diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260510_215518/_tabddpm_train.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260510_215518/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..774a52227cc7ba5106651e5e85375a7c49a3f9c2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260510_215518/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260510_215518/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260510_215518/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260510_215518/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260510_215518/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..058eecd4abe7d96d18345541801a43a67fd11758 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260510_215518/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260510_215518/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260510_215518/data" +model_type = "mlp" +num_numerical_features = 149 +device = "cuda:0" + +[model_params] +d_in = 170 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 256 +seed = 0 diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260510_215518/data/X_cat_train.npy 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0000000000000000000000000000000000000000..9b148cde6e315b01b30652b19f39a11f211dff1c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/.gitignore @@ -0,0 +1,22 @@ +.DS_Store +__pycache__/ +catboost_info/ +**/**.pt +**/**.ipynb +!agg_results.ipynb +**/**.npy +**/**.gz +**/**.sh +**/**.obj +**/**.png +**/**.tar +**/**.code-workspace +**/**.csv +exp/**/**/results_catboost.json +exp/**/**/results_mlp.json + +configs/ +data/ +junk/ +RF/ +exps/ \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/.gitmodules b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/.gitmodules new file mode 100644 index 0000000000000000000000000000000000000000..8c677ab737cbbdbc5c806cf58d27960efefcb11a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/.gitmodules @@ -0,0 +1,9 @@ +[submodule "ctgan"] + # path = CTGAN/CTGAN + url = https://github.com/sdv-dev/CTGAN +[submodule "ctabgan"] + # path = CTAB-GAN + url = https://github.com/Team-TUD/CTAB-GAN +[submodule "ctabgan+"] + # path = CTAB-GAN-Plus + url = https://github.com/Team-TUD/CTAB-GAN-Plus \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CONFIG_DESCRIPTION.md b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CONFIG_DESCRIPTION.md new file mode 100644 index 0000000000000000000000000000000000000000..646a0c00f329b55bc736d5cdbd1e797d1143cd1c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CONFIG_DESCRIPTION.md @@ -0,0 +1,78 @@ +# Description of .toml config for TabDDPM +First of all, `train.T` and `eval.T` denote preprocessing for training and for evaluation, respectively. + +Here we list non-obvious parameters. + +Main part: +- `seed = 0` -- evaluation seed (and training, but for training it is fixed to 0) +- `parent_dir = "exp/abalone/check"` -- exp folder +- `real_data_path = "data/abalone/"` +- `model_type = "mlp"` -- model type that approximates the reverse process +- `num_numerical_features ` -- a number of numerical features in dataset +- `device = "cuda:0"` + +Model params: +- `is_y_cond` -- false for regression, true for classification +- `d_in` -- input dimension (not necessary, since scripts calculate it automatically) +- `num_calsses` -- zero for regression, a number of classes for classification +- `rtdl_params` -- MLP parameters + +```toml +seed = 0 +parent_dir = "exp/abalone/check" +real_data_path = "data/abalone/" +model_type = "mlp" +num_numerical_features = 7 +device = "cuda:0" + +[model_params] +is_y_cond = false +d_in = 11 +num_classes = 0 + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 20800 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +``` \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/.gitignore b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..2473a164760acb3c77f225f094e19e2e0f523912 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/.gitignore @@ -0,0 +1 @@ +**/**.csv \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/README.md b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e873924cc56b6603669d17e6ab3badd4aba0657a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/README.md @@ -0,0 +1,49 @@ +# CTAB-GAN+ +This is the official git paper [CTAB-GAN+: Enhancing Tabular Data Synthesis](https://arxiv.org/abs/2204.00401). Current code is without differential privacy part. +If you have any question, please contact `z.zhao-8@tudelft.nl` for more information. + + +## Prerequisite + +The required package version +``` +numpy==1.21.0 +torch==1.9.1 +pandas==1.2.4 +sklearn==0.24.1 +dython==0.6.4.post1 +scipy==1.4.1 +``` +The sklean package in newer version has updated its function for `sklearn.mixture.BayesianGaussianMixture`. Therefore, user should use this proposed sklearn version to successfully run the code! + +## Example +`Experiment_Script_Adult.ipynb` `Experiment_Script_king.ipynb` are two example notebooks for training CTAB-GAN+ with Adult (classification) and king (regression) datasets. The datasets are alread under `Real_Datasets` folder. +The evaluation code is also provided. + +## Problem type + +You can either indicate your dataset problem type as Classification, Regression. If there is no problem type, you can leave the problem type as None as follows: +``` +problem_type= {None: None} +``` + +## For large dataset + +If your dataset has large number of column, you may encounter the problem that our currnet code cannot encode all of your data since CTAB-GAN+ will wrap the encoded data into an image-like format. What you can do is changing the line 378 and 385 in `model/synthesizer/ctabgan_synthesizer.py`. The number in the `slide` list +``` +sides = [4, 8, 16, 24, 32] +``` +is the side size of image. You can enlarge the list to [4, 8, 16, 24, 32, 64] or [4, 8, 16, 24, 32, 64, 128] for accepting larger dataset. + +## Bibtex + +To cite this paper, you could use this bibtex + +``` +@article{zhao2022ctab, + title={CTAB-GAN+: Enhancing Tabular Data Synthesis}, + author={Zhao, Zilong and Kunar, Aditya and Birke, Robert and Chen, Lydia Y}, + journal={arXiv preprint arXiv:2204.00401}, + year={2022} +} +``` \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/columns.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/columns.json new file mode 100644 index 0000000000000000000000000000000000000000..a893dbbac0f5d33d236271e26831b3dd4175ecf1 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/columns.json @@ -0,0 +1,119 @@ +{ + "churn2": { + "categorical_columns": ["7", "8", "9", "10", "y"], + "mixed_columns": {"0": [850.0], "3": [0.0]}, + "integer_columns": ["2", "4"], + "general_columns": ["1", "5", "6"], + "problem_type": {"Classification": "y"} + }, + "adult": { + "categorical_columns": ["6", "7", "8", "9", "10", "11", "12", "13", "y"], + "mixed_columns": {"3": [0.0], "4": [0.0]}, + "integer_columns": ["0", "1", "2", "5"], + "general_columns": ["0", "1", "7"], + "problem_type": {"Classification": "y"} + }, + "california": { + "categorical_columns": [], + "mixed_columns": {"1": [52.0]}, + "integer_columns": ["4"], + "general_columns": ["0"], + "problem_type": {"Regression": "y"} + }, + "default": { + "categorical_columns": ["20", "21", "22", "y"], + "mixed_columns": {}, + "general_columns": [], + "integer_columns": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19"], + "problem_type": {"Classification": "y"} + }, + "buddy": { + "categorical_columns": ["4", "5", "6", "7", "8", "y"], + "mixed_columns": {}, + "integer_columns": ["0", "1"], + "general_columns": ["1", "3", "5"], + "problem_type": {"Classification": "y"} + }, + "gesture": { + "categorical_columns": ["y"], + "mixed_columns": {}, + "integer_columns": [], + "general_columns": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23"], + "problem_type": {"Classification": "y"} + }, + "wilt": { + "categorical_columns": ["y"], + "mixed_columns": {}, + "integer_columns": [], + "general_columns": ["0", "3"], + "problem_type": {"Classification": "y"} + }, + "satellite": { + "categorical_columns": ["y"], + "mixed_columns": {}, + "integer_columns": [], + "problem_type": {"Classification": "y"} + }, + "higgs-small": { + "categorical_columns": ["y"], + "mixed_columns": {}, + "integer_columns": [], + "general_columns": ["1", "2", "4", "6", "10", "11", "14", "15", "18","19"], + "problem_type": {"Classification": "y"} + }, + "diabetes": { + "categorical_columns": ["y"], + "mixed_columns": {"3": [0.0], "4": [0.0]}, + "general_columns": [], + "integer_columns": ["0", "1", "2", "5", "7"], + "problem_type": {"Classification": "y"} + }, + "abalone": { + "categorical_columns": ["7"], + "mixed_columns": {}, + "integer_columns": ["y"], + "general_columns": [], + "problem_type": {"Regression": "y"} + }, + "insurance": { + "categorical_columns": ["3", "4", "5"], + "mixed_columns": {}, + "general_columns": [], + "integer_columns": ["0", "2"], + "problem_type": {"Regression": "y"} + }, + "king": { + "categorical_columns": ["17", "18", "19"], + "mixed_columns": {"9": [0.0], "11":[0.0]}, + "general_columns": ["2", "6", "7"], + "integer_columns": ["0", "2", "5", "7", "8", "9", "12"], + "problem_type": {"Regression": "y"} + }, + "cardio": { + "categorical_columns": ["5", "6", "7", "8", "9", "10", "y"], + "mixed_columns": {}, + "integer_columns": ["0", "1", "3", "4"], + "problem_type": {"Classification": "y"} + }, + "house": { + "categorical_columns": [], + "mixed_columns": {"2": [0.0], "6": [0.0], "8": [0.0], "11": [0.0], "12": [1.0], "14": [0.0]}, + "general_columns": ["1", "7"], + "integer_columns": ["0"], + "problem_type": {"Regression": "y"} + }, + "miniboone": { + "categorical_columns": ["y"], + "mixed_columns": {}, + "integer_columns": [], + "general_columns": ["8", "9", "10", "19", "20", "21", "28", "29", "35", "39", "45", "49"], + "problem_type": {"Classification": "y"} + }, + "fb-comments": { + "categorical_columns": ["36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50"], + "mixed_columns": {"1": [0.0], "8": [0.0], "10": [0.0]}, + "general_columns": ["26", "36"], + "integer_columns": [], + "problem_type": {"Regression": "y"} + } +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/ctabgan.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/ctabgan.py new file mode 100644 index 0000000000000000000000000000000000000000..81782dcde9f07f3d8178c29ba08ceb129c40f58f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/ctabgan.py @@ -0,0 +1,70 @@ +""" +Generative model training algorithm based on the CTABGANSynthesiser + +""" +import pandas as pd +import time +from model.pipeline.data_preparation import DataPrep +from model.synthesizer.ctabgan_synthesizer import CTABGANSynthesizer + +import warnings + +warnings.filterwarnings("ignore") + +class CTABGAN(): + + def __init__(self, + df, + test_ratio = 0.20, + categorical_columns = [ 'workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'gender', 'native-country', 'income'], + log_columns = [], + mixed_columns= {'capital-loss':[0.0],'capital-gain':[0.0]}, + general_columns = ["age"], + non_categorical_columns = [], + integer_columns = ['age', 'fnlwgt','capital-gain', 'capital-loss','hours-per-week'], + problem_type= {"Classification": "income"}, + class_dim=(256, 256, 256, 256), + random_dim=100, + num_channels=64, + l2scale=1e-5, + batch_size=500, + epochs=150, + device="cpu"): + + self.__name__ = 'CTABGAN' + + self.synthesizer = CTABGANSynthesizer( + class_dim=class_dim, + random_dim=random_dim, + num_channels=num_channels, + l2scale=l2scale, + batch_size=batch_size, + epochs=epochs, + device=device + ) + self.raw_df = df + self.test_ratio = test_ratio + self.categorical_columns = categorical_columns + self.log_columns = log_columns + self.mixed_columns = mixed_columns + self.general_columns = general_columns + self.non_categorical_columns = non_categorical_columns + self.integer_columns = integer_columns + self.problem_type = problem_type + + def fit(self): + + start_time = time.time() + self.data_prep = DataPrep(self.raw_df,self.categorical_columns,self.log_columns,self.mixed_columns,self.general_columns,self.non_categorical_columns,self.integer_columns,self.problem_type,self.test_ratio) + self.synthesizer.fit(train_data=self.data_prep.df, categorical = self.data_prep.column_types["categorical"], mixed = self.data_prep.column_types["mixed"], + general = self.data_prep.column_types["general"], non_categorical = self.data_prep.column_types["non_categorical"], type=self.problem_type) + end_time = time.time() + print('Finished training in',end_time-start_time," seconds.") + + + def generate_samples(self, seed=0): + + sample = self.synthesizer.sample(len(self.raw_df), seed) + sample_df = self.data_prep.inverse_prep(sample) + + return sample_df diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/eval/evaluation.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/eval/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..1492fcf80cfb907f95b3844e4c0f38f15effbdb0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/eval/evaluation.py @@ -0,0 +1,193 @@ +import numpy as np +import pandas as pd +from sklearn import metrics +from sklearn import model_selection +from sklearn.preprocessing import MinMaxScaler,StandardScaler +from sklearn.neural_network import MLPClassifier +from sklearn.linear_model import LogisticRegression +from sklearn import svm,tree +from sklearn.ensemble import RandomForestClassifier +from dython.nominal import compute_associations +from scipy.stats import wasserstein_distance +from scipy.spatial import distance +import warnings + +warnings.filterwarnings("ignore") + +def supervised_model_training(x_train, y_train, x_test, + y_test, model_name): + + + if model_name == 'lr': + model = LogisticRegression(random_state=42,max_iter=500) + elif model_name == 'svm': + model = svm.SVC(random_state=42,probability=True) + elif model_name == 'dt': + model = tree.DecisionTreeClassifier(random_state=42) + elif model_name == 'rf': + model = RandomForestClassifier(random_state=42) + elif model_name == "mlp": + model = MLPClassifier(random_state=42,max_iter=100) + + model.fit(x_train, y_train) + pred = model.predict(x_test) + + if len(np.unique(y_train))>2: + predict = model.predict_proba(x_test) + acc = metrics.accuracy_score(y_test,pred)*100 + auc = metrics.roc_auc_score(y_test, predict,average="weighted",multi_class="ovr") + f1_score = metrics.precision_recall_fscore_support(y_test, pred,average="weighted")[2] + return [acc, auc,f1_score] + + else: + predict = model.predict_proba(x_test)[:,1] + acc = metrics.accuracy_score(y_test,pred)*100 + auc = metrics.roc_auc_score(y_test, predict) + f1_score = metrics.precision_recall_fscore_support(y_test,pred)[2].mean() + return [acc, auc,f1_score] + + +def get_utility_metrics(real_path,fake_paths,scaler="MinMax",classifiers=["lr","dt","rf","mlp"],test_ratio=.20): + + data_real = pd.read_csv(real_path).to_numpy() + data_dim = data_real.shape[1] + + data_real_y = data_real[:,-1] + data_real_X = data_real[:,:data_dim-1] + X_train_real, X_test_real, y_train_real, y_test_real = model_selection.train_test_split(data_real_X ,data_real_y, test_size=test_ratio, stratify=data_real_y,random_state=42) + + if scaler=="MinMax": + scaler_real = MinMaxScaler() + else: + scaler_real = StandardScaler() + + scaler_real.fit(data_real_X) + X_train_real_scaled = scaler_real.transform(X_train_real) + X_test_real_scaled = scaler_real.transform(X_test_real) + + all_real_results = [] + for classifier in classifiers: + real_results = supervised_model_training(X_train_real_scaled,y_train_real,X_test_real_scaled,y_test_real,classifier) + all_real_results.append(real_results) + + all_fake_results_avg = [] + + for fake_path in fake_paths: + data_fake = pd.read_csv(fake_path).to_numpy() + data_fake_y = data_fake[:,-1] + data_fake_X = data_fake[:,:data_dim-1] + X_train_fake, _ , y_train_fake, _ = model_selection.train_test_split(data_fake_X ,data_fake_y, test_size=test_ratio, stratify=data_fake_y,random_state=42) + + if scaler=="MinMax": + scaler_fake = MinMaxScaler() + else: + scaler_fake = StandardScaler() + + scaler_fake.fit(data_fake_X) + + X_train_fake_scaled = scaler_fake.transform(X_train_fake) + + all_fake_results = [] + for classifier in classifiers: + fake_results = supervised_model_training(X_train_fake_scaled,y_train_fake,X_test_real_scaled,y_test_real,classifier) + all_fake_results.append(fake_results) + + all_fake_results_avg.append(all_fake_results) + + diff_results = np.array(all_real_results)- np.array(all_fake_results_avg).mean(axis=0) + + return diff_results + +def stat_sim(real_path,fake_path,cat_cols=None): + + Stat_dict={} + + real = pd.read_csv(real_path) + fake = pd.read_csv(fake_path) + + really = real.copy() + fakey = fake.copy() + + real_corr = compute_associations(real, nominal_columns=cat_cols) + + fake_corr = compute_associations(fake, nominal_columns=cat_cols) + + corr_dist = np.linalg.norm(real_corr - fake_corr) + + cat_stat = [] + num_stat = [] + + for column in real.columns: + + if column in cat_cols: + + real_pdf=(really[column].value_counts()/really[column].value_counts().sum()) + fake_pdf=(fakey[column].value_counts()/fakey[column].value_counts().sum()) + categories = (fakey[column].value_counts()/fakey[column].value_counts().sum()).keys().tolist() + sorted_categories = sorted(categories) + + real_pdf_values = [] + fake_pdf_values = [] + + for i in sorted_categories: + real_pdf_values.append(real_pdf[i]) + fake_pdf_values.append(fake_pdf[i]) + + if len(real_pdf)!=len(fake_pdf): + zero_cats = set(really[column].value_counts().keys())-set(fakey[column].value_counts().keys()) + for z in zero_cats: + real_pdf_values.append(real_pdf[z]) + fake_pdf_values.append(0) + Stat_dict[column]=(distance.jensenshannon(real_pdf_values,fake_pdf_values, 2.0)) + cat_stat.append(Stat_dict[column]) + else: + scaler = MinMaxScaler() + scaler.fit(real[column].values.reshape(-1,1)) + l1 = scaler.transform(real[column].values.reshape(-1,1)).flatten() + l2 = scaler.transform(fake[column].values.reshape(-1,1)).flatten() + Stat_dict[column]= (wasserstein_distance(l1,l2)) + num_stat.append(Stat_dict[column]) + + return [np.mean(num_stat),np.mean(cat_stat),corr_dist] + +def privacy_metrics(real_path,fake_path,data_percent=15): + + real = pd.read_csv(real_path).drop_duplicates(keep=False) + fake = pd.read_csv(fake_path).drop_duplicates(keep=False) + + real_refined = real.sample(n=int(len(real)*(.01*data_percent)), random_state=42).to_numpy() + fake_refined = fake.sample(n=int(len(fake)*(.01*data_percent)), random_state=42).to_numpy() + + scalerR = StandardScaler() + scalerR.fit(real_refined) + scalerF = StandardScaler() + scalerF.fit(fake_refined) + df_real_scaled = scalerR.transform(real_refined) + df_fake_scaled = scalerF.transform(fake_refined) + + dist_rf = metrics.pairwise_distances(df_real_scaled, Y=df_fake_scaled, metric='minkowski', n_jobs=-1) + dist_rr = metrics.pairwise_distances(df_real_scaled, Y=None, metric='minkowski', n_jobs=-1) + rd_dist_rr = dist_rr[~np.eye(dist_rr.shape[0],dtype=bool)].reshape(dist_rr.shape[0],-1) + dist_ff = metrics.pairwise_distances(df_fake_scaled, Y=None, metric='minkowski', n_jobs=-1) + rd_dist_ff = dist_ff[~np.eye(dist_ff.shape[0],dtype=bool)].reshape(dist_ff.shape[0],-1) + smallest_two_indexes_rf = [dist_rf[i].argsort()[:2] for i in range(len(dist_rf))] + smallest_two_rf = [dist_rf[i][smallest_two_indexes_rf[i]] for i in range(len(dist_rf))] + smallest_two_indexes_rr = [rd_dist_rr[i].argsort()[:2] for i in range(len(rd_dist_rr))] + smallest_two_rr = [rd_dist_rr[i][smallest_two_indexes_rr[i]] for i in range(len(rd_dist_rr))] + smallest_two_indexes_ff = [rd_dist_ff[i].argsort()[:2] for i in range(len(rd_dist_ff))] + smallest_two_ff = [rd_dist_ff[i][smallest_two_indexes_ff[i]] for i in range(len(rd_dist_ff))] + nn_ratio_rr = np.array([i[0]/i[1] for i in smallest_two_rr]) + nn_ratio_ff = np.array([i[0]/i[1] for i in smallest_two_ff]) + nn_ratio_rf = np.array([i[0]/i[1] for i in smallest_two_rf]) + nn_fifth_perc_rr = np.percentile(nn_ratio_rr,5) + nn_fifth_perc_ff = np.percentile(nn_ratio_ff,5) + nn_fifth_perc_rf = np.percentile(nn_ratio_rf,5) + + min_dist_rf = np.array([i[0] for i in smallest_two_rf]) + fifth_perc_rf = np.percentile(min_dist_rf,5) + min_dist_rr = np.array([i[0] for i in smallest_two_rr]) + fifth_perc_rr = np.percentile(min_dist_rr,5) + min_dist_ff = np.array([i[0] for i in smallest_two_ff]) + fifth_perc_ff = np.percentile(min_dist_ff,5) + + return np.array([fifth_perc_rf,fifth_perc_rr,fifth_perc_ff,nn_fifth_perc_rf,nn_fifth_perc_rr,nn_fifth_perc_ff]).reshape(1,6) \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/pipeline/data_preparation.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/pipeline/data_preparation.py new file mode 100644 index 0000000000000000000000000000000000000000..abe1f725f7d09f7284abef0dd9c1187f0e3c96bf --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/pipeline/data_preparation.py @@ -0,0 +1,130 @@ +import numpy as np +import pandas as pd +from sklearn import preprocessing +from sklearn import model_selection + +class DataPrep(object): + + def __init__(self, raw_df: pd.DataFrame, categorical: list, log:list, mixed:dict, general:list, non_categorical:list, integer:list, type:dict, test_ratio:float): + + + self.categorical_columns = categorical + self.log_columns = log + self.mixed_columns = mixed + self.general_columns = general + self.non_categorical_columns = non_categorical + self.integer_columns = integer + self.column_types = dict() + self.column_types["categorical"] = [] + self.column_types["mixed"] = {} + self.column_types["general"] = [] + self.column_types["non_categorical"] = [] + self.lower_bounds = {} + self.label_encoder_list = [] + + target_col = list(type.values())[0] + if target_col is not None: + y_real = raw_df[target_col] + X_real = raw_df.drop(columns=[target_col]) + X_train_real, _, y_train_real, _ = model_selection.train_test_split(X_real ,y_real, test_size=test_ratio, stratify=y_real,random_state=42) + + X_train_real[target_col]= y_train_real + + self.df = X_train_real + else: + self.df = raw_df + + self.df = self.df.replace(r' ', np.nan) + self.df = self.df.fillna('empty') + + all_columns= set(self.df.columns) + irrelevant_missing_columns = set(self.categorical_columns) + relevant_missing_columns = list(all_columns - irrelevant_missing_columns) + + for i in relevant_missing_columns: + if i in self.log_columns: + if "empty" in list(self.df[i].values): + self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x) + self.mixed_columns[i] = [-9999999] + elif i in list(self.mixed_columns.keys()): + if "empty" in list(self.df[i].values): + self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x ) + self.mixed_columns[i].append(-9999999) + else: + if "empty" in list(self.df[i].values): + self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x) + self.mixed_columns[i] = [-9999999] + + if self.log_columns: + for log_column in self.log_columns: + valid_indices = [] + for idx,val in enumerate(self.df[log_column].values): + if val!=-9999999: + valid_indices.append(idx) + eps = 1 + lower = np.min(self.df[log_column].iloc[valid_indices].values) + self.lower_bounds[log_column] = lower + if lower>0: + self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x) if x!=-9999999 else -9999999) + elif lower == 0: + self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x+eps) if x!=-9999999 else -9999999) + else: + self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x-lower+eps) if x!=-9999999 else -9999999) + + for column_index, column in enumerate(self.df.columns): + if column in self.categorical_columns: + label_encoder = preprocessing.LabelEncoder() + self.df[column] = self.df[column].astype(str) + label_encoder.fit(self.df[column]) + current_label_encoder = dict() + current_label_encoder['column'] = column + current_label_encoder['label_encoder'] = label_encoder + transformed_column = label_encoder.transform(self.df[column]) + self.df[column] = transformed_column + self.label_encoder_list.append(current_label_encoder) + self.column_types["categorical"].append(column_index) + + if column in self.general_columns: + self.column_types["general"].append(column_index) + + if column in self.non_categorical_columns: + self.column_types["non_categorical"].append(column_index) + + elif column in self.mixed_columns: + self.column_types["mixed"][column_index] = self.mixed_columns[column] + + elif column in self.general_columns: + self.column_types["general"].append(column_index) + + + super().__init__() + + def inverse_prep(self, data, eps=1): + + df_sample = pd.DataFrame(data,columns=self.df.columns) + + for i in range(len(self.label_encoder_list)): + le = self.label_encoder_list[i]["label_encoder"] + df_sample[self.label_encoder_list[i]["column"]] = df_sample[self.label_encoder_list[i]["column"]].astype(int) + df_sample[self.label_encoder_list[i]["column"]] = le.inverse_transform(df_sample[self.label_encoder_list[i]["column"]]) + + if self.log_columns: + for i in df_sample: + if i in self.log_columns: + lower_bound = self.lower_bounds[i] + if lower_bound>0: + df_sample[i].apply(lambda x: np.exp(x)) + elif lower_bound==0: + df_sample[i] = df_sample[i].apply(lambda x: np.ceil(np.exp(x)-eps) if (np.exp(x)-eps) < 0 else (np.exp(x)-eps)) + else: + df_sample[i] = df_sample[i].apply(lambda x: np.exp(x)-eps+lower_bound) + + if self.integer_columns: + for column in self.integer_columns: + df_sample[column]= (np.round(df_sample[column].values)) + df_sample[column] = df_sample[column].astype(int) + + df_sample.replace(-9999999, np.nan,inplace=True) + df_sample.replace('empty', np.nan,inplace=True) + + return df_sample diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/privacy_utils/rdp_accountant.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/privacy_utils/rdp_accountant.py new file mode 100644 index 0000000000000000000000000000000000000000..5e225cbb0994fa6b9e9726f38d873754bbfe82cf --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/privacy_utils/rdp_accountant.py @@ -0,0 +1,280 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import sys + +import numpy as np +from scipy import special +import six + +######################## +# LOG-SPACE ARITHMETIC # +######################## + + +def _log_add(logx, logy): + """Add two numbers in the log space.""" + a, b = min(logx, logy), max(logx, logy) + if a == -np.inf: # adding 0 + return b + # Use exp(a) + exp(b) = (exp(a - b) + 1) * exp(b) + return math.log1p(math.exp(a - b)) + b # log1p(x) = log(x + 1) + + +def _log_sub(logx, logy): + """Subtract two numbers in the log space. Answer must be non-negative.""" + if logx < logy: + raise ValueError("The result of subtraction must be non-negative.") + if logy == -np.inf: # subtracting 0 + return logx + if logx == logy: + return -np.inf # 0 is represented as -np.inf in the log space. + + try: + # Use exp(x) - exp(y) = (exp(x - y) - 1) * exp(y). + return math.log(math.expm1(logx - logy)) + logy # expm1(x) = exp(x) - 1 + except OverflowError: + return logx + + +def _log_print(logx): + """Pretty print.""" + if logx < math.log(sys.float_info.max): + return "{}".format(math.exp(logx)) + else: + return "exp({})".format(logx) + + +def _compute_log_a_int(q, sigma, alpha): + """Compute log(A_alpha) for integer alpha. 0 < q < 1.""" + assert isinstance(alpha, six.integer_types) + + # Initialize with 0 in the log space. + log_a = -np.inf + + for i in range(alpha + 1): + log_coef_i = ( + math.log(special.binom(alpha, i)) + i * math.log(q) + + (alpha - i) * math.log(1 - q)) + + s = log_coef_i + (i * i - i) / (2 * (sigma**2)) + log_a = _log_add(log_a, s) + + return float(log_a) + + +def _compute_log_a_frac(q, sigma, alpha): + """Compute log(A_alpha) for fractional alpha. 0 < q < 1.""" + # The two parts of A_alpha, integrals over (-inf,z0] and [z0, +inf), are + # initialized to 0 in the log space: + log_a0, log_a1 = -np.inf, -np.inf + i = 0 + + z0 = sigma**2 * math.log(1 / q - 1) + .5 + + while True: # do ... until loop + coef = special.binom(alpha, i) + log_coef = math.log(abs(coef)) + j = alpha - i + + log_t0 = log_coef + i * math.log(q) + j * math.log(1 - q) + log_t1 = log_coef + j * math.log(q) + i * math.log(1 - q) + + log_e0 = math.log(.5) + _log_erfc((i - z0) / (math.sqrt(2) * sigma)) + log_e1 = math.log(.5) + _log_erfc((z0 - j) / (math.sqrt(2) * sigma)) + + log_s0 = log_t0 + (i * i - i) / (2 * (sigma**2)) + log_e0 + log_s1 = log_t1 + (j * j - j) / (2 * (sigma**2)) + log_e1 + + if coef > 0: + log_a0 = _log_add(log_a0, log_s0) + log_a1 = _log_add(log_a1, log_s1) + else: + log_a0 = _log_sub(log_a0, log_s0) + log_a1 = _log_sub(log_a1, log_s1) + + i += 1 + if max(log_s0, log_s1) < -30: + break + + return _log_add(log_a0, log_a1) + + +def _compute_log_a(q, sigma, alpha): + """Compute log(A_alpha) for any positive finite alpha.""" + if float(alpha).is_integer(): + return _compute_log_a_int(q, sigma, int(alpha)) + else: + return _compute_log_a_frac(q, sigma, alpha) + + +def _log_erfc(x): + """Compute log(erfc(x)) with high accuracy for large x.""" + try: + return math.log(2) + special.log_ndtr(-x * 2**.5) + except NameError: + # If log_ndtr is not available, approximate as follows: + r = special.erfc(x) + if r == 0.0: + # Using the Laurent series at infinity for the tail of the erfc function: + # erfc(x) ~ exp(-x^2-.5/x^2+.625/x^4)/(x*pi^.5) + # To verify in Mathematica: + # Series[Log[Erfc[x]] + Log[x] + Log[Pi]/2 + x^2, {x, Infinity, 6}] + return (-math.log(math.pi) / 2 - math.log(x) - x**2 - .5 * x**-2 + + .625 * x**-4 - 37. / 24. * x**-6 + 353. / 64. * x**-8) + else: + return math.log(r) + + +def _compute_delta(orders, rdp, eps): + """Compute delta given a list of RDP values and target epsilon. + + Args: + orders: An array (or a scalar) of orders. + rdp: A list (or a scalar) of RDP guarantees. + eps: The target epsilon. + + Returns: + Pair of (delta, optimal_order). + + Raises: + ValueError: If input is malformed. + + """ + orders_vec = np.atleast_1d(orders) + rdp_vec = np.atleast_1d(rdp) + + if len(orders_vec) != len(rdp_vec): + raise ValueError("Input lists must have the same length.") + + deltas = np.exp((rdp_vec - eps) * (orders_vec - 1)) + idx_opt = np.argmin(deltas) + return min(deltas[idx_opt], 1.), orders_vec[idx_opt] + + +def _compute_eps(orders, rdp, delta): + """Compute epsilon given a list of RDP values and target delta. + + Args: + orders: An array (or a scalar) of orders. + rdp: A list (or a scalar) of RDP guarantees. + delta: The target delta. + + Returns: + Pair of (eps, optimal_order). + + Raises: + ValueError: If input is malformed. + + """ + orders_vec = np.atleast_1d(orders) + rdp_vec = np.atleast_1d(rdp) + + if len(orders_vec) != len(rdp_vec): + raise ValueError("Input lists must have the same length.") + + eps = rdp_vec - math.log(delta) / (orders_vec - 1) + + idx_opt = np.nanargmin(eps) # Ignore NaNs + return eps[idx_opt], orders_vec[idx_opt] + + +def _compute_rdp(q, sigma, alpha): + """Compute RDP of the Sampled Gaussian mechanism at order alpha. + + Args: + q: The sampling rate. + sigma: The std of the additive Gaussian noise. + alpha: The order at which RDP is computed. + + Returns: + RDP at alpha, can be np.inf. + """ + if q == 0: + return 0 + + if q == 1.: + return alpha / (2 * sigma**2) + + if np.isinf(alpha): + return np.inf + + return _compute_log_a(q, sigma, alpha) / (alpha - 1) + + +def compute_rdp(q, noise_multiplier, steps, orders): + """Compute RDP of the Sampled Gaussian Mechanism. + + Args: + q: The sampling rate. + noise_multiplier: The ratio of the standard deviation of the Gaussian noise + to the l2-sensitivity of the function to which it is added. + steps: The number of steps. + orders: An array (or a scalar) of RDP orders. + + Returns: + The RDPs at all orders, can be np.inf. + """ + if np.isscalar(orders): + rdp = _compute_rdp(q, noise_multiplier, orders) + else: + rdp = np.array([_compute_rdp(q, noise_multiplier, order) + for order in orders]) + + return rdp * steps + + +def get_privacy_spent(orders, rdp, target_eps=None, target_delta=None): + """Compute delta (or eps) for given eps (or delta) from RDP values. + + Args: + orders: An array (or a scalar) of RDP orders. + rdp: An array of RDP values. Must be of the same length as the orders list. + target_eps: If not None, the epsilon for which we compute the corresponding + delta. + target_delta: If not None, the delta for which we compute the corresponding + epsilon. Exactly one of target_eps and target_delta must be None. + + Returns: + eps, delta, opt_order. + + Raises: + ValueError: If target_eps and target_delta are messed up. + """ + if target_eps is None and target_delta is None: + raise ValueError( + "Exactly one out of eps and delta must be None. (Both are).") + + if target_eps is not None and target_delta is not None: + raise ValueError( + "Exactly one out of eps and delta must be None. (None is).") + + if target_eps is not None: + delta, opt_order = _compute_delta(orders, rdp, target_eps) + return target_eps, delta, opt_order + else: + eps, opt_order = _compute_eps(orders, rdp, target_delta) + return eps, target_delta, opt_order + + +def compute_rdp_from_ledger(ledger, orders): + """Compute RDP of Sampled Gaussian Mechanism from ledger. + + Args: + ledger: A formatted privacy ledger. + orders: An array (or a scalar) of RDP orders. + + Returns: + RDP at all orders, can be np.inf. + """ + total_rdp = np.zeros_like(orders, dtype=float) + for sample in ledger: + # Compute equivalent z from l2_clip_bounds and noise stddevs in sample. + # See https://arxiv.org/pdf/1812.06210.pdf for derivation of this formula. + effective_z = sum([ + (q.noise_stddev / q.l2_norm_bound)**-2 for q in sample.queries])**-0.5 + total_rdp += compute_rdp( + sample.selection_probability, effective_z, 1, orders) + return total_rdp \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/synthesizer/ctabgan_synthesizer.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/synthesizer/ctabgan_synthesizer.py new file mode 100644 index 0000000000000000000000000000000000000000..9fd6845ffaf8f3a991acceba92af143941e5c11f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/synthesizer/ctabgan_synthesizer.py @@ -0,0 +1,601 @@ +import numpy as np +import pandas as pd +import torch +import torch.utils.data +import torch.optim as optim +from torch.optim import Adam +from torch.nn import functional as F +from torch.nn import (Dropout, LeakyReLU, Linear, Module, ReLU, Sequential, +Conv2d, ConvTranspose2d, Sigmoid, init, BCELoss, CrossEntropyLoss,SmoothL1Loss,LayerNorm) +from model.synthesizer.transformer import ImageTransformer,DataTransformer +from model.privacy_utils.rdp_accountant import compute_rdp, get_privacy_spent +from tqdm import tqdm + + +class Classifier(Module): + def __init__(self,input_dim, dis_dims,st_ed): + super(Classifier,self).__init__() + dim = input_dim-(st_ed[1]-st_ed[0]) + seq = [] + self.str_end = st_ed + for item in list(dis_dims): + seq += [ + Linear(dim, item), + LeakyReLU(0.2), + Dropout(0.5) + ] + dim = item + + if (st_ed[1]-st_ed[0])==1: + seq += [Linear(dim, 1)] + + elif (st_ed[1]-st_ed[0])==2: + seq += [Linear(dim, 1),Sigmoid()] + else: + seq += [Linear(dim,(st_ed[1]-st_ed[0]))] + + self.seq = Sequential(*seq) + + def forward(self, input): + + label=None + + if (self.str_end[1]-self.str_end[0])==1: + label = input[:, self.str_end[0]:self.str_end[1]] + else: + label = torch.argmax(input[:, self.str_end[0]:self.str_end[1]], axis=-1) + + new_imp = torch.cat((input[:,:self.str_end[0]],input[:,self.str_end[1]:]),1) + + if ((self.str_end[1]-self.str_end[0])==2) | ((self.str_end[1]-self.str_end[0])==1): + return self.seq(new_imp).view(-1), label + else: + return self.seq(new_imp), label + +def apply_activate(data, output_info): + data_t = [] + st = 0 + for item in output_info: + if item[1] == 'tanh': + ed = st + item[0] + data_t.append(torch.tanh(data[:, st:ed])) + st = ed + elif item[1] == 'softmax': + ed = st + item[0] + data_t.append(F.gumbel_softmax(data[:, st:ed], tau=0.2)) + st = ed + return torch.cat(data_t, dim=1) + +def get_st_ed(target_col_index,output_info): + st = 0 + c= 0 + tc= 0 + + for item in output_info: + if c==target_col_index: + break + if item[1]=='tanh': + st += item[0] + if item[2] == 'yes_g': + c+=1 + elif item[1] == 'softmax': + st += item[0] + c+=1 + tc+=1 + + ed= st+output_info[tc][0] + + return (st,ed) + +def random_choice_prob_index_sampling(probs,col_idx): + option_list = [] + for i in col_idx: + pp = probs[i] + option_list.append(np.random.choice(np.arange(len(probs[i])), p=pp)) + + return np.array(option_list).reshape(col_idx.shape) + +def random_choice_prob_index(a, axis=1): + r = np.expand_dims(np.random.rand(a.shape[1 - axis]), axis=axis) + return (a.cumsum(axis=axis) > r).argmax(axis=axis) + +def maximum_interval(output_info): + max_interval = 0 + for item in output_info: + max_interval = max(max_interval, item[0]) + return max_interval + +class Cond(object): + def __init__(self, data, output_info): + + self.model = [] + st = 0 + counter = 0 + for item in output_info: + + if item[1] == 'tanh': + st += item[0] + continue + elif item[1] == 'softmax': + ed = st + item[0] + counter += 1 + self.model.append(np.argmax(data[:, st:ed], axis=-1)) + st = ed + + self.interval = [] + self.n_col = 0 + self.n_opt = 0 + st = 0 + self.p = np.zeros((counter, maximum_interval(output_info))) + self.p_sampling = [] + for item in output_info: + if item[1] == 'tanh': + st += item[0] + continue + elif item[1] == 'softmax': + ed = st + item[0] + tmp = np.sum(data[:, st:ed], axis=0) + tmp_sampling = np.sum(data[:, st:ed], axis=0) + tmp = np.log(tmp + 1) + tmp = tmp / np.sum(tmp) + tmp_sampling = tmp_sampling / np.sum(tmp_sampling) + self.p_sampling.append(tmp_sampling) + self.p[self.n_col, :item[0]] = tmp + self.interval.append((self.n_opt, item[0])) + self.n_opt += item[0] + self.n_col += 1 + st = ed + + self.interval = np.asarray(self.interval) + + def sample_train(self, batch): + if self.n_col == 0: + return None + batch = batch + + idx = np.random.choice(np.arange(self.n_col), batch) + + vec = np.zeros((batch, self.n_opt), dtype='float32') + mask = np.zeros((batch, self.n_col), dtype='float32') + mask[np.arange(batch), idx] = 1 + opt1prime = random_choice_prob_index(self.p[idx]) + for i in np.arange(batch): + vec[i, self.interval[idx[i], 0] + opt1prime[i]] = 1 + + return vec, mask, idx, opt1prime + + def sample(self, batch): + if self.n_col == 0: + return None + batch = batch + + idx = np.random.choice(np.arange(self.n_col), batch) + + vec = np.zeros((batch, self.n_opt), dtype='float32') + opt1prime = random_choice_prob_index_sampling(self.p_sampling,idx) + + for i in np.arange(batch): + vec[i, self.interval[idx[i], 0] + opt1prime[i]] = 1 + + return vec + +def cond_loss(data, output_info, c, m): + loss = [] + st = 0 + st_c = 0 + for item in output_info: + if item[1] == 'tanh': + st += item[0] + continue + + elif item[1] == 'softmax': + ed = st + item[0] + ed_c = st_c + item[0] + tmp = F.cross_entropy( + data[:, st:ed], + torch.argmax(c[:, st_c:ed_c], dim=1), + reduction='none') + loss.append(tmp) + st = ed + st_c = ed_c + + loss = torch.stack(loss, dim=1) + return (loss * m).sum() / data.size()[0] + +class Sampler(object): + def __init__(self, data, output_info): + super(Sampler, self).__init__() + self.data = data + self.model = [] + self.n = len(data) + st = 0 + for item in output_info: + if item[1] == 'tanh': + st += item[0] + continue + elif item[1] == 'softmax': + ed = st + item[0] + tmp = [] + for j in range(item[0]): + tmp.append(np.nonzero(data[:, st + j])[0]) + self.model.append(tmp) + st = ed + + def sample(self, n, col, opt): + if col is None: + idx = np.random.choice(np.arange(self.n), n) + return self.data[idx] + idx = [] + for c, o in zip(col, opt): + idx.append(np.random.choice(self.model[c][o])) + return self.data[idx] + +class Discriminator(Module): + def __init__(self, side, layers): + super(Discriminator, self).__init__() + self.side = side + info = len(layers)-2 + self.seq = Sequential(*layers) + self.seq_info = Sequential(*layers[:info]) + + def forward(self, input): + return (self.seq(input)), self.seq_info(input) + +class Generator(Module): + def __init__(self, side, layers): + super(Generator, self).__init__() + self.side = side + self.seq = Sequential(*layers) + + def forward(self, input_): + return self.seq(input_) + +def determine_layers_disc(side, num_channels): + assert side >= 4 and side <= 64 + + layer_dims = [(1, side), (num_channels, side // 2)] + + while layer_dims[-1][1] > 3 and len(layer_dims) < 4: + layer_dims.append((layer_dims[-1][0] * 2, layer_dims[-1][1] // 2)) + + layerNorms = [] + num_c = num_channels + num_s = side / 2 + for l in range(len(layer_dims) - 1): + layerNorms.append([int(num_c), int(num_s), int(num_s)]) + num_c = num_c * 2 + num_s = num_s / 2 + + layers_D = [] + + for prev, curr, ln in zip(layer_dims, layer_dims[1:], layerNorms): + layers_D += [ + Conv2d(prev[0], curr[0], 4, 2, 1, bias=False), + LayerNorm(ln), + LeakyReLU(0.2, inplace=True), + ] + + layers_D += [Conv2d(layer_dims[-1][0], 1, layer_dims[-1][1], 1, 0), ReLU(True)] + + return layers_D + +def determine_layers_gen(side, random_dim, num_channels): + assert side >= 4 and side <= 64 + + layer_dims = [(1, side), (num_channels, side // 2)] + + while layer_dims[-1][1] > 3 and len(layer_dims) < 4: + layer_dims.append((layer_dims[-1][0] * 2, layer_dims[-1][1] // 2)) + + layerNorms = [] + + num_c = num_channels * (2 ** (len(layer_dims) - 2)) + num_s = int(side / (2 ** (len(layer_dims) - 1))) + for l in range(len(layer_dims) - 1): + layerNorms.append([int(num_c), int(num_s), int(num_s)]) + num_c = num_c / 2 + num_s = num_s * 2 + + layers_G = [ConvTranspose2d(random_dim, layer_dims[-1][0], layer_dims[-1][1], 1, 0, output_padding=0, bias=False)] + + for prev, curr, ln in zip(reversed(layer_dims), reversed(layer_dims[:-1]), layerNorms): + layers_G += [LayerNorm(ln), ReLU(True), ConvTranspose2d(prev[0], curr[0], 4, 2, 1, output_padding=0, bias=True)] + return layers_G + +def slerp(val, low, high): + low_norm = low/torch.norm(low, dim=1, keepdim=True) + high_norm = high/torch.norm(high, dim=1, keepdim=True) + omega = torch.acos((low_norm*high_norm).sum(1)).view(val.size(0), 1) + so = torch.sin(omega) + res = (torch.sin((1.0-val)*omega)/so)*low + (torch.sin(val*omega)/so) * high + + return res + +def calc_gradient_penalty_slerp(netD, real_data, fake_data, transformer, device='cpu', lambda_=10): + batchsize = real_data.shape[0] + alpha = torch.rand(batchsize, 1, device=device) + interpolates = slerp(alpha, real_data, fake_data) + interpolates = interpolates.to(device) + interpolates = transformer.transform(interpolates) + interpolates = torch.autograd.Variable(interpolates, requires_grad=True) + disc_interpolates,_ = netD(interpolates) + + gradients = torch.autograd.grad(outputs=disc_interpolates, inputs=interpolates, + grad_outputs=torch.ones(disc_interpolates.size()).to(device), + create_graph=True, retain_graph=True, only_inputs=True)[0] + + gradients_norm = gradients.norm(2, dim=1) + gradient_penalty = ((gradients_norm - 1) ** 2).mean() * lambda_ + + return gradient_penalty + +def weights_init(m): + classname = m.__class__.__name__ + + if classname.find('Conv') != -1: + init.normal_(m.weight.data, 0.0, 0.02) + + elif classname.find('BatchNorm') != -1: + init.normal_(m.weight.data, 1.0, 0.02) + init.constant_(m.bias.data, 0) + +class CTABGANSynthesizer: + def __init__(self, + class_dim=(256, 256, 256, 256), + random_dim=100, + num_channels=64, + l2scale=1e-5, + batch_size=500, + epochs=150, + device="cpu"): + + + self.random_dim = random_dim + self.class_dim = class_dim + self.num_channels = num_channels + self.dside = None + self.gside = None + self.l2scale = l2scale + self.batch_size = batch_size + self.epochs = epochs + self.device = torch.device(device) + + def fit(self, train_data=pd.DataFrame, categorical=[], mixed={}, general=[], non_categorical=[], type={}): + + problem_type = None + target_index=None + if type: + problem_type = list(type.keys())[0] + if problem_type: + target_index = train_data.columns.get_loc(type[problem_type]) + + self.transformer = DataTransformer(train_data=train_data, categorical_list=categorical, mixed_dict=mixed, general_list=general, non_categorical_list=non_categorical) + self.transformer.fit() + train_data = self.transformer.transform(train_data.values) + data_sampler = Sampler(train_data, self.transformer.output_info) + data_dim = self.transformer.output_dim + self.cond_generator = Cond(train_data, self.transformer.output_info) + + sides = [4, 8, 16, 24, 64] + col_size_d = data_dim + self.cond_generator.n_opt + for i in sides: + if i * i >= col_size_d: + self.dside = i + break + + sides = [4, 8, 16, 24, 64] + col_size_g = data_dim + for i in sides: + if i * i >= col_size_g: + self.gside = i + break + + + layers_G = determine_layers_gen(self.gside, self.random_dim+self.cond_generator.n_opt, self.num_channels) + layers_D = determine_layers_disc(self.dside, self.num_channels) + + self.generator = Generator(self.gside, layers_G).to(self.device) + discriminator = Discriminator(self.dside, layers_D).to(self.device) + optimizer_params = dict(lr=2e-4, betas=(0.5, 0.9), eps=1e-3, weight_decay=self.l2scale) + optimizerG = Adam(self.generator.parameters(), **optimizer_params) + optimizerD = Adam(discriminator.parameters(), **optimizer_params) + + st_ed = None + classifier=None + optimizerC= None + if target_index != None: + st_ed= get_st_ed(target_index,self.transformer.output_info) + classifier = Classifier(data_dim,self.class_dim,st_ed).to(self.device) + optimizerC = optim.Adam(classifier.parameters(),**optimizer_params) + + + self.generator.apply(weights_init) + discriminator.apply(weights_init) + + self.Gtransformer = ImageTransformer(self.gside) + self.Dtransformer = ImageTransformer(self.dside) + + epsilon = 0 + epoch = 0 + steps = 0 + ci = 1 + + for i in tqdm(range(self.epochs)): + + + for _ in range(ci): + noisez = torch.randn(self.batch_size, self.random_dim, device=self.device) + condvec = self.cond_generator.sample_train(self.batch_size) + + c, m, col, opt = condvec + c = torch.from_numpy(c).to(self.device) + m = torch.from_numpy(m).to(self.device) + noisez = torch.cat([noisez, c], dim=1) + noisez = noisez.view(self.batch_size,self.random_dim+self.cond_generator.n_opt,1,1) + + perm = np.arange(self.batch_size) + np.random.shuffle(perm) + real = data_sampler.sample(self.batch_size, col[perm], opt[perm]) + c_perm = c[perm] + + real = torch.from_numpy(real.astype('float32')).to(self.device) + + fake = self.generator(noisez) + faket = self.Gtransformer.inverse_transform(fake) + fakeact = apply_activate(faket, self.transformer.output_info) + + fake_cat = torch.cat([fakeact, c], dim=1) + real_cat = torch.cat([real, c_perm], dim=1) + + real_cat_d = self.Dtransformer.transform(real_cat) + fake_cat_d = self.Dtransformer.transform(fake_cat) + + optimizerD.zero_grad() + + d_real,_ = discriminator(real_cat_d) + + + d_real = -torch.mean(d_real) + d_real.backward() + + + d_fake,_ = discriminator(fake_cat_d) + + d_fake = torch.mean(d_fake) + + d_fake.backward() + + pen = calc_gradient_penalty_slerp(discriminator, real_cat, fake_cat, self.Dtransformer , self.device) + + pen.backward() + + optimizerD.step() + + noisez = torch.randn(self.batch_size, self.random_dim, device=self.device) + + condvec = self.cond_generator.sample_train(self.batch_size) + + c, m, col, opt = condvec + c = torch.from_numpy(c).to(self.device) + m = torch.from_numpy(m).to(self.device) + noisez = torch.cat([noisez, c], dim=1) + noisez = noisez.view(self.batch_size,self.random_dim+self.cond_generator.n_opt,1,1) + + optimizerG.zero_grad() + + fake = self.generator(noisez) + faket = self.Gtransformer.inverse_transform(fake) + fakeact = apply_activate(faket, self.transformer.output_info) + + fake_cat = torch.cat([fakeact, c], dim=1) + fake_cat = self.Dtransformer.transform(fake_cat) + + y_fake,info_fake = discriminator(fake_cat) + + cross_entropy = cond_loss(faket, self.transformer.output_info, c, m) + + _,info_real = discriminator(real_cat_d) + + + g = -torch.mean(y_fake) + cross_entropy + g.backward(retain_graph=True) + loss_mean = torch.norm(torch.mean(info_fake.view(self.batch_size,-1), dim=0) - torch.mean(info_real.view(self.batch_size,-1), dim=0), 1) + loss_std = torch.norm(torch.std(info_fake.view(self.batch_size,-1), dim=0) - torch.std(info_real.view(self.batch_size,-1), dim=0), 1) + loss_info = loss_mean + loss_std + loss_info.backward() + optimizerG.step() + + + if problem_type: + + fake = self.generator(noisez) + + faket = self.Gtransformer.inverse_transform(fake) + + fakeact = apply_activate(faket, self.transformer.output_info) + + real_pre, real_label = classifier(real) + fake_pre, fake_label = classifier(fakeact) + + c_loss = CrossEntropyLoss() + + if (st_ed[1] - st_ed[0])==1: + c_loss= SmoothL1Loss() + real_label = real_label.type_as(real_pre) + fake_label = fake_label.type_as(fake_pre) + real_label = torch.reshape(real_label,real_pre.size()) + fake_label = torch.reshape(fake_label,fake_pre.size()) + + + elif (st_ed[1] - st_ed[0])==2: + c_loss = BCELoss() + real_label = real_label.type_as(real_pre) + fake_label = fake_label.type_as(fake_pre) + + loss_cc = c_loss(real_pre, real_label) + loss_cg = c_loss(fake_pre, fake_label) + + optimizerG.zero_grad() + loss_cg.backward() + optimizerG.step() + + optimizerC.zero_grad() + loss_cc.backward() + optimizerC.step() + + + + + @torch.no_grad() + def sample(self, n, seed=0): + + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + sample_batch_size = 8092 + self.generator.eval() + + output_info = self.transformer.output_info + steps = n // sample_batch_size + 1 + + data = [] + + for i in range(steps): + noisez = torch.randn(self.batch_size, self.random_dim, device=self.device) + condvec = self.cond_generator.sample(self.batch_size) + c = condvec + c = torch.from_numpy(c).to(self.device) + noisez = torch.cat([noisez, c], dim=1) + noisez = noisez.view(self.batch_size,self.random_dim+self.cond_generator.n_opt,1,1) + + fake = self.generator(noisez) + faket = self.Gtransformer.inverse_transform(fake) + fakeact = apply_activate(faket,output_info) + data.append(fakeact.detach().cpu().numpy()) + + data = np.concatenate(data, axis=0) + result,resample = self.transformer.inverse_transform(data) + + while len(result) < n: + data_resample = [] + steps_left = resample// self.batch_size + 1 + + for i in range(steps_left): + noisez = torch.randn(self.batch_size, self.random_dim, device=self.device) + condvec = self.cond_generator.sample(self.batch_size) + c = condvec + c = torch.from_numpy(c).to(self.device) + noisez = torch.cat([noisez, c], dim=1) + noisez = noisez.view(self.batch_size,self.random_dim+self.cond_generator.n_opt,1,1) + + fake = self.generator(noisez) + faket = self.Gtransformer.inverse_transform(fake) + fakeact = apply_activate(faket, output_info) + data_resample.append(fakeact.detach().cpu().numpy()) + + data_resample = np.concatenate(data_resample, axis=0) + + res,resample = self.transformer.inverse_transform(data_resample) + result = np.concatenate([result,res],axis=0) + + return result[0:n] + diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/synthesizer/transformer.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/synthesizer/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..ddad92266dc6d8b47e9ea1f4b4528795b9126e7d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model copy/synthesizer/transformer.py @@ -0,0 +1,429 @@ +import numpy as np +import pandas as pd +import torch +from sklearn.mixture import BayesianGaussianMixture + +class DataTransformer(): + + def __init__(self, train_data=pd.DataFrame, categorical_list=[], mixed_dict={}, general_list=[], non_categorical_list=[], n_clusters=10, eps=0.005): + self.meta = None + self.n_clusters = n_clusters + self.eps = eps + self.train_data = train_data + self.categorical_columns= categorical_list + self.mixed_columns= mixed_dict + self.general_columns = general_list + self.non_categorical_columns= non_categorical_list + + def get_metadata(self): + + meta = [] + + for index in range(self.train_data.shape[1]): + column = self.train_data.iloc[:,index] + if index in self.categorical_columns: + if index in self.non_categorical_columns: + meta.append({ + "name": index, + "type": "continuous", + "min": column.min(), + "max": column.max(), + }) + else: + mapper = column.value_counts().index.tolist() + meta.append({ + "name": index, + "type": "categorical", + "size": len(mapper), + "i2s": mapper + }) + + elif index in self.mixed_columns.keys(): + meta.append({ + "name": index, + "type": "mixed", + "min": column.min(), + "max": column.max(), + "modal": self.mixed_columns[index] + }) + else: + meta.append({ + "name": index, + "type": "continuous", + "min": column.min(), + "max": column.max(), + }) + + return meta + + def fit(self): + data = self.train_data.values + self.meta = self.get_metadata() + model = [] + self.ordering = [] + self.output_info = [] + self.output_dim = 0 + self.components = [] + self.filter_arr = [] + for id_, info in enumerate(self.meta): + if info['type'] == "continuous": + if id_ not in self.general_columns: + gm = BayesianGaussianMixture( + n_components = self.n_clusters, + weight_concentration_prior_type='dirichlet_process', + weight_concentration_prior=0.001, + max_iter=100,n_init=1, random_state=42) + gm.fit(data[:, id_].reshape([-1, 1])) + mode_freq = (pd.Series(gm.predict(data[:, id_].reshape([-1, 1]))).value_counts().keys()) + model.append(gm) + old_comp = gm.weights_ > self.eps + comp = [] + for i in range(self.n_clusters): + if (i in (mode_freq)) & old_comp[i]: + comp.append(True) + else: + comp.append(False) + self.components.append(comp) + self.output_info += [(1, 'tanh','no_g'), (np.sum(comp), 'softmax')] + self.output_dim += 1 + np.sum(comp) + else: + model.append(None) + self.components.append(None) + self.output_info += [(1, 'tanh','yes_g')] + self.output_dim += 1 + + elif info['type'] == "mixed": + + gm1 = BayesianGaussianMixture( + n_components = self.n_clusters, + weight_concentration_prior_type='dirichlet_process', + weight_concentration_prior=0.001, max_iter=100, + n_init=1,random_state=42) + gm2 = BayesianGaussianMixture( + n_components = self.n_clusters, + weight_concentration_prior_type='dirichlet_process', + weight_concentration_prior=0.001, max_iter=100, + n_init=1,random_state=42) + + gm1.fit(data[:, id_].reshape([-1, 1])) + + filter_arr = [] + for element in data[:, id_]: + if element not in info['modal']: + filter_arr.append(True) + else: + filter_arr.append(False) + + gm2.fit(data[:, id_][filter_arr].reshape([-1, 1])) + mode_freq = (pd.Series(gm2.predict(data[:, id_][filter_arr].reshape([-1, 1]))).value_counts().keys()) + self.filter_arr.append(filter_arr) + model.append((gm1,gm2)) + + old_comp = gm2.weights_ > self.eps + + comp = [] + + for i in range(self.n_clusters): + if (i in (mode_freq)) & old_comp[i]: + comp.append(True) + else: + comp.append(False) + + self.components.append(comp) + + self.output_info += [(1, 'tanh',"no_g"), (np.sum(comp) + len(info['modal']), 'softmax')] + self.output_dim += 1 + np.sum(comp) + len(info['modal']) + else: + model.append(None) + self.components.append(None) + self.output_info += [(info['size'], 'softmax')] + self.output_dim += info['size'] + self.model = model + + def transform(self, data, ispositive = False, positive_list = None): + values = [] + mixed_counter = 0 + for id_, info in enumerate(self.meta): + current = data[:, id_] + if info['type'] == "continuous": + if id_ not in self.general_columns: + current = current.reshape([-1, 1]) + means = self.model[id_].means_.reshape((1, self.n_clusters)) + stds = np.sqrt(self.model[id_].covariances_).reshape((1, self.n_clusters)) + features = np.empty(shape=(len(current),self.n_clusters)) + if ispositive == True: + if id_ in positive_list: + features = np.abs(current - means) / (4 * stds) + else: + features = (current - means) / (4 * stds) + + probs = self.model[id_].predict_proba(current.reshape([-1, 1])) + n_opts = sum(self.components[id_]) + features = features[:, self.components[id_]] + probs = probs[:, self.components[id_]] + + opt_sel = np.zeros(len(data), dtype='int') + for i in range(len(data)): + pp = probs[i] + 1e-6 + pp = pp / sum(pp) + opt_sel[i] = np.random.choice(np.arange(n_opts), p=pp) + + idx = np.arange((len(features))) + features = features[idx, opt_sel].reshape([-1, 1]) + features = np.clip(features, -.99, .99) + probs_onehot = np.zeros_like(probs) + probs_onehot[np.arange(len(probs)), opt_sel] = 1 + + re_ordered_phot = np.zeros_like(probs_onehot) + + col_sums = probs_onehot.sum(axis=0) + + + n = probs_onehot.shape[1] + largest_indices = np.argsort(-1*col_sums)[:n] + self.ordering.append(largest_indices) + for id,val in enumerate(largest_indices): + re_ordered_phot[:,id] = probs_onehot[:,val] + + + values += [features, re_ordered_phot] + + else: + + self.ordering.append(None) + + if id_ in self.non_categorical_columns: + info['min'] = -1e-3 + info['max'] = info['max'] + 1e-3 + + current = (current - (info['min'])) / (info['max'] - info['min']) + current = current * 2 - 1 + current = current.reshape([-1, 1]) + values.append(current) + + elif info['type'] == "mixed": + + means_0 = self.model[id_][0].means_.reshape([-1]) + stds_0 = np.sqrt(self.model[id_][0].covariances_).reshape([-1]) + + zero_std_list = [] + means_needed = [] + stds_needed = [] + + for mode in info['modal']: + if mode!=-9999999: + dist = [] + for idx,val in enumerate(list(means_0.flatten())): + dist.append(abs(mode-val)) + index_min = np.argmin(np.array(dist)) + zero_std_list.append(index_min) + else: continue + + for idx in zero_std_list: + means_needed.append(means_0[idx]) + stds_needed.append(stds_0[idx]) + + + mode_vals = [] + + for i,j,k in zip(info['modal'],means_needed,stds_needed): + this_val = np.abs(i - j) / (4*k) + mode_vals.append(this_val) + + if -9999999 in info["modal"]: + mode_vals.append(0) + + current = current.reshape([-1, 1]) + filter_arr = self.filter_arr[mixed_counter] + current = current[filter_arr] + + means = self.model[id_][1].means_.reshape((1, self.n_clusters)) + stds = np.sqrt(self.model[id_][1].covariances_).reshape((1, self.n_clusters)) + features = np.empty(shape=(len(current),self.n_clusters)) + if ispositive == True: + if id_ in positive_list: + features = np.abs(current - means) / (4 * stds) + else: + features = (current - means) / (4 * stds) + + probs = self.model[id_][1].predict_proba(current.reshape([-1, 1])) + + n_opts = sum(self.components[id_]) # 8 + features = features[:, self.components[id_]] + probs = probs[:, self.components[id_]] + + opt_sel = np.zeros(len(current), dtype='int') + for i in range(len(current)): + pp = probs[i] + 1e-6 + pp = pp / sum(pp) + opt_sel[i] = np.random.choice(np.arange(n_opts), p=pp) + idx = np.arange((len(features))) + features = features[idx, opt_sel].reshape([-1, 1]) + features = np.clip(features, -.99, .99) + probs_onehot = np.zeros_like(probs) + probs_onehot[np.arange(len(probs)), opt_sel] = 1 + extra_bits = np.zeros([len(current), len(info['modal'])]) + temp_probs_onehot = np.concatenate([extra_bits,probs_onehot], axis = 1) + final = np.zeros([len(data), 1 + probs_onehot.shape[1] + len(info['modal'])]) + features_curser = 0 + for idx, val in enumerate(data[:, id_]): + if val in info['modal']: + category_ = list(map(info['modal'].index, [val]))[0] + final[idx, 0] = mode_vals[category_] + final[idx, (category_+1)] = 1 + + else: + final[idx, 0] = features[features_curser] + final[idx, (1+len(info['modal'])):] = temp_probs_onehot[features_curser][len(info['modal']):] + features_curser = features_curser + 1 + + just_onehot = final[:,1:] + re_ordered_jhot= np.zeros_like(just_onehot) + n = just_onehot.shape[1] + col_sums = just_onehot.sum(axis=0) + largest_indices = np.argsort(-1*col_sums)[:n] + self.ordering.append(largest_indices) + for id,val in enumerate(largest_indices): + re_ordered_jhot[:,id] = just_onehot[:,val] + final_features = final[:,0].reshape([-1, 1]) + values += [final_features, re_ordered_jhot] + mixed_counter = mixed_counter + 1 + + else: + self.ordering.append(None) + col_t = np.zeros([len(data), info['size']]) + idx = list(map(info['i2s'].index, current)) + col_t[np.arange(len(data)), idx] = 1 + values.append(col_t) + + return np.concatenate(values, axis=1) + + def inverse_transform(self, data): + data_t = np.zeros([len(data), len(self.meta)]) + invalid_ids = [] + st = 0 + for id_, info in enumerate(self.meta): + if info['type'] == "continuous": + if id_ not in self.general_columns: + u = data[:, st] + v = data[:, st + 1:st + 1 + np.sum(self.components[id_])] + order = self.ordering[id_] + v_re_ordered = np.zeros_like(v) + + for id,val in enumerate(order): + v_re_ordered[:,val] = v[:,id] + + v = v_re_ordered + + u = np.clip(u, -1, 1) + v_t = np.ones((data.shape[0], self.n_clusters)) * -100 + v_t[:, self.components[id_]] = v + v = v_t + st += 1 + np.sum(self.components[id_]) + means = self.model[id_].means_.reshape([-1]) + stds = np.sqrt(self.model[id_].covariances_).reshape([-1]) + p_argmax = np.argmax(v, axis=1) + std_t = stds[p_argmax] + mean_t = means[p_argmax] + tmp = u * 4 * std_t + mean_t + + for idx,val in enumerate(tmp): + if (val < info["min"]) | (val > info['max']): + invalid_ids.append(idx) + + if id_ in self.non_categorical_columns: + + tmp = np.round(tmp) + + data_t[:, id_] = tmp + + else: + u = data[:, st] + u = (u + 1) / 2 + u = np.clip(u, 0, 1) + u = u * (info['max'] - info['min']) + info['min'] + if id_ in self.non_categorical_columns: + data_t[:, id_] = np.round(u) + else: data_t[:, id_] = u + + st += 1 + + elif info['type'] == "mixed": + + u = data[:, st] + full_v = data[:,(st+1):(st+1)+len(info['modal'])+np.sum(self.components[id_])] + order = self.ordering[id_] + full_v_re_ordered = np.zeros_like(full_v) + + for id,val in enumerate(order): + full_v_re_ordered[:,val] = full_v[:,id] + + full_v = full_v_re_ordered + + + mixed_v = full_v[:,:len(info['modal'])] + v = full_v[:,-np.sum(self.components[id_]):] + + u = np.clip(u, -1, 1) + v_t = np.ones((data.shape[0], self.n_clusters)) * -100 + v_t[:, self.components[id_]] = v + v = np.concatenate([mixed_v,v_t], axis=1) + + st += 1 + np.sum(self.components[id_]) + len(info['modal']) + means = self.model[id_][1].means_.reshape([-1]) + stds = np.sqrt(self.model[id_][1].covariances_).reshape([-1]) + p_argmax = np.argmax(v, axis=1) + + result = np.zeros_like(u) + + for idx in range(len(data)): + if p_argmax[idx] < len(info['modal']): + argmax_value = p_argmax[idx] + result[idx] = float(list(map(info['modal'].__getitem__, [argmax_value]))[0]) + else: + std_t = stds[(p_argmax[idx]-len(info['modal']))] + mean_t = means[(p_argmax[idx]-len(info['modal']))] + result[idx] = u[idx] * 4 * std_t + mean_t + + for idx,val in enumerate(result): + if (val < info["min"]) | (val > info['max']): + invalid_ids.append(idx) + + data_t[:, id_] = result + + else: + current = data[:, st:st + info['size']] + st += info['size'] + idx = np.argmax(current, axis=1) + data_t[:, id_] = list(map(info['i2s'].__getitem__, idx)) + + + invalid_ids = np.unique(np.array(invalid_ids)) + all_ids = np.arange(0,len(data)) + valid_ids = list(set(all_ids) - set(invalid_ids)) + + return data_t[valid_ids],len(invalid_ids) + + +class ImageTransformer(): + + def __init__(self, side): + + self.height = side + + def transform(self, data): + + if self.height * self.height > len(data[0]): + + padding = torch.zeros((len(data), self.height * self.height - len(data[0]))).to(data.device) + data = torch.cat([data, padding], axis=1) + + return data.view(-1, 1, self.height, self.height) + + def inverse_transform(self, data): + + data = data.view(-1, self.height * self.height) + + return data + + diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/ctabgan.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/ctabgan.py new file mode 100644 index 0000000000000000000000000000000000000000..a99b5d587da5f3e49b3923e43975e02e9c11e6e1 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/ctabgan.py @@ -0,0 +1,72 @@ +""" +Generative model training algorithm based on the CTABGANSynthesiser + +""" +import pandas as pd +import time +from model.pipeline.data_preparation import DataPrep +from model.synthesizer.ctabgan_synthesizer import CTABGANSynthesizer + +import warnings + +warnings.filterwarnings("ignore") + +class CTABGAN(): + + def __init__(self, + df, + test_ratio = 0.20, + categorical_columns = [], + log_columns = [], + mixed_columns= {}, + general_columns = [], + non_categorical_columns = [], + integer_columns = [], + problem_type= {}, + class_dim=(256, 256, 256, 256), + random_dim=100, + num_channels=64, + l2scale=1e-5, + batch_size=500, + epochs=150, + lr=2e-4, + device="cpu"): + + self.__name__ = 'CTABGAN' + + self.synthesizer = CTABGANSynthesizer( + class_dim=class_dim, + random_dim=random_dim, + num_channels=num_channels, + l2scale=l2scale, + lr=lr, + batch_size=batch_size, + epochs=epochs, + device=device + ) + self.raw_df = df + self.test_ratio = test_ratio + self.categorical_columns = categorical_columns + self.log_columns = log_columns + self.mixed_columns = mixed_columns + self.general_columns = general_columns + self.non_categorical_columns = non_categorical_columns + self.integer_columns = integer_columns + self.problem_type = problem_type + + def fit(self): + + start_time = time.time() + self.data_prep = DataPrep(self.raw_df,self.categorical_columns,self.log_columns,self.mixed_columns,self.general_columns,self.non_categorical_columns,self.integer_columns,self.problem_type,self.test_ratio) + self.synthesizer.fit(train_data=self.data_prep.df, categorical = self.data_prep.column_types["categorical"], mixed = self.data_prep.column_types["mixed"], + general = self.data_prep.column_types["general"], non_categorical = self.data_prep.column_types["non_categorical"], type=self.problem_type) + end_time = time.time() + print('Finished training in',end_time-start_time," seconds.") + + + def generate_samples(self, num_samples, seed=0): + + sample = self.synthesizer.sample(num_samples, seed) + sample_df = self.data_prep.inverse_prep(sample) + + return sample_df \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/eval/evaluation.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/eval/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..1492fcf80cfb907f95b3844e4c0f38f15effbdb0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/eval/evaluation.py @@ -0,0 +1,193 @@ +import numpy as np +import pandas as pd +from sklearn import metrics +from sklearn import model_selection +from sklearn.preprocessing import MinMaxScaler,StandardScaler +from sklearn.neural_network import MLPClassifier +from sklearn.linear_model import LogisticRegression +from sklearn import svm,tree +from sklearn.ensemble import RandomForestClassifier +from dython.nominal import compute_associations +from scipy.stats import wasserstein_distance +from scipy.spatial import distance +import warnings + +warnings.filterwarnings("ignore") + +def supervised_model_training(x_train, y_train, x_test, + y_test, model_name): + + + if model_name == 'lr': + model = LogisticRegression(random_state=42,max_iter=500) + elif model_name == 'svm': + model = svm.SVC(random_state=42,probability=True) + elif model_name == 'dt': + model = tree.DecisionTreeClassifier(random_state=42) + elif model_name == 'rf': + model = RandomForestClassifier(random_state=42) + elif model_name == "mlp": + model = MLPClassifier(random_state=42,max_iter=100) + + model.fit(x_train, y_train) + pred = model.predict(x_test) + + if len(np.unique(y_train))>2: + predict = model.predict_proba(x_test) + acc = metrics.accuracy_score(y_test,pred)*100 + auc = metrics.roc_auc_score(y_test, predict,average="weighted",multi_class="ovr") + f1_score = metrics.precision_recall_fscore_support(y_test, pred,average="weighted")[2] + return [acc, auc,f1_score] + + else: + predict = model.predict_proba(x_test)[:,1] + acc = metrics.accuracy_score(y_test,pred)*100 + auc = metrics.roc_auc_score(y_test, predict) + f1_score = metrics.precision_recall_fscore_support(y_test,pred)[2].mean() + return [acc, auc,f1_score] + + +def get_utility_metrics(real_path,fake_paths,scaler="MinMax",classifiers=["lr","dt","rf","mlp"],test_ratio=.20): + + data_real = pd.read_csv(real_path).to_numpy() + data_dim = data_real.shape[1] + + data_real_y = data_real[:,-1] + data_real_X = data_real[:,:data_dim-1] + X_train_real, X_test_real, y_train_real, y_test_real = model_selection.train_test_split(data_real_X ,data_real_y, test_size=test_ratio, stratify=data_real_y,random_state=42) + + if scaler=="MinMax": + scaler_real = MinMaxScaler() + else: + scaler_real = StandardScaler() + + scaler_real.fit(data_real_X) + X_train_real_scaled = scaler_real.transform(X_train_real) + X_test_real_scaled = scaler_real.transform(X_test_real) + + all_real_results = [] + for classifier in classifiers: + real_results = supervised_model_training(X_train_real_scaled,y_train_real,X_test_real_scaled,y_test_real,classifier) + all_real_results.append(real_results) + + all_fake_results_avg = [] + + for fake_path in fake_paths: + data_fake = pd.read_csv(fake_path).to_numpy() + data_fake_y = data_fake[:,-1] + data_fake_X = data_fake[:,:data_dim-1] + X_train_fake, _ , y_train_fake, _ = model_selection.train_test_split(data_fake_X ,data_fake_y, test_size=test_ratio, stratify=data_fake_y,random_state=42) + + if scaler=="MinMax": + scaler_fake = MinMaxScaler() + else: + scaler_fake = StandardScaler() + + scaler_fake.fit(data_fake_X) + + X_train_fake_scaled = scaler_fake.transform(X_train_fake) + + all_fake_results = [] + for classifier in classifiers: + fake_results = supervised_model_training(X_train_fake_scaled,y_train_fake,X_test_real_scaled,y_test_real,classifier) + all_fake_results.append(fake_results) + + all_fake_results_avg.append(all_fake_results) + + diff_results = np.array(all_real_results)- np.array(all_fake_results_avg).mean(axis=0) + + return diff_results + +def stat_sim(real_path,fake_path,cat_cols=None): + + Stat_dict={} + + real = pd.read_csv(real_path) + fake = pd.read_csv(fake_path) + + really = real.copy() + fakey = fake.copy() + + real_corr = compute_associations(real, nominal_columns=cat_cols) + + fake_corr = compute_associations(fake, nominal_columns=cat_cols) + + corr_dist = np.linalg.norm(real_corr - fake_corr) + + cat_stat = [] + num_stat = [] + + for column in real.columns: + + if column in cat_cols: + + real_pdf=(really[column].value_counts()/really[column].value_counts().sum()) + fake_pdf=(fakey[column].value_counts()/fakey[column].value_counts().sum()) + categories = (fakey[column].value_counts()/fakey[column].value_counts().sum()).keys().tolist() + sorted_categories = sorted(categories) + + real_pdf_values = [] + fake_pdf_values = [] + + for i in sorted_categories: + real_pdf_values.append(real_pdf[i]) + fake_pdf_values.append(fake_pdf[i]) + + if len(real_pdf)!=len(fake_pdf): + zero_cats = set(really[column].value_counts().keys())-set(fakey[column].value_counts().keys()) + for z in zero_cats: + real_pdf_values.append(real_pdf[z]) + fake_pdf_values.append(0) + Stat_dict[column]=(distance.jensenshannon(real_pdf_values,fake_pdf_values, 2.0)) + cat_stat.append(Stat_dict[column]) + else: + scaler = MinMaxScaler() + scaler.fit(real[column].values.reshape(-1,1)) + l1 = scaler.transform(real[column].values.reshape(-1,1)).flatten() + l2 = scaler.transform(fake[column].values.reshape(-1,1)).flatten() + Stat_dict[column]= (wasserstein_distance(l1,l2)) + num_stat.append(Stat_dict[column]) + + return [np.mean(num_stat),np.mean(cat_stat),corr_dist] + +def privacy_metrics(real_path,fake_path,data_percent=15): + + real = pd.read_csv(real_path).drop_duplicates(keep=False) + fake = pd.read_csv(fake_path).drop_duplicates(keep=False) + + real_refined = real.sample(n=int(len(real)*(.01*data_percent)), random_state=42).to_numpy() + fake_refined = fake.sample(n=int(len(fake)*(.01*data_percent)), random_state=42).to_numpy() + + scalerR = StandardScaler() + scalerR.fit(real_refined) + scalerF = StandardScaler() + scalerF.fit(fake_refined) + df_real_scaled = scalerR.transform(real_refined) + df_fake_scaled = scalerF.transform(fake_refined) + + dist_rf = metrics.pairwise_distances(df_real_scaled, Y=df_fake_scaled, metric='minkowski', n_jobs=-1) + dist_rr = metrics.pairwise_distances(df_real_scaled, Y=None, metric='minkowski', n_jobs=-1) + rd_dist_rr = dist_rr[~np.eye(dist_rr.shape[0],dtype=bool)].reshape(dist_rr.shape[0],-1) + dist_ff = metrics.pairwise_distances(df_fake_scaled, Y=None, metric='minkowski', n_jobs=-1) + rd_dist_ff = dist_ff[~np.eye(dist_ff.shape[0],dtype=bool)].reshape(dist_ff.shape[0],-1) + smallest_two_indexes_rf = [dist_rf[i].argsort()[:2] for i in range(len(dist_rf))] + smallest_two_rf = [dist_rf[i][smallest_two_indexes_rf[i]] for i in range(len(dist_rf))] + smallest_two_indexes_rr = [rd_dist_rr[i].argsort()[:2] for i in range(len(rd_dist_rr))] + smallest_two_rr = [rd_dist_rr[i][smallest_two_indexes_rr[i]] for i in range(len(rd_dist_rr))] + smallest_two_indexes_ff = [rd_dist_ff[i].argsort()[:2] for i in range(len(rd_dist_ff))] + smallest_two_ff = [rd_dist_ff[i][smallest_two_indexes_ff[i]] for i in range(len(rd_dist_ff))] + nn_ratio_rr = np.array([i[0]/i[1] for i in smallest_two_rr]) + nn_ratio_ff = np.array([i[0]/i[1] for i in smallest_two_ff]) + nn_ratio_rf = np.array([i[0]/i[1] for i in smallest_two_rf]) + nn_fifth_perc_rr = np.percentile(nn_ratio_rr,5) + nn_fifth_perc_ff = np.percentile(nn_ratio_ff,5) + nn_fifth_perc_rf = np.percentile(nn_ratio_rf,5) + + min_dist_rf = np.array([i[0] for i in smallest_two_rf]) + fifth_perc_rf = np.percentile(min_dist_rf,5) + min_dist_rr = np.array([i[0] for i in smallest_two_rr]) + fifth_perc_rr = np.percentile(min_dist_rr,5) + min_dist_ff = np.array([i[0] for i in smallest_two_ff]) + fifth_perc_ff = np.percentile(min_dist_ff,5) + + return np.array([fifth_perc_rf,fifth_perc_rr,fifth_perc_ff,nn_fifth_perc_rf,nn_fifth_perc_rr,nn_fifth_perc_ff]).reshape(1,6) \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/pipeline/data_preparation.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/pipeline/data_preparation.py new file mode 100644 index 0000000000000000000000000000000000000000..3f4b7293d63db51bc7c9da6c38b84c6c033d779b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/pipeline/data_preparation.py @@ -0,0 +1,131 @@ +import numpy as np +import pandas as pd +from sklearn import preprocessing +from sklearn import model_selection + +class DataPrep(object): + + def __init__(self, raw_df: pd.DataFrame, categorical: list, log:list, mixed:dict, general:list, non_categorical:list, integer:list, type:dict, test_ratio:float): + + + self.categorical_columns = categorical + self.log_columns = log + self.mixed_columns = mixed + self.general_columns = general + self.non_categorical_columns = non_categorical + self.integer_columns = integer + self.column_types = dict() + self.column_types["categorical"] = [] + self.column_types["mixed"] = {} + self.column_types["general"] = [] + self.column_types["non_categorical"] = [] + self.lower_bounds = {} + self.label_encoder_list = [] + + target_col = list(type.values())[0] + if target_col is not None: + y_real = raw_df[target_col] + X_real = raw_df.drop(columns=[target_col]) + # X_train_real, _, y_train_real, _ = model_selection.train_test_split(X_real ,y_real, test_size=test_ratio, stratify=y_real,random_state=42) + X_train_real, y_train_real = X_real, y_real + + X_train_real[target_col]= y_train_real + + self.df = X_train_real + else: + self.df = raw_df + + self.df = self.df.replace(r' ', np.nan) + self.df = self.df.fillna('empty') + + all_columns= set(self.df.columns) + irrelevant_missing_columns = set(self.categorical_columns) + relevant_missing_columns = list(all_columns - irrelevant_missing_columns) + + for i in relevant_missing_columns: + if i in self.log_columns: + if "empty" in list(self.df[i].values): + self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x) + self.mixed_columns[i] = [-9999999] + elif i in list(self.mixed_columns.keys()): + if "empty" in list(self.df[i].values): + self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x ) + self.mixed_columns[i].append(-9999999) + else: + if "empty" in list(self.df[i].values): + self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x) + self.mixed_columns[i] = [-9999999] + + if self.log_columns: + for log_column in self.log_columns: + valid_indices = [] + for idx,val in enumerate(self.df[log_column].values): + if val!=-9999999: + valid_indices.append(idx) + eps = 1 + lower = np.min(self.df[log_column].iloc[valid_indices].values) + self.lower_bounds[log_column] = lower + if lower>0: + self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x) if x!=-9999999 else -9999999) + elif lower == 0: + self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x+eps) if x!=-9999999 else -9999999) + else: + self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x-lower+eps) if x!=-9999999 else -9999999) + + for column_index, column in enumerate(self.df.columns): + if column in self.categorical_columns: + label_encoder = preprocessing.LabelEncoder() + self.df[column] = self.df[column].astype(str) + label_encoder.fit(self.df[column]) + current_label_encoder = dict() + current_label_encoder['column'] = column + current_label_encoder['label_encoder'] = label_encoder + transformed_column = label_encoder.transform(self.df[column]) + self.df[column] = transformed_column + self.label_encoder_list.append(current_label_encoder) + self.column_types["categorical"].append(column_index) + + if column in self.general_columns: + self.column_types["general"].append(column_index) + + if column in self.non_categorical_columns: + self.column_types["non_categorical"].append(column_index) + + elif column in self.mixed_columns: + self.column_types["mixed"][column_index] = self.mixed_columns[column] + + elif column in self.general_columns: + self.column_types["general"].append(column_index) + + + super().__init__() + + def inverse_prep(self, data, eps=1): + + df_sample = pd.DataFrame(data,columns=self.df.columns) + + for i in range(len(self.label_encoder_list)): + le = self.label_encoder_list[i]["label_encoder"] + df_sample[self.label_encoder_list[i]["column"]] = df_sample[self.label_encoder_list[i]["column"]].astype(int) + df_sample[self.label_encoder_list[i]["column"]] = le.inverse_transform(df_sample[self.label_encoder_list[i]["column"]]) + + if self.log_columns: + for i in df_sample: + if i in self.log_columns: + lower_bound = self.lower_bounds[i] + if lower_bound>0: + df_sample[i].apply(lambda x: np.exp(x)) + elif lower_bound==0: + df_sample[i] = df_sample[i].apply(lambda x: np.ceil(np.exp(x)-eps) if (np.exp(x)-eps) < 0 else (np.exp(x)-eps)) + else: + df_sample[i] = df_sample[i].apply(lambda x: np.exp(x)-eps+lower_bound) + + if self.integer_columns: + for column in self.integer_columns: + df_sample[column]= (np.round(df_sample[column].values)) + df_sample[column] = df_sample[column].astype(int) + + df_sample.replace(-9999999, np.nan,inplace=True) + df_sample.replace('empty', np.nan,inplace=True) + + return df_sample diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/privacy_utils/rdp_accountant.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/privacy_utils/rdp_accountant.py new file mode 100644 index 0000000000000000000000000000000000000000..5e225cbb0994fa6b9e9726f38d873754bbfe82cf --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/privacy_utils/rdp_accountant.py @@ -0,0 +1,280 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import sys + +import numpy as np +from scipy import special +import six + +######################## +# LOG-SPACE ARITHMETIC # +######################## + + +def _log_add(logx, logy): + """Add two numbers in the log space.""" + a, b = min(logx, logy), max(logx, logy) + if a == -np.inf: # adding 0 + return b + # Use exp(a) + exp(b) = (exp(a - b) + 1) * exp(b) + return math.log1p(math.exp(a - b)) + b # log1p(x) = log(x + 1) + + +def _log_sub(logx, logy): + """Subtract two numbers in the log space. Answer must be non-negative.""" + if logx < logy: + raise ValueError("The result of subtraction must be non-negative.") + if logy == -np.inf: # subtracting 0 + return logx + if logx == logy: + return -np.inf # 0 is represented as -np.inf in the log space. + + try: + # Use exp(x) - exp(y) = (exp(x - y) - 1) * exp(y). + return math.log(math.expm1(logx - logy)) + logy # expm1(x) = exp(x) - 1 + except OverflowError: + return logx + + +def _log_print(logx): + """Pretty print.""" + if logx < math.log(sys.float_info.max): + return "{}".format(math.exp(logx)) + else: + return "exp({})".format(logx) + + +def _compute_log_a_int(q, sigma, alpha): + """Compute log(A_alpha) for integer alpha. 0 < q < 1.""" + assert isinstance(alpha, six.integer_types) + + # Initialize with 0 in the log space. + log_a = -np.inf + + for i in range(alpha + 1): + log_coef_i = ( + math.log(special.binom(alpha, i)) + i * math.log(q) + + (alpha - i) * math.log(1 - q)) + + s = log_coef_i + (i * i - i) / (2 * (sigma**2)) + log_a = _log_add(log_a, s) + + return float(log_a) + + +def _compute_log_a_frac(q, sigma, alpha): + """Compute log(A_alpha) for fractional alpha. 0 < q < 1.""" + # The two parts of A_alpha, integrals over (-inf,z0] and [z0, +inf), are + # initialized to 0 in the log space: + log_a0, log_a1 = -np.inf, -np.inf + i = 0 + + z0 = sigma**2 * math.log(1 / q - 1) + .5 + + while True: # do ... until loop + coef = special.binom(alpha, i) + log_coef = math.log(abs(coef)) + j = alpha - i + + log_t0 = log_coef + i * math.log(q) + j * math.log(1 - q) + log_t1 = log_coef + j * math.log(q) + i * math.log(1 - q) + + log_e0 = math.log(.5) + _log_erfc((i - z0) / (math.sqrt(2) * sigma)) + log_e1 = math.log(.5) + _log_erfc((z0 - j) / (math.sqrt(2) * sigma)) + + log_s0 = log_t0 + (i * i - i) / (2 * (sigma**2)) + log_e0 + log_s1 = log_t1 + (j * j - j) / (2 * (sigma**2)) + log_e1 + + if coef > 0: + log_a0 = _log_add(log_a0, log_s0) + log_a1 = _log_add(log_a1, log_s1) + else: + log_a0 = _log_sub(log_a0, log_s0) + log_a1 = _log_sub(log_a1, log_s1) + + i += 1 + if max(log_s0, log_s1) < -30: + break + + return _log_add(log_a0, log_a1) + + +def _compute_log_a(q, sigma, alpha): + """Compute log(A_alpha) for any positive finite alpha.""" + if float(alpha).is_integer(): + return _compute_log_a_int(q, sigma, int(alpha)) + else: + return _compute_log_a_frac(q, sigma, alpha) + + +def _log_erfc(x): + """Compute log(erfc(x)) with high accuracy for large x.""" + try: + return math.log(2) + special.log_ndtr(-x * 2**.5) + except NameError: + # If log_ndtr is not available, approximate as follows: + r = special.erfc(x) + if r == 0.0: + # Using the Laurent series at infinity for the tail of the erfc function: + # erfc(x) ~ exp(-x^2-.5/x^2+.625/x^4)/(x*pi^.5) + # To verify in Mathematica: + # Series[Log[Erfc[x]] + Log[x] + Log[Pi]/2 + x^2, {x, Infinity, 6}] + return (-math.log(math.pi) / 2 - math.log(x) - x**2 - .5 * x**-2 + + .625 * x**-4 - 37. / 24. * x**-6 + 353. / 64. * x**-8) + else: + return math.log(r) + + +def _compute_delta(orders, rdp, eps): + """Compute delta given a list of RDP values and target epsilon. + + Args: + orders: An array (or a scalar) of orders. + rdp: A list (or a scalar) of RDP guarantees. + eps: The target epsilon. + + Returns: + Pair of (delta, optimal_order). + + Raises: + ValueError: If input is malformed. + + """ + orders_vec = np.atleast_1d(orders) + rdp_vec = np.atleast_1d(rdp) + + if len(orders_vec) != len(rdp_vec): + raise ValueError("Input lists must have the same length.") + + deltas = np.exp((rdp_vec - eps) * (orders_vec - 1)) + idx_opt = np.argmin(deltas) + return min(deltas[idx_opt], 1.), orders_vec[idx_opt] + + +def _compute_eps(orders, rdp, delta): + """Compute epsilon given a list of RDP values and target delta. + + Args: + orders: An array (or a scalar) of orders. + rdp: A list (or a scalar) of RDP guarantees. + delta: The target delta. + + Returns: + Pair of (eps, optimal_order). + + Raises: + ValueError: If input is malformed. + + """ + orders_vec = np.atleast_1d(orders) + rdp_vec = np.atleast_1d(rdp) + + if len(orders_vec) != len(rdp_vec): + raise ValueError("Input lists must have the same length.") + + eps = rdp_vec - math.log(delta) / (orders_vec - 1) + + idx_opt = np.nanargmin(eps) # Ignore NaNs + return eps[idx_opt], orders_vec[idx_opt] + + +def _compute_rdp(q, sigma, alpha): + """Compute RDP of the Sampled Gaussian mechanism at order alpha. + + Args: + q: The sampling rate. + sigma: The std of the additive Gaussian noise. + alpha: The order at which RDP is computed. + + Returns: + RDP at alpha, can be np.inf. + """ + if q == 0: + return 0 + + if q == 1.: + return alpha / (2 * sigma**2) + + if np.isinf(alpha): + return np.inf + + return _compute_log_a(q, sigma, alpha) / (alpha - 1) + + +def compute_rdp(q, noise_multiplier, steps, orders): + """Compute RDP of the Sampled Gaussian Mechanism. + + Args: + q: The sampling rate. + noise_multiplier: The ratio of the standard deviation of the Gaussian noise + to the l2-sensitivity of the function to which it is added. + steps: The number of steps. + orders: An array (or a scalar) of RDP orders. + + Returns: + The RDPs at all orders, can be np.inf. + """ + if np.isscalar(orders): + rdp = _compute_rdp(q, noise_multiplier, orders) + else: + rdp = np.array([_compute_rdp(q, noise_multiplier, order) + for order in orders]) + + return rdp * steps + + +def get_privacy_spent(orders, rdp, target_eps=None, target_delta=None): + """Compute delta (or eps) for given eps (or delta) from RDP values. + + Args: + orders: An array (or a scalar) of RDP orders. + rdp: An array of RDP values. Must be of the same length as the orders list. + target_eps: If not None, the epsilon for which we compute the corresponding + delta. + target_delta: If not None, the delta for which we compute the corresponding + epsilon. Exactly one of target_eps and target_delta must be None. + + Returns: + eps, delta, opt_order. + + Raises: + ValueError: If target_eps and target_delta are messed up. + """ + if target_eps is None and target_delta is None: + raise ValueError( + "Exactly one out of eps and delta must be None. (Both are).") + + if target_eps is not None and target_delta is not None: + raise ValueError( + "Exactly one out of eps and delta must be None. (None is).") + + if target_eps is not None: + delta, opt_order = _compute_delta(orders, rdp, target_eps) + return target_eps, delta, opt_order + else: + eps, opt_order = _compute_eps(orders, rdp, target_delta) + return eps, target_delta, opt_order + + +def compute_rdp_from_ledger(ledger, orders): + """Compute RDP of Sampled Gaussian Mechanism from ledger. + + Args: + ledger: A formatted privacy ledger. + orders: An array (or a scalar) of RDP orders. + + Returns: + RDP at all orders, can be np.inf. + """ + total_rdp = np.zeros_like(orders, dtype=float) + for sample in ledger: + # Compute equivalent z from l2_clip_bounds and noise stddevs in sample. + # See https://arxiv.org/pdf/1812.06210.pdf for derivation of this formula. + effective_z = sum([ + (q.noise_stddev / q.l2_norm_bound)**-2 for q in sample.queries])**-0.5 + total_rdp += compute_rdp( + sample.selection_probability, effective_z, 1, orders) + return total_rdp \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/synthesizer/ctabgan_synthesizer.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/synthesizer/ctabgan_synthesizer.py new file mode 100644 index 0000000000000000000000000000000000000000..521a29aff4e8db1d45c7c3be98d593892c65c8d1 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/synthesizer/ctabgan_synthesizer.py @@ -0,0 +1,605 @@ +import numpy as np +import pandas as pd +import torch +import torch.utils.data +import torch.optim as optim +from torch.optim import Adam +from torch.nn import functional as F +from torch.nn import (Dropout, LeakyReLU, Linear, Module, ReLU, Sequential, +Conv2d, ConvTranspose2d, Sigmoid, init, BCELoss, CrossEntropyLoss,SmoothL1Loss,LayerNorm) +from model.synthesizer.transformer import ImageTransformer,DataTransformer +from model.privacy_utils.rdp_accountant import compute_rdp, get_privacy_spent +from tqdm import tqdm, trange +import time + + +class Classifier(Module): + def __init__(self,input_dim, dis_dims,st_ed): + super(Classifier,self).__init__() + dim = input_dim-(st_ed[1]-st_ed[0]) + seq = [] + self.str_end = st_ed + for item in list(dis_dims): + seq += [ + Linear(dim, item), + LeakyReLU(0.2), + Dropout(0.5) + ] + dim = item + + if (st_ed[1]-st_ed[0])==1: + seq += [Linear(dim, 1)] + + elif (st_ed[1]-st_ed[0])==2: + seq += [Linear(dim, 1),Sigmoid()] + else: + seq += [Linear(dim,(st_ed[1]-st_ed[0]))] + + self.seq = Sequential(*seq) + + def forward(self, input): + + label=None + + if (self.str_end[1]-self.str_end[0])==1: + label = input[:, self.str_end[0]:self.str_end[1]] + else: + label = torch.argmax(input[:, self.str_end[0]:self.str_end[1]], axis=-1) + + new_imp = torch.cat((input[:,:self.str_end[0]],input[:,self.str_end[1]:]),1) + + if ((self.str_end[1]-self.str_end[0])==2) | ((self.str_end[1]-self.str_end[0])==1): + return self.seq(new_imp).view(-1), label + else: + return self.seq(new_imp), label + +def apply_activate(data, output_info): + data_t = [] + st = 0 + for item in output_info: + if item[1] == 'tanh': + ed = st + item[0] + data_t.append(torch.tanh(data[:, st:ed])) + st = ed + elif item[1] == 'softmax': + ed = st + item[0] + data_t.append(F.gumbel_softmax(data[:, st:ed], tau=0.2)) + st = ed + return torch.cat(data_t, dim=1) + +def get_st_ed(target_col_index,output_info): + st = 0 + c= 0 + tc= 0 + + for item in output_info: + if c==target_col_index: + break + if item[1]=='tanh': + st += item[0] + if item[2] == 'yes_g': + c+=1 + elif item[1] == 'softmax': + st += item[0] + c+=1 + tc+=1 + + ed= st+output_info[tc][0] + + return (st,ed) + +def random_choice_prob_index_sampling(probs,col_idx): + option_list = [] + for i in col_idx: + pp = probs[i] + option_list.append(np.random.choice(np.arange(len(probs[i])), p=pp)) + + return np.array(option_list).reshape(col_idx.shape) + +def random_choice_prob_index(a, axis=1): + r = np.expand_dims(np.random.rand(a.shape[1 - axis]), axis=axis) + return (a.cumsum(axis=axis) > r).argmax(axis=axis) + +def maximum_interval(output_info): + max_interval = 0 + for item in output_info: + max_interval = max(max_interval, item[0]) + return max_interval + +class Cond(object): + def __init__(self, data, output_info): + + self.model = [] + st = 0 + counter = 0 + for item in output_info: + + if item[1] == 'tanh': + st += item[0] + continue + elif item[1] == 'softmax': + ed = st + item[0] + counter += 1 + self.model.append(np.argmax(data[:, st:ed], axis=-1)) + st = ed + + self.interval = [] + self.n_col = 0 + self.n_opt = 0 + st = 0 + self.p = np.zeros((counter, maximum_interval(output_info))) + self.p_sampling = [] + for item in output_info: + if item[1] == 'tanh': + st += item[0] + continue + elif item[1] == 'softmax': + ed = st + item[0] + tmp = np.sum(data[:, st:ed], axis=0) + tmp_sampling = np.sum(data[:, st:ed], axis=0) + tmp = np.log(tmp + 1) + tmp = tmp / np.sum(tmp) + tmp_sampling = tmp_sampling / np.sum(tmp_sampling) + self.p_sampling.append(tmp_sampling) + self.p[self.n_col, :item[0]] = tmp + self.interval.append((self.n_opt, item[0])) + self.n_opt += item[0] + self.n_col += 1 + st = ed + + self.interval = np.asarray(self.interval) + + def sample_train(self, batch): + if self.n_col == 0: + return None + batch = batch + + idx = np.random.choice(np.arange(self.n_col), batch) + + vec = np.zeros((batch, self.n_opt), dtype='float32') + mask = np.zeros((batch, self.n_col), dtype='float32') + mask[np.arange(batch), idx] = 1 + opt1prime = random_choice_prob_index(self.p[idx]) + for i in np.arange(batch): + vec[i, self.interval[idx[i], 0] + opt1prime[i]] = 1 + + return vec, mask, idx, opt1prime + + def sample(self, batch): + if self.n_col == 0: + return None + batch = batch + + idx = np.random.choice(np.arange(self.n_col), batch) + + vec = np.zeros((batch, self.n_opt), dtype='float32') + opt1prime = random_choice_prob_index_sampling(self.p_sampling,idx) + + for i in np.arange(batch): + vec[i, self.interval[idx[i], 0] + opt1prime[i]] = 1 + + return vec + +def cond_loss(data, output_info, c, m): + loss = [] + st = 0 + st_c = 0 + for item in output_info: + if item[1] == 'tanh': + st += item[0] + continue + + elif item[1] == 'softmax': + ed = st + item[0] + ed_c = st_c + item[0] + tmp = F.cross_entropy( + data[:, st:ed], + torch.argmax(c[:, st_c:ed_c], dim=1), + reduction='none') + loss.append(tmp) + st = ed + st_c = ed_c + + loss = torch.stack(loss, dim=1) + return (loss * m).sum() / data.size()[0] + +class Sampler(object): + def __init__(self, data, output_info): + super(Sampler, self).__init__() + self.data = data + self.model = [] + self.n = len(data) + st = 0 + for item in output_info: + if item[1] == 'tanh': + st += item[0] + continue + elif item[1] == 'softmax': + ed = st + item[0] + tmp = [] + for j in range(item[0]): + tmp.append(np.nonzero(data[:, st + j])[0]) + self.model.append(tmp) + st = ed + + def sample(self, n, col, opt): + if col is None: + idx = np.random.choice(np.arange(self.n), n) + return self.data[idx] + idx = [] + for c, o in zip(col, opt): + idx.append(np.random.choice(self.model[c][o])) + return self.data[idx] + +class Discriminator(Module): + def __init__(self, side, layers): + super(Discriminator, self).__init__() + self.side = side + info = len(layers)-2 + self.seq = Sequential(*layers) + self.seq_info = Sequential(*layers[:info]) + + def forward(self, input): + return (self.seq(input)), self.seq_info(input) + +class Generator(Module): + def __init__(self, side, layers): + super(Generator, self).__init__() + self.side = side + self.seq = Sequential(*layers) + + def forward(self, input_): + return self.seq(input_) + +def determine_layers_disc(side, num_channels): + assert side >= 4 and side <= 64 + + layer_dims = [(1, side), (num_channels, side // 2)] + + while layer_dims[-1][1] > 3 and len(layer_dims) < 4: + layer_dims.append((layer_dims[-1][0] * 2, layer_dims[-1][1] // 2)) + + layerNorms = [] + num_c = num_channels + num_s = side / 2 + for l in range(len(layer_dims) - 1): + layerNorms.append([int(num_c), int(num_s), int(num_s)]) + num_c = num_c * 2 + num_s = num_s / 2 + + layers_D = [] + + for prev, curr, ln in zip(layer_dims, layer_dims[1:], layerNorms): + layers_D += [ + Conv2d(prev[0], curr[0], 4, 2, 1, bias=False), + LayerNorm(ln), + LeakyReLU(0.2, inplace=True), + ] + + layers_D += [Conv2d(layer_dims[-1][0], 1, layer_dims[-1][1], 1, 0), ReLU(True)] + + return layers_D + +def determine_layers_gen(side, random_dim, num_channels): + assert side >= 4 and side <= 64 + + layer_dims = [(1, side), (num_channels, side // 2)] + + while layer_dims[-1][1] > 3 and len(layer_dims) < 4: + layer_dims.append((layer_dims[-1][0] * 2, layer_dims[-1][1] // 2)) + + layerNorms = [] + + num_c = num_channels * (2 ** (len(layer_dims) - 2)) + num_s = int(side / (2 ** (len(layer_dims) - 1))) + for l in range(len(layer_dims) - 1): + layerNorms.append([int(num_c), int(num_s), int(num_s)]) + num_c = num_c / 2 + num_s = num_s * 2 + + layers_G = [ConvTranspose2d(random_dim, layer_dims[-1][0], layer_dims[-1][1], 1, 0, output_padding=0, bias=False)] + + for prev, curr, ln in zip(reversed(layer_dims), reversed(layer_dims[:-1]), layerNorms): + layers_G += [LayerNorm(ln), ReLU(True), ConvTranspose2d(prev[0], curr[0], 4, 2, 1, output_padding=0, bias=True)] + return layers_G + +def slerp(val, low, high): + low_norm = low/torch.norm(low, dim=1, keepdim=True) + high_norm = high/torch.norm(high, dim=1, keepdim=True) + omega = torch.acos((low_norm*high_norm).sum(1)).view(val.size(0), 1) + so = torch.sin(omega) + res = (torch.sin((1.0-val)*omega)/so)*low + (torch.sin(val*omega)/so) * high + + return res + +def calc_gradient_penalty_slerp(netD, real_data, fake_data, transformer, device='cpu', lambda_=10): + batchsize = real_data.shape[0] + alpha = torch.rand(batchsize, 1, device=device) + interpolates = slerp(alpha, real_data, fake_data) + interpolates = interpolates.to(device) + interpolates = transformer.transform(interpolates) + interpolates = torch.autograd.Variable(interpolates, requires_grad=True) + disc_interpolates,_ = netD(interpolates) + + gradients = torch.autograd.grad(outputs=disc_interpolates, inputs=interpolates, + grad_outputs=torch.ones(disc_interpolates.size()).to(device), + create_graph=True, retain_graph=True, only_inputs=True)[0] + + gradients_norm = gradients.norm(2, dim=1) + gradient_penalty = ((gradients_norm - 1) ** 2).mean() * lambda_ + + return gradient_penalty + +def weights_init(m): + classname = m.__class__.__name__ + + if classname.find('Conv') != -1: + init.normal_(m.weight.data, 0.0, 0.02) + + elif classname.find('BatchNorm') != -1: + init.normal_(m.weight.data, 1.0, 0.02) + init.constant_(m.bias.data, 0) + +class CTABGANSynthesizer: + def __init__(self, + class_dim=(256, 256, 256, 256), + random_dim=100, + num_channels=64, + l2scale=1e-5, + batch_size=500, + epochs=150, + lr=2e-4, + device="cpu"): + + + self.random_dim = random_dim + self.class_dim = class_dim + self.num_channels = num_channels + self.dside = None + self.gside = None + self.l2scale = l2scale + self.lr = lr + self.batch_size = batch_size + self.epochs = epochs + self.device = torch.device(device) + + def fit(self, train_data=pd.DataFrame, categorical=[], mixed={}, general=[], non_categorical=[], type={}): + + problem_type = None + target_index=None + if type: + problem_type = list(type.keys())[0] + if problem_type: + target_index = train_data.columns.get_loc(type[problem_type]) + + self.transformer = DataTransformer(train_data=train_data, categorical_list=categorical, mixed_dict=mixed, general_list=general, non_categorical_list=non_categorical) + self.transformer.fit() + train_data = self.transformer.transform(train_data.values) + data_sampler = Sampler(train_data, self.transformer.output_info) + data_dim = self.transformer.output_dim + self.cond_generator = Cond(train_data, self.transformer.output_info) + + sides = [4, 8, 16, 24, 32] + col_size_d = data_dim + self.cond_generator.n_opt + for i in sides: + if i * i >= col_size_d: + self.dside = i + break + + sides = [4, 8, 16, 24, 32] + col_size_g = data_dim + for i in sides: + if i * i >= col_size_g: + self.gside = i + break + + + layers_G = determine_layers_gen(self.gside, self.random_dim+self.cond_generator.n_opt, self.num_channels) + layers_D = determine_layers_disc(self.dside, self.num_channels) + + self.generator = Generator(self.gside, layers_G).to(self.device) + discriminator = Discriminator(self.dside, layers_D).to(self.device) + optimizer_params = dict(lr=self.lr, betas=(0.5, 0.9), eps=1e-3, weight_decay=self.l2scale) + optimizerG = Adam(self.generator.parameters(), **optimizer_params) + optimizerD = Adam(discriminator.parameters(), **optimizer_params) + + st_ed = None + classifier=None + optimizerC= None + if target_index != None: + st_ed= get_st_ed(target_index,self.transformer.output_info) + classifier = Classifier(data_dim,self.class_dim,st_ed).to(self.device) + optimizerC = optim.Adam(classifier.parameters(),**optimizer_params) + + + self.generator.apply(weights_init) + discriminator.apply(weights_init) + + self.Gtransformer = ImageTransformer(self.gside) + self.Dtransformer = ImageTransformer(self.dside) + + epsilon = 0 + epoch = 0 + steps = 0 + ci = 1 + + for i in tqdm(range(self.epochs)): + + + for _ in range(ci): + noisez = torch.randn(self.batch_size, self.random_dim, device=self.device) + condvec = self.cond_generator.sample_train(self.batch_size) + + c, m, col, opt = condvec + c = torch.from_numpy(c).to(self.device) + m = torch.from_numpy(m).to(self.device) + noisez = torch.cat([noisez, c], dim=1) + noisez = noisez.view(self.batch_size,self.random_dim+self.cond_generator.n_opt,1,1) + + perm = np.arange(self.batch_size) + np.random.shuffle(perm) + real = data_sampler.sample(self.batch_size, col[perm], opt[perm]) + c_perm = c[perm] + + real = torch.from_numpy(real.astype('float32')).to(self.device) + + fake = self.generator(noisez) + faket = self.Gtransformer.inverse_transform(fake) + fakeact = apply_activate(faket, self.transformer.output_info) + + fake_cat = torch.cat([fakeact, c], dim=1) + real_cat = torch.cat([real, c_perm], dim=1) + + real_cat_d = self.Dtransformer.transform(real_cat) + fake_cat_d = self.Dtransformer.transform(fake_cat) + + optimizerD.zero_grad() + + d_real,_ = discriminator(real_cat_d) + + + d_real = -torch.mean(d_real) + d_real.backward() + + + d_fake,_ = discriminator(fake_cat_d) + + d_fake = torch.mean(d_fake) + + d_fake.backward() + + pen = calc_gradient_penalty_slerp(discriminator, real_cat, fake_cat, self.Dtransformer , self.device) + + pen.backward() + + optimizerD.step() + + noisez = torch.randn(self.batch_size, self.random_dim, device=self.device) + + condvec = self.cond_generator.sample_train(self.batch_size) + + c, m, col, opt = condvec + c = torch.from_numpy(c).to(self.device) + m = torch.from_numpy(m).to(self.device) + noisez = torch.cat([noisez, c], dim=1) + noisez = noisez.view(self.batch_size,self.random_dim+self.cond_generator.n_opt,1,1) + + optimizerG.zero_grad() + + fake = self.generator(noisez) + faket = self.Gtransformer.inverse_transform(fake) + fakeact = apply_activate(faket, self.transformer.output_info) + + fake_cat = torch.cat([fakeact, c], dim=1) + fake_cat = self.Dtransformer.transform(fake_cat) + + y_fake,info_fake = discriminator(fake_cat) + + cross_entropy = cond_loss(faket, self.transformer.output_info, c, m) + + _,info_real = discriminator(real_cat_d) + + + g = -torch.mean(y_fake) + cross_entropy + g.backward(retain_graph=True) + loss_mean = torch.norm(torch.mean(info_fake.view(self.batch_size,-1), dim=0) - torch.mean(info_real.view(self.batch_size,-1), dim=0), 1) + loss_std = torch.norm(torch.std(info_fake.view(self.batch_size,-1), dim=0) - torch.std(info_real.view(self.batch_size,-1), dim=0), 1) + loss_info = loss_mean + loss_std + loss_info.backward() + optimizerG.step() + + + if problem_type: + + fake = self.generator(noisez) + + faket = self.Gtransformer.inverse_transform(fake) + + fakeact = apply_activate(faket, self.transformer.output_info) + + real_pre, real_label = classifier(real) + fake_pre, fake_label = classifier(fakeact) + + c_loss = CrossEntropyLoss() + + if (st_ed[1] - st_ed[0])==1: + c_loss= SmoothL1Loss() + real_label = real_label.type_as(real_pre) + fake_label = fake_label.type_as(fake_pre) + real_label = torch.reshape(real_label,real_pre.size()) + fake_label = torch.reshape(fake_label,fake_pre.size()) + + + elif (st_ed[1] - st_ed[0])==2: + c_loss = BCELoss() + real_label = real_label.type_as(real_pre) + fake_label = fake_label.type_as(fake_pre) + + loss_cc = c_loss(real_pre, real_label) + loss_cg = c_loss(fake_pre, fake_label) + + optimizerG.zero_grad() + loss_cg.backward() + optimizerG.step() + + optimizerC.zero_grad() + loss_cc.backward() + optimizerC.step() + + + + + @torch.no_grad() + def sample(self, n, seed=0): + print(n) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + sample_batch_size = 8092 + self.generator.eval() + + output_info = self.transformer.output_info + steps = n // sample_batch_size + 1 + + data = [] + + for i in range(steps): + noisez = torch.randn(sample_batch_size, self.random_dim, device=self.device) + condvec = self.cond_generator.sample(sample_batch_size) + c = condvec + c = torch.from_numpy(c).to(self.device) + noisez = torch.cat([noisez, c], dim=1) + noisez = noisez.view(sample_batch_size,self.random_dim+self.cond_generator.n_opt,1,1) + + fake = self.generator(noisez) + faket = self.Gtransformer.inverse_transform(fake) + fakeact = apply_activate(faket,output_info) + data.append(fakeact.detach().cpu().numpy()) + + data = np.concatenate(data, axis=0) + result,resample = self.transformer.inverse_transform(data) + + t0 = time.time() + while len(result) < n and (time.time() - t0) <= 600: + data_resample = [] + steps_left = resample// sample_batch_size + 1 + # print(f"Sampling: {len(result)}/{n}") + for i in range(steps_left): + noisez = torch.randn(sample_batch_size, self.random_dim, device=self.device) + condvec = self.cond_generator.sample(sample_batch_size) + c = condvec + c = torch.from_numpy(c).to(self.device) + noisez = torch.cat([noisez, c], dim=1) + noisez = noisez.view(sample_batch_size,self.random_dim+self.cond_generator.n_opt,1,1) + + fake = self.generator(noisez) + faket = self.Gtransformer.inverse_transform(fake) + fakeact = apply_activate(faket, output_info) + data_resample.append(fakeact.detach().cpu().numpy()) + + data_resample = np.concatenate(data_resample, axis=0) + + res,resample = self.transformer.inverse_transform(data_resample) + result = np.concatenate([result,res],axis=0) + + return result[0:n] + diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/synthesizer/transformer.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/synthesizer/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..ddad92266dc6d8b47e9ea1f4b4528795b9126e7d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/model/synthesizer/transformer.py @@ -0,0 +1,429 @@ +import numpy as np +import pandas as pd +import torch +from sklearn.mixture import BayesianGaussianMixture + +class DataTransformer(): + + def __init__(self, train_data=pd.DataFrame, categorical_list=[], mixed_dict={}, general_list=[], non_categorical_list=[], n_clusters=10, eps=0.005): + self.meta = None + self.n_clusters = n_clusters + self.eps = eps + self.train_data = train_data + self.categorical_columns= categorical_list + self.mixed_columns= mixed_dict + self.general_columns = general_list + self.non_categorical_columns= non_categorical_list + + def get_metadata(self): + + meta = [] + + for index in range(self.train_data.shape[1]): + column = self.train_data.iloc[:,index] + if index in self.categorical_columns: + if index in self.non_categorical_columns: + meta.append({ + "name": index, + "type": "continuous", + "min": column.min(), + "max": column.max(), + }) + else: + mapper = column.value_counts().index.tolist() + meta.append({ + "name": index, + "type": "categorical", + "size": len(mapper), + "i2s": mapper + }) + + elif index in self.mixed_columns.keys(): + meta.append({ + "name": index, + "type": "mixed", + "min": column.min(), + "max": column.max(), + "modal": self.mixed_columns[index] + }) + else: + meta.append({ + "name": index, + "type": "continuous", + "min": column.min(), + "max": column.max(), + }) + + return meta + + def fit(self): + data = self.train_data.values + self.meta = self.get_metadata() + model = [] + self.ordering = [] + self.output_info = [] + self.output_dim = 0 + self.components = [] + self.filter_arr = [] + for id_, info in enumerate(self.meta): + if info['type'] == "continuous": + if id_ not in self.general_columns: + gm = BayesianGaussianMixture( + n_components = self.n_clusters, + weight_concentration_prior_type='dirichlet_process', + weight_concentration_prior=0.001, + max_iter=100,n_init=1, random_state=42) + gm.fit(data[:, id_].reshape([-1, 1])) + mode_freq = (pd.Series(gm.predict(data[:, id_].reshape([-1, 1]))).value_counts().keys()) + model.append(gm) + old_comp = gm.weights_ > self.eps + comp = [] + for i in range(self.n_clusters): + if (i in (mode_freq)) & old_comp[i]: + comp.append(True) + else: + comp.append(False) + self.components.append(comp) + self.output_info += [(1, 'tanh','no_g'), (np.sum(comp), 'softmax')] + self.output_dim += 1 + np.sum(comp) + else: + model.append(None) + self.components.append(None) + self.output_info += [(1, 'tanh','yes_g')] + self.output_dim += 1 + + elif info['type'] == "mixed": + + gm1 = BayesianGaussianMixture( + n_components = self.n_clusters, + weight_concentration_prior_type='dirichlet_process', + weight_concentration_prior=0.001, max_iter=100, + n_init=1,random_state=42) + gm2 = BayesianGaussianMixture( + n_components = self.n_clusters, + weight_concentration_prior_type='dirichlet_process', + weight_concentration_prior=0.001, max_iter=100, + n_init=1,random_state=42) + + gm1.fit(data[:, id_].reshape([-1, 1])) + + filter_arr = [] + for element in data[:, id_]: + if element not in info['modal']: + filter_arr.append(True) + else: + filter_arr.append(False) + + gm2.fit(data[:, id_][filter_arr].reshape([-1, 1])) + mode_freq = (pd.Series(gm2.predict(data[:, id_][filter_arr].reshape([-1, 1]))).value_counts().keys()) + self.filter_arr.append(filter_arr) + model.append((gm1,gm2)) + + old_comp = gm2.weights_ > self.eps + + comp = [] + + for i in range(self.n_clusters): + if (i in (mode_freq)) & old_comp[i]: + comp.append(True) + else: + comp.append(False) + + self.components.append(comp) + + self.output_info += [(1, 'tanh',"no_g"), (np.sum(comp) + len(info['modal']), 'softmax')] + self.output_dim += 1 + np.sum(comp) + len(info['modal']) + else: + model.append(None) + self.components.append(None) + self.output_info += [(info['size'], 'softmax')] + self.output_dim += info['size'] + self.model = model + + def transform(self, data, ispositive = False, positive_list = None): + values = [] + mixed_counter = 0 + for id_, info in enumerate(self.meta): + current = data[:, id_] + if info['type'] == "continuous": + if id_ not in self.general_columns: + current = current.reshape([-1, 1]) + means = self.model[id_].means_.reshape((1, self.n_clusters)) + stds = np.sqrt(self.model[id_].covariances_).reshape((1, self.n_clusters)) + features = np.empty(shape=(len(current),self.n_clusters)) + if ispositive == True: + if id_ in positive_list: + features = np.abs(current - means) / (4 * stds) + else: + features = (current - means) / (4 * stds) + + probs = self.model[id_].predict_proba(current.reshape([-1, 1])) + n_opts = sum(self.components[id_]) + features = features[:, self.components[id_]] + probs = probs[:, self.components[id_]] + + opt_sel = np.zeros(len(data), dtype='int') + for i in range(len(data)): + pp = probs[i] + 1e-6 + pp = pp / sum(pp) + opt_sel[i] = np.random.choice(np.arange(n_opts), p=pp) + + idx = np.arange((len(features))) + features = features[idx, opt_sel].reshape([-1, 1]) + features = np.clip(features, -.99, .99) + probs_onehot = np.zeros_like(probs) + probs_onehot[np.arange(len(probs)), opt_sel] = 1 + + re_ordered_phot = np.zeros_like(probs_onehot) + + col_sums = probs_onehot.sum(axis=0) + + + n = probs_onehot.shape[1] + largest_indices = np.argsort(-1*col_sums)[:n] + self.ordering.append(largest_indices) + for id,val in enumerate(largest_indices): + re_ordered_phot[:,id] = probs_onehot[:,val] + + + values += [features, re_ordered_phot] + + else: + + self.ordering.append(None) + + if id_ in self.non_categorical_columns: + info['min'] = -1e-3 + info['max'] = info['max'] + 1e-3 + + current = (current - (info['min'])) / (info['max'] - info['min']) + current = current * 2 - 1 + current = current.reshape([-1, 1]) + values.append(current) + + elif info['type'] == "mixed": + + means_0 = self.model[id_][0].means_.reshape([-1]) + stds_0 = np.sqrt(self.model[id_][0].covariances_).reshape([-1]) + + zero_std_list = [] + means_needed = [] + stds_needed = [] + + for mode in info['modal']: + if mode!=-9999999: + dist = [] + for idx,val in enumerate(list(means_0.flatten())): + dist.append(abs(mode-val)) + index_min = np.argmin(np.array(dist)) + zero_std_list.append(index_min) + else: continue + + for idx in zero_std_list: + means_needed.append(means_0[idx]) + stds_needed.append(stds_0[idx]) + + + mode_vals = [] + + for i,j,k in zip(info['modal'],means_needed,stds_needed): + this_val = np.abs(i - j) / (4*k) + mode_vals.append(this_val) + + if -9999999 in info["modal"]: + mode_vals.append(0) + + current = current.reshape([-1, 1]) + filter_arr = self.filter_arr[mixed_counter] + current = current[filter_arr] + + means = self.model[id_][1].means_.reshape((1, self.n_clusters)) + stds = np.sqrt(self.model[id_][1].covariances_).reshape((1, self.n_clusters)) + features = np.empty(shape=(len(current),self.n_clusters)) + if ispositive == True: + if id_ in positive_list: + features = np.abs(current - means) / (4 * stds) + else: + features = (current - means) / (4 * stds) + + probs = self.model[id_][1].predict_proba(current.reshape([-1, 1])) + + n_opts = sum(self.components[id_]) # 8 + features = features[:, self.components[id_]] + probs = probs[:, self.components[id_]] + + opt_sel = np.zeros(len(current), dtype='int') + for i in range(len(current)): + pp = probs[i] + 1e-6 + pp = pp / sum(pp) + opt_sel[i] = np.random.choice(np.arange(n_opts), p=pp) + idx = np.arange((len(features))) + features = features[idx, opt_sel].reshape([-1, 1]) + features = np.clip(features, -.99, .99) + probs_onehot = np.zeros_like(probs) + probs_onehot[np.arange(len(probs)), opt_sel] = 1 + extra_bits = np.zeros([len(current), len(info['modal'])]) + temp_probs_onehot = np.concatenate([extra_bits,probs_onehot], axis = 1) + final = np.zeros([len(data), 1 + probs_onehot.shape[1] + len(info['modal'])]) + features_curser = 0 + for idx, val in enumerate(data[:, id_]): + if val in info['modal']: + category_ = list(map(info['modal'].index, [val]))[0] + final[idx, 0] = mode_vals[category_] + final[idx, (category_+1)] = 1 + + else: + final[idx, 0] = features[features_curser] + final[idx, (1+len(info['modal'])):] = temp_probs_onehot[features_curser][len(info['modal']):] + features_curser = features_curser + 1 + + just_onehot = final[:,1:] + re_ordered_jhot= np.zeros_like(just_onehot) + n = just_onehot.shape[1] + col_sums = just_onehot.sum(axis=0) + largest_indices = np.argsort(-1*col_sums)[:n] + self.ordering.append(largest_indices) + for id,val in enumerate(largest_indices): + re_ordered_jhot[:,id] = just_onehot[:,val] + final_features = final[:,0].reshape([-1, 1]) + values += [final_features, re_ordered_jhot] + mixed_counter = mixed_counter + 1 + + else: + self.ordering.append(None) + col_t = np.zeros([len(data), info['size']]) + idx = list(map(info['i2s'].index, current)) + col_t[np.arange(len(data)), idx] = 1 + values.append(col_t) + + return np.concatenate(values, axis=1) + + def inverse_transform(self, data): + data_t = np.zeros([len(data), len(self.meta)]) + invalid_ids = [] + st = 0 + for id_, info in enumerate(self.meta): + if info['type'] == "continuous": + if id_ not in self.general_columns: + u = data[:, st] + v = data[:, st + 1:st + 1 + np.sum(self.components[id_])] + order = self.ordering[id_] + v_re_ordered = np.zeros_like(v) + + for id,val in enumerate(order): + v_re_ordered[:,val] = v[:,id] + + v = v_re_ordered + + u = np.clip(u, -1, 1) + v_t = np.ones((data.shape[0], self.n_clusters)) * -100 + v_t[:, self.components[id_]] = v + v = v_t + st += 1 + np.sum(self.components[id_]) + means = self.model[id_].means_.reshape([-1]) + stds = np.sqrt(self.model[id_].covariances_).reshape([-1]) + p_argmax = np.argmax(v, axis=1) + std_t = stds[p_argmax] + mean_t = means[p_argmax] + tmp = u * 4 * std_t + mean_t + + for idx,val in enumerate(tmp): + if (val < info["min"]) | (val > info['max']): + invalid_ids.append(idx) + + if id_ in self.non_categorical_columns: + + tmp = np.round(tmp) + + data_t[:, id_] = tmp + + else: + u = data[:, st] + u = (u + 1) / 2 + u = np.clip(u, 0, 1) + u = u * (info['max'] - info['min']) + info['min'] + if id_ in self.non_categorical_columns: + data_t[:, id_] = np.round(u) + else: data_t[:, id_] = u + + st += 1 + + elif info['type'] == "mixed": + + u = data[:, st] + full_v = data[:,(st+1):(st+1)+len(info['modal'])+np.sum(self.components[id_])] + order = self.ordering[id_] + full_v_re_ordered = np.zeros_like(full_v) + + for id,val in enumerate(order): + full_v_re_ordered[:,val] = full_v[:,id] + + full_v = full_v_re_ordered + + + mixed_v = full_v[:,:len(info['modal'])] + v = full_v[:,-np.sum(self.components[id_]):] + + u = np.clip(u, -1, 1) + v_t = np.ones((data.shape[0], self.n_clusters)) * -100 + v_t[:, self.components[id_]] = v + v = np.concatenate([mixed_v,v_t], axis=1) + + st += 1 + np.sum(self.components[id_]) + len(info['modal']) + means = self.model[id_][1].means_.reshape([-1]) + stds = np.sqrt(self.model[id_][1].covariances_).reshape([-1]) + p_argmax = np.argmax(v, axis=1) + + result = np.zeros_like(u) + + for idx in range(len(data)): + if p_argmax[idx] < len(info['modal']): + argmax_value = p_argmax[idx] + result[idx] = float(list(map(info['modal'].__getitem__, [argmax_value]))[0]) + else: + std_t = stds[(p_argmax[idx]-len(info['modal']))] + mean_t = means[(p_argmax[idx]-len(info['modal']))] + result[idx] = u[idx] * 4 * std_t + mean_t + + for idx,val in enumerate(result): + if (val < info["min"]) | (val > info['max']): + invalid_ids.append(idx) + + data_t[:, id_] = result + + else: + current = data[:, st:st + info['size']] + st += info['size'] + idx = np.argmax(current, axis=1) + data_t[:, id_] = list(map(info['i2s'].__getitem__, idx)) + + + invalid_ids = np.unique(np.array(invalid_ids)) + all_ids = np.arange(0,len(data)) + valid_ids = list(set(all_ids) - set(invalid_ids)) + + return data_t[valid_ids],len(invalid_ids) + + +class ImageTransformer(): + + def __init__(self, side): + + self.height = side + + def transform(self, data): + + if self.height * self.height > len(data[0]): + + padding = torch.zeros((len(data), self.height * self.height - len(data[0]))).to(data.device) + data = torch.cat([data, padding], axis=1) + + return data.view(-1, 1, self.height, self.height) + + def inverse_transform(self, data): + + data = data.view(-1, self.height * self.height) + + return data + + diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/pipeline_ctabganp.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/pipeline_ctabganp.py new file mode 100644 index 0000000000000000000000000000000000000000..a584f91844e866d04f3c59297ca66017f1e7dd6c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/pipeline_ctabganp.py @@ -0,0 +1,81 @@ +import tomli +import shutil +import os +import argparse +from train_sample_ctabganp import train_ctabgan, sample_ctabgan +from scripts.eval_catboost import train_catboost +import zero +import lib +from model.ctabgan import CTABGAN + +def load_config(path) : + with open(path, 'rb') as f: + return tomli.load(f) + +def save_file(parent_dir, config_path): + try: + dst = os.path.join(parent_dir) + os.makedirs(os.path.dirname(dst), exist_ok=True) + shutil.copyfile(os.path.abspath(config_path), dst) + except shutil.SameFileError: + pass + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--config', metavar='FILE') + parser.add_argument('--train', action='store_true', default=False) + parser.add_argument('--sample', action='store_true', default=False) + parser.add_argument('--eval', action='store_true', default=False) + parser.add_argument('--change_val', action='store_true', default=False) + + args = parser.parse_args() + raw_config = lib.load_config(args.config) + timer = zero.Timer() + timer.run() + save_file(os.path.join(raw_config['parent_dir'], 'config.toml'), args.config) + ctabgan = None + if args.train: + ctabgan = train_ctabgan( + parent_dir=raw_config['parent_dir'], + real_data_path=raw_config['real_data_path'], + train_params=raw_config['train_params'], + change_val=args.change_val, + device=raw_config['device'] + ) + if args.sample: + sample_ctabgan( + synthesizer=ctabgan, + parent_dir=raw_config['parent_dir'], + real_data_path=raw_config['real_data_path'], + num_samples=raw_config['sample']['num_samples'], + train_params=raw_config['train_params'], + change_val=args.change_val, + seed=raw_config['sample']['seed'], + device=raw_config['device'] + ) + + save_file(os.path.join(raw_config['parent_dir'], 'info.json'), os.path.join(raw_config['real_data_path'], 'info.json')) + if args.eval: + if raw_config['eval']['type']['eval_model'] == 'catboost': + train_catboost( + parent_dir=raw_config['parent_dir'], + real_data_path=raw_config['real_data_path'], + eval_type=raw_config['eval']['type']['eval_type'], + T_dict=raw_config['eval']['T'], + seed=raw_config['seed'], + change_val=args.change_val + ) + # elif raw_config['eval']['type']['eval_model'] == 'mlp': + # train_mlp( + # parent_dir=raw_config['parent_dir'], + # real_data_path=raw_config['real_data_path'], + # eval_type=raw_config['eval']['type']['eval_type'], + # T_dict=raw_config['eval']['T'], + # seed=raw_config['seed'], + # change_val=args.change_val + # ) + + print(f'Elapsed time: {str(timer)}') + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/train_sample_ctabganp.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/train_sample_ctabganp.py new file mode 100644 index 0000000000000000000000000000000000000000..c1f668fe5b5d3d5255a3587982e968276e60273d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/train_sample_ctabganp.py @@ -0,0 +1,110 @@ +import lib +import os +import numpy as np +import argparse +from model.ctabgan import CTABGAN +from pathlib import Path +import torch +import pickle + + +def train_ctabgan( + parent_dir, + real_data_path, + train_params = {"batch_size": 512}, + change_val=False, + device = "cpu" +): + real_data_path = Path(real_data_path) + parent_dir = Path(parent_dir) + device = torch.device(device) + + if change_val: + X_num_train, X_cat_train, y_train, _, _, _ = lib.read_changed_val(real_data_path) + else: + X_num_train, X_cat_train, y_train = lib.read_pure_data(real_data_path, 'train') + + X = lib.concat_to_pd(X_num_train, X_cat_train, y_train) + + X.columns = [str(_) for _ in X.columns] + + ctabgan_params = lib.load_json("CTAB-GAN-Plus/columns.json")[real_data_path.name] + train_params["batch_size"] = min(y_train.shape[0], train_params["batch_size"]) + + print(train_params) + synthesizer = CTABGAN( + df = X, + test_ratio = 0.0, + **ctabgan_params, + **train_params, + device=device + ) + + synthesizer.fit() + + # save_ctabgan(synthesizer, parent_dir) + with open(parent_dir / "ctabgan.obj", "wb") as f: + pickle.dump(synthesizer, f) + + return synthesizer + +def sample_ctabgan( + synthesizer, + parent_dir, + real_data_path, + num_samples, + train_params = {"batch_size": 512}, + change_val=False, + device="cpu", + seed=0 +): + real_data_path = Path(real_data_path) + parent_dir = Path(parent_dir) + device = torch.device(device) + + if change_val: + X_num_train, X_cat_train, y_train, _, _, _ = lib.read_changed_val(real_data_path) + else: + X_num_train, X_cat_train, y_train = lib.read_pure_data(real_data_path, 'train') + + X = lib.concat_to_pd(X_num_train, X_cat_train, y_train) + + X.columns = [str(_) for _ in X.columns] + + ctabgan_params = lib.load_json("CTAB-GAN-Plus/columns.json")[real_data_path.name] + + cat_features = ctabgan_params["categorical_columns"] + # if synthesizer is None: + # synthesizer = load_ctabgan(X, ctabgan_params, train_params, parent_dir) + with open(parent_dir / "ctabgan.obj", 'rb') as f: + synthesizer = pickle.load(f) + synthesizer.synthesizer.generator = synthesizer.synthesizer.generator.to(device) + gen_data = synthesizer.generate_samples(num_samples, seed) + + y = gen_data['y'].values + if len(np.unique(y)) == 1: + y[0] = 0 + y[1] = 1 + + X_cat = gen_data[cat_features].drop('y', axis=1, errors="ignore").values if len(cat_features) else None + X_num = gen_data.values[:, :X_num_train.shape[1]] if X_num_train is not None else None + + if X_num_train is not None: + np.save(parent_dir / 'X_num_train', X_num.astype(float)) + if X_cat_train is not None: + np.save(parent_dir / 'X_cat_train', X_cat.astype(str)) + np.save(parent_dir / 'y_train', y.astype(float).astype(int)) # only clf !!! + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('real_data_path', type=str) + parser.add_argument('parent_dir', type=str) + parser.add_argument('train_size', type=int) + args = parser.parse_args() + + ctabgan = train_ctabgan(args.parent_dir, args.real_data_path, change_val=True) + sample_ctabgan(ctabgan, args.parent_dir, args.real_data_path, args.train_size, change_val=True) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/tune_ctabgan.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/tune_ctabgan.py new file mode 100644 index 0000000000000000000000000000000000000000..ed19426b03246e546e234a2501a7c06f173d9f03 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN-Plus/tune_ctabgan.py @@ -0,0 +1,153 @@ +from multiprocessing.sharedctypes import RawValue +from random import random +import tempfile +import subprocess +import lib +import os +import optuna +import argparse +from pathlib import Path +from train_sample_ctabganp import train_ctabgan, sample_ctabgan +from scripts.eval_catboost import train_catboost + +parser = argparse.ArgumentParser() +parser.add_argument('data_path', type=str) +parser.add_argument('train_size', type=int) +parser.add_argument('eval_type', type=str) +parser.add_argument('device', type=str) + +args = parser.parse_args() +real_data_path = args.data_path +eval_type = args.eval_type +train_size = args.train_size +device = args.device +assert eval_type in ('merged', 'synthetic') + +def objective(trial): + + lr = trial.suggest_loguniform('lr', 0.00001, 0.003) + + def suggest_dim(name): + t = trial.suggest_int(name, d_min, d_max) + return 2 ** t + + # construct model + min_n_layers, max_n_layers, d_min, d_max = 1, 4, 6, 8 + n_layers = trial.suggest_int('n_layers', min_n_layers, max_n_layers) + d_first = [suggest_dim('d_first')] if n_layers else [] + d_middle = ( + [suggest_dim('d_middle')] * (n_layers - 2) + if n_layers > 2 + else [] + ) + d_last = [suggest_dim('d_last')] if n_layers > 1 else [] + d_layers = d_first + d_middle + d_last + #### + + steps = trial.suggest_categorical('steps', [1000, 5000, 10000]) + # steps = trial.suggest_categorical('steps', [10]) + batch_size = 2 ** trial.suggest_int('batch_size', 9, 11) + random_dim = 2 ** trial.suggest_int('random_dim', 4, 7) + num_channels = 2 ** trial.suggest_int('num_channels', 4, 6) + + # steps = trial.suggest_categorical('steps', [1000]) + + num_samples = int(train_size * (2 ** trial.suggest_int('frac_samples', -2, 3))) + + train_params = { + "lr": lr, + "epochs": steps, + "class_dim": d_layers, + "batch_size": batch_size, + "random_dim": random_dim, + "num_channels": num_channels + } + trial.set_user_attr("train_params", train_params) + trial.set_user_attr("num_samples", num_samples) + + score = 0.0 + with tempfile.TemporaryDirectory() as dir_: + dir_ = Path(dir_) + ctabgan = train_ctabgan( + parent_dir=dir_, + real_data_path=real_data_path, + train_params=train_params, + change_val=True, + device=device + ) + + for sample_seed in range(5): + sample_ctabgan( + ctabgan, + parent_dir=dir_, + real_data_path=real_data_path, + num_samples=num_samples, + train_params=train_params, + change_val=True, + seed=sample_seed, + device=device + ) + + T_dict = { + "seed": 0, + "normalization": None, + "num_nan_policy": None, + "cat_nan_policy": None, + "cat_min_frequency": None, + "cat_encoding": None, + "y_policy": "default" + } + metrics = train_catboost( + parent_dir=dir_, + real_data_path=real_data_path, + eval_type=eval_type, + T_dict=T_dict, + change_val=True, + seed = 0 + ) + + score += metrics.get_val_score() + return score / 5 + + +study = optuna.create_study( + direction='maximize', + sampler=optuna.samplers.TPESampler(seed=0), +) + +study.optimize(objective, n_trials=35, show_progress_bar=True) + +os.makedirs(f"exp/{Path(real_data_path).name}/ctabgan-plus/", exist_ok=True) +config = { + "parent_dir": f"exp/{Path(real_data_path).name}/ctabgan-plus/", + "real_data_path": real_data_path, + "seed": 0, + "device": args.device, + "train_params": study.best_trial.user_attrs["train_params"], + "sample": {"seed": 0, "num_samples": study.best_trial.user_attrs["num_samples"]}, + "eval": { + "type": {"eval_model": "catboost", "eval_type": eval_type}, + "T": { + "seed": 0, + "normalization": None, + "num_nan_policy": None, + "cat_nan_policy": None, + "cat_min_frequency": None, + "cat_encoding": None, + "y_policy": "default" + }, + } +} + +train_ctabgan( + parent_dir=f"exp/{Path(real_data_path).name}/ctabgan-plus/", + real_data_path=real_data_path, + train_params=study.best_trial.user_attrs["train_params"], + change_val=False, + device=device +) + +lib.dump_config(config, config["parent_dir"]+"config.toml") + +subprocess.run(['python3.9', "scripts/eval_seeds.py", '--config', f'{config["parent_dir"]+"config.toml"}', + '10', "ctabgan-plus", eval_type, "catboost", "5"], check=True) \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/.gitignore b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..2473a164760acb3c77f225f094e19e2e0f523912 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/.gitignore @@ -0,0 +1 @@ +**/**.csv \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/LICENSE b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/License.txt b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/License.txt new file mode 100644 index 0000000000000000000000000000000000000000..5404b6f9e08e11c3a28a214518830c963060c885 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/License.txt @@ -0,0 +1,15 @@ +Distributed learning systems Lab at TU Delft & Generatrix, hereby disclaims all copyright interest in the program "CTAB-GAN" (which synthesizes tabular data) + +Copyright 2020-2022 Distributed learning systems Lab at TU Delft & Generatrix. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + https://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/README.md b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/README.md new file mode 100644 index 0000000000000000000000000000000000000000..7fd4f2ac92abf9b244313a9ecf7d8328932cfc8c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/README.md @@ -0,0 +1,50 @@ +# CTAB-GAN +This is the official git paper [CTAB-GAN: Effective Table Data Synthesizing](https://proceedings.mlr.press/v157/zhao21a.html). The paper is published on Asian Conference on Machine Learning (ACML 2021), please check our pdf on PMLR website for our newest version of [paper](https://proceedings.mlr.press/v157/zhao21a.html), it adds more content on time consumption analysis of training CTAB-GAN. If you have any question, please contact `z.zhao-8@tudelft.nl` for more information. + + +## Prerequisite + +The required package version +``` +numpy==1.21.0 +torch==1.9.1 +pandas==1.2.4 +sklearn==0.24.1 +dython==0.6.4.post1 +scipy==1.4.1 +``` + +## Example +`Experiment_Script_Adult.ipynb` is an example notebook for training CTAB-GAN with Adult dataset. The dataset is alread under `Real_Datasets` folder. +The evaluation code is also provided. + +## For large dataset + +If your dataset has large number of column, you may encounter the problem that our currnet code cannot encode all of your data since CTAB-GAN will wrap the encoded data into an image-like format. What you can do is changing the line 341 and 348 in `model/synthesizer/ctabgan_synthesizer.py`. The number in the `slide` list +``` +sides = [4, 8, 16, 24, 32] +``` +is the side size of image. You can enlarge the list to [4, 8, 16, 24, 32, 64] or [4, 8, 16, 24, 32, 64, 128] for accepting larger dataset. + +## Bibtex + +To cite this paper, you could use this bibtex + +``` +@InProceedings{zhao21, + title = {CTAB-GAN: Effective Table Data Synthesizing}, + author = {Zhao, Zilong and Kunar, Aditya and Birke, Robert and Chen, Lydia Y.}, + booktitle = {Proceedings of The 13th Asian Conference on Machine Learning}, + pages = {97--112}, + year = {2021}, + editor = {Balasubramanian, Vineeth N. and Tsang, Ivor}, + volume = {157}, + series = {Proceedings of Machine Learning Research}, + month = {17--19 Nov}, + publisher = {PMLR}, + pdf = {https://proceedings.mlr.press/v157/zhao21a/zhao21a.pdf}, + url = {https://proceedings.mlr.press/v157/zhao21a.html} +} + + +``` diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/columns.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/columns.json new file mode 100644 index 0000000000000000000000000000000000000000..8a3d4ed342c50c91577173e825c3cffe777c1dcd --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/columns.json @@ -0,0 +1,74 @@ +{ + "churn2": { + "categorical_columns": ["7", "8", "9", "10", "y"], + "mixed_columns": {"0": [850.0], "3": [0.0]}, + "integer_columns": ["1", "2", "4"], + "problem_type": {"Classification": "y"} + }, + "adult": { + "categorical_columns": ["6", "7", "8", "9", "10", "11", "12", "13", "y"], + "mixed_columns": {"3": [0.0], "4": [0.0]}, + "integer_columns": ["0", "2", "5"], + "problem_type": {"Classification": "y"} + }, + "buddy": { + "categorical_columns": ["4", "5", "6", "7", "8", "y"], + "mixed_columns": {}, + "integer_columns": ["0", "1"], + "problem_type": {"Classification": "y"} + }, + "gesture": { + "categorical_columns": ["y"], + "mixed_columns": {}, + "integer_columns": [], + "problem_type": {"Classification": "y"} + }, + "wilt": { + "categorical_columns": ["y"], + "mixed_columns": {}, + "integer_columns": [], + "problem_type": {"Classification": "y"} + }, + "satellite": { + "categorical_columns": ["y"], + "mixed_columns": {}, + "integer_columns": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35"], + "problem_type": {"Classification": "y"} + }, + "higgs-small": { + "categorical_columns": ["y"], + "mixed_columns": {}, + "integer_columns": [], + "problem_type": {"Classification": "y"} + }, + "diabetes": { + "categorical_columns": ["y"], + "mixed_columns": {"3": [0.0], "4": [0.0]}, + "integer_columns": ["0", "1", "2", "5", "7"], + "problem_type": {"Classification": "y"} + }, + "default": { + "categorical_columns": ["20", "21", "22", "y"], + "mixed_columns": {}, + "integer_columns": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19"], + "problem_type": {"Classification": "y"} + }, + "otto": { + "categorical_columns": ["y"], + "mixed_columns": {}, + "integer_columns": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92"], + "problem_type": {"Classification": "y"} + }, + "cardio": { + "categorical_columns": ["5", "6", "7", "8", "9", "10", "y"], + "mixed_columns": {}, + "integer_columns": ["0", "1", "3", "4"], + "problem_type": {"Classification": "y"} + }, + "miniboone": { + "categorical_columns": ["y"], + "mixed_columns": {}, + "integer_columns": [], + "problem_type": {"Classification": "y"} + } +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/__init__.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/ctabgan.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/ctabgan.py new file mode 100644 index 0000000000000000000000000000000000000000..d12c1a3c05d486c698bca7012d45bf31bc50ffbf --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/ctabgan.py @@ -0,0 +1,58 @@ +""" +Generative model training algorithm based on the CTABGANSynthesiser + +""" +import pandas as pd +import time +from model.pipeline.data_preparation import DataPrep +from model.synthesizer.ctabgan_synthesizer import CTABGANSynthesizer + +import warnings + +warnings.filterwarnings("ignore") + +class CTABGAN(): + + def __init__(self, + df, + test_ratio = 0.20, + categorical_columns = [ 'workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'gender', 'native-country', 'income'], + log_columns = [], + mixed_columns= {'capital-loss':[0.0],'capital-gain':[0.0]}, + integer_columns = ['age', 'fnlwgt','capital-gain', 'capital-loss','hours-per-week'], + problem_type= {"Classification": 'income'}, + batch_size = 512, + class_dim = (256, 256, 256, 256), + lr = 2e-4, + epochs = 10, + device=None): + + self.__name__ = 'CTABGAN' + + self.synthesizer = CTABGANSynthesizer(lr = lr, epochs = epochs, batch_size = batch_size, class_dim = class_dim, device = device) + self.raw_df = df + print(self.raw_df.shape) + self.test_ratio = test_ratio + self.categorical_columns = categorical_columns + self.log_columns = log_columns + self.mixed_columns = mixed_columns + self.integer_columns = integer_columns + self.problem_type = problem_type + + def fit(self, no_train=False): + print("-"*100) + start_time = time.time() + self.data_prep = DataPrep(self.raw_df,self.categorical_columns,self.log_columns,self.mixed_columns,self.integer_columns,self.problem_type,self.test_ratio) + self.synthesizer.fit(train_data=self.data_prep.df, categorical = self.data_prep.column_types["categorical"], + mixed = self.data_prep.column_types["mixed"],type=self.problem_type, no_train=no_train) + end_time = time.time() + print('Finished training in',end_time-start_time," seconds.") + print("-"*100) + + + def generate_samples(self, num_samples, seed=0): + + sample = self.synthesizer.sample(num_samples, seed) + sample_df = self.data_prep.inverse_prep(sample) + + return sample_df diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/eval/evaluation.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/eval/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..2cc965efb802eac7263640e94ffba0cbdfff8c7e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/eval/evaluation.py @@ -0,0 +1,191 @@ +import numpy as np +import pandas as pd +from sklearn import metrics +from sklearn import model_selection +from sklearn.preprocessing import MinMaxScaler,StandardScaler +from sklearn.neural_network import MLPClassifier +from sklearn.linear_model import LogisticRegression +from sklearn import svm,tree +from sklearn.ensemble import RandomForestClassifier +from dython.nominal import compute_associations +from scipy.stats import wasserstein_distance +from scipy.spatial import distance +import warnings + +warnings.filterwarnings("ignore") + +def supervised_model_training(x_train, y_train, x_test, + y_test, model_name): + + if model_name == 'lr': + model = LogisticRegression(random_state=42,max_iter=500) + elif model_name == 'svm': + model = svm.SVC(random_state=42,probability=True) + elif model_name == 'dt': + model = tree.DecisionTreeClassifier(random_state=42) + elif model_name == 'rf': + model = RandomForestClassifier(random_state=42) + elif model_name == "mlp": + model = MLPClassifier(random_state=42,max_iter=100) + + model.fit(x_train, y_train) + pred = model.predict(x_test) + + if len(np.unique(y_train))>2: + predict = model.predict_proba(x_test) + acc = metrics.accuracy_score(y_test,pred)*100 + auc = metrics.roc_auc_score(y_test, predict,average="weighted",multi_class="ovr") + f1_score = metrics.precision_recall_fscore_support(y_test, pred,average="weighted")[2] + return [acc, auc,f1_score] + + else: + predict = model.predict_proba(x_test)[:,1] + acc = metrics.accuracy_score(y_test,pred)*100 + auc = metrics.roc_auc_score(y_test, predict) + f1_score = metrics.precision_recall_fscore_support(y_test,pred)[2].mean() + return [acc, auc,f1_score] + + +def get_utility_metrics(real_path,fake_paths,scaler="MinMax",classifiers=["lr","dt","rf","mlp"],test_ratio=.20): + + data_real = pd.read_csv(real_path).to_numpy() + data_dim = data_real.shape[1] + + data_real_y = data_real[:,-1] + data_real_X = data_real[:,:data_dim-1] + X_train_real, X_test_real, y_train_real, y_test_real = model_selection.train_test_split(data_real_X ,data_real_y, test_size=test_ratio, stratify=data_real_y,random_state=42) + + if scaler=="MinMax": + scaler_real = MinMaxScaler() + else: + scaler_real = StandardScaler() + + scaler_real.fit(X_train_real) + X_train_real_scaled = scaler_real.transform(X_train_real) + X_test_real_scaled = scaler_real.transform(X_test_real) + + all_real_results = [] + for classifier in classifiers: + real_results = supervised_model_training(X_train_real_scaled,y_train_real,X_test_real_scaled,y_test_real,classifier) + all_real_results.append(real_results) + + all_fake_results_avg = [] + + for fake_path in fake_paths: + data_fake = pd.read_csv(fake_path).to_numpy() + data_fake_y = data_fake[:,-1] + data_fake_X = data_fake[:,:data_dim-1] + X_train_fake, _ , y_train_fake, _ = model_selection.train_test_split(data_fake_X ,data_fake_y, test_size=test_ratio, stratify=data_fake_y,random_state=42) + + if scaler=="MinMax": + scaler_fake = MinMaxScaler() + else: + scaler_fake = StandardScaler() + + scaler_fake.fit(data_fake_X) + + X_train_fake_scaled = scaler_fake.transform(X_train_fake) + + all_fake_results = [] + for classifier in classifiers: + fake_results = supervised_model_training(X_train_fake_scaled,y_train_fake,X_test_real_scaled,y_test_real,classifier) + all_fake_results.append(fake_results) + + all_fake_results_avg.append(all_fake_results) + + diff_results = np.array(all_real_results)- np.array(all_fake_results_avg).mean(axis=0) + + return diff_results + +def stat_sim(real_path,fake_path,cat_cols=None): + + Stat_dict={} + + real = pd.read_csv(real_path) + fake = pd.read_csv(fake_path) + + really = real.copy() + fakey = fake.copy() + + real_corr = compute_associations(real, nominal_columns=cat_cols) + + fake_corr = compute_associations(fake, nominal_columns=cat_cols) + + corr_dist = np.linalg.norm(real_corr - fake_corr) + + cat_stat = [] + num_stat = [] + + for column in real.columns: + + if column in cat_cols: + real_pdf=(really[column].value_counts()/really[column].value_counts().sum()) + fake_pdf=(fakey[column].value_counts()/fakey[column].value_counts().sum()) + categories = (fakey[column].value_counts()/fakey[column].value_counts().sum()).keys().tolist() + sorted_categories = sorted(categories) + + real_pdf_values = [] + fake_pdf_values = [] + + for i in sorted_categories: + real_pdf_values.append(real_pdf[i]) + fake_pdf_values.append(fake_pdf[i]) + + if len(real_pdf)!=len(fake_pdf): + zero_cats = set(really[column].value_counts().keys())-set(fakey[column].value_counts().keys()) + for z in zero_cats: + real_pdf_values.append(real_pdf[z]) + fake_pdf_values.append(0) + Stat_dict[column]=(distance.jensenshannon(real_pdf_values,fake_pdf_values, 2.0)) + cat_stat.append(Stat_dict[column]) + else: + scaler = MinMaxScaler() + scaler.fit(real[column].values.reshape(-1,1)) + l1 = scaler.transform(real[column].values.reshape(-1,1)).flatten() + l2 = scaler.transform(fake[column].values.reshape(-1,1)).flatten() + Stat_dict[column]= (wasserstein_distance(l1,l2)) + num_stat.append(Stat_dict[column]) + + return [np.mean(num_stat),np.mean(cat_stat),corr_dist] + +def privacy_metrics(real_path,fake_path,data_percent=15): + + real = pd.read_csv(real_path).drop_duplicates(keep=False) + fake = pd.read_csv(fake_path).drop_duplicates(keep=False) + + real_refined = real.sample(n=int(len(real)*(.01*data_percent)), random_state=42).to_numpy() + fake_refined = fake.sample(n=int(len(fake)*(.01*data_percent)), random_state=42).to_numpy() + + scalerR = StandardScaler() + scalerR.fit(real_refined) + scalerF = StandardScaler() + scalerF.fit(fake_refined) + df_real_scaled = scalerR.transform(real_refined) + df_fake_scaled = scalerF.transform(fake_refined) + + dist_rf = metrics.pairwise_distances(df_real_scaled, Y=df_fake_scaled, metric='minkowski', n_jobs=-1) + dist_rr = metrics.pairwise_distances(df_real_scaled, Y=None, metric='minkowski', n_jobs=-1) + rd_dist_rr = dist_rr[~np.eye(dist_rr.shape[0],dtype=bool)].reshape(dist_rr.shape[0],-1) + dist_ff = metrics.pairwise_distances(df_fake_scaled, Y=None, metric='minkowski', n_jobs=-1) + rd_dist_ff = dist_ff[~np.eye(dist_ff.shape[0],dtype=bool)].reshape(dist_ff.shape[0],-1) + smallest_two_indexes_rf = [dist_rf[i].argsort()[:2] for i in range(len(dist_rf))] + smallest_two_rf = [dist_rf[i][smallest_two_indexes_rf[i]] for i in range(len(dist_rf))] + smallest_two_indexes_rr = [rd_dist_rr[i].argsort()[:2] for i in range(len(rd_dist_rr))] + smallest_two_rr = [rd_dist_rr[i][smallest_two_indexes_rr[i]] for i in range(len(rd_dist_rr))] + smallest_two_indexes_ff = [rd_dist_ff[i].argsort()[:2] for i in range(len(rd_dist_ff))] + smallest_two_ff = [rd_dist_ff[i][smallest_two_indexes_ff[i]] for i in range(len(rd_dist_ff))] + nn_ratio_rr = np.array([i[0]/i[1] for i in smallest_two_rr]) + nn_ratio_ff = np.array([i[0]/i[1] for i in smallest_two_ff]) + nn_ratio_rf = np.array([i[0]/i[1] for i in smallest_two_rf]) + nn_fifth_perc_rr = np.percentile(nn_ratio_rr,5) + nn_fifth_perc_ff = np.percentile(nn_ratio_ff,5) + nn_fifth_perc_rf = np.percentile(nn_ratio_rf,5) + + min_dist_rf = np.array([i[0] for i in smallest_two_rf]) + fifth_perc_rf = np.percentile(min_dist_rf,5) + min_dist_rr = np.array([i[0] for i in smallest_two_rr]) + fifth_perc_rr = np.percentile(min_dist_rr,5) + min_dist_ff = np.array([i[0] for i in smallest_two_ff]) + fifth_perc_ff = np.percentile(min_dist_ff,5) + + return np.array([fifth_perc_rf,fifth_perc_rr,fifth_perc_ff,nn_fifth_perc_rf,nn_fifth_perc_rr,nn_fifth_perc_ff]).reshape(1,6) \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/pipeline/data_preparation.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/pipeline/data_preparation.py new file mode 100644 index 0000000000000000000000000000000000000000..dec2dc9715af03ecabf6efb3cff333588e5aa25c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/pipeline/data_preparation.py @@ -0,0 +1,114 @@ +import numpy as np +import pandas as pd +from sklearn import preprocessing +from sklearn import model_selection + +class DataPrep(object): + + def __init__(self, raw_df: pd.DataFrame, categorical: list, log:list, mixed:dict, integer:list, type:dict, test_ratio:float): + + + self.categorical_columns = categorical + self.log_columns = log + self.mixed_columns = mixed + self.integer_columns = integer + self.column_types = dict() + self.column_types["categorical"] = [] + self.column_types["mixed"] = {} + self.lower_bounds = {} + self.label_encoder_list = [] + + + target_col = list(type.values())[0] + y_real = raw_df[target_col] + X_real = raw_df.drop(columns=[target_col]) + # X_train_real, _, y_train_real, _ = model_selection.train_test_split(X_real ,y_real, test_size=test_ratio, stratify=y_real,random_state=42) + X_train_real, y_train_real = X_real, y_real + X_train_real[target_col]= y_train_real + + self.df = X_train_real + + self.df = self.df.replace(r' ', np.nan) + self.df = self.df.fillna('empty') + + all_columns= set(self.df.columns) + irrelevant_missing_columns = set(self.categorical_columns) + relevant_missing_columns = list(all_columns - irrelevant_missing_columns) + + for i in relevant_missing_columns: + if i in self.log_columns: + if "empty" in list(self.df[i].values): + self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x) + self.mixed_columns[i] = [-9999999] + elif i in list(self.mixed_columns.keys()): + if "empty" in list(self.df[i].values): + self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x ) + self.mixed_columns[i].append(-9999999) + else: + if "empty" in list(self.df[i].values): + self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x) + self.mixed_columns[i] = [-9999999] + + if self.log_columns: + for log_column in self.log_columns: + valid_indices = [] + for idx,val in enumerate(self.df[log_column].values): + if val!=-9999999: + valid_indices.append(idx) + eps = 1 + lower = np.min(self.df[log_column].iloc[valid_indices].values) + self.lower_bounds[log_column] = lower + if lower>0: + self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x) if x!=-9999999 else -9999999) + elif lower == 0: + self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x+eps) if x!=-9999999 else -9999999) + else: + self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x-lower+eps) if x!=-9999999 else -9999999) + + for column_index, column in enumerate(self.df.columns): + if column in self.categorical_columns: + label_encoder = preprocessing.LabelEncoder() + self.df[column] = self.df[column].astype(str) + label_encoder.fit(self.df[column]) + current_label_encoder = dict() + current_label_encoder['column'] = column + current_label_encoder['label_encoder'] = label_encoder + transformed_column = label_encoder.transform(self.df[column]) + self.df[column] = transformed_column + self.label_encoder_list.append(current_label_encoder) + self.column_types["categorical"].append(column_index) + + elif column in self.mixed_columns: + self.column_types["mixed"][column_index] = self.mixed_columns[column] + + super().__init__() + + def inverse_prep(self, data, eps=1): + + df_sample = pd.DataFrame(data,columns=self.df.columns) + + for i in range(len(self.label_encoder_list)): + le = self.label_encoder_list[i]["label_encoder"] + df_sample[self.label_encoder_list[i]["column"]] = df_sample[self.label_encoder_list[i]["column"]].astype(int) + df_sample[self.label_encoder_list[i]["column"]] = le.inverse_transform(df_sample[self.label_encoder_list[i]["column"]]) + + if self.log_columns: + for i in df_sample: + if i in self.log_columns: + lower_bound = self.lower_bounds[i] + if lower_bound>0: + df_sample[i].apply(lambda x: np.exp(x)) + elif lower_bound==0: + df_sample[i] = df_sample[i].apply(lambda x: np.ceil(np.exp(x)-eps) if (np.exp(x)-eps) < 0 else (np.exp(x)-eps)) + else: + df_sample[i] = df_sample[i].apply(lambda x: np.exp(x)-eps+lower_bound) + + if self.integer_columns: + for column in self.integer_columns: + df_sample[column]= (np.round(df_sample[column].values)) + df_sample[column] = df_sample[column].astype(int) + + df_sample.replace(-9999999, np.nan,inplace=True) + df_sample.replace('empty', np.nan,inplace=True) + + return df_sample diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/synthesizer/ctabgan_synthesizer.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/synthesizer/ctabgan_synthesizer.py new file mode 100644 index 0000000000000000000000000000000000000000..7e4dd5cc2ecfb7c730b8cdab498c3cba332a318f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/synthesizer/ctabgan_synthesizer.py @@ -0,0 +1,526 @@ +import numpy as np +import pandas as pd +import torch +import torch.utils.data +import torch.optim as optim +from torch.optim import Adam +from torch.nn import functional as F +from torch.nn import (Dropout, LeakyReLU, Linear, Module, ReLU, Sequential, +Conv2d, ConvTranspose2d, BatchNorm2d, Sigmoid, init, BCELoss, CrossEntropyLoss,SmoothL1Loss) +from model.synthesizer.transformer import ImageTransformer,DataTransformer +from tqdm import tqdm + + +class Classifier(Module): + def __init__(self,input_dim, dis_dims,st_ed): + super(Classifier,self).__init__() + dim = input_dim-(st_ed[1]-st_ed[0]) + seq = [] + self.str_end = st_ed + for item in list(dis_dims): + seq += [ + Linear(dim, item), + LeakyReLU(0.2), + Dropout(0.5) + ] + dim = item + + if (st_ed[1]-st_ed[0])==1: + seq += [Linear(dim, 1)] + + elif (st_ed[1]-st_ed[0])==2: + seq += [Linear(dim, 1),Sigmoid()] + else: + seq += [Linear(dim,(st_ed[1]-st_ed[0]))] + + self.seq = Sequential(*seq) + + def forward(self, input): + + label=None + + if (self.str_end[1]-self.str_end[0])==1: + label = input[:, self.str_end[0]:self.str_end[1]] + else: + label = torch.argmax(input[:, self.str_end[0]:self.str_end[1]], axis=-1) + + new_imp = torch.cat((input[:,:self.str_end[0]],input[:,self.str_end[1]:]),1) + + if ((self.str_end[1]-self.str_end[0])==2) | ((self.str_end[1]-self.str_end[0])==1): + return self.seq(new_imp).view(-1), label + else: + return self.seq(new_imp), label + +def apply_activate(data, output_info): + data_t = [] + st = 0 + for item in output_info: + if item[1] == 'tanh': + ed = st + item[0] + data_t.append(torch.tanh(data[:, st:ed])) + st = ed + elif item[1] == 'softmax': + ed = st + item[0] + data_t.append(F.gumbel_softmax(data[:, st:ed], tau=0.2)) + st = ed + return torch.cat(data_t, dim=1) + +def get_st_ed(target_col_index,output_info): + st = 0 + c= 0 + tc= 0 + for item in output_info: + if c==target_col_index: + break + if item[1]=='tanh': + st += item[0] + elif item[1] == 'softmax': + st += item[0] + c+=1 + tc+=1 + ed= st+output_info[tc][0] + return (st,ed) + +def random_choice_prob_index_sampling(probs,col_idx): + option_list = [] + for i in col_idx: + pp = probs[i] + option_list.append(np.random.choice(np.arange(len(probs[i])), p=pp)) + + return np.array(option_list).reshape(col_idx.shape) + +def random_choice_prob_index(a, axis=1): + r = np.expand_dims(np.random.rand(a.shape[1 - axis]), axis=axis) + return (a.cumsum(axis=axis) > r).argmax(axis=axis) + +def maximum_interval(output_info): + max_interval = 0 + for item in output_info: + max_interval = max(max_interval, item[0]) + return max_interval + +class Cond(object): + def __init__(self, data, output_info): + + self.model = [] + st = 0 + counter = 0 + for item in output_info: + + if item[1] == 'tanh': + st += item[0] + continue + elif item[1] == 'softmax': + ed = st + item[0] + counter += 1 + self.model.append(np.argmax(data[:, st:ed], axis=-1)) + st = ed + + self.interval = [] + self.n_col = 0 + self.n_opt = 0 + st = 0 + self.p = np.zeros((counter, maximum_interval(output_info))) + self.p_sampling = [] + for item in output_info: + if item[1] == 'tanh': + st += item[0] + continue + elif item[1] == 'softmax': + ed = st + item[0] + tmp = np.sum(data[:, st:ed], axis=0) + tmp_sampling = np.sum(data[:, st:ed], axis=0) + tmp = np.log(tmp + 1) + tmp = tmp / np.sum(tmp) + tmp_sampling = tmp_sampling / np.sum(tmp_sampling) + self.p_sampling.append(tmp_sampling) + self.p[self.n_col, :item[0]] = tmp + self.interval.append((self.n_opt, item[0])) + self.n_opt += item[0] + self.n_col += 1 + st = ed + + self.interval = np.asarray(self.interval) + + def sample_train(self, batch): + if self.n_col == 0: + return None + batch = batch + + idx = np.random.choice(np.arange(self.n_col), batch) + + vec = np.zeros((batch, self.n_opt), dtype='float32') + mask = np.zeros((batch, self.n_col), dtype='float32') + mask[np.arange(batch), idx] = 1 + opt1prime = random_choice_prob_index(self.p[idx]) + for i in np.arange(batch): + vec[i, self.interval[idx[i], 0] + opt1prime[i]] = 1 + + return vec, mask, idx, opt1prime + + def sample(self, batch): + if self.n_col == 0: + return None + batch = batch + + idx = np.random.choice(np.arange(self.n_col), batch) + + vec = np.zeros((batch, self.n_opt), dtype='float32') + opt1prime = random_choice_prob_index_sampling(self.p_sampling,idx) + + for i in np.arange(batch): + vec[i, self.interval[idx[i], 0] + opt1prime[i]] = 1 + + return vec + +def cond_loss(data, output_info, c, m): + loss = [] + st = 0 + st_c = 0 + for item in output_info: + if item[1] == 'tanh': + st += item[0] + continue + + elif item[1] == 'softmax': + ed = st + item[0] + ed_c = st_c + item[0] + tmp = F.cross_entropy( + data[:, st:ed], + torch.argmax(c[:, st_c:ed_c], dim=1), + reduction='none') + loss.append(tmp) + st = ed + st_c = ed_c + + loss = torch.stack(loss, dim=1) + return (loss * m).sum() / data.size()[0] + +class Sampler(object): + def __init__(self, data, output_info): + super(Sampler, self).__init__() + self.data = data + self.model = [] + self.n = len(data) + st = 0 + for item in output_info: + if item[1] == 'tanh': + st += item[0] + continue + elif item[1] == 'softmax': + ed = st + item[0] + tmp = [] + for j in range(item[0]): + tmp.append(np.nonzero(data[:, st + j])[0]) + self.model.append(tmp) + st = ed + + def sample(self, n, col, opt): + if col is None: + idx = np.random.choice(np.arange(self.n), n) + return self.data[idx] + idx = [] + for c, o in zip(col, opt): + idx.append(np.random.choice(self.model[c][o])) + return self.data[idx] + +class Discriminator(Module): + def __init__(self, side, layers): + super(Discriminator, self).__init__() + self.side = side + info = len(layers)-2 + self.seq = Sequential(*layers) + self.seq_info = Sequential(*layers[:info]) + + def forward(self, input): + return (self.seq(input)), self.seq_info(input) + +class Generator(Module): + def __init__(self, side, layers): + super(Generator, self).__init__() + self.side = side + self.seq = Sequential(*layers) + + def forward(self, input_): + return self.seq(input_) + +def determine_layers_disc(side, num_channels): + assert side >= 4 and side <= 32 + + layer_dims = [(1, side), (num_channels, side // 2)] + + while layer_dims[-1][1] > 3 and len(layer_dims) < 4: + layer_dims.append((layer_dims[-1][0] * 2, layer_dims[-1][1] // 2)) + + layers_D = [] + for prev, curr in zip(layer_dims, layer_dims[1:]): + layers_D += [ + Conv2d(prev[0], curr[0], 4, 2, 1, bias=False), + BatchNorm2d(curr[0]), + LeakyReLU(0.2, inplace=True) + ] + print() + layers_D += [ + + Conv2d(layer_dims[-1][0], 1, layer_dims[-1][1], 1, 0), + Sigmoid() + ] + + return layers_D + +def determine_layers_gen(side, random_dim, num_channels): + assert side >= 4 and side <= 32 + + layer_dims = [(1, side), (num_channels, side // 2)] + + while layer_dims[-1][1] > 3 and len(layer_dims) < 4: + layer_dims.append((layer_dims[-1][0] * 2, layer_dims[-1][1] // 2)) + + layers_G = [ + ConvTranspose2d( + random_dim, layer_dims[-1][0], layer_dims[-1][1], 1, 0, output_padding=0, bias=False) + ] + + for prev, curr in zip(reversed(layer_dims), reversed(layer_dims[:-1])): + layers_G += [ + BatchNorm2d(prev[0]), + ReLU(True), + ConvTranspose2d(prev[0], curr[0], 4, 2, 1, output_padding=0, bias=True) + ] + return layers_G + + +def weights_init(m): + classname = m.__class__.__name__ + + if classname.find('Conv') != -1: + init.normal_(m.weight.data, 0.0, 0.02) + + elif classname.find('BatchNorm') != -1: + init.normal_(m.weight.data, 1.0, 0.02) + init.constant_(m.bias.data, 0) + +class CTABGANSynthesizer: + def __init__(self, + lr=2e-4, + class_dim=(256, 256, 256, 256), + random_dim=128, + num_channels=64, + l2scale=1e-5, + batch_size=1024, + epochs=1, + device=torch.device("cpu")): + + + self.random_dim = random_dim + self.class_dim = class_dim + self.num_channels = num_channels + self.dside = None + self.gside = None + self.l2scale = l2scale + self.lr = lr + self.batch_size = batch_size + self.epochs = epochs + self.device = device + + def fit(self, train_data=pd.DataFrame, categorical=[], mixed={}, type={}, no_train=False): + print("Fit started.") + problem_type = None + target_index=None + if type: + problem_type = list(type.keys())[0] + if problem_type: + target_index = train_data.columns.get_loc(type[problem_type]) + + self.transformer = DataTransformer(train_data=train_data, categorical_list=categorical, mixed_dict=mixed) + self.transformer.fit() + + train_data = self.transformer.transform(train_data.values) + + data_sampler = Sampler(train_data, self.transformer.output_info) + data_dim = self.transformer.output_dim + self.cond_generator = Cond(train_data, self.transformer.output_info) + + sides = [4, 8, 16, 24, 32] + col_size_d = data_dim + self.cond_generator.n_opt + for i in sides: + if i * i >= col_size_d: + self.dside = i + break + + sides = [4, 8, 16, 24, 32] + col_size_g = data_dim + for i in sides: + if i * i >= col_size_g: + self.gside = i + break + + layers_G = determine_layers_gen(self.gside, self.random_dim+self.cond_generator.n_opt, self.num_channels) + layers_D = determine_layers_disc(self.dside, self.num_channels) + + self.generator = Generator(self.gside, layers_G).to(self.device) + discriminator = Discriminator(self.dside, layers_D).to(self.device) + optimizer_params = dict(lr=self.lr, betas=(0.5, 0.9), eps=1e-3, weight_decay=self.l2scale) + optimizerG = Adam(self.generator.parameters(), **optimizer_params) + optimizerD = Adam(discriminator.parameters(), **optimizer_params) + + st_ed = None + classifier=None + optimizerC= None + if target_index != None: + st_ed= get_st_ed(target_index,self.transformer.output_info) + classifier = Classifier(data_dim,self.class_dim,st_ed).to(self.device) + optimizerC = optim.Adam(classifier.parameters(),**optimizer_params) + + + self.generator.apply(weights_init) + discriminator.apply(weights_init) + + self.Gtransformer = ImageTransformer(self.gside) + self.Dtransformer = ImageTransformer(self.dside) + + + if no_train: return + + print("Training started.") + for i in range(self.epochs): + # for _ in range(steps_per_epoch): + + noisez = torch.randn(self.batch_size, self.random_dim, device=self.device) + condvec = self.cond_generator.sample_train(self.batch_size) + + c, m, col, opt = condvec + c = torch.from_numpy(c).to(self.device) + m = torch.from_numpy(m).to(self.device) + noisez = torch.cat([noisez, c], dim=1) + noisez = noisez.view(self.batch_size,self.random_dim+self.cond_generator.n_opt,1,1) + + perm = np.arange(self.batch_size) + np.random.shuffle(perm) + real = data_sampler.sample(self.batch_size, col[perm], opt[perm]) + c_perm = c[perm] + + real = torch.from_numpy(real.astype('float32')).to(self.device) + + fake = self.generator(noisez) + faket = self.Gtransformer.inverse_transform(fake) + fakeact = apply_activate(faket, self.transformer.output_info) + + fake_cat = torch.cat([fakeact, c], dim=1) + real_cat = torch.cat([real, c_perm], dim=1) + + real_cat_d = self.Dtransformer.transform(real_cat) + fake_cat_d = self.Dtransformer.transform(fake_cat) + + optimizerD.zero_grad() + y_real,_ = discriminator(real_cat_d) + y_fake,_ = discriminator(fake_cat_d) + loss_d = (-(torch.log(y_real + 1e-4).mean()) - (torch.log(1. - y_fake + 1e-4).mean())) + loss_d.backward() + optimizerD.step() + + noisez = torch.randn(self.batch_size, self.random_dim, device=self.device) + + condvec = self.cond_generator.sample_train(self.batch_size) + + c, m, col, opt = condvec + c = torch.from_numpy(c).to(self.device) + m = torch.from_numpy(m).to(self.device) + noisez = torch.cat([noisez, c], dim=1) + noisez = noisez.view(self.batch_size,self.random_dim+self.cond_generator.n_opt,1,1) + + optimizerG.zero_grad() + + fake = self.generator(noisez) + faket = self.Gtransformer.inverse_transform(fake) + fakeact = apply_activate(faket, self.transformer.output_info) + + fake_cat = torch.cat([fakeact, c], dim=1) + fake_cat = self.Dtransformer.transform(fake_cat) + + y_fake,info_fake = discriminator(fake_cat) + + cross_entropy = cond_loss(faket, self.transformer.output_info, c, m) + + _,info_real = discriminator(real_cat_d) + + g = -(torch.log(y_fake + 1e-4).mean()) + cross_entropy + g.backward(retain_graph=True) + loss_mean = torch.norm(torch.mean(info_fake.view(self.batch_size,-1), dim=0) - torch.mean(info_real.view(self.batch_size,-1), dim=0), 1) + loss_std = torch.norm(torch.std(info_fake.view(self.batch_size,-1), dim=0) - torch.std(info_real.view(self.batch_size,-1), dim=0), 1) + loss_info = loss_mean + loss_std + loss_info.backward() + optimizerG.step() + + if (i + 1) % 500 == 0: + print(f"Step: {i}/{self.epochs} Loss: {loss_mean:.4f}") + + if problem_type: + + fake = self.generator(noisez) + + faket = self.Gtransformer.inverse_transform(fake) + + fakeact = apply_activate(faket, self.transformer.output_info) + + real_pre, real_label = classifier(real) + fake_pre, fake_label = classifier(fakeact) + + c_loss = CrossEntropyLoss() + + if (st_ed[1] - st_ed[0])==1: + c_loss= SmoothL1Loss() + real_label = real_label.type_as(real_pre) + fake_label = fake_label.type_as(fake_pre) + real_label = torch.reshape(real_label,real_pre.size()) + fake_label = torch.reshape(fake_label,fake_pre.size()) + + + elif (st_ed[1] - st_ed[0])==2: + c_loss = BCELoss() + real_label = real_label.type_as(real_pre) + fake_label = fake_label.type_as(fake_pre) + + loss_cc = c_loss(real_pre, real_label) + loss_cg = c_loss(fake_pre, fake_label) + + optimizerG.zero_grad() + loss_cg.backward() + optimizerG.step() + + optimizerC.zero_grad() + loss_cc.backward() + optimizerC.step() + + @torch.no_grad() + def sample(self, n, seed=0): + + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + sample_batch_size = 8092 + self.generator.eval() + + output_info = self.transformer.output_info + steps = n // sample_batch_size + 1 + + data = [] + + for i in range(steps): + noisez = torch.randn(sample_batch_size, self.random_dim, device=self.device) + condvec = self.cond_generator.sample(sample_batch_size) + c = condvec + c = torch.from_numpy(c).to(self.device) + noisez = torch.cat([noisez, c], dim=1) + noisez = noisez.view(sample_batch_size,self.random_dim+self.cond_generator.n_opt,1,1) + + fake = self.generator(noisez) + faket = self.Gtransformer.inverse_transform(fake) + fakeact = apply_activate(faket,output_info) + # print(len(data)) + data.append(fakeact.detach().cpu().numpy()) + + data = np.concatenate(data, axis=0) + result = self.transformer.inverse_transform(data) + + return result[0:n] + diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/synthesizer/transformer.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/synthesizer/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..18fc3c492a99c7f2455ebf2c2cddf09525333b92 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/model/synthesizer/transformer.py @@ -0,0 +1,363 @@ +import numpy as np +import pandas as pd +import torch +from sklearn.mixture import BayesianGaussianMixture + +class DataTransformer(): + + def __init__(self, train_data=pd.DataFrame, categorical_list=[], mixed_dict={}, n_clusters=10, eps=0.005): + self.meta = None + self.n_clusters = n_clusters + self.eps = eps + self.train_data = train_data + self.categorical_columns= categorical_list + self.mixed_columns= mixed_dict + + def get_metadata(self): + + meta = [] + + for index in range(self.train_data.shape[1]): + column = self.train_data.iloc[:,index] + if index in self.categorical_columns: + mapper = column.value_counts().index.tolist() + meta.append({ + "name": index, + "type": "categorical", + "size": len(mapper), + "i2s": mapper + }) + elif index in self.mixed_columns.keys(): + meta.append({ + "name": index, + "type": "mixed", + "min": column.min(), + "max": column.max(), + "modal": self.mixed_columns[index] + }) + else: + meta.append({ + "name": index, + "type": "continuous", + "min": column.min(), + "max": column.max(), + }) + + return meta + + def fit(self): + data = self.train_data.values + self.meta = self.get_metadata() + model = [] + self.ordering = [] + self.output_info = [] + self.output_dim = 0 + self.components = [] + self.filter_arr = [] + for id_, info in enumerate(self.meta): + if info['type'] == "continuous": + gm = BayesianGaussianMixture(n_components=self.n_clusters, + weight_concentration_prior_type='dirichlet_process', + weight_concentration_prior=0.001, + max_iter=100,n_init=1, random_state=42) + gm.fit(data[:, id_].reshape([-1, 1])) + mode_freq = (pd.Series(gm.predict(data[:, id_].reshape([-1, 1]))).value_counts().keys()) + model.append(gm) + old_comp = gm.weights_ > self.eps + comp = [] + for i in range(self.n_clusters): + if (i in (mode_freq)) & old_comp[i]: + comp.append(True) + else: + comp.append(False) + self.components.append(comp) + self.output_info += [(1, 'tanh'), (np.sum(comp), 'softmax')] + self.output_dim += 1 + np.sum(comp) + + elif info['type'] == "mixed": + + gm1 = BayesianGaussianMixture(n_components=self.n_clusters, + weight_concentration_prior_type='dirichlet_process', + weight_concentration_prior=0.001, max_iter=100, + n_init=1,random_state=42) + gm2 = BayesianGaussianMixture(n_components=self.n_clusters, + weight_concentration_prior_type='dirichlet_process', + weight_concentration_prior=0.001, max_iter=100, + n_init=1,random_state=42) + + gm1.fit(data[:, id_].reshape([-1, 1])) + + filter_arr = [] + for element in data[:, id_]: + if element not in info['modal']: + filter_arr.append(True) + else: + filter_arr.append(False) + + gm2.fit(data[:, id_][filter_arr].reshape([-1, 1])) + mode_freq = (pd.Series(gm2.predict(data[:, id_][filter_arr].reshape([-1, 1]))).value_counts().keys()) + self.filter_arr.append(filter_arr) + model.append((gm1,gm2)) + + old_comp = gm2.weights_ > self.eps + + comp = [] + + for i in range(self.n_clusters): + if (i in (mode_freq)) & old_comp[i]: + comp.append(True) + else: + comp.append(False) + + self.components.append(comp) + + self.output_info += [(1, 'tanh'), (np.sum(comp) + len(info['modal']), 'softmax')] + self.output_dim += 1 + np.sum(comp) + len(info['modal']) + + else: + model.append(None) + self.components.append(None) + self.output_info += [(info['size'], 'softmax')] + self.output_dim += info['size'] + + self.model = model + + def transform(self, data, ispositive = False, positive_list = None): + values = [] + mixed_counter = 0 + for id_, info in enumerate(self.meta): + current = data[:, id_] + if info['type'] == "continuous": + current = current.reshape([-1, 1]) + means = self.model[id_].means_.reshape((1, self.n_clusters)) + stds = np.sqrt(self.model[id_].covariances_).reshape((1, self.n_clusters)) + features = np.empty(shape=(len(current),self.n_clusters)) + if ispositive == True: + if id_ in positive_list: + features = np.abs(current - means) / (4 * stds) + else: + features = (current - means) / (4 * stds) + + probs = self.model[id_].predict_proba(current.reshape([-1, 1])) + n_opts = sum(self.components[id_]) + features = features[:, self.components[id_]] + probs = probs[:, self.components[id_]] + + opt_sel = np.zeros(len(data), dtype='int') + for i in range(len(data)): + pp = probs[i] + 1e-6 + pp = pp / sum(pp) + opt_sel[i] = np.random.choice(np.arange(n_opts), p=pp) + + idx = np.arange((len(features))) + features = features[idx, opt_sel].reshape([-1, 1]) + features = np.clip(features, -.99, .99) + probs_onehot = np.zeros_like(probs) + probs_onehot[np.arange(len(probs)), opt_sel] = 1 + + re_ordered_phot = np.zeros_like(probs_onehot) + + col_sums = probs_onehot.sum(axis=0) + + + n = probs_onehot.shape[1] + largest_indices = np.argsort(-1*col_sums)[:n] + self.ordering.append(largest_indices) + for id,val in enumerate(largest_indices): + re_ordered_phot[:,id] = probs_onehot[:,val] + + + values += [features, re_ordered_phot] + + elif info['type'] == "mixed": + + means_0 = self.model[id_][0].means_.reshape([-1]) + stds_0 = np.sqrt(self.model[id_][0].covariances_).reshape([-1]) + + zero_std_list = [] + means_needed = [] + stds_needed = [] + + for mode in info['modal']: + if mode!=-9999999: + dist = [] + for idx,val in enumerate(list(means_0.flatten())): + dist.append(abs(mode-val)) + index_min = np.argmin(np.array(dist)) + zero_std_list.append(index_min) + else: continue + + for idx in zero_std_list: + means_needed.append(means_0[idx]) + stds_needed.append(stds_0[idx]) + + + mode_vals = [] + + for i,j,k in zip(info['modal'],means_needed,stds_needed): + this_val = np.abs(i - j) / (4*k) + mode_vals.append(this_val) + + if -9999999 in info["modal"]: + mode_vals.append(0) + + current = current.reshape([-1, 1]) + filter_arr = self.filter_arr[mixed_counter] + current = current[filter_arr] + + means = self.model[id_][1].means_.reshape((1, self.n_clusters)) + stds = np.sqrt(self.model[id_][1].covariances_).reshape((1, self.n_clusters)) + features = np.empty(shape=(len(current),self.n_clusters)) + if ispositive == True: + if id_ in positive_list: + features = np.abs(current - means) / (4 * stds) + else: + features = (current - means) / (4 * stds) + + probs = self.model[id_][1].predict_proba(current.reshape([-1, 1])) + + n_opts = sum(self.components[id_]) # 8 + features = features[:, self.components[id_]] + probs = probs[:, self.components[id_]] + + opt_sel = np.zeros(len(current), dtype='int') + for i in range(len(current)): + pp = probs[i] + 1e-6 + pp = pp / sum(pp) + opt_sel[i] = np.random.choice(np.arange(n_opts), p=pp) + idx = np.arange((len(features))) + features = features[idx, opt_sel].reshape([-1, 1]) + features = np.clip(features, -.99, .99) + probs_onehot = np.zeros_like(probs) + probs_onehot[np.arange(len(probs)), opt_sel] = 1 + extra_bits = np.zeros([len(current), len(info['modal'])]) + temp_probs_onehot = np.concatenate([extra_bits,probs_onehot], axis = 1) + final = np.zeros([len(data), 1 + probs_onehot.shape[1] + len(info['modal'])]) + features_curser = 0 + for idx, val in enumerate(data[:, id_]): + if val in info['modal']: + category_ = list(map(info['modal'].index, [val]))[0] + final[idx, 0] = mode_vals[category_] + final[idx, (category_+1)] = 1 + + else: + final[idx, 0] = features[features_curser] + final[idx, (1+len(info['modal'])):] = temp_probs_onehot[features_curser][len(info['modal']):] + features_curser = features_curser + 1 + + just_onehot = final[:,1:] + re_ordered_jhot= np.zeros_like(just_onehot) + n = just_onehot.shape[1] + col_sums = just_onehot.sum(axis=0) + largest_indices = np.argsort(-1*col_sums)[:n] + self.ordering.append(largest_indices) + for id,val in enumerate(largest_indices): + re_ordered_jhot[:,id] = just_onehot[:,val] + final_features = final[:,0].reshape([-1, 1]) + values += [final_features, re_ordered_jhot] + mixed_counter = mixed_counter + 1 + + else: + self.ordering.append(None) + col_t = np.zeros([len(data), info['size']]) + idx = list(map(info['i2s'].index, current)) + col_t[np.arange(len(data)), idx] = 1 + values.append(col_t) + + return np.concatenate(values, axis=1) + + def inverse_transform(self, data): + data_t = np.zeros([len(data), len(self.meta)]) + st = 0 + for id_, info in enumerate(self.meta): + if info['type'] == "continuous": + u = data[:, st] + v = data[:, st + 1:st + 1 + np.sum(self.components[id_])] + order = self.ordering[id_] + v_re_ordered = np.zeros_like(v) + + for id,val in enumerate(order): + v_re_ordered[:,val] = v[:,id] + + v = v_re_ordered + + u = np.clip(u, -1, 1) + v_t = np.ones((data.shape[0], self.n_clusters)) * -100 + v_t[:, self.components[id_]] = v + v = v_t + st += 1 + np.sum(self.components[id_]) + means = self.model[id_].means_.reshape([-1]) + stds = np.sqrt(self.model[id_].covariances_).reshape([-1]) + p_argmax = np.argmax(v, axis=1) + std_t = stds[p_argmax] + mean_t = means[p_argmax] + tmp = u * 4 * std_t + mean_t + data_t[:, id_] = tmp + + elif info['type'] == "mixed": + + u = data[:, st] + full_v = data[:,(st+1):(st+1)+len(info['modal'])+np.sum(self.components[id_])] + order = self.ordering[id_] + full_v_re_ordered = np.zeros_like(full_v) + + for id,val in enumerate(order): + full_v_re_ordered[:,val] = full_v[:,id] + + full_v = full_v_re_ordered + mixed_v = full_v[:,:len(info['modal'])] + v = full_v[:,-np.sum(self.components[id_]):] + + u = np.clip(u, -1, 1) + v_t = np.ones((data.shape[0], self.n_clusters)) * -100 + v_t[:, self.components[id_]] = v + v = np.concatenate([mixed_v,v_t], axis=1) + + st += 1 + np.sum(self.components[id_]) + len(info['modal']) + means = self.model[id_][1].means_.reshape([-1]) + stds = np.sqrt(self.model[id_][1].covariances_).reshape([-1]) + p_argmax = np.argmax(v, axis=1) + + result = np.zeros_like(u) + + for idx in range(len(data)): + if p_argmax[idx] < len(info['modal']): + argmax_value = p_argmax[idx] + result[idx] = float(list(map(info['modal'].__getitem__, [argmax_value]))[0]) + else: + std_t = stds[(p_argmax[idx]-len(info['modal']))] + mean_t = means[(p_argmax[idx]-len(info['modal']))] + result[idx] = u[idx] * 4 * std_t + mean_t + + data_t[:, id_] = result + + else: + current = data[:, st:st + info['size']] + st += info['size'] + idx = np.argmax(current, axis=1) + data_t[:, id_] = list(map(info['i2s'].__getitem__, idx)) + + return data_t + +class ImageTransformer(): + + def __init__(self, side): + + self.height = side + + def transform(self, data): + + if self.height * self.height > len(data[0]): + + padding = torch.zeros((len(data), self.height * self.height - len(data[0]))).to(data.device) + data = torch.cat([data, padding], axis=1) + + return data.view(-1, 1, self.height, self.height) + + def inverse_transform(self, data): + + data = data.view(-1, self.height * self.height) + + return data + + diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/pipeline_ctabgan.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/pipeline_ctabgan.py new file mode 100644 index 0000000000000000000000000000000000000000..9ed3e80f4a226ec8e973f5f15db87540e4fa01a5 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/pipeline_ctabgan.py @@ -0,0 +1,80 @@ +import tomli +import shutil +import os +import argparse +from train_sample_ctabgan import train_ctabgan, sample_ctabgan +from scripts.eval_catboost import train_catboost +import zero +import lib + +def load_config(path) : + with open(path, 'rb') as f: + return tomli.load(f) + +def save_file(parent_dir, config_path): + try: + dst = os.path.join(parent_dir) + os.makedirs(os.path.dirname(dst), exist_ok=True) + shutil.copyfile(os.path.abspath(config_path), dst) + except shutil.SameFileError: + pass + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--config', metavar='FILE') + parser.add_argument('--train', action='store_true', default=False) + parser.add_argument('--sample', action='store_true', default=False) + parser.add_argument('--eval', action='store_true', default=False) + parser.add_argument('--change_val', action='store_true', default=False) + + args = parser.parse_args() + raw_config = lib.load_config(args.config) + timer = zero.Timer() + timer.run() + save_file(os.path.join(raw_config['parent_dir'], 'config.toml'), args.config) + ctabgan = None + if args.train: + ctabgan = train_ctabgan( + parent_dir=raw_config['parent_dir'], + real_data_path=raw_config['real_data_path'], + train_params=raw_config['train_params'], + change_val=args.change_val, + device=raw_config['device'] + ) + if args.sample: + sample_ctabgan( + synthesizer=ctabgan, + parent_dir=raw_config['parent_dir'], + real_data_path=raw_config['real_data_path'], + num_samples=raw_config['sample']['num_samples'], + train_params=raw_config['train_params'], + change_val=args.change_val, + seed=raw_config['sample']['seed'], + device=raw_config['device'] + ) + + save_file(os.path.join(raw_config['parent_dir'], 'info.json'), os.path.join(raw_config['real_data_path'], 'info.json')) + if args.eval: + if raw_config['eval']['type']['eval_model'] == 'catboost': + train_catboost( + parent_dir=raw_config['parent_dir'], + real_data_path=raw_config['real_data_path'], + eval_type=raw_config['eval']['type']['eval_type'], + T_dict=raw_config['eval']['T'], + seed=raw_config['seed'], + change_val=args.change_val + ) + # elif raw_config['eval']['type']['eval_model'] == 'mlp': + # train_mlp( + # parent_dir=raw_config['parent_dir'], + # real_data_path=raw_config['real_data_path'], + # eval_type=raw_config['eval']['type']['eval_type'], + # T_dict=raw_config['eval']['T'], + # seed=raw_config['seed'], + # change_val=args.change_val + # ) + + print(f'Elapsed time: {str(timer)}') + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/requirements.txt b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..1144dbbc7b9c35690b609f5717d766abd4ba9567 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/requirements.txt @@ -0,0 +1,6 @@ +numpy==1.21.0 +torch==1.9.1 +pandas==1.2.4 +sklearn==0.24.1 +dython==0.6.4.post1 +scipy==1.4.1 diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/train_sample_ctabgan.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/train_sample_ctabgan.py new file mode 100644 index 0000000000000000000000000000000000000000..be74a48ec44624d341dda0f684780ac698ec3851 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/train_sample_ctabgan.py @@ -0,0 +1,108 @@ +import lib +import os +import numpy as np +import argparse +from pathlib import Path +from model.ctabgan import CTABGAN +import torch +import pickle + +def train_ctabgan( + parent_dir, + real_data_path, + train_params = {"batch_size": 512}, + change_val=False, + device = "cpu" +): + real_data_path = Path(real_data_path) + parent_dir = Path(parent_dir) + device = torch.device(device) + + if change_val: + X_num_train, X_cat_train, y_train, _, _, _ = lib.read_changed_val(real_data_path) + else: + X_num_train, X_cat_train, y_train = lib.read_pure_data(real_data_path, 'train') + + X = lib.concat_to_pd(X_num_train, X_cat_train, y_train) + + X.columns = [str(_) for _ in X.columns] + + ctabgan_params = lib.load_json("CTAB-GAN/columns.json")[real_data_path.name] + train_params["batch_size"] = min(y_train.shape[0], train_params["batch_size"]) + + print(train_params) + synthesizer = CTABGAN( + df = X, + test_ratio = 0.0, + **ctabgan_params, + **train_params, + device=device + ) + + synthesizer.fit() + + # save_ctabgan(synthesizer, parent_dir) + with open(parent_dir / "ctabgan.obj", "wb") as f: + pickle.dump(synthesizer, f) + + return synthesizer + +def sample_ctabgan( + synthesizer, + parent_dir, + real_data_path, + num_samples, + train_params = {"batch_size": 512}, + change_val=False, + device="cpu", + seed=0 +): + real_data_path = Path(real_data_path) + parent_dir = Path(parent_dir) + device = torch.device(device) + + if change_val: + X_num_train, X_cat_train, y_train, _, _, _ = lib.read_changed_val(real_data_path) + else: + X_num_train, X_cat_train, y_train = lib.read_pure_data(real_data_path, 'train') + + X = lib.concat_to_pd(X_num_train, X_cat_train, y_train) + + X.columns = [str(_) for _ in X.columns] + + ctabgan_params = lib.load_json("CTAB-GAN/columns.json")[real_data_path.name] + + cat_features = ctabgan_params["categorical_columns"] + # if synthesizer is None: + # synthesizer = load_ctabgan(X, ctabgan_params, train_params, parent_dir) + with open(parent_dir / "ctabgan.obj", 'rb') as f: + synthesizer = pickle.load(f) + synthesizer.synthesizer.generator = synthesizer.synthesizer.generator.to(device) + gen_data = synthesizer.generate_samples(num_samples, seed) + + y = gen_data['y'].values + if len(np.unique(y)) == 1: + y[0] = 1 + + X_cat = gen_data[cat_features].drop('y', axis=1).values if len(cat_features) else None + X_num = gen_data.values[:, :X_num_train.shape[1]] if X_num_train is not None else None + + if X_num_train is not None: + np.save(parent_dir / 'X_num_train', X_num.astype(float)) + if X_cat_train is not None: + np.save(parent_dir / 'X_cat_train', X_cat.astype(str)) + np.save(parent_dir / 'y_train', y.astype(float).astype(int)) # only clf !!! + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('real_data_path', type=str) + parser.add_argument('parent_dir', type=str) + parser.add_argument('train_size', type=int) + args = parser.parse_args() + + ctabgan = train_ctabgan(args.parent_dir, args.real_data_path, change_val=True) + sample_ctabgan(ctabgan, args.parent_dir, args.real_data_path, args.train_size, change_val=True) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/tune_ctabgan.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/tune_ctabgan.py new file mode 100644 index 0000000000000000000000000000000000000000..71e73f1edc23821ea1ea099a10095a6b1de032ee --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTAB-GAN/tune_ctabgan.py @@ -0,0 +1,150 @@ +from multiprocessing.sharedctypes import RawValue +import tempfile +import subprocess +import lib +import os +import optuna +import argparse +from pathlib import Path +from train_sample_ctabgan import train_ctabgan, sample_ctabgan +from scripts.eval_catboost import train_catboost + +parser = argparse.ArgumentParser() +parser.add_argument('data_path', type=str) +parser.add_argument('train_size', type=int) +parser.add_argument('eval_type', type=str) +parser.add_argument('device', type=str) + +args = parser.parse_args() +real_data_path = args.data_path +eval_type = args.eval_type +train_size = args.train_size +device = args.device +assert eval_type in ('merged', 'synthetic') + +def objective(trial): + + lr = trial.suggest_loguniform('lr', 0.00001, 0.003) + + def suggest_dim(name): + t = trial.suggest_int(name, d_min, d_max) + return 2 ** t + + # construct model + min_n_layers, max_n_layers, d_min, d_max = 1, 4, 6, 8 + n_layers = trial.suggest_int('n_layers', min_n_layers, max_n_layers) + d_first = [suggest_dim('d_first')] if n_layers else [] + d_middle = ( + [suggest_dim('d_middle')] * (n_layers - 2) + if n_layers > 2 + else [] + ) + d_last = [suggest_dim('d_last')] if n_layers > 1 else [] + d_layers = d_first + d_middle + d_last + #### + + steps = trial.suggest_categorical('steps', [1000, 5000, 10000]) + # steps = trial.suggest_categorical('steps', [10]) + batch_size = 2 ** trial.suggest_int('batch_size', 9, 11) + random_dim = 2 ** trial.suggest_int('random_dim', 4, 7) + num_channels = 2 ** trial.suggest_int('num_channels', 4, 6) + + num_samples = int(train_size * (2 ** trial.suggest_int('frac_samples', -2, 3))) + + train_params = { + "lr": lr, + "epochs": steps, + "class_dim": d_layers, + "batch_size": batch_size, + "random_dim": random_dim, + "num_channels": num_channels + } + trial.set_user_attr("train_params", train_params) + trial.set_user_attr("num_samples", num_samples) + + score = 0.0 + with tempfile.TemporaryDirectory() as dir_: + dir_ = Path(dir_) + ctabgan = train_ctabgan( + parent_dir=dir_, + real_data_path=real_data_path, + train_params=train_params, + change_val=True, + device=device + ) + + for sample_seed in range(5): + sample_ctabgan( + ctabgan, + parent_dir=dir_, + real_data_path=real_data_path, + num_samples=num_samples, + train_params=train_params, + change_val=True, + seed=sample_seed, + device=device + ) + + T_dict = { + "seed": 0, + "normalization": None, + "num_nan_policy": None, + "cat_nan_policy": None, + "cat_min_frequency": None, + "cat_encoding": None, + "y_policy": "default" + } + metrics = train_catboost( + parent_dir=dir_, + real_data_path=real_data_path, + eval_type=eval_type, + T_dict=T_dict, + change_val=True, + seed = 0 + ) + + score += metrics.get_val_score() + return score / 5 + + +study = optuna.create_study( + direction='maximize', + sampler=optuna.samplers.TPESampler(seed=0), +) + +study.optimize(objective, n_trials=35, show_progress_bar=True) + +os.makedirs(f"exp/{Path(real_data_path).name}/ctabgan/", exist_ok=True) +config = { + "parent_dir": f"exp/{Path(real_data_path).name}/ctabgan/", + "real_data_path": real_data_path, + "seed": 0, + "device": args.device, + "train_params": study.best_trial.user_attrs["train_params"], + "sample": {"seed": 0, "num_samples": study.best_trial.user_attrs["num_samples"]}, + "eval": { + "type": {"eval_model": "catboost", "eval_type": eval_type}, + "T": { + "seed": 0, + "normalization": None, + "num_nan_policy": None, + "cat_nan_policy": None, + "cat_min_frequency": None, + "cat_encoding": None, + "y_policy": "default" + }, + } +} + +train_ctabgan( + parent_dir=f"exp/{Path(real_data_path).name}/ctabgan/", + real_data_path=real_data_path, + train_params=study.best_trial.user_attrs["train_params"], + change_val=False, + device=device +) + +lib.dump_config(config, config["parent_dir"]+"config.toml") + +subprocess.run(['python3.9', "scripts/eval_seeds.py", '--config', f'{config["parent_dir"]+"config.toml"}', + '10', "ctabgan", eval_type, "catboost", "5"], check=True) \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.editorconfig b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.editorconfig new file mode 100644 index 0000000000000000000000000000000000000000..8558c49851ee32e7577c83758bf92941054d2c5c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.editorconfig @@ -0,0 +1,24 @@ +# http://editorconfig.org + +root = true + +[*] +indent_style = space +indent_size = 4 +trim_trailing_whitespace = true +insert_final_newline = true +charset = utf-8 +end_of_line = lf + +[*.py] +max_line_length = 99 + +[*.bat] +indent_style = tab +end_of_line = crlf + +[LICENSE] +insert_final_newline = false + +[Makefile] +indent_style = tab diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/CODEOWNERS b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/CODEOWNERS new file mode 100644 index 0000000000000000000000000000000000000000..e4db0356f3f62a8480ae07791b86c28adff16739 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/CODEOWNERS @@ -0,0 +1,2 @@ +# Global rule: +* @sdv-dev/core-contributors diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/ISSUE_TEMPLATE/bug_report.md b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000000000000000000000000000000000000..16e0decde54dd915131ba85aa981029946dc53aa --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,33 @@ +--- +name: Bug report +about: Report an error that you found when using CTGAN +title: '' +labels: bug, pending review +assignees: '' + +--- + +### Environment Details + +Please indicate the following details about the environment in which you found the bug: + +* CTGAN version: +* Python version: +* Operating System: + +### Error Description + + + +### Steps to reproduce + + + +``` +Paste the command(s) you ran and the output. +If there was a crash, please include the traceback here. +``` diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/ISSUE_TEMPLATE/feature_request.md b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000000000000000000000000000000000000..54d7b6a17c3456c79fd59efaa5819d071e0101b0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,24 @@ +--- +name: Feature request +about: Request a new feature that you would like to see implemented in CTGAN +title: '' +labels: new feature, pending review +assignees: '' + +--- + +### Problem Description + + + +### Expected behavior + + + +### Additional context + + diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/ISSUE_TEMPLATE/question.md b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/ISSUE_TEMPLATE/question.md new file mode 100644 index 0000000000000000000000000000000000000000..20aca671d77560c3c52bd7d00ae129ca2f160f8a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/ISSUE_TEMPLATE/question.md @@ -0,0 +1,33 @@ +--- +name: Question +about: Doubts about CTGAN usage +title: '' +labels: question, pending review +assignees: '' + +--- + +### Environment details + +If you are already running CTGAN, please indicate the following details about the environment in +which you are running it: + +* CTGAN version: +* Python version: +* Operating System: + +### Problem description + + + +### What I already tried + + + +``` +Paste the command(s) you ran and the output. +If there was a crash, please include the traceback here. +``` diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/integration.yml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/integration.yml new file mode 100644 index 0000000000000000000000000000000000000000..fdc744eeec5f5a1a18ce12ab91a8796f1c25d9fc --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/integration.yml @@ -0,0 +1,31 @@ +name: Integration Tests + +on: + - push + - pull_request + +jobs: + unit: + runs-on: ${{ matrix.os }} + strategy: + matrix: + python-version: [3.6, 3.7, 3.8, 3.9] + os: [ubuntu-latest, macos-10.15, windows-latest] + steps: + - uses: actions/checkout@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - if: matrix.os == 'windows-latest' + name: Install dependencies - Windows + run: | + python -m pip install --upgrade pip + python -m pip install 'torch>=1.8,<2' -f https://download.pytorch.org/whl/cpu/torch/ + python -m pip install 'torchvision>=0.9.0,<1' -f https://download.pytorch.org/whl/cpu/torchvision/ + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install invoke .[test] + - name: Run integration tests + run: invoke integration diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/lint.yml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/lint.yml new file mode 100644 index 0000000000000000000000000000000000000000..07dde39e9f898c598379e9bd39dac1304be9456e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/lint.yml @@ -0,0 +1,21 @@ +name: Style Checks + +on: + - push + - pull_request + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v1 + - name: Set up Python 3.8 + uses: actions/setup-python@v2 + with: + python-version: 3.8 + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install invoke .[dev] + - name: Run lint checks + run: invoke lint diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/minimum.yml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/minimum.yml new file mode 100644 index 0000000000000000000000000000000000000000..1989c957943adecaf4e9fb851971e961c9188b3d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/minimum.yml @@ -0,0 +1,31 @@ +name: Unit Tests Minimum Versions + +on: + - push + - pull_request + +jobs: + minimum: + runs-on: ${{ matrix.os }} + strategy: + matrix: + python-version: [3.6, 3.7, 3.8, 3.9] + os: [ubuntu-latest, macos-10.15, windows-latest] + steps: + - uses: actions/checkout@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - if: matrix.os == 'windows-latest' + name: Install dependencies - Windows + run: | + python -m pip install --upgrade pip + python -m pip install 'torch==1.8' -f https://download.pytorch.org/whl/cpu/torch/ + python -m pip install 'torchvision==0.9.0' -f https://download.pytorch.org/whl/cpu/torchvision/ + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install invoke .[test] + - name: Test with minimum versions + run: invoke minimum diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/readme.yml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/readme.yml new file mode 100644 index 0000000000000000000000000000000000000000..5a623b543050f215dce8a6ba0d9aeecf2875b779 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/readme.yml @@ -0,0 +1,25 @@ +name: Test README + +on: + - push + - pull_request + +jobs: + readme: + runs-on: ${{ matrix.os }} + strategy: + matrix: + python-version: [3.6, 3.7, 3.8, 3.9] + os: [ubuntu-latest, macos-10.15] # skip windows bc rundoc fails + steps: + - uses: actions/checkout@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install invoke rundoc . + - name: Run the README.md + run: invoke readme diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/unit.yml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/unit.yml new file mode 100644 index 0000000000000000000000000000000000000000..6a63f0e006e1c8d3b635c441661c5ceb0282066b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/unit.yml @@ -0,0 +1,34 @@ +name: Unit Tests + +on: + - push + - pull_request + +jobs: + unit: + runs-on: ${{ matrix.os }} + strategy: + matrix: + python-version: [3.6, 3.7, 3.8, 3.9] + os: [ubuntu-latest, macos-10.15, windows-latest] + steps: + - uses: actions/checkout@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - if: matrix.os == 'windows-latest' + name: Install dependencies - Windows + run: | + python -m pip install --upgrade pip + python -m pip install 'torch>=1.8,<2' -f https://download.pytorch.org/whl/cpu/torch/ + python -m pip install 'torchvision>=0.9.0,<1' -f https://download.pytorch.org/whl/cpu/torchvision/ + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install invoke .[test] + - name: Run unit tests + run: invoke unit + - if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.8 + name: Upload codecov report + uses: codecov/codecov-action@v2 diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.gitignore b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..1609a70e22920b86a4a717e9c9aae645184b0831 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.gitignore @@ -0,0 +1,107 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ +tests/readme_test/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ +docs/api/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv +venv/ +ENV/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +# Vim +.*.swp diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.travis.yml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.travis.yml new file mode 100644 index 0000000000000000000000000000000000000000..ecfa96045b11d59a33951d94e2358ea6b30646b9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/.travis.yml @@ -0,0 +1,15 @@ +# Config file for automatic testing at travis-ci.org +dist: bionic +language: python +python: + - 3.8 + - 3.7 + - 3.6 + +# Command to install dependencies +install: pip install -U tox-travis codecov + +after_success: codecov + +# Command to run tests +script: tox diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/AUTHORS.rst b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/AUTHORS.rst new file mode 100644 index 0000000000000000000000000000000000000000..1ea30d0908426597369b5193f52f1ec2b6ff4826 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/AUTHORS.rst @@ -0,0 +1,13 @@ +Credits +======= + +Research and Development Lead +----------------------------- + +* Lei Xu + +Contributors +------------ + +* Carles Sala +* Kevin Kuo diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/CONTRIBUTING.rst b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/CONTRIBUTING.rst new file mode 100644 index 0000000000000000000000000000000000000000..a21b70abaec7c75cf14208a5438d0af3dfac03b3 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/CONTRIBUTING.rst @@ -0,0 +1,237 @@ +.. highlight:: shell + +============ +Contributing +============ + +Contributions are welcome, and they are greatly appreciated! Every little bit +helps, and credit will always be given. + +You can contribute in many ways: + +Types of Contributions +---------------------- + +Report Bugs +~~~~~~~~~~~ + +Report bugs at the `GitHub Issues page`_. + +If you are reporting a bug, please include: + +* Your operating system name and version. +* Any details about your local setup that might be helpful in troubleshooting. +* Detailed steps to reproduce the bug. + +Fix Bugs +~~~~~~~~ + +Look through the GitHub issues for bugs. Anything tagged with "bug" and "help +wanted" is open to whoever wants to implement it. + +Implement Features +~~~~~~~~~~~~~~~~~~ + +Look through the GitHub issues for features. Anything tagged with "enhancement" +and "help wanted" is open to whoever wants to implement it. + +Write Documentation +~~~~~~~~~~~~~~~~~~~ + +CTGAN could always use more documentation, whether as part of the +official CTGAN docs, in docstrings, or even on the web in blog posts, +articles, and such. + +Submit Feedback +~~~~~~~~~~~~~~~ + +The best way to send feedback is to file an issue at the `GitHub Issues page`_. + +If you are proposing a feature: + +* Explain in detail how it would work. +* Keep the scope as narrow as possible, to make it easier to implement. +* Remember that this is a volunteer-driven project, and that contributions + are welcome :) + +Get Started! +------------ + +Ready to contribute? Here's how to set up `CTGAN` for local development. + +1. Fork the `CTGAN` repo on GitHub. +2. Clone your fork locally:: + + $ git clone git@github.com:your_name_here/CTGAN.git + +3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, + this is how you set up your fork for local development:: + + $ mkvirtualenv CTGAN + $ cd CTGAN/ + $ make install-develop + +4. Create a branch for local development:: + + $ git checkout -b name-of-your-bugfix-or-feature + + Try to use the naming scheme of prefixing your branch with ``gh-X`` where X is + the associated issue, such as ``gh-3-fix-foo-bug``. And if you are not + developing on your own fork, further prefix the branch with your GitHub + username, like ``githubusername/gh-3-fix-foo-bug``. + + Now you can make your changes locally. + +5. While hacking your changes, make sure to cover all your developments with the required + unit tests, and that none of the old tests fail as a consequence of your changes. + For this, make sure to run the tests suite and check the code coverage:: + + $ make lint # Check code styling + $ make test # Run the tests + $ make coverage # Get the coverage report + +6. When you're done making changes, check that your changes pass all the styling checks and + tests, including other Python supported versions, using:: + + $ make test-all + +7. Make also sure to include the necessary documentation in the code as docstrings following + the `Google docstrings style`_. + If you want to view how your documentation will look like when it is published, you can + generate and view the docs with this command:: + + $ make view-docs + +8. Commit your changes and push your branch to GitHub:: + + $ git add . + $ git commit -m "Your detailed description of your changes." + $ git push origin name-of-your-bugfix-or-feature + +9. Submit a pull request through the GitHub website. + +Pull Request Guidelines +----------------------- + +Before you submit a pull request, check that it meets these guidelines: + +1. It resolves an open GitHub Issue and contains its reference in the title or + the comment. If there is no associated issue, feel free to create one. +2. Whenever possible, it resolves only **one** issue. If your PR resolves more than + one issue, try to split it in more than one pull request. +3. The pull request should include unit tests that cover all the changed code +4. If the pull request adds functionality, the docs should be updated. Put + your new functionality into a function with a docstring, and add the + feature to the documentation in an appropriate place. +5. The pull request should work for all the supported Python versions. Check the `Travis Build + Status page`_ and make sure that all the checks pass. + +Unit Testing Guidelines +----------------------- + +All the Unit Tests should comply with the following requirements: + +1. Unit Tests should be based only in unittest and pytest modules. + +2. The tests that cover a module called ``ctgan/path/to/a_module.py`` + should be implemented in a separated module called + ``tests/ctgan/path/to/test_a_module.py``. + Note that the module name has the ``test_`` prefix and is located in a path similar + to the one of the tested module, just inside the ``tests`` folder. + +3. Each method of the tested module should have at least one associated test method, and + each test method should cover only **one** use case or scenario. + +4. Test case methods should start with the ``test_`` prefix and have descriptive names + that indicate which scenario they cover. + Names such as ``test_some_methed_input_none``, ``test_some_method_value_error`` or + ``test_some_method_timeout`` are right, but names like ``test_some_method_1``, + ``some_method`` or ``test_error`` are not. + +5. Each test should validate only what the code of the method being tested does, and not + cover the behavior of any third party package or tool being used, which is assumed to + work properly as far as it is being passed the right values. + +6. Any third party tool that may have any kind of random behavior, such as some Machine + Learning models, databases or Web APIs, will be mocked using the ``mock`` library, and + the only thing that will be tested is that our code passes the right values to them. + +7. Unit tests should not use anything from outside the test and the code being tested. This + includes not reading or writing to any file system or database, which will be properly + mocked. + +Tips +---- + +To run a subset of tests:: + + $ python -m pytest tests.test_ctgan + $ python -m pytest -k 'foo' + +Release Workflow +---------------- + +The process of releasing a new version involves several steps combining both ``git`` and +``bumpversion`` which, briefly: + +1. Merge what is in ``master`` branch into ``stable`` branch. +2. Update the version in ``setup.cfg``, ``ctgan/__init__.py`` and + ``HISTORY.md`` files. +3. Create a new git tag pointing at the corresponding commit in ``stable`` branch. +4. Merge the new commit from ``stable`` into ``master``. +5. Update the version in ``setup.cfg`` and ``ctgan/__init__.py`` + to open the next development iteration. + +.. note:: Before starting the process, make sure that ``HISTORY.md`` has been updated with a new + entry that explains the changes that will be included in the new version. + Normally this is just a list of the Pull Requests that have been merged to master + since the last release. + +Once this is done, run of the following commands: + +1. If you are releasing a patch version:: + + make release + +2. If you are releasing a minor version:: + + make release-minor + +3. If you are releasing a major version:: + + make release-major + +Release Candidates +~~~~~~~~~~~~~~~~~~ + +Sometimes it is necessary or convenient to upload a release candidate to PyPi as a pre-release, +in order to make some of the new features available for testing on other projects before they +are included in an actual full-blown release. + +In order to perform such an action, you can execute:: + + make release-candidate + +This will perform the following actions: + +1. Build and upload the current version to PyPi as a pre-release, with the format ``X.Y.Z.devN`` + +2. Bump the current version to the next release candidate, ``X.Y.Z.dev(N+1)`` + +After this is done, the new pre-release can be installed by including the ``dev`` section in the +dependency specification, either in ``setup.py``:: + + install_requires = [ + ... + 'ctgan>=X.Y.Z.dev', + ... + ] + +or in command line:: + + pip install 'ctgan>=X.Y.Z.dev' + + +.. _GitHub issues page: https://github.com/sdv-dev/CTGAN/issues +.. _Travis Build Status page: https://travis-ci.org/sdv-dev/CTGAN/pull_requests +.. _Google docstrings style: https://google.github.io/styleguide/pyguide.html?showone=Comments#Comments diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/HISTORY.md b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/HISTORY.md new file mode 100644 index 0000000000000000000000000000000000000000..df2595e5dbbc2a9c44d571d9ae63bf86670e6cf2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/HISTORY.md @@ -0,0 +1,147 @@ +# History + +## v0.5.1 - 2022-02-25 + +This release fixes a bug with the decoder instantiation, and also allows users to set a random state for the model +fitting and sampling. + +### Issues closed + +* Update self.decoder with correct variable name - Issue [#203](https://github.com/sdv-dev/CTGAN/issues/203) by @tejuafonja +* Add random state - Issue [#204](https://github.com/sdv-dev/CTGAN/issues/204) by @katxiao + +## v0.5.0 - 2021-11-18 + +This release adds support for Python 3.9 and updates dependencies to ensure compatibility with the +rest of the SDV ecosystem, and upgrades to the latests [RDT](https://github.com/sdv-dev/RDT/releases/tag/v0.6.1) +release. + +### Issues closed + +* Add support for Python 3.9 - Issue [#177](https://github.com/sdv-dev/CTGAN/issues/177) by @pvk-developer +* Add pip check to CI workflows - Issue [#174](https://github.com/sdv-dev/CTGAN/issues/174) by @pvk-developer +* Typo in `CTGAN` code - Issue [#158](https://github.com/sdv-dev/CTGAN/issues/158) by @ori-katz100 and @fealho + +## v0.4.3 - 2021-07-12 + +Dependency upgrades to ensure compatibility with the rest of the SDV ecosystem. + +## v0.4.2 - 2021-04-27 + +In this release, the way in which the loss function of the TVAE model was computed has been fixed. +In addition, the default value of the `discriminator_decay` has been changed to a more optimal +value. Also some improvements to the tests were added. + +### Issues closed + +* `TVAE`: loss function - Issue [#143](https://github.com/sdv-dev/CTGAN/issues/143) by @fealho and @DingfanChen +* Set `discriminator_decay` to `1e-6` - Pull request [#145](https://github.com/sdv-dev/CTGAN/pull/145/) by @fealho +* Adds unit tests - Pull requests [#140](https://github.com/sdv-dev/CTGAN/pull/140) by @fealho + +## v0.4.1 - 2021-03-30 + +This release exposes all the hyperparameters which the user may find useful for both `CTGAN` +and `TVAE`. Also `TVAE` can now be fitted on datasets that are shorter than the batch +size and drops the last batch only if the data size is not divisible by the batch size. + +### Issues closed + +* `TVAE`: Adapt `batch_size` to data size - Issue [#135](https://github.com/sdv-dev/CTGAN/issues/135) by @fealho and @csala +* `ValueError` from `validate_discre_columns` with `uniqueCombinationConstraint` - Issue [133](https://github.com/sdv-dev/CTGAN/issues/133) by @fealho and @MLjungg + +## v0.4.0 - 2021-02-24 + +Maintenance relese to upgrade dependencies to ensure compatibility with the rest +of the SDV libraries. + +Also add a validation on the CTGAN `condition_column` and `condition_value` inputs. + +### Improvements + +* Validate condition_column and condition_value - Issue [#124](https://github.com/sdv-dev/CTGAN/issues/124) by @fealho + +## v0.3.1 - 2021-01-27 + +### Improvements + +* Check discrete_columns valid before fitting - [Issue #35](https://github.com/sdv-dev/CTGAN/issues/35) by @fealho + +## Bugs fixed + +* ValueError: max() arg is an empty sequence - [Issue #115](https://github.com/sdv-dev/CTGAN/issues/115) by @fealho + +## v0.3.0 - 2020-12-18 + +In this release we add a new TVAE model which was presented in the original CTGAN paper. +It also exposes more hyperparameters and moves epochs and log_frequency from fit to the constructor. + +A new verbose argument has been added to optionally disable unnecessary printing, and a new hyperparameter +called `discriminator_steps` has been added to CTGAN to control the number of optimization steps performed +in the discriminator for each generator epoch. + +The code has also been reorganized and cleaned up for better readability and interpretability. + +Special thanks to @Baukebrenninkmeijer @fealho @leix28 @csala for the contributions! + +### Improvements + +* Add TVAE - [Issue #111](https://github.com/sdv-dev/CTGAN/issues/111) by @fealho +* Move `log_frequency` to `__init__` - [Issue #102](https://github.com/sdv-dev/CTGAN/issues/102) by @fealho +* Add discriminator steps hyperparameter - [Issue #101](https://github.com/sdv-dev/CTGAN/issues/101) by @Baukebrenninkmeijer +* Code cleanup / Expose hyperparameters - [Issue #59](https://github.com/sdv-dev/CTGAN/issues/59) by @fealho and @leix28 +* Publish to conda repo - [Issue #54](https://github.com/sdv-dev/CTGAN/issues/54) by @fealho + +### Bugs fixed + +* Fixed NaN != NaN counting bug. - [Issue #100](https://github.com/sdv-dev/CTGAN/issues/100) by @fealho +* Update dependencies and testing - [Issue #90](https://github.com/sdv-dev/CTGAN/issues/90) by @csala + +## v0.2.2 - 2020-11-13 + +In this release we introduce several minor improvements to make CTGAN more versatile and +propertly support new types of data, such as categorical NaN values, as well as conditional +sampling and features to save and load models. + +Additionally, the dependency ranges and python versions have been updated to support up +to date runtimes. + +Many thanks @fealho @leix28 @csala @oregonpillow and @lurosenb for working on making this release possible! + +### Improvements + +* Drop Python 3.5 support - [Issue #79](https://github.com/sdv-dev/CTGAN/issues/79) by @fealho +* Support NaN values in categorical variables - [Issue #78](https://github.com/sdv-dev/CTGAN/issues/78) by @fealho +* Sample synthetic data conditioning on a discrete column - [Issue #69](https://github.com/sdv-dev/CTGAN/issues/69) by @leix28 +* Support recent versions of pandas - [Issue #57](https://github.com/sdv-dev/CTGAN/issues/57) by @csala +* Easy solution for restoring original dtypes - [Issue #26](https://github.com/sdv-dev/CTGAN/issues/26) by @oregonpillow + +### Bugs fixed + +* Loss to nan - [Issue #73](https://github.com/sdv-dev/CTGAN/issues/73) by @fealho +* Swapped the sklearn utils testing import statement - [Issue #53](https://github.com/sdv-dev/CTGAN/issues/53) by @lurosenb + +## v0.2.1 - 2020-01-27 + +Minor version including changes to ensure the logs are properly printed and +the option to disable the log transformation to the discrete column frequencies. + +Special thanks to @kevinykuo for the contributions! + +### Issues Resolved: + +* Option to sample from true data frequency instead of logged frequency - [Issue #16](https://github.com/sdv-dev/CTGAN/issues/16) by @kevinykuo +* Flush stdout buffer for epoch updates - [Issue #14](https://github.com/sdv-dev/CTGAN/issues/14) by @kevinykuo + +## v0.2.0 - 2019-12-18 + +Reorganization of the project structure with a new Python API, new Command Line Interface +and increased data format support. + +### Issues Resolved: + +* Reorganize the project structure - [Issue #10](https://github.com/sdv-dev/CTGAN/issues/10) by @csala +* Move epochs to the fit method - [Issue #5](https://github.com/sdv-dev/CTGAN/issues/5) by @csala + +## v0.1.0 - 2019-11-07 + +First Release - NeurIPS 2019 Version. diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/LICENSE b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..f6f8213bcfc525df1b533a0dd4b06f05f9a14ea8 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/LICENSE @@ -0,0 +1,22 @@ +MIT License + +Copyright (c) 2019, MIT Data To AI Lab + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/MANIFEST.in b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/MANIFEST.in new file mode 100644 index 0000000000000000000000000000000000000000..469520f584a4ffc098d13e23c0d273f89e01a06e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/MANIFEST.in @@ -0,0 +1,11 @@ +include AUTHORS.rst +include CONTRIBUTING.rst +include HISTORY.md +include LICENSE +include README.md + +recursive-include tests * +recursive-exclude * __pycache__ +recursive-exclude * *.py[co] + +recursive-include docs *.md *.rst conf.py Makefile make.bat *.jpg *.png *.gif diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/Makefile b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..bb667a145994b12c34f88661ec6a566f06a4518d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/Makefile @@ -0,0 +1,240 @@ +.DEFAULT_GOAL := help + +define BROWSER_PYSCRIPT +import os, webbrowser, sys + +try: + from urllib import pathname2url +except: + from urllib.request import pathname2url + +webbrowser.open("file://" + pathname2url(os.path.abspath(sys.argv[1]))) +endef +export BROWSER_PYSCRIPT + +define PRINT_HELP_PYSCRIPT +import re, sys + +for line in sys.stdin: + match = re.match(r'^([a-zA-Z_-]+):.*?## (.*)$$', line) + if match: + target, help = match.groups() + print("%-20s %s" % (target, help)) +endef +export PRINT_HELP_PYSCRIPT + +BROWSER := python -c "$$BROWSER_PYSCRIPT" + +.PHONY: help +help: + @python -c "$$PRINT_HELP_PYSCRIPT" < $(MAKEFILE_LIST) + + +# CLEAN TARGETS + +.PHONY: clean-build +clean-build: ## remove build artifacts + rm -fr build/ + rm -fr dist/ + rm -fr .eggs/ + find . -name '*.egg-info' -exec rm -fr {} + + find . -name '*.egg' -exec rm -f {} + + +.PHONY: clean-pyc +clean-pyc: ## remove Python file artifacts + find . -name '*.pyc' -exec rm -f {} + + find . -name '*.pyo' -exec rm -f {} + + find . -name '*~' -exec rm -f {} + + find . -name '__pycache__' -exec rm -fr {} + + +.PHONY: clean-coverage +clean-coverage: ## remove coverage artifacts + rm -f .coverage + rm -f .coverage.* + rm -fr htmlcov/ + +.PHONY: clean-test +clean-test: ## remove test artifacts + rm -fr .tox/ + rm -fr .pytest_cache + +.PHONY: clean +clean: clean-build clean-pyc clean-test clean-coverage ## remove all build, test, coverage and Python artifacts + + +# INSTALL TARGETS + +.PHONY: install +install: clean-build clean-pyc ## install the package to the active Python's site-packages + pip install . + +.PHONY: install-test +install-test: clean-build clean-pyc ## install the package and test dependencies + pip install .[test] + +.PHONY: install-develop +install-develop: clean-build clean-pyc ## install the package in editable mode and dependencies for development + pip install -e .[dev] + +MINIMUM := $(shell sed -n '/install_requires = \[/,/]/p' setup.py | head -n-1 | tail -n+2 | sed 's/ *\(.*\),$?$$/\1/g' | tr '>' '=') + +.PHONY: install-minimum +install-minimum: ## install the minimum supported versions of the package dependencies + pip install $(MINIMUM) + + +# LINT TARGETS + + +.PHONY: lint +lint: ## check style with flake8 and isort + invoke lint + +.PHONY: fix-lint +fix-lint: ## fix lint issues using autoflake, autopep8, and isort + find ctgan tests -name '*.py' | xargs autoflake --in-place --remove-all-unused-imports --remove-unused-variables + autopep8 --in-place --recursive --aggressive ctgan tests + isort --apply --atomic --recursive ctgan tests + + +# TEST TARGETS + +.PHONY: test-unit +test-unit: ## run unit tests using pytest + invoke unit + +.PHONY: test-integration +test-integration: ## run integration tests using pytest + invoke integration + +.PHONY: test-readme +test-readme: ## run the readme snippets + invoke readme + +.PHONY: check-dependencies +check-dependencies: ## test if there are any broken dependencies + pip check + +.PHONY: test +test: test-unit test-integration test-readme ## test everything that needs test dependencies + +.PHONY: test-devel +test-devel: lint ## test everything that needs development dependencies + +.PHONY: test-all +test-all: ## run tests on every Python version with tox + tox -r + + +.PHONY: coverage +coverage: ## check code coverage quickly with the default Python + coverage run --source ctgan -m pytest + coverage report -m + coverage html + $(BROWSER) htmlcov/index.html + + +# RELEASE TARGETS + +.PHONY: dist +dist: clean ## builds source and wheel package + python setup.py sdist + python setup.py bdist_wheel + ls -l dist + +.PHONY: publish-confirm +publish-confirm: + @echo "WARNING: This will irreversibly upload a new version to PyPI!" + @echo -n "Please type 'confirm' to proceed: " \ + && read answer \ + && [ "$${answer}" = "confirm" ] + +.PHONY: publish-test +publish-test: dist publish-confirm ## package and upload a release on TestPyPI + twine upload --repository-url https://test.pypi.org/legacy/ dist/* + +.PHONY: publish +publish: dist publish-confirm ## package and upload a release + twine upload dist/* + +.PHONY: bumpversion-release +bumpversion-release: ## Merge master to stable and bumpversion release + git checkout stable || git checkout -b stable + git merge --no-ff master -m"make release-tag: Merge branch 'master' into stable" + bumpversion release + git push --tags origin stable + +.PHONY: bumpversion-release-test +bumpversion-release-test: ## Merge master to stable and bumpversion release + git checkout stable || git checkout -b stable + git merge --no-ff master -m"make release-tag: Merge branch 'master' into stable" + bumpversion release --no-tag + @echo git push --tags origin stable + +.PHONY: bumpversion-patch +bumpversion-patch: ## Merge stable to master and bumpversion patch + git checkout master + git merge stable + bumpversion --no-tag patch + git push + +.PHONY: bumpversion-candidate +bumpversion-candidate: ## Bump the version to the next candidate + bumpversion candidate --no-tag + +.PHONY: bumpversion-minor +bumpversion-minor: ## Bump the version the next minor skipping the release + bumpversion --no-tag minor + +.PHONY: bumpversion-major +bumpversion-major: ## Bump the version the next major skipping the release + bumpversion --no-tag major + +.PHONY: bumpversion-revert +bumpversion-revert: ## Undo a previous bumpversion-release + git checkout master + git branch -D stable + +CLEAN_DIR := $(shell git status --short | grep -v ??) +CURRENT_BRANCH := $(shell git rev-parse --abbrev-ref HEAD 2>/dev/null) +CHANGELOG_LINES := $(shell git diff HEAD..origin/stable HISTORY.md 2>&1 | wc -l) + +.PHONY: check-clean +check-clean: ## Check if the directory has uncommitted changes +ifneq ($(CLEAN_DIR),) + $(error There are uncommitted changes) +endif + +.PHONY: check-master +check-master: ## Check if we are in master branch +ifneq ($(CURRENT_BRANCH),master) + $(error Please make the release from master branch\n) +endif + +.PHONY: check-history +check-history: ## Check if HISTORY.md has been modified +ifeq ($(CHANGELOG_LINES),0) + $(error Please insert the release notes in HISTORY.md before releasing) +endif + +.PHONY: check-release +check-release: check-clean check-master check-history ## Check if the release can be made + @echo "A new release can be made" + +.PHONY: release +release: check-release bumpversion-release publish bumpversion-patch + +.PHONY: release-test +release-test: check-release bumpversion-release-test publish-test bumpversion-revert + +.PHONY: release-candidate +release-candidate: check-master publish bumpversion-candidate + +.PHONY: release-candidate-test +release-candidate-test: check-clean check-master publish-test + +.PHONY: release-minor +release-minor: check-release bumpversion-minor release + +.PHONY: release-major +release-major: check-release bumpversion-major release diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/README.md b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/README.md new file mode 100644 index 0000000000000000000000000000000000000000..6c8ab04c4ff5f505eaf5f1bf68c55f0161593f07 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/README.md @@ -0,0 +1,182 @@ +
+
+

+ This repository is part of The Synthetic Data Vault Project, a project from DataCebo. +

+ +[![Development Status](https://img.shields.io/badge/Development%20Status-2%20--%20Pre--Alpha-yellow)](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha) +[![PyPI Shield](https://img.shields.io/pypi/v/ctgan.svg)](https://pypi.python.org/pypi/ctgan) +[![Unit Tests](https://github.com/sdv-dev/CTGAN/actions/workflows/unit.yml/badge.svg)](https://github.com/sdv-dev/CTGAN/actions/workflows/unit.yml) +[![Downloads](https://pepy.tech/badge/ctgan)](https://pepy.tech/project/ctgan) +[![Coverage Status](https://codecov.io/gh/sdv-dev/CTGAN/branch/master/graph/badge.svg)](https://codecov.io/gh/sdv-dev/CTGAN) + +
+
+

+ + + +

+
+ +
+ +# Overview + +CTGAN is a collection of Deep Learning based Synthetic Data Generators for single table data, which are able to learn from real data and generate synthetic clones with high fidelity. + +| Important Links | | +| --------------------------------------------- | -------------------------------------------------------------------- | +| :computer: **[Website]** | Check out the SDV Website for more information about the project. | +| :orange_book: **[SDV Blog]** | Regular publshing of useful content about Synthetic Data Generation. | +| :book: **[Documentation]** | Quickstarts, User and Development Guides, and API Reference. | +| :octocat: **[Repository]** | The link to the Github Repository of this library. | +| :scroll: **[License]** | The entire ecosystem is published under the MIT License. | +| :keyboard: **[Development Status]** | This software is in its Pre-Alpha stage. | +| [![][Slack Logo] **Community**][Community] | Join our Slack Workspace for announcements and discussions. | +| [![][MyBinder Logo] **Tutorials**][Tutorials] | Run the SDV Tutorials in a Binder environment. | + +[Website]: https://sdv.dev +[SDV Blog]: https://sdv.dev/blog +[Documentation]: https://sdv.dev/SDV +[Repository]: https://github.com/sdv-dev/CTGAN +[License]: https://github.com/sdv-dev/CTGAN/blob/master/LICENSE +[Development Status]: https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha +[Slack Logo]: https://github.com/sdv-dev/SDV/blob/master/docs/images/slack.png +[Community]: https://join.slack.com/t/sdv-space/shared_invite/zt-gdsfcb5w-0QQpFMVoyB2Yd6SRiMplcw +[MyBinder Logo]: https://github.com/sdv-dev/SDV/blob/master/docs/images/mybinder.png +[Tutorials]: https://mybinder.org/v2/gh/sdv-dev/SDV/master?filepath=tutorials + +## Implemented Models + +Currently, this library implements the **CTGAN** and **TVAE** models proposed in the [Modeling Tabular data using Conditional GAN](https://arxiv.org/abs/1907.00503) paper. For more information about these models, please check out the respective user guides: +* [CTGAN User Guide](https://sdv.dev/SDV/user_guides/single_table/ctgan.html). +* [TVAE User Guide](https://sdv.dev/SDV/user_guides/single_table/tvae.html). + +# Install + +**CTGAN** is part of the **SDV** project and is automatically installed alongside it. For +details about this process please visit the [SDV Installation Guide]( +https://sdv.dev/SDV/getting_started/install.html) + +Optionally, **CTGAN** can also be installed as a standalone library using the following commands: + +**Using `pip`:** + +```bash +pip install ctgan +``` + +**Using `conda`:** + +```bash +conda install -c pytorch -c conda-forge ctgan +``` + +For more installation options please visit the [CTGAN installation Guide](INSTALL.md) + +# Usage Example + +> :warning: **WARNING**: If you're just getting started with synthetic data, we recommend using the SDV library which provides user-friendly APIs for interacting with CTGAN. To learn more about using CTGAN through SDV, check out the user guide [here](https://sdv.dev/SDV/user_guides/single_table/ctgan.html). + +To get started with CTGAN, you should prepare your data as either a `numpy.ndarray` or a `pandas.DataFrame` object with two types of columns: + +* **Continuous Columns**: can contain any numerical value. +* **Discrete Columns**: contain a finite number values, whether these are string values or not. + +In this example we load the [Adult Census Dataset](https://archive.ics.uci.edu/ml/datasets/adult) which is a built-in demo dataset. We then model it using the **CTGANSynthesizer** and generate a synthetic copy of it. + + +```python3 +from ctgan import CTGANSynthesizer +from ctgan import load_demo + +data = load_demo() + +# Names of the columns that are discrete +discrete_columns = [ + 'workclass', + 'education', + 'marital-status', + 'occupation', + 'relationship', + 'race', + 'sex', + 'native-country', + 'income' +] + +ctgan = CTGANSynthesizer(epochs=10) +ctgan.fit(data, discrete_columns) + +# Synthetic copy +samples = ctgan.sample(1000) +``` + + + +# Join our community + + +1. Please have a look at the [Contributing Guide](https://sdv.dev/SDV/developer_guides/contributing.html) to see how you can contribute to the project. +2. If you have any doubts, feature requests or detect an error, please [open an issue on github](https://github.com/sdv-dev/CTGAN/issues) or [join our Slack Workspace](https://sdv-space.slack.com/join/shared_invite/zt-gdsfcb5w-0QQpFMVoyB2Yd6SRiMplcw#/). +3. Also, do not forget to check the [project documentation site](https://sdv.dev/SDV/)! + + +# Citing TGAN + +If you use CTGAN, please cite the following work: + +- *Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni.* **Modeling Tabular data using Conditional GAN**. NeurIPS, 2019. + +```LaTeX +@inproceedings{xu2019modeling, + title={Modeling Tabular data using Conditional GAN}, + author={Xu, Lei and Skoularidou, Maria and Cuesta-Infante, Alfredo and Veeramachaneni, Kalyan}, + booktitle={Advances in Neural Information Processing Systems}, + year={2019} +} +``` + +# Related Projects +Please note that these libraries are external contributions and are not maintained nor supervised by +the MIT DAI-Lab team. + +## R interface for CTGAN + +A wrapper around **CTGAN** has been implemented by Kevin Kuo @kevinykuo, bringing the functionalities +of **CTGAN** to **R** users. + +More details can be found in the corresponding repository: https://github.com/kasaai/ctgan + +## CTGAN Server CLI + +A package to easily deploy **CTGAN** onto a remote server. This package is developed by Timothy Pillow @oregonpillow. + +More details can be found in the corresponding repository: https://github.com/oregonpillow/ctgan-server-cli + +--- + + +
+ +
+
+
+ +[The Synthetic Data Vault Project](https://sdv.dev) was first created at MIT's [Data to AI Lab]( +https://dai.lids.mit.edu/) in 2016. After 4 years of research and traction with enterprise, we +created [DataCebo](https://datacebo.com) in 2020 with the goal of growing the project. +Today, DataCebo is the proud developer of SDV, the largest ecosystem for +synthetic data generation & evaluation. It is home to multiple libraries that support synthetic +data, including: + +* 🔄 Data discovery & transformation. Reverse the transforms to reproduce realistic data. +* 🧠 Multiple machine learning models -- ranging from Copulas to Deep Learning -- to create tabular, + multi table and time series data. +* 📊 Measuring quality and privacy of synthetic data, and comparing different synthetic data + generation models. + +[Get started using the SDV package](https://sdv.dev/SDV/getting_started/install.html) -- a fully +integrated solution and your one-stop shop for synthetic data. Or, use the standalone libraries +for specific needs. diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/conda/README.md b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/conda/README.md new file mode 100644 index 0000000000000000000000000000000000000000..92e2ec5aa40b081c940ac1e918efaeeed8623584 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/conda/README.md @@ -0,0 +1,29 @@ +## Instructions + +These are instructions to deploy the latest version of **CTGAN** to [conda](https://docs.conda.io/en/latest/). +It should be done after every new release. + +## Update the recipe +Prior to making the release on PyPI, you should update the meta.yaml to reflect any changes in the dependencies. +Note that you do not need to edit the version number as that is managed by bumpversion. + +## Make the PyPI release +Follow the standard release instructions to make a PyPI release. Then, return here to make the conda release. + +## Build a package +As part of the PyPI release, you will have updated the stable branch. You should now check out the stable +branch and build the conda package. + +```bash +git checkout stable +cd conda +conda build -c sdv-dev -c pytorch -c conda-forge . +``` + +## Upload to Anaconda +Finally, you can upload the resulting package to Anaconda. + +```bash +anaconda login +anaconda upload -u sdv-dev +``` \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/conda/meta.yaml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/conda/meta.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9d26c28c6ada4f4977d517986a05ceca138c7090 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/conda/meta.yaml @@ -0,0 +1,51 @@ +{% set name = 'ctgan' %} +{% set version = '0.5.2.dev0' %} + +package: + name: "{{ name|lower }}" + version: "{{ version }}" + +source: + url: "https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz" + +build: + number: 0 + noarch: python + entry_points: + - ctgan=ctgan.__main__:main + script: "{{ PYTHON }} -m pip install . -vv" + +requirements: + host: + - pip + - pytest-runner + - packaging >=20,<22 + - python >=3.6,<3.10 + - numpy >=1.18.0,<2 + - pandas >=1.1.3,<2 + - scikit-learn >=0.24,<1 + - pytorch >=1.8.0,<2 + - torchvision >=0.9.0,<1 + - rdt >=0.6.2,<0.7 + run: + - packaging >=20,<22 + - python >=3.6,<3.10 + - numpy >=1.18.0,<2 + - pandas >=1.1.3,<2 + - scikit-learn >=0.24,<1 + - pytorch >=1.8.0,<2 + - torchvision >=0.9.0,<1 + - rdt >=0.6.2,<0.7 + +about: + home: "https://github.com/sdv-dev/CTGAN" + license: MIT + license_family: MIT + license_file: + summary: "Conditional GAN for Tabular Data" + doc_url: + dev_url: + +extra: + recipe-maintainers: + - sdv-dev diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/__init__.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e3abb1c03b685802c21428ba050b1620e1a435fa --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/__init__.py @@ -0,0 +1,17 @@ +# -*- coding: utf-8 -*- + +"""Top-level package for ctgan.""" + +__author__ = 'MIT Data To AI Lab' +__email__ = 'dailabmit@gmail.com' +__version__ = '0.5.2.dev0' + +from .demo import load_demo +from .synthesizers.ctgan import CTGANSynthesizer +from .synthesizers.tvae import TVAESynthesizer + +__all__ = ( + 'CTGANSynthesizer', + 'TVAESynthesizer', + 'load_demo' +) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/__main__.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..8291c1879b60d0de49db79fb6ca60d33f09c8763 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/__main__.py @@ -0,0 +1,102 @@ +"""CLI.""" + +import argparse + +from ctgan.data import read_csv, read_tsv, write_tsv +from ctgan.synthesizers.ctgan import CTGANSynthesizer + + +def _parse_args(): + parser = argparse.ArgumentParser(description='CTGAN Command Line Interface') + parser.add_argument('-e', '--epochs', default=300, type=int, + help='Number of training epochs') + parser.add_argument('-t', '--tsv', action='store_true', + help='Load data in TSV format instead of CSV') + parser.add_argument('--no-header', dest='header', action='store_false', + help='The CSV file has no header. Discrete columns will be indices.') + + parser.add_argument('-m', '--metadata', help='Path to the metadata') + parser.add_argument('-d', '--discrete', + help='Comma separated list of discrete columns without whitespaces.') + parser.add_argument('-n', '--num-samples', type=int, + help='Number of rows to sample. Defaults to the training data size') + + parser.add_argument('--generator_lr', type=float, default=2e-4, + help='Learning rate for the generator.') + parser.add_argument('--discriminator_lr', type=float, default=2e-4, + help='Learning rate for the discriminator.') + + parser.add_argument('--generator_decay', type=float, default=1e-6, + help='Weight decay for the generator.') + parser.add_argument('--discriminator_decay', type=float, default=0, + help='Weight decay for the discriminator.') + + parser.add_argument('--embedding_dim', type=int, default=128, + help='Dimension of input z to the generator.') + parser.add_argument('--generator_dim', type=str, default='256,256', + help='Dimension of each generator layer. ' + 'Comma separated integers with no whitespaces.') + parser.add_argument('--discriminator_dim', type=str, default='256,256', + help='Dimension of each discriminator layer. ' + 'Comma separated integers with no whitespaces.') + + parser.add_argument('--batch_size', type=int, default=500, + help='Batch size. Must be an even number.') + parser.add_argument('--save', default=None, type=str, + help='A filename to save the trained synthesizer.') + parser.add_argument('--load', default=None, type=str, + help='A filename to load a trained synthesizer.') + + parser.add_argument('--sample_condition_column', default=None, type=str, + help='Select a discrete column name.') + parser.add_argument('--sample_condition_column_value', default=None, type=str, + help='Specify the value of the selected discrete column.') + + parser.add_argument('data', help='Path to training data') + parser.add_argument('output', help='Path of the output file') + + return parser.parse_args() + + +def main(): + """CLI.""" + args = _parse_args() + if args.tsv: + data, discrete_columns = read_tsv(args.data, args.metadata) + else: + data, discrete_columns = read_csv(args.data, args.metadata, args.header, args.discrete) + + if args.load: + model = CTGANSynthesizer.load(args.load) + else: + generator_dim = [int(x) for x in args.generator_dim.split(',')] + discriminator_dim = [int(x) for x in args.discriminator_dim.split(',')] + model = CTGANSynthesizer( + embedding_dim=args.embedding_dim, generator_dim=generator_dim, + discriminator_dim=discriminator_dim, generator_lr=args.generator_lr, + generator_decay=args.generator_decay, discriminator_lr=args.discriminator_lr, + discriminator_decay=args.discriminator_decay, batch_size=args.batch_size, + epochs=args.epochs) + model.fit(data, discrete_columns) + + if args.save is not None: + model.save(args.save) + + num_samples = args.num_samples or len(data) + + if args.sample_condition_column is not None: + assert args.sample_condition_column_value is not None + + sampled = model.sample( + num_samples, + args.sample_condition_column, + args.sample_condition_column_value) + + if args.tsv: + write_tsv(sampled, args.metadata, args.output) + else: + sampled.to_csv(args.output, index=False) + + +if __name__ == '__main__': + main() diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/data.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/data.py new file mode 100644 index 0000000000000000000000000000000000000000..1c3ec72e9ec972f9c7d148b66f212bf3e366a340 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/data.py @@ -0,0 +1,94 @@ +"""Data loading.""" + +import json + +import numpy as np +import pandas as pd + + +def read_csv(csv_filename, meta_filename=None, header=True, discrete=None): + """Read a csv file.""" + data = pd.read_csv(csv_filename, header='infer' if header else None) + + if meta_filename: + with open(meta_filename) as meta_file: + metadata = json.load(meta_file) + + discrete_columns = [ + column['name'] + for column in metadata['columns'] + if column['type'] != 'continuous' + ] + + elif discrete: + discrete_columns = discrete.split(',') + if not header: + discrete_columns = [int(i) for i in discrete_columns] + + else: + discrete_columns = [] + + return data, discrete_columns + + +def read_tsv(data_filename, meta_filename): + """Read a tsv file.""" + with open(meta_filename) as f: + column_info = f.readlines() + + column_info_raw = [ + x.replace('{', ' ').replace('}', ' ').split() + for x in column_info + ] + + discrete = [] + continuous = [] + column_info = [] + + for idx, item in enumerate(column_info_raw): + if item[0] == 'C': + continuous.append(idx) + column_info.append((float(item[1]), float(item[2]))) + else: + assert item[0] == 'D' + discrete.append(idx) + column_info.append(item[1:]) + + meta = { + 'continuous_columns': continuous, + 'discrete_columns': discrete, + 'column_info': column_info + } + + with open(data_filename) as f: + lines = f.readlines() + + data = [] + for row in lines: + row_raw = row.split() + row = [] + for idx, col in enumerate(row_raw): + if idx in continuous: + row.append(col) + else: + assert idx in discrete + row.append(column_info[idx].index(col)) + + data.append(row) + + return np.asarray(data, dtype='float32'), meta['discrete_columns'] + + +def write_tsv(data, meta, output_filename): + """Write to a tsv file.""" + with open(output_filename, 'w') as f: + + for row in data: + for idx, col in enumerate(row): + if idx in meta['continuous_columns']: + print(col, end=' ', file=f) + else: + assert idx in meta['discrete_columns'] + print(meta['column_info'][idx][int(col)], end=' ', file=f) + + print(file=f) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/data_sampler.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/data_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..5cbf339dac0980c5368d2d11e2934a268fb9d43a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/data_sampler.py @@ -0,0 +1,156 @@ +"""DataSampler module.""" + +import numpy as np + + +class DataSampler(object): + """DataSampler samples the conditional vector and corresponding data for CTGAN.""" + + def __init__(self, data, output_info, log_frequency): + self._data = data + + def is_discrete_column(column_info): + return (len(column_info) == 1 + and column_info[0].activation_fn == 'softmax') + + n_discrete_columns = sum( + [1 for column_info in output_info if is_discrete_column(column_info)]) + + self._discrete_column_matrix_st = np.zeros( + n_discrete_columns, dtype='int32') + + # Store the row id for each category in each discrete column. + # For example _rid_by_cat_cols[a][b] is a list of all rows with the + # a-th discrete column equal value b. + self._rid_by_cat_cols = [] + + # Compute _rid_by_cat_cols + st = 0 + for column_info in output_info: + if is_discrete_column(column_info): + span_info = column_info[0] + ed = st + span_info.dim + + rid_by_cat = [] + for j in range(span_info.dim): + rid_by_cat.append(np.nonzero(data[:, st + j])[0]) + self._rid_by_cat_cols.append(rid_by_cat) + st = ed + else: + st += sum([span_info.dim for span_info in column_info]) + assert st == data.shape[1] + + # Prepare an interval matrix for efficiently sample conditional vector + max_category = max([ + column_info[0].dim + for column_info in output_info + if is_discrete_column(column_info) + ], default=0) + + self._discrete_column_cond_st = np.zeros(n_discrete_columns, dtype='int32') + self._discrete_column_n_category = np.zeros(n_discrete_columns, dtype='int32') + self._discrete_column_category_prob = np.zeros((n_discrete_columns, max_category)) + self._n_discrete_columns = n_discrete_columns + self._n_categories = sum([ + column_info[0].dim + for column_info in output_info + if is_discrete_column(column_info) + ]) + + st = 0 + current_id = 0 + current_cond_st = 0 + for column_info in output_info: + if is_discrete_column(column_info): + span_info = column_info[0] + ed = st + span_info.dim + category_freq = np.sum(data[:, st:ed], axis=0) + if log_frequency: + category_freq = np.log(category_freq + 1) + category_prob = category_freq / np.sum(category_freq) + self._discrete_column_category_prob[current_id, :span_info.dim] = category_prob + self._discrete_column_cond_st[current_id] = current_cond_st + self._discrete_column_n_category[current_id] = span_info.dim + current_cond_st += span_info.dim + current_id += 1 + st = ed + else: + st += sum([span_info.dim for span_info in column_info]) + + def _random_choice_prob_index(self, discrete_column_id): + probs = self._discrete_column_category_prob[discrete_column_id] + r = np.expand_dims(np.random.rand(probs.shape[0]), axis=1) + return (probs.cumsum(axis=1) > r).argmax(axis=1) + + def sample_condvec(self, batch): + """Generate the conditional vector for training. + + Returns: + cond (batch x #categories): + The conditional vector. + mask (batch x #discrete columns): + A one-hot vector indicating the selected discrete column. + discrete column id (batch): + Integer representation of mask. + category_id_in_col (batch): + Selected category in the selected discrete column. + """ + if self._n_discrete_columns == 0: + return None + + discrete_column_id = np.random.choice( + np.arange(self._n_discrete_columns), batch) + + cond = np.zeros((batch, self._n_categories), dtype='float32') + mask = np.zeros((batch, self._n_discrete_columns), dtype='float32') + mask[np.arange(batch), discrete_column_id] = 1 + category_id_in_col = self._random_choice_prob_index(discrete_column_id) + category_id = (self._discrete_column_cond_st[discrete_column_id] + category_id_in_col) + cond[np.arange(batch), category_id] = 1 + + return cond, mask, discrete_column_id, category_id_in_col + + def sample_original_condvec(self, batch): + """Generate the conditional vector for generation use original frequency.""" + if self._n_discrete_columns == 0: + return None + + cond = np.zeros((batch, self._n_categories), dtype='float32') + + for i in range(batch): + row_idx = np.random.randint(0, len(self._data)) + col_idx = np.random.randint(0, self._n_discrete_columns) + matrix_st = self._discrete_column_matrix_st[col_idx] + matrix_ed = matrix_st + self._discrete_column_n_category[col_idx] + pick = np.argmax(self._data[row_idx, matrix_st:matrix_ed]) + cond[i, pick + self._discrete_column_cond_st[col_idx]] = 1 + + return cond + + def sample_data(self, n, col, opt): + """Sample data from original training data satisfying the sampled conditional vector. + + Returns: + n rows of matrix data. + """ + if col is None: + idx = np.random.randint(len(self._data), size=n) + return self._data[idx] + + idx = [] + for c, o in zip(col, opt): + idx.append(np.random.choice(self._rid_by_cat_cols[c][o])) + + return self._data[idx] + + def dim_cond_vec(self): + """Return the total number of categories.""" + return self._n_categories + + def generate_cond_from_condition_column_info(self, condition_info, batch): + """Generate the condition vector.""" + vec = np.zeros((batch, self._n_categories), dtype='float32') + id_ = self._discrete_column_matrix_st[condition_info['discrete_column_id']] + id_ += condition_info['value_id'] + vec[:, id_] = 1 + return vec diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/data_transformer.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/data_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..06cfd128247f187be963c4b8c26067b06a02cbce --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/data_transformer.py @@ -0,0 +1,217 @@ +"""DataTransformer module.""" + +from collections import namedtuple + +import numpy as np +import pandas as pd +from rdt.transformers import BayesGMMTransformer, OneHotEncodingTransformer + +SpanInfo = namedtuple('SpanInfo', ['dim', 'activation_fn']) +ColumnTransformInfo = namedtuple( + 'ColumnTransformInfo', [ + 'column_name', 'column_type', 'transform', 'output_info', 'output_dimensions' + ] +) + + +class DataTransformer(object): + """Data Transformer. + + Model continuous columns with a BayesianGMM and normalized to a scalar [0, 1] and a vector. + Discrete columns are encoded using a scikit-learn OneHotEncoder. + """ + + def __init__(self, max_clusters=10, weight_threshold=0.005): + """Create a data transformer. + + Args: + max_clusters (int): + Maximum number of Gaussian distributions in Bayesian GMM. + weight_threshold (float): + Weight threshold for a Gaussian distribution to be kept. + """ + self._max_clusters = max_clusters + self._weight_threshold = weight_threshold + + def _fit_continuous(self, data): + """Train Bayesian GMM for continuous columns. + + Args: + data (pd.DataFrame): + A dataframe containing a column. + + Returns: + namedtuple: + A ``ColumnTransformInfo`` object. + """ + column_name = data.columns[0] + gm = BayesGMMTransformer(max_clusters=min(len(data), 10)) + gm.fit(data, [column_name]) + num_components = sum(gm.valid_component_indicator) + + return ColumnTransformInfo( + column_name=column_name, column_type='continuous', transform=gm, + output_info=[SpanInfo(1, 'tanh'), SpanInfo(num_components, 'softmax')], + output_dimensions=1 + num_components) + + def _fit_discrete(self, data): + """Fit one hot encoder for discrete column. + + Args: + data (pd.DataFrame): + A dataframe containing a column. + + Returns: + namedtuple: + A ``ColumnTransformInfo`` object. + """ + column_name = data.columns[0] + ohe = OneHotEncodingTransformer() + ohe.fit(data, [column_name]) + num_categories = len(ohe.dummies) + + return ColumnTransformInfo( + column_name=column_name, column_type='discrete', transform=ohe, + output_info=[SpanInfo(num_categories, 'softmax')], + output_dimensions=num_categories) + + def fit(self, raw_data, discrete_columns=()): + """Fit the ``DataTransformer``. + + Fits a ``BayesGMMTransformer`` for continuous columns and a + ``OneHotEncodingTransformer`` for discrete columns. + + This step also counts the #columns in matrix data and span information. + """ + self.output_info_list = [] + self.output_dimensions = 0 + self.dataframe = True + + if not isinstance(raw_data, pd.DataFrame): + self.dataframe = False + # work around for RDT issue #328 Fitting with numerical column names fails + discrete_columns = [str(column) for column in discrete_columns] + column_names = [str(num) for num in range(raw_data.shape[1])] + raw_data = pd.DataFrame(raw_data, columns=column_names) + + self._column_raw_dtypes = raw_data.infer_objects().dtypes + self._column_transform_info_list = [] + for column_name in raw_data.columns: + if column_name in discrete_columns: + column_transform_info = self._fit_discrete(raw_data[[column_name]]) + else: + column_transform_info = self._fit_continuous(raw_data[[column_name]]) + + self.output_info_list.append(column_transform_info.output_info) + self.output_dimensions += column_transform_info.output_dimensions + self._column_transform_info_list.append(column_transform_info) + + def _transform_continuous(self, column_transform_info, data): + column_name = data.columns[0] + data.loc[:, column_name] = data[column_name].to_numpy().flatten() + gm = column_transform_info.transform + transformed = gm.transform(data, [column_name]) + + # Converts the transformed data to the appropriate output format. + # The first column (ending in '.normalized') stays the same, + # but the lable encoded column (ending in '.component') is one hot encoded. + output = np.zeros((len(transformed), column_transform_info.output_dimensions)) + output[:, 0] = transformed[f'{column_name}.normalized'].to_numpy() + index = transformed[f'{column_name}.component'].to_numpy().astype(int) + output[np.arange(index.size), index + 1] = 1.0 + + return output + + def _transform_discrete(self, column_transform_info, data): + ohe = column_transform_info.transform + return ohe.transform(data).to_numpy() + + def transform(self, raw_data): + """Take raw data and output a matrix data.""" + if not isinstance(raw_data, pd.DataFrame): + column_names = [str(num) for num in range(raw_data.shape[1])] + raw_data = pd.DataFrame(raw_data, columns=column_names) + + column_data_list = [] + for column_transform_info in self._column_transform_info_list: + column_name = column_transform_info.column_name + data = raw_data[[column_name]] + if column_transform_info.column_type == 'continuous': + column_data_list.append(self._transform_continuous(column_transform_info, data)) + else: + column_data_list.append(self._transform_discrete(column_transform_info, data)) + + return np.concatenate(column_data_list, axis=1).astype(float) + + def _inverse_transform_continuous(self, column_transform_info, column_data, sigmas, st): + gm = column_transform_info.transform + data = pd.DataFrame(column_data[:, :2], columns=list(gm.get_output_types())) + data.iloc[:, 1] = np.argmax(column_data[:, 1:], axis=1) + if sigmas is not None: + selected_normalized_value = np.random.normal(data.iloc[:, 0], sigmas[st]) + data.iloc[:, 0] = selected_normalized_value + + return gm.reverse_transform(data, [column_transform_info.column_name]) + + def _inverse_transform_discrete(self, column_transform_info, column_data): + ohe = column_transform_info.transform + data = pd.DataFrame(column_data, columns=list(ohe.get_output_types())) + return ohe.reverse_transform(data)[column_transform_info.column_name] + + def inverse_transform(self, data, sigmas=None): + """Take matrix data and output raw data. + + Output uses the same type as input to the transform function. + Either np array or pd dataframe. + """ + st = 0 + recovered_column_data_list = [] + column_names = [] + for column_transform_info in self._column_transform_info_list: + dim = column_transform_info.output_dimensions + column_data = data[:, st:st + dim] + if column_transform_info.column_type == 'continuous': + recovered_column_data = self._inverse_transform_continuous( + column_transform_info, column_data, sigmas, st) + else: + recovered_column_data = self._inverse_transform_discrete( + column_transform_info, column_data) + + recovered_column_data_list.append(recovered_column_data) + column_names.append(column_transform_info.column_name) + st += dim + + recovered_data = np.column_stack(recovered_column_data_list) + recovered_data = (pd.DataFrame(recovered_data, columns=column_names) + .astype(self._column_raw_dtypes)) + if not self.dataframe: + recovered_data = recovered_data.to_numpy() + + return recovered_data + + def convert_column_name_value_to_id(self, column_name, value): + """Get the ids of the given `column_name`.""" + discrete_counter = 0 + column_id = 0 + for column_transform_info in self._column_transform_info_list: + if column_transform_info.column_name == column_name: + break + if column_transform_info.column_type == 'discrete': + discrete_counter += 1 + + column_id += 1 + + else: + raise ValueError(f"The column_name `{column_name}` doesn't exist in the data.") + + ohe = column_transform_info.transform + data = pd.DataFrame([value], columns=[column_transform_info.column_name]) + one_hot = ohe.transform(data).to_numpy()[0] + if sum(one_hot) == 0: + raise ValueError(f"The value `{value}` doesn't exist in the column `{column_name}`.") + + return { + 'discrete_column_id': discrete_counter, + 'column_id': column_id, + 'value_id': np.argmax(one_hot) + } diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/demo.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..a99f90aa576a31f07ebfa7081ee6e4e7817ed02f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/demo.py @@ -0,0 +1,10 @@ +"""Demo module.""" + +import pandas as pd + +DEMO_URL = 'http://ctgan-data.s3.amazonaws.com/census.csv.gz' + + +def load_demo(): + """Load the demo.""" + return pd.read_csv(DEMO_URL, compression='gzip') diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/synthesizers/__init__.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/synthesizers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2f67c77a7892dad39ae429b80b46e8672a3c69df --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/synthesizers/__init__.py @@ -0,0 +1,16 @@ +"""Synthesizers module.""" + +from .ctgan import CTGANSynthesizer +from .tvae import TVAESynthesizer + +__all__ = ( + 'CTGANSynthesizer', + 'TVAESynthesizer' +) + + +def get_all_synthesizers(): + return { + name: globals()[name] + for name in __all__ + } diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/synthesizers/base.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/synthesizers/base.py new file mode 100644 index 0000000000000000000000000000000000000000..5afb66d4a6d2700c27516d8a322ab7b30a9356eb --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/synthesizers/base.py @@ -0,0 +1,105 @@ +"""BaseSynthesizer module.""" + +import contextlib + +import numpy as np +import torch + + +@contextlib.contextmanager +def set_random_states(random_state, set_model_random_state): + """Context manager for managing the random state. + + Args: + random_state (int or tuple): + The random seed or a tuple of (numpy.random.RandomState, torch.Generator). + set_model_random_state (function): + Function to set the random state on the model. + """ + original_np_state = np.random.get_state() + original_torch_state = torch.get_rng_state() + + random_np_state, random_torch_state = random_state + + np.random.set_state(random_np_state.get_state()) + torch.set_rng_state(random_torch_state.get_state()) + + try: + yield + finally: + current_np_state = np.random.RandomState() + current_np_state.set_state(np.random.get_state()) + current_torch_state = torch.Generator() + current_torch_state.set_state(torch.get_rng_state()) + set_model_random_state((current_np_state, current_torch_state)) + + np.random.set_state(original_np_state) + torch.set_rng_state(original_torch_state) + + +def random_state(function): + """Set the random state before calling the function. + + Args: + function (Callable): + The function to wrap around. + """ + def wrapper(self, *args, **kwargs): + if self.random_states is None: + return function(self, *args, **kwargs) + + else: + with set_random_states(self.random_states, self.set_random_state): + return function(self, *args, **kwargs) + + return wrapper + + +class BaseSynthesizer: + """Base class for all default synthesizers of ``CTGAN``. + + This should contain the save/load methods. + """ + + random_states = None + + def save(self, path): + """Save the model in the passed `path`.""" + device_backup = self._device + self.set_device(torch.device('cpu')) + torch.save(self, path) + self.set_device(device_backup) + + @classmethod + def load(cls, path): + """Load the model stored in the passed `path`.""" + device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') + model = torch.load(path) + model.set_device(device) + return model + + def set_random_state(self, random_state): + """Set the random state. + + Args: + random_state (int, tuple, or None): + Either a tuple containing the (numpy.random.RandomState, torch.Generator) + or an int representing the random seed to use for both random states. + """ + if random_state is None: + self.random_states = random_state + elif isinstance(random_state, int): + self.random_states = ( + np.random.RandomState(seed=random_state), + torch.Generator().manual_seed(random_state), + ) + elif ( + isinstance(random_state, tuple) and + isinstance(random_state[0], np.random.RandomState) and + isinstance(random_state[1], torch.Generator) + ): + self.random_states = random_state + else: + raise TypeError( + f'`random_state` {random_state} expected to be an int or a tuple of ' + '(`np.random.RandomState`, `torch.Generator`)') diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/synthesizers/ctgan.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/synthesizers/ctgan.py new file mode 100644 index 0000000000000000000000000000000000000000..e4253e87d6349cd2bccb669f46146edfd61e23a8 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/synthesizers/ctgan.py @@ -0,0 +1,482 @@ +"""CTGANSynthesizer module.""" + +import warnings + +import numpy as np +import pandas as pd +import torch +from packaging import version +from torch import optim +from torch.nn import BatchNorm1d, Dropout, LeakyReLU, Linear, Module, ReLU, Sequential, functional + +from ..data_sampler import DataSampler +from ..data_transformer import DataTransformer +from .base import BaseSynthesizer, random_state + + +class Discriminator(Module): + """Discriminator for the CTGANSynthesizer.""" + + def __init__(self, input_dim, discriminator_dim, pac=10): + super(Discriminator, self).__init__() + dim = input_dim * pac + self.pac = pac + self.pacdim = dim + seq = [] + for item in list(discriminator_dim): + seq += [Linear(dim, item), LeakyReLU(0.2), Dropout(0.5)] + dim = item + + seq += [Linear(dim, 1)] + self.seq = Sequential(*seq) + + def calc_gradient_penalty(self, real_data, fake_data, device='cpu', pac=10, lambda_=10): + """Compute the gradient penalty.""" + alpha = torch.rand(real_data.size(0) // pac, 1, 1, device=device) + alpha = alpha.repeat(1, pac, real_data.size(1)) + alpha = alpha.view(-1, real_data.size(1)) + + interpolates = alpha * real_data + ((1 - alpha) * fake_data) + + disc_interpolates = self(interpolates) + + gradients = torch.autograd.grad( + outputs=disc_interpolates, inputs=interpolates, + grad_outputs=torch.ones(disc_interpolates.size(), device=device), + create_graph=True, retain_graph=True, only_inputs=True + )[0] + + gradients_view = gradients.view(-1, pac * real_data.size(1)).norm(2, dim=1) - 1 + gradient_penalty = ((gradients_view) ** 2).mean() * lambda_ + + return gradient_penalty + + def forward(self, input_): + """Apply the Discriminator to the `input_`.""" + assert input_.size()[0] % self.pac == 0 + return self.seq(input_.view(-1, self.pacdim)) + + +class Residual(Module): + """Residual layer for the CTGANSynthesizer.""" + + def __init__(self, i, o): + super(Residual, self).__init__() + self.fc = Linear(i, o) + self.bn = BatchNorm1d(o) + self.relu = ReLU() + + def forward(self, input_): + """Apply the Residual layer to the `input_`.""" + out = self.fc(input_) + out = self.bn(out) + out = self.relu(out) + return torch.cat([out, input_], dim=1) + + +class Generator(Module): + """Generator for the CTGANSynthesizer.""" + + def __init__(self, embedding_dim, generator_dim, data_dim): + super(Generator, self).__init__() + dim = embedding_dim + seq = [] + for item in list(generator_dim): + seq += [Residual(dim, item)] + dim += item + seq.append(Linear(dim, data_dim)) + self.seq = Sequential(*seq) + + def forward(self, input_): + """Apply the Generator to the `input_`.""" + data = self.seq(input_) + return data + + +class CTGANSynthesizer(BaseSynthesizer): + """Conditional Table GAN Synthesizer. + + This is the core class of the CTGAN project, where the different components + are orchestrated together. + For more details about the process, please check the [Modeling Tabular data using + Conditional GAN](https://arxiv.org/abs/1907.00503) paper. + + Args: + embedding_dim (int): + Size of the random sample passed to the Generator. Defaults to 128. + generator_dim (tuple or list of ints): + Size of the output samples for each one of the Residuals. A Residual Layer + will be created for each one of the values provided. Defaults to (256, 256). + discriminator_dim (tuple or list of ints): + Size of the output samples for each one of the Discriminator Layers. A Linear Layer + will be created for each one of the values provided. Defaults to (256, 256). + generator_lr (float): + Learning rate for the generator. Defaults to 2e-4. + generator_decay (float): + Generator weight decay for the Adam Optimizer. Defaults to 1e-6. + discriminator_lr (float): + Learning rate for the discriminator. Defaults to 2e-4. + discriminator_decay (float): + Discriminator weight decay for the Adam Optimizer. Defaults to 1e-6. + batch_size (int): + Number of data samples to process in each step. + discriminator_steps (int): + Number of discriminator updates to do for each generator update. + From the WGAN paper: https://arxiv.org/abs/1701.07875. WGAN paper + default is 5. Default used is 1 to match original CTGAN implementation. + log_frequency (boolean): + Whether to use log frequency of categorical levels in conditional + sampling. Defaults to ``True``. + verbose (boolean): + Whether to have print statements for progress results. Defaults to ``False``. + epochs (int): + Number of training epochs. Defaults to 300. + pac (int): + Number of samples to group together when applying the discriminator. + Defaults to 10. + cuda (bool): + Whether to attempt to use cuda for GPU computation. + If this is False or CUDA is not available, CPU will be used. + Defaults to ``True``. + """ + + def __init__(self, embedding_dim=128, generator_dim=(256, 256), discriminator_dim=(256, 256), + generator_lr=2e-4, generator_decay=1e-6, discriminator_lr=2e-4, + discriminator_decay=1e-6, batch_size=500, discriminator_steps=1, + log_frequency=True, verbose=False, epochs=300, pac=10, cuda=True): + + assert batch_size % 2 == 0 + + self._embedding_dim = embedding_dim + self._generator_dim = generator_dim + self._discriminator_dim = discriminator_dim + + self._generator_lr = generator_lr + self._generator_decay = generator_decay + self._discriminator_lr = discriminator_lr + self._discriminator_decay = discriminator_decay + + self._batch_size = batch_size + self._discriminator_steps = discriminator_steps + self._log_frequency = log_frequency + self._verbose = verbose + self._epochs = epochs + self.pac = pac + + if not cuda or not torch.cuda.is_available(): + device = 'cpu' + elif isinstance(cuda, str): + device = cuda + else: + device = 'cuda' + + self._device = torch.device(device) + + self._transformer = None + self._data_sampler = None + self._generator = None + + @staticmethod + def _gumbel_softmax(logits, tau=1, hard=False, eps=1e-10, dim=-1): + """Deals with the instability of the gumbel_softmax for older versions of torch. + + For more details about the issue: + https://drive.google.com/file/d/1AA5wPfZ1kquaRtVruCd6BiYZGcDeNxyP/view?usp=sharing + + Args: + logits […, num_features]: + Unnormalized log probabilities + tau: + Non-negative scalar temperature + hard (bool): + If True, the returned samples will be discretized as one-hot vectors, + but will be differentiated as if it is the soft sample in autograd + dim (int): + A dimension along which softmax will be computed. Default: -1. + + Returns: + Sampled tensor of same shape as logits from the Gumbel-Softmax distribution. + """ + if version.parse(torch.__version__) < version.parse('1.2.0'): + for i in range(10): + transformed = functional.gumbel_softmax(logits, tau=tau, hard=hard, + eps=eps, dim=dim) + if not torch.isnan(transformed).any(): + return transformed + raise ValueError('gumbel_softmax returning NaN.') + + return functional.gumbel_softmax(logits, tau=tau, hard=hard, eps=eps, dim=dim) + + def _apply_activate(self, data): + """Apply proper activation function to the output of the generator.""" + data_t = [] + st = 0 + for column_info in self._transformer.output_info_list: + for span_info in column_info: + if span_info.activation_fn == 'tanh': + ed = st + span_info.dim + data_t.append(torch.tanh(data[:, st:ed])) + st = ed + elif span_info.activation_fn == 'softmax': + ed = st + span_info.dim + transformed = self._gumbel_softmax(data[:, st:ed], tau=0.2) + data_t.append(transformed) + st = ed + else: + raise ValueError(f'Unexpected activation function {span_info.activation_fn}.') + + return torch.cat(data_t, dim=1) + + def _cond_loss(self, data, c, m): + """Compute the cross entropy loss on the fixed discrete column.""" + loss = [] + st = 0 + st_c = 0 + for column_info in self._transformer.output_info_list: + for span_info in column_info: + if len(column_info) != 1 or span_info.activation_fn != 'softmax': + # not discrete column + st += span_info.dim + else: + ed = st + span_info.dim + ed_c = st_c + span_info.dim + tmp = functional.cross_entropy( + data[:, st:ed], + torch.argmax(c[:, st_c:ed_c], dim=1), + reduction='none' + ) + loss.append(tmp) + st = ed + st_c = ed_c + + loss = torch.stack(loss, dim=1) # noqa: PD013 + + return (loss * m).sum() / data.size()[0] + + def _validate_discrete_columns(self, train_data, discrete_columns): + """Check whether ``discrete_columns`` exists in ``train_data``. + + Args: + train_data (numpy.ndarray or pandas.DataFrame): + Training Data. It must be a 2-dimensional numpy array or a pandas.DataFrame. + discrete_columns (list-like): + List of discrete columns to be used to generate the Conditional + Vector. If ``train_data`` is a Numpy array, this list should + contain the integer indices of the columns. Otherwise, if it is + a ``pandas.DataFrame``, this list should contain the column names. + """ + if isinstance(train_data, pd.DataFrame): + invalid_columns = set(discrete_columns) - set(train_data.columns) + elif isinstance(train_data, np.ndarray): + invalid_columns = [] + for column in discrete_columns: + if column < 0 or column >= train_data.shape[1]: + invalid_columns.append(column) + else: + raise TypeError('``train_data`` should be either pd.DataFrame or np.array.') + + if invalid_columns: + raise ValueError(f'Invalid columns found: {invalid_columns}') + + @random_state + def fit(self, train_data, discrete_columns=(), epochs=None): + """Fit the CTGAN Synthesizer models to the training data. + + Args: + train_data (numpy.ndarray or pandas.DataFrame): + Training Data. It must be a 2-dimensional numpy array or a pandas.DataFrame. + discrete_columns (list-like): + List of discrete columns to be used to generate the Conditional + Vector. If ``train_data`` is a Numpy array, this list should + contain the integer indices of the columns. Otherwise, if it is + a ``pandas.DataFrame``, this list should contain the column names. + """ + self._validate_discrete_columns(train_data, discrete_columns) + + if epochs is None: + epochs = self._epochs + else: + warnings.warn( + ('`epochs` argument in `fit` method has been deprecated and will be removed ' + 'in a future version. Please pass `epochs` to the constructor instead'), + DeprecationWarning + ) + + self._transformer = DataTransformer() + self._transformer.fit(train_data, discrete_columns) + + train_data = self._transformer.transform(train_data) + + self._data_sampler = DataSampler( + train_data, + self._transformer.output_info_list, + self._log_frequency) + + data_dim = self._transformer.output_dimensions + + self._generator = Generator( + self._embedding_dim + self._data_sampler.dim_cond_vec(), + self._generator_dim, + data_dim + ).to(self._device) + + discriminator = Discriminator( + data_dim + self._data_sampler.dim_cond_vec(), + self._discriminator_dim, + pac=self.pac + ).to(self._device) + + optimizerG = optim.Adam( + self._generator.parameters(), lr=self._generator_lr, betas=(0.5, 0.9), + weight_decay=self._generator_decay + ) + + optimizerD = optim.Adam( + discriminator.parameters(), lr=self._discriminator_lr, + betas=(0.5, 0.9), weight_decay=self._discriminator_decay + ) + + mean = torch.zeros(self._batch_size, self._embedding_dim, device=self._device) + std = mean + 1 + + print('CTGAN training') + steps_per_epoch = max(len(train_data) // self._batch_size, 1) + for i in range(epochs): + for n in range(self._discriminator_steps): + fakez = torch.normal(mean=mean, std=std) + + condvec = self._data_sampler.sample_condvec(self._batch_size) + if condvec is None: + c1, m1, col, opt = None, None, None, None + real = self._data_sampler.sample_data(self._batch_size, col, opt) + else: + c1, m1, col, opt = condvec + c1 = torch.from_numpy(c1).to(self._device) + m1 = torch.from_numpy(m1).to(self._device) + fakez = torch.cat([fakez, c1], dim=1) + + perm = np.arange(self._batch_size) + np.random.shuffle(perm) + real = self._data_sampler.sample_data( + self._batch_size, col[perm], opt[perm]) + c2 = c1[perm] + + fake = self._generator(fakez) + fakeact = self._apply_activate(fake) + + real = torch.from_numpy(real.astype('float32')).to(self._device) + + if c1 is not None: + fake_cat = torch.cat([fakeact, c1], dim=1) + real_cat = torch.cat([real, c2], dim=1) + else: + real_cat = real + fake_cat = fakeact + + y_fake = discriminator(fake_cat) + y_real = discriminator(real_cat) + + pen = discriminator.calc_gradient_penalty( + real_cat, fake_cat, self._device, self.pac) + loss_d = -(torch.mean(y_real) - torch.mean(y_fake)) + + optimizerD.zero_grad() + pen.backward(retain_graph=True) + loss_d.backward() + optimizerD.step() + + fakez = torch.normal(mean=mean, std=std) + condvec = self._data_sampler.sample_condvec(self._batch_size) + + if condvec is None: + c1, m1, col, opt = None, None, None, None + else: + c1, m1, col, opt = condvec + c1 = torch.from_numpy(c1).to(self._device) + m1 = torch.from_numpy(m1).to(self._device) + fakez = torch.cat([fakez, c1], dim=1) + + fake = self._generator(fakez) + fakeact = self._apply_activate(fake) + + if c1 is not None: + y_fake = discriminator(torch.cat([fakeact, c1], dim=1)) + else: + y_fake = discriminator(fakeact) + + if condvec is None: + cross_entropy = 0 + else: + cross_entropy = self._cond_loss(fake, c1, m1) + + loss_g = -torch.mean(y_fake) + cross_entropy + + optimizerG.zero_grad() + loss_g.backward() + optimizerG.step() + + if self._verbose and (i + 1) % 1000 == 0: + print(f'Epoch {i+1}, Loss G: {loss_g.detach().cpu(): .4f},' # noqa: T001 + f'Loss D: {loss_d.detach().cpu(): .4f}', + flush=True) + + @random_state + def sample(self, n, condition_column=None, condition_value=None): + """Sample data similar to the training data. + + Choosing a condition_column and condition_value will increase the probability of the + discrete condition_value happening in the condition_column. + + Args: + n (int): + Number of rows to sample. + condition_column (string): + Name of a discrete column. + condition_value (string): + Name of the category in the condition_column which we wish to increase the + probability of happening. + + Returns: + numpy.ndarray or pandas.DataFrame + """ + if condition_column is not None and condition_value is not None: + condition_info = self._transformer.convert_column_name_value_to_id( + condition_column, condition_value) + global_condition_vec = self._data_sampler.generate_cond_from_condition_column_info( + condition_info, self._batch_size) + else: + global_condition_vec = None + + steps = n // self._batch_size + 1 + data = [] + for i in range(steps): + mean = torch.zeros(self._batch_size, self._embedding_dim) + std = mean + 1 + fakez = torch.normal(mean=mean, std=std).to(self._device) + + if global_condition_vec is not None: + condvec = global_condition_vec.copy() + else: + condvec = self._data_sampler.sample_original_condvec(self._batch_size) + + if condvec is None: + pass + else: + c1 = condvec + c1 = torch.from_numpy(c1).to(self._device) + fakez = torch.cat([fakez, c1], dim=1) + + fake = self._generator(fakez) + fakeact = self._apply_activate(fake) + data.append(fakeact.detach().cpu().numpy()) + + data = np.concatenate(data, axis=0) + data = data[:n] + + return self._transformer.inverse_transform(data) + + def set_device(self, device): + """Set the `device` to be used ('GPU' or 'CPU).""" + self._device = device + if self._generator is not None: + self._generator.to(self._device) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/synthesizers/tvae.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/synthesizers/tvae.py new file mode 100644 index 0000000000000000000000000000000000000000..1baa3f38a8157b80fb866c205491616543fb0470 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/ctgan/synthesizers/tvae.py @@ -0,0 +1,218 @@ +"""TVAESynthesizer module.""" + +import numpy as np +import torch +from torch.nn import Linear, Module, Parameter, ReLU, Sequential +from torch.nn.functional import cross_entropy +from torch.optim import Adam +from torch.utils.data import DataLoader, TensorDataset + +from ..data_transformer import DataTransformer +from .base import BaseSynthesizer, random_state + + +class Encoder(Module): + """Encoder for the TVAESynthesizer. + + Args: + data_dim (int): + Dimensions of the data. + compress_dims (tuple or list of ints): + Size of each hidden layer. + embedding_dim (int): + Size of the output vector. + """ + + def __init__(self, data_dim, compress_dims, embedding_dim): + super(Encoder, self).__init__() + dim = data_dim + seq = [] + for item in list(compress_dims): + seq += [ + Linear(dim, item), + ReLU() + ] + dim = item + + self.seq = Sequential(*seq) + self.fc1 = Linear(dim, embedding_dim) + self.fc2 = Linear(dim, embedding_dim) + + def forward(self, input_): + """Encode the passed `input_`.""" + feature = self.seq(input_) + mu = self.fc1(feature) + logvar = self.fc2(feature) + std = torch.exp(0.5 * logvar) + return mu, std, logvar + + +class Decoder(Module): + """Decoder for the TVAESynthesizer. + + Args: + embedding_dim (int): + Size of the input vector. + decompress_dims (tuple or list of ints): + Size of each hidden layer. + data_dim (int): + Dimensions of the data. + """ + + def __init__(self, embedding_dim, decompress_dims, data_dim): + super(Decoder, self).__init__() + dim = embedding_dim + seq = [] + for item in list(decompress_dims): + seq += [Linear(dim, item), ReLU()] + dim = item + + seq.append(Linear(dim, data_dim)) + self.seq = Sequential(*seq) + self.sigma = Parameter(torch.ones(data_dim) * 0.1) + + def forward(self, input_): + """Decode the passed `input_`.""" + return self.seq(input_), self.sigma + + +def _loss_function(recon_x, x, sigmas, mu, logvar, output_info, factor): + st = 0 + loss = [] + for column_info in output_info: + for span_info in column_info: + if span_info.activation_fn != 'softmax': + ed = st + span_info.dim + std = sigmas[st] + eq = x[:, st] - torch.tanh(recon_x[:, st]) + loss.append((eq ** 2 / 2 / (std ** 2)).sum()) + loss.append(torch.log(std) * x.size()[0]) + st = ed + + else: + ed = st + span_info.dim + loss.append(cross_entropy( + recon_x[:, st:ed], torch.argmax(x[:, st:ed], dim=-1), reduction='sum')) + st = ed + + assert st == recon_x.size()[1] + KLD = -0.5 * torch.sum(1 + logvar - mu**2 - logvar.exp()) + return sum(loss) * factor / x.size()[0], KLD / x.size()[0] + + +class TVAESynthesizer(BaseSynthesizer): + """TVAESynthesizer.""" + + def __init__( + self, + embedding_dim=128, + compress_dims=(128, 128), + decompress_dims=(128, 128), + l2scale=1e-5, + batch_size=500, + epochs=300, + lr=1e-3, + loss_factor=2, + device="cuda:0" + ): + self.embedding_dim = embedding_dim + self.compress_dims = compress_dims + self.decompress_dims = decompress_dims + + self.lr = lr + self.l2scale = l2scale + self.batch_size = batch_size + self.loss_factor = loss_factor + self.epochs = epochs + + + self._device = torch.device(device) + + @random_state + def fit(self, train_data, discrete_columns=()): + """Fit the TVAE Synthesizer models to the training data. + + Args: + train_data (numpy.ndarray or pandas.DataFrame): + Training Data. It must be a 2-dimensional numpy array or a pandas.DataFrame. + discrete_columns (list-like): + List of discrete columns to be used to generate the Conditional + Vector. If ``train_data`` is a Numpy array, this list should + contain the integer indices of the columns. Otherwise, if it is + a ``pandas.DataFrame``, this list should contain the column names. + """ + self.transformer = DataTransformer() + self.transformer.fit(train_data, discrete_columns) + train_data = self.transformer.transform(train_data) + dataset = TensorDataset(torch.from_numpy(train_data.astype('float32'))) + loader = DataLoader(dataset, batch_size=self.batch_size, shuffle=True, drop_last=False) + + data_dim = self.transformer.output_dimensions + encoder = Encoder(data_dim, self.compress_dims, self.embedding_dim).to(self._device) + self.decoder = Decoder(self.embedding_dim, self.decompress_dims, data_dim).to(self._device) + optimizerAE = Adam( + list(encoder.parameters()) + list(self.decoder.parameters()), + lr=self.lr, + weight_decay=self.l2scale) + data_iter = iter(loader) + print('Training:') + for i in range(self.epochs): + try: + data = next(data_iter) + except: + data_iter = iter(loader) + data = next(data_iter) + + optimizerAE.zero_grad() + real = data[0].to(self._device) + mu, std, logvar = encoder(real) + eps = torch.randn_like(std) + emb = eps * std + mu + rec, sigmas = self.decoder(emb) + loss_1, loss_2 = _loss_function( + rec, real, sigmas, mu, logvar, + self.transformer.output_info_list, self.loss_factor + ) + loss = loss_1 + loss_2 + loss.backward() + optimizerAE.step() + self.decoder.sigma.data.clamp_(0.01, 1.0) + if (i + 1) % 1000 == 0: + print(f"{i + 1}/{self.epochs} {loss}", flush=True) + + @random_state + def sample(self, samples, seed=0): + """Sample data similar to the training data. + + Args: + samples (int): + Number of rows to sample. + + Returns: + numpy.ndarray or pandas.DataFrame + """ + + torch.cuda.manual_seed(seed) + torch.manual_seed(seed) + + self.decoder.eval() + + sample_batch_size = 8092 + steps = samples // sample_batch_size + 1 + data = [] + for _ in range(steps): + mean = torch.zeros(sample_batch_size, self.embedding_dim) + std = mean + 1 + noise = torch.normal(mean=mean, std=std).to(self._device) + fake, sigmas = self.decoder(noise) + fake = torch.tanh(fake) + data.append(fake.detach().cpu().numpy()) + + data = np.concatenate(data, axis=0) + data = data[:samples] + return self.transformer.inverse_transform(data, sigmas.detach().cpu().numpy()) + + def set_device(self, device): + """Set the `device` to be used ('GPU' or 'CPU).""" + self._device = device + self.decoder.to(self._device) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/setup.cfg b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/setup.cfg new file mode 100644 index 0000000000000000000000000000000000000000..8398eee0de75e378fa157456b8594f9ec383c45c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/setup.cfg @@ -0,0 +1,59 @@ +[bumpversion] +current_version = 0.5.2.dev0 +commit = True +tag = True +parse = (?P\d+)\.(?P\d+)\.(?P\d+)(\.(?P[a-z]+)(?P\d+))? +serialize = + {major}.{minor}.{patch}.{release}{candidate} + {major}.{minor}.{patch} + +[bumpversion:part:release] +optional_value = release +first_value = dev +values = + dev + release + +[bumpversion:part:candidate] + +[bumpversion:file:setup.py] +search = version='{current_version}' +replace = version='{new_version}' + +[bumpversion:file:ctgan/__init__.py] +search = __version__ = '{current_version}' +replace = __version__ = '{new_version}' + +[bumpversion:file:conda/meta.yaml] +search = version = '{current_version}' +replace = version = '{new_version}' + +[bdist_wheel] +universal = 1 + +[flake8] +convention = google +max-line-length = 99 +exclude = docs, .tox, .git, __pycache__, .ipynb_checkpoints +extend-ignore = D107, # Missing docstring in __init__ + D407, # Missing dashed underline after section + D417, # Missing argument descriptions in the docstring + SFS3, # String literal formatting using f-string. + VNE001 # Single letter variable names are not allowed +per-file-ignores = + ctgan/data.py:T001 + +[isort] +include_trailing_comment = True +line_length = 99 +lines_between_types = 0 +multi_line_output = 4 +not_skip = __init__.py +use_parentheses = True + +[aliases] +test = pytest + +[tool:pytest] +collect_ignore = ['setup.py'] + diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/setup.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..83d973eb603ce5a4f4dc31f3bac459eeabdd1ec5 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/setup.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +"""The setup script.""" + +from setuptools import find_packages, setup + +with open('README.md', encoding='utf-8') as readme_file: + readme = readme_file.read() + +with open('HISTORY.md', encoding='utf-8') as history_file: + history = history_file.read() + +install_requires = [ + 'packaging>=20,<22', + "numpy>=1.18.0,<1.20.0;python_version<'3.7'", + "numpy>=1.20.0,<2;python_version>='3.7'", + 'pandas>=1.1.3,<2', + 'scikit-learn>=0.24,<2', + 'torch>=1.8.0,<2', + 'torchvision>=0.9.0,<1', + 'rdt>=0.6.2,<0.7', +] + +setup_requires = [ + 'pytest-runner>=2.11.1', +] + +tests_require = [ + 'pytest>=3.4.2', + 'pytest-rerunfailures>=9.1.1,<10', + 'pytest-cov>=2.6.0', +] + +development_requires = [ + # general + 'pip>=9.0.1', + 'bumpversion>=0.5.3,<0.6', + 'watchdog>=0.8.3,<0.11', + + # style check + 'flake8>=3.7.7,<4', + 'isort>=4.3.4,<5', + 'dlint>=0.11.0,<0.12', # code security addon for flake8 + 'flake8-debugger>=4.0.0,<4.1', + 'flake8-mock>=0.3,<0.4', + 'flake8-mutable>=1.2.0,<1.3', + 'flake8-absolute-import>=1.0,<2', + 'flake8-multiline-containers>=0.0.18,<0.1', + 'flake8-print>=4.0.0,<4.1', + 'flake8-quotes>=3.3.0,<4', + 'flake8-fixme>=1.1.1,<1.2', + 'flake8-expression-complexity>=0.0.9,<0.1', + 'flake8-eradicate>=1.1.0,<1.2', + 'flake8-builtins>=1.5.3,<1.6', + 'flake8-variables-names>=0.0.4,<0.1', + 'pandas-vet>=0.2.2,<0.3', + 'flake8-comprehensions>=3.6.1,<3.7', + 'dlint>=0.11.0,<0.12', + 'flake8-docstrings>=1.5.0,<2', + 'flake8-sfs>=0.0.3,<0.1', + 'flake8-pytest-style>=1.5.0,<2', + + # fix style issues + 'autoflake>=1.1,<2', + 'autopep8>=1.4.3,<1.6', + + # distribute on PyPI + 'twine>=1.10.0,<4', + 'wheel>=0.30.0', + + # Advanced testing + 'coverage>=4.5.1,<6', + 'tox>=2.9.1,<4', + + 'invoke', +] + +setup( + author='MIT Data To AI Lab', + author_email='dailabmit@gmail.com', + classifiers=[ + 'Development Status :: 2 - Pre-Alpha', + 'Intended Audience :: Developers', + 'License :: OSI Approved :: MIT License', + 'Natural Language :: English', + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.6', + 'Programming Language :: Python :: 3.7', + 'Programming Language :: Python :: 3.8', + 'Programming Language :: Python :: 3.9', + ], + description='Conditional GAN for Tabular Data', + entry_points={ + 'console_scripts': [ + 'ctgan=ctgan.__main__:main' + ], + }, + extras_require={ + 'test': tests_require, + 'dev': development_requires + tests_require, + }, + install_package_data=True, + install_requires=install_requires, + license='MIT license', + long_description=readme + '\n\n' + history, + long_description_content_type='text/markdown', + include_package_data=True, + keywords='ctgan CTGAN', + name='ctgan', + packages=find_packages(include=['ctgan', 'ctgan.*']), + python_requires='>=3.6,<3.10', + setup_requires=setup_requires, + test_suite='tests', + tests_require=tests_require, + url='https://github.com/sdv-dev/CTGAN', + version='0.5.2.dev0', + zip_safe=False, +) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tasks.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tasks.py new file mode 100644 index 0000000000000000000000000000000000000000..78730cea28cec928ff175364db655f10abe6143e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tasks.py @@ -0,0 +1,121 @@ +import glob +import operator +import os +import re +import platform +import shutil +import stat +from pathlib import Path + +from invoke import task + +COMPARISONS = { + '>=': operator.ge, + '>': operator.gt, + '<': operator.lt, + '<=': operator.le +} + + +@task +def check_dependencies(c): + c.run('python -m pip check') + + +@task +def unit(c): + c.run('python -m pytest ./tests/unit --cov=ctgan --cov-report=xml') + + +@task +def integration(c): + c.run('python -m pytest ./tests/integration --reruns 3') + + +@task +def readme(c): + test_path = Path('tests/readme_test') + if test_path.exists() and test_path.is_dir(): + shutil.rmtree(test_path) + + cwd = os.getcwd() + os.makedirs(test_path, exist_ok=True) + shutil.copy('README.md', test_path / 'README.md') + os.chdir(test_path) + c.run('rundoc run --single-session python3 -t python3 README.md') + os.chdir(cwd) + shutil.rmtree(test_path) + + +def _validate_python_version(line): + python_version_match = re.search(r"python_version(<=?|>=?)\'(\d\.?)+\'", line) + if python_version_match: + python_version = python_version_match.group(0) + comparison = re.search(r'(>=?|<=?)', python_version).group(0) + version_number = python_version.split(comparison)[-1].replace("'", "") + comparison_function = COMPARISONS[comparison] + return comparison_function(platform.python_version(), version_number) + + return True + + +@task +def install_minimum(c): + with open('setup.py', 'r') as setup_py: + lines = setup_py.read().splitlines() + + versions = [] + started = False + for line in lines: + if started: + if line == ']': + started = False + continue + + line = line.strip() + if _validate_python_version(line): + requirement = re.match(r'[^>]*', line).group(0) + requirement = re.sub(r"""['",]""", '', requirement) + version = re.search(r'>=?[^(,|#)]*', line).group(0) + if version: + version = re.sub(r'>=?', '==', version) + version = re.sub(r"""['",]""", '', version) + requirement += version + + versions.append(requirement) + + elif (line.startswith('install_requires = [') or + line.startswith('pomegranate_requires = [')): + started = True + + c.run(f'python -m pip install {" ".join(versions)}') + + +@task +def minimum(c): + install_minimum(c) + check_dependencies(c) + unit(c) + integration(c) + + +@task +def lint(c): + check_dependencies(c) + c.run('flake8 ctgan') + c.run('flake8 tests --ignore=D101') + c.run('isort -c --recursive ctgan tests') + + +def remove_readonly(func, path, _): + "Clear the readonly bit and reattempt the removal" + os.chmod(path, stat.S_IWRITE) + func(path) + + +@task +def rmdir(c, path): + try: + shutil.rmtree(path, onerror=remove_readonly) + except PermissionError: + pass diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/integration/synthesizer/test_ctgan.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/integration/synthesizer/test_ctgan.py new file mode 100644 index 0000000000000000000000000000000000000000..a750d3dabc716a49fb722dbbb87e7a2120fc5fd4 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/integration/synthesizer/test_ctgan.py @@ -0,0 +1,275 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +"""Integration tests for ctgan. + +These tests only ensure that the software does not crash and that +the API works as expected in terms of input and output data formats, +but correctness of the data values and the internal behavior of the +model are not checked. +""" + +import tempfile as tf + +import numpy as np +import pandas as pd +import pytest + +from ctgan.synthesizers.ctgan import CTGANSynthesizer + + +def test_ctgan_no_categoricals(): + """Test the CTGANSynthesizer with no categorical values.""" + data = pd.DataFrame({ + 'continuous': np.random.random(1000) + }) + + ctgan = CTGANSynthesizer(epochs=1) + ctgan.fit(data, []) + + sampled = ctgan.sample(100) + + assert sampled.shape == (100, 1) + assert isinstance(sampled, pd.DataFrame) + assert set(sampled.columns) == {'continuous'} + + +def test_ctgan_dataframe(): + """Test the CTGANSynthesizer when passed a dataframe.""" + data = pd.DataFrame({ + 'continuous': np.random.random(100), + 'discrete': np.random.choice(['a', 'b', 'c'], 100) + }) + discrete_columns = ['discrete'] + + ctgan = CTGANSynthesizer(epochs=1) + ctgan.fit(data, discrete_columns) + + sampled = ctgan.sample(100) + + assert sampled.shape == (100, 2) + assert isinstance(sampled, pd.DataFrame) + assert set(sampled.columns) == {'continuous', 'discrete'} + assert set(sampled['discrete'].unique()) == {'a', 'b', 'c'} + + +def test_ctgan_numpy(): + """Test the CTGANSynthesizer when passed a numpy array.""" + data = pd.DataFrame({ + 'continuous': np.random.random(100), + 'discrete': np.random.choice(['a', 'b', 'c'], 100) + }) + discrete_columns = [1] + + ctgan = CTGANSynthesizer(epochs=1) + ctgan.fit(data.to_numpy(), discrete_columns) + + sampled = ctgan.sample(100) + + assert sampled.shape == (100, 2) + assert isinstance(sampled, np.ndarray) + assert set(np.unique(sampled[:, 1])) == {'a', 'b', 'c'} + + +def test_log_frequency(): + """Test the CTGANSynthesizer with no `log_frequency` set to False.""" + data = pd.DataFrame({ + 'continuous': np.random.random(1000), + 'discrete': np.repeat(['a', 'b', 'c'], [950, 25, 25]) + }) + + discrete_columns = ['discrete'] + + ctgan = CTGANSynthesizer(epochs=100) + ctgan.fit(data, discrete_columns) + + sampled = ctgan.sample(10000) + counts = sampled['discrete'].value_counts() + assert counts['a'] < 6500 + + ctgan = CTGANSynthesizer(log_frequency=False, epochs=100) + ctgan.fit(data, discrete_columns) + + sampled = ctgan.sample(10000) + counts = sampled['discrete'].value_counts() + assert counts['a'] > 9000 + + +def test_categorical_nan(): + """Test the CTGANSynthesizer with no categorical values.""" + data = pd.DataFrame({ + 'continuous': np.random.random(30), + # This must be a list (not a np.array) or NaN will be cast to a string. + 'discrete': [np.nan, 'b', 'c'] * 10 + }) + discrete_columns = ['discrete'] + + ctgan = CTGANSynthesizer(epochs=1) + ctgan.fit(data, discrete_columns) + + sampled = ctgan.sample(100) + + assert sampled.shape == (100, 2) + assert isinstance(sampled, pd.DataFrame) + assert set(sampled.columns) == {'continuous', 'discrete'} + + # since np.nan != np.nan, we need to be careful here + values = set(sampled['discrete'].unique()) + assert len(values) == 3 + assert any(pd.isna(x) for x in values) + assert {'b', 'c'}.issubset(values) + + +def test_synthesizer_sample(): + """Test the CTGANSynthesizer samples the correct datatype.""" + data = pd.DataFrame({ + 'discrete': np.random.choice(['a', 'b', 'c'], 100) + }) + discrete_columns = ['discrete'] + + ctgan = CTGANSynthesizer(epochs=1) + ctgan.fit(data, discrete_columns) + + samples = ctgan.sample(1000, 'discrete', 'a') + assert isinstance(samples, pd.DataFrame) + + +def test_save_load(): + """Test the CTGANSynthesizer load/save methods.""" + data = pd.DataFrame({ + 'continuous': np.random.random(100), + 'discrete': np.random.choice(['a', 'b', 'c'], 100) + }) + discrete_columns = ['discrete'] + + ctgan = CTGANSynthesizer(epochs=1) + ctgan.fit(data, discrete_columns) + + with tf.TemporaryDirectory() as temporary_directory: + ctgan.save(temporary_directory + 'test_tvae.pkl') + ctgan = CTGANSynthesizer.load(temporary_directory + 'test_tvae.pkl') + + sampled = ctgan.sample(1000) + assert set(sampled.columns) == {'continuous', 'discrete'} + assert set(sampled['discrete'].unique()) == {'a', 'b', 'c'} + + +def test_wrong_discrete_columns_dataframe(): + """Test the CTGANSynthesizer correctly crashes when passed non-existing discrete columns.""" + data = pd.DataFrame({ + 'discrete': ['a', 'b'] + }) + discrete_columns = ['b', 'c'] + + ctgan = CTGANSynthesizer(epochs=1) + with pytest.raises(ValueError, match="Invalid columns found: {'.*', '.*'}"): + ctgan.fit(data, discrete_columns) + + +def test_wrong_discrete_columns_numpy(): + """Test the CTGANSynthesizer correctly crashes when passed non-existing discrete columns.""" + data = pd.DataFrame({ + 'discrete': ['a', 'b'] + }) + discrete_columns = [0, 1] + + ctgan = CTGANSynthesizer(epochs=1) + with pytest.raises(ValueError, match=r'Invalid columns found: \[1\]'): + ctgan.fit(data.to_numpy(), discrete_columns) + + +def test_wrong_sampling_conditions(): + """Test the CTGANSynthesizer correctly crashes when passed incorrect sampling conditions.""" + data = pd.DataFrame({ + 'continuous': np.random.random(100), + 'discrete': np.random.choice(['a', 'b', 'c'], 100) + }) + discrete_columns = ['discrete'] + + ctgan = CTGANSynthesizer(epochs=1) + ctgan.fit(data, discrete_columns) + + with pytest.raises(ValueError, match="The column_name `cardinal` doesn't exist in the data."): + ctgan.sample(1, 'cardinal', "doesn't matter") + + with pytest.raises(ValueError): # noqa: RDT currently incorrectly raises a tuple instead of a string + ctgan.sample(1, 'discrete', 'd') + + +def test_fixed_random_seed(): + """Test the CTGANSynthesizer with a fixed seed. + + Expect that when the random seed is reset with the same seed, the same sequence + of data will be produced. Expect that the data generated with the seed is + different than randomly sampled data. + """ + # Setup + data = pd.DataFrame({ + 'continuous': np.random.random(100), + 'discrete': np.random.choice(['a', 'b', 'c'], 100) + }) + discrete_columns = ['discrete'] + + ctgan = CTGANSynthesizer(epochs=1) + + # Run + ctgan.fit(data, discrete_columns) + sampled_random = ctgan.sample(10) + + ctgan.set_random_state(0) + sampled_0_0 = ctgan.sample(10) + sampled_0_1 = ctgan.sample(10) + + ctgan.set_random_state(0) + sampled_1_0 = ctgan.sample(10) + sampled_1_1 = ctgan.sample(10) + + # Assert + assert not np.array_equal(sampled_random, sampled_0_0) + assert not np.array_equal(sampled_random, sampled_0_1) + np.testing.assert_array_equal(sampled_0_0, sampled_1_0) + np.testing.assert_array_equal(sampled_0_1, sampled_1_1) + + +# Below are CTGAN tests that should be implemented in the future +def test_continuous(): + """Test training the CTGAN synthesizer on a continuous dataset.""" + # assert the distribution of the samples is close to the distribution of the data + # using kstest: + # - uniform (assert p-value > 0.05) + # - gaussian (assert p-value > 0.05) + # - inversely correlated (assert correlation < 0) + + +def test_categorical(): + """Test training the CTGAN synthesizer on a categorical dataset.""" + # assert the distribution of the samples is close to the distribution of the data + # using cstest: + # - uniform (assert p-value > 0.05) + # - very skewed / biased? (assert p-value > 0.05) + # - inversely correlated (assert correlation < 0) + + +def test_categorical_log_frequency(): + """Test training the CTGAN synthesizer on a small categorical dataset.""" + # assert the distribution of the samples is close to the distribution of the data + # using cstest: + # - uniform (assert p-value > 0.05) + # - very skewed / biased? (assert p-value > 0.05) + # - inversely correlated (assert correlation < 0) + + +def test_mixed(): + """Test training the CTGAN synthesizer on a small mixed-type dataset.""" + # assert the distribution of the samples is close to the distribution of the data + # using a kstest for continuous + a cstest for categorical. + + +def test_conditional(): + """Test training the CTGAN synthesizer and sampling conditioned on a categorical.""" + # verify that conditioning increases the likelihood of getting a sample with the specified + # categorical value + + +def test_batch_size_pack_size(): + """Test that if batch size is not a multiple of pack size, it raises a sane error.""" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/integration/synthesizer/test_tvae.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/integration/synthesizer/test_tvae.py new file mode 100644 index 0000000000000000000000000000000000000000..ab8c583e0dd11fee3e1ce3d5cd4ac8f0a847a192 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/integration/synthesizer/test_tvae.py @@ -0,0 +1,131 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +"""Integration tests for tvae. + +These tests only ensure that the software does not crash and that +the API works as expected in terms of input and output data formats, +but correctness of the data values and the internal behavior of the +model are not checked. +""" + +import numpy as np +import pandas as pd +from sklearn import datasets + +from ctgan.synthesizers.tvae import TVAESynthesizer + + +def test_tvae(tmpdir): + """Test the TVAESynthesizer load/save methods.""" + iris = datasets.load_iris() + data = pd.DataFrame(iris.data, columns=iris.feature_names) + data['class'] = pd.Series(iris.target).map(iris.target_names.__getitem__) + + tvae = TVAESynthesizer(epochs=10) + tvae.fit(data, ['class']) + + path = str(tmpdir / 'test_tvae.pkl') + tvae.save(path) + tvae = TVAESynthesizer.load(path) + + sampled = tvae.sample(100) + + assert sampled.shape == (100, 5) + assert isinstance(sampled, pd.DataFrame) + assert set(sampled.columns) == set(data.columns) + assert set(sampled.dtypes) == set(data.dtypes) + + +def test_drop_last_false(): + """Test the TVAESynthesizer predicts the correct values.""" + data = pd.DataFrame({ + '1': ['a', 'b', 'c'] * 150, + '2': ['a', 'b', 'c'] * 150 + }) + + tvae = TVAESynthesizer(epochs=300) + tvae.fit(data, ['1', '2']) + + sampled = tvae.sample(100) + correct = 0 + for _, row in sampled.iterrows(): + if row['1'] == row['2']: + correct += 1 + + assert correct >= 95 + + +# TVAE tests that should be implemented in the future. +def test_continuous(): + """Test training the TVAE synthesizer on a small continuous dataset.""" + # verify that the distribution of the samples is close to the distribution of the data + # using a kstest. + + +def test_categorical(): + """Test training the TVAE synthesizer on a small categorical dataset.""" + # verify that the distribution of the samples is close to the distribution of the data + # using a cstest. + + +def test_mixed(): + """Test training the TVAE synthesizer on a small mixed-type dataset.""" + # verify that the distribution of the samples is close to the distribution of the data + # using a kstest for continuous + a cstest for categorical. + + +def test__loss_function(): + """Test the TVAESynthesizer produces average values similar to the training data.""" + data = pd.DataFrame({ + '1': [float(i) for i in range(1000)], + '2': [float(2 * i) for i in range(1000)] + }) + + tvae = TVAESynthesizer(epochs=300) + tvae.fit(data) + + num_samples = 1000 + sampled = tvae.sample(num_samples) + error = 0 + for _, row in sampled.iterrows(): + error += abs(2 * row['1'] - row['2']) + + avg_error = error / num_samples + + assert avg_error < 400 + + +def test_fixed_random_seed(): + """Test the TVAESynthesizer with a fixed seed. + + Expect that when the random seed is reset with the same seed, the same sequence + of data will be produced. Expect that the data generated with the seed is + different than randomly sampled data. + """ + # Setup + data = pd.DataFrame({ + 'continuous': np.random.random(100), + 'discrete': np.random.choice(['a', 'b', 'c'], 100) + }) + discrete_columns = ['discrete'] + + tvae = TVAESynthesizer(epochs=1) + + # Run + tvae.fit(data, discrete_columns) + sampled_random = tvae.sample(10) + + tvae.set_random_state(0) + sampled_0_0 = tvae.sample(10) + sampled_0_1 = tvae.sample(10) + + tvae.set_random_state(0) + sampled_1_0 = tvae.sample(10) + sampled_1_1 = tvae.sample(10) + + # Assert + assert not np.array_equal(sampled_random, sampled_0_0) + assert not np.array_equal(sampled_random, sampled_0_1) + np.testing.assert_array_equal(sampled_0_0, sampled_1_0) + np.testing.assert_array_equal(sampled_0_1, sampled_1_1) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/integration/test_data_transformer.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/integration/test_data_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..2f0314ca08a4cbd05d2dba7560edeeab57780306 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/integration/test_data_transformer.py @@ -0,0 +1,42 @@ +"""Data transformer intergration testing module.""" + + +# Data Transformer tests that should be implemented in the future. +def test_constant(): + """Test transforming a dataframe containing constant values.""" + + +def test_df_continuous(): + """Test transforming a dataframe containing only continuous values.""" + # validate output ranges [0, 1] + # validate output shape (# samples, # output dims) + # validate that forward transform is **not** deterministic + # make sure it can be inverted + + +def test_df_categorical(): + """Test transforming a dataframe containing only categorical values.""" + # validate output ranges [0, 1] + # validate output shape (# samples, # output dims) + # validate that forward transform is deterministic + # make sure it can be inverted + + +def test_df_mixed(): + """Test transforming a dataframe containing mixed data types.""" + + +def test_df_mixed_nan(): + """Test transforming a dataframe containing mixed data types + NaN for categoricals.""" + + +def test_np_continuous(): + """Test transforming a np.array containing only continuous values.""" + + +def test_np_categorical(): + """Test transforming a np.array containing only categorical values.""" + + +def test_np_mixed(): + """Test transforming a np.array containing mixed data types.""" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/__init__.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..60ff4d04eff14032ccb698a80a37aa16cb4ff1ce --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/__init__.py @@ -0,0 +1 @@ +"""Unit testing module.""" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/synthesizer/__init__.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/synthesizer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..80d7afbb190ef7d1204849cae14f6a89312c39f7 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/synthesizer/__init__.py @@ -0,0 +1 @@ +"""CTGANSynthesizer testing module.""" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/synthesizer/test_base.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/synthesizer/test_base.py new file mode 100644 index 0000000000000000000000000000000000000000..5859d93deb2bebe6f07faf0a7c3b08c841e66546 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/synthesizer/test_base.py @@ -0,0 +1,111 @@ + +"""BaseSynthesizer unit testing module.""" + +from unittest.mock import MagicMock, call, patch + +import numpy as np +import torch + +from ctgan.synthesizers.base import BaseSynthesizer, random_state + + +@patch('ctgan.synthesizers.base.torch') +@patch('ctgan.synthesizers.base.np.random') +def test_valid_random_state(random_mock, torch_mock): + """Test the ``random_state`` attribute with a valid random state. + + Expect that the decorated function uses the random_state attribute. + """ + # Setup + my_function = MagicMock() + instance = MagicMock() + + random_state_mock = MagicMock() + random_state_mock.get_state.return_value = 'desired numpy state' + torch_generator_mock = MagicMock() + torch_generator_mock.get_state.return_value = 'desired torch state' + instance.random_states = (random_state_mock, torch_generator_mock) + + args = {'some', 'args'} + kwargs = {'keyword': 'value'} + + random_mock.RandomState.return_value = random_state_mock + random_mock.get_state.return_value = 'random state' + torch_mock.Generator.return_value = torch_generator_mock + torch_mock.get_rng_state.return_value = 'torch random state' + + # Run + decorated_function = random_state(my_function) + decorated_function(instance, *args, **kwargs) + + # Assert + my_function.assert_called_once_with(instance, *args, **kwargs) + + instance.assert_not_called + assert random_mock.get_state.call_count == 2 + assert torch_mock.get_rng_state.call_count == 2 + random_mock.RandomState.assert_has_calls( + [call().get_state(), call(), call().set_state('random state')]) + random_mock.set_state.assert_has_calls([call('desired numpy state'), call('random state')]) + torch_mock.set_rng_state.assert_has_calls( + [call('desired torch state'), call('torch random state')]) + + +@patch('ctgan.synthesizers.base.torch') +@patch('ctgan.synthesizers.base.np.random') +def test_no_random_seed(random_mock, torch_mock): + """Test the ``random_state`` attribute with no random state. + + Expect that the decorated function calls the original function + when there is no random state. + """ + # Setup + my_function = MagicMock() + instance = MagicMock() + instance.random_states = None + + args = {'some', 'args'} + kwargs = {'keyword': 'value'} + + # Run + decorated_function = random_state(my_function) + decorated_function(instance, *args, **kwargs) + + # Assert + my_function.assert_called_once_with(instance, *args, **kwargs) + + instance.assert_not_called + random_mock.get_state.assert_not_called() + random_mock.RandomState.assert_not_called() + random_mock.set_state.assert_not_called() + torch_mock.get_rng_state.assert_not_called() + torch_mock.Generator.assert_not_called() + torch_mock.set_rng_state.assert_not_called() + + +class TestBaseSynthesizer: + + def test_set_random_state(self): + """Test ``set_random_state`` works as expected.""" + # Setup + instance = BaseSynthesizer() + + # Run + instance.set_random_state(3) + + # Assert + assert isinstance(instance.random_states, tuple) + assert isinstance(instance.random_states[0], np.random.RandomState) + assert isinstance(instance.random_states[1], torch.Generator) + + def test_set_random_state_with_none(self): + """Test ``set_random_state`` with None.""" + # Setup + instance = BaseSynthesizer() + + # Run and assert + instance.set_random_state(3) + assert instance.random_states is not None + + instance.set_random_state(None) + assert instance.random_states is None diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/synthesizer/test_ctgan.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/synthesizer/test_ctgan.py new file mode 100644 index 0000000000000000000000000000000000000000..7a724d30779cea4e9512fc480e8417c17a21da7a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/synthesizer/test_ctgan.py @@ -0,0 +1,343 @@ +"""CTGANSynthesizer unit testing module.""" + +from unittest import TestCase +from unittest.mock import Mock + +import pandas as pd +import pytest +import torch + +from ctgan.data_transformer import SpanInfo +from ctgan.synthesizers.ctgan import CTGANSynthesizer, Discriminator, Generator, Residual + + +class TestDiscriminator(TestCase): + + def test___init__(self): + """Test `__init__` for a generic case. + + Make sure 'self.seq' has same length as 3*`discriminator_dim` + 1. + + Setup: + - Create Discriminator + + Input: + - input_dim = positive integer + - discriminator_dim = list of integers + - pack = positive integer + + Output: + - None + + Side Effects: + - Set `self.seq`, `self.pack` and `self.packdim` + """ + discriminator_dim = [1, 2, 3] + discriminator = Discriminator(input_dim=50, discriminator_dim=discriminator_dim, pac=7) + + assert discriminator.pac == 7 + assert discriminator.pacdim == 350 + assert len(discriminator.seq) == 3 * len(discriminator_dim) + 1 + + def test_forward(self): + """Test `test_forward` for a generic case. + + Check that the output shapes are correct. + We can also test that all parameters have a gradient attached to them + by running `encoder.parameters()`. To do that, we just need to use `loss.backward()` + for some loss, like `loss = torch.mean(output)`. Notice that the input_dim = input_size. + + Setup: + - initialize with input_size, discriminator_dim, pac + - Create random tensor as input + + Input: + - input = random tensor of shape (N, input_size) + + Output: + - tensor of shape (N/pac, 1) + """ + discriminator = Discriminator(input_dim=50, discriminator_dim=[100, 200, 300], pac=7) + output = discriminator(torch.randn(70, 50)) + assert output.shape == (10, 1) + + # Check to make sure no gradients attached + for parameter in discriminator.parameters(): + assert parameter.grad is None + + # Backpropagate + output.mean().backward() + + # Check to make sure all parameters have gradients + for parameter in discriminator.parameters(): + assert parameter.grad is not None + + +class TestResidual(TestCase): + + def test_forward(self): + """Test `test_forward` for a generic case. + + Check that the output shapes are correct. + We can also test that all parameters have a gradient attached to them + by running `encoder.parameters()`. To do that, we just need to use `loss.backward()` + for some loss, like `loss = torch.mean(output)`. + + Setup: + - initialize with input_size, output_size + - Create random tensor as input + + Input: + - input = random tensor of shape (N, input_size) + + Output: + - tensor of shape (N, input_size + output_size) + """ + residual = Residual(10, 2) + output = residual(torch.randn(100, 10)) + assert output.shape == (100, 12) + + # Check to make sure no gradients attached + for parameter in residual.parameters(): + assert parameter.grad is None + + # Backpropagate + output.mean().backward() + + # Check to make sure all parameters have gradients + for parameter in residual.parameters(): + assert parameter.grad is not None + + +class TestGenerator(TestCase): + + def test___init__(self): + """Test `__init__` for a generic case. + + Make sure `self.seq` has same length as `generator_dim` + 1. + + Setup: + - Create Generator + + Input: + - embedding_dim = positive integer + - generator_dim = list of integers + - data_dim = positive integer + + Output: + - None + + Side Effects: + - Set `self.seq` + """ + generator_dim = [1, 2, 3] + generator = Generator(embedding_dim=50, generator_dim=generator_dim, data_dim=7) + + assert len(generator.seq) == len(generator_dim) + 1 + + def test_forward(self): + """Test `test_forward` for a generic case. + + Check that the output shapes are correct. + We can also test that all parameters have a gradient attached to them + by running `encoder.parameters()`. To do that, we just need to use `loss.backward()` + for some loss, like `loss = torch.mean(output)`. + + Setup: + - initialize with embedding_dim, generator_dim, data_dim + - Create random tensor as input + + Input: + - input = random tensor of shape (N, input_size) + + Output: + - tensor of shape (N, data_dim) + """ + generator = Generator(embedding_dim=60, generator_dim=[100, 200, 300], data_dim=500) + output = generator(torch.randn(70, 60)) + assert output.shape == (70, 500) + + # Check to make sure no gradients attached + for parameter in generator.parameters(): + assert parameter.grad is None + + # Backpropagate + output.mean().backward() + + # Check to make sure all parameters have gradients + for parameter in generator.parameters(): + assert parameter.grad is not None + + +def _assert_is_between(data, lower, upper): + """Assert all values of the tensor 'data' are within range.""" + assert all((data >= lower).numpy().tolist()) + assert all((data <= upper).numpy().tolist()) + + +class TestCTGANSynthesizer(TestCase): + + def test__apply_activate_(self): + """Test `_apply_activate` for tables with both continuous and categoricals. + + Check every continuous column has all values between -1 and 1 + (since they are normalized), and check every categorical column adds up to 1. + + Setup: + - Mock `self._transformer.output_info_list` + + Input: + - data = tensor of shape (N, data_dims) + + Output: + - tensor = tensor of shape (N, data_dims) + """ + model = CTGANSynthesizer() + model._transformer = Mock() + model._transformer.output_info_list = [ + [SpanInfo(3, 'softmax')], + [SpanInfo(1, 'tanh'), SpanInfo(2, 'softmax')] + ] + + data = torch.randn(100, 6) + result = model._apply_activate(data) + + assert result.shape == (100, 6) + _assert_is_between(result[:, 0:3], 0.0, 1.0) + _assert_is_between(result[: 3], -1.0, 1.0) + _assert_is_between(result[:, 4:6], 0.0, 1.0) + + def test__cond_loss(self): + """Test `_cond_loss`. + + Test that the loss is purely a function of the target categorical. + + Setup: + - mock transformer.output_info_list + - create two categoricals, one continuous + - compute the conditional loss, conditioned on the 1st categorical + - compare the loss to the cross-entropy of the 1st categorical, manually computed + + Input: + data - the synthetic data generated by the model + c - a tensor with the same shape as the data but with only a specific one-hot vector + corresponding to the target column filled in + m - binary mask used to select the categorical column to condition on + + Output: + loss scalar; this should only be affected by the target column + + Note: + - even though the implementation of this is probably right, I'm not sure if the idea + behind it is correct + """ + model = CTGANSynthesizer() + model._transformer = Mock() + model._transformer.output_info_list = [ + [SpanInfo(1, 'tanh'), SpanInfo(2, 'softmax')], + [SpanInfo(3, 'softmax')], # this is the categorical column we are conditioning on + [SpanInfo(2, 'softmax')], # this is the categorical column we are bry jrbec on + ] + + data = torch.tensor([ + # first 3 dims ignored, next 3 dims are the prediction, last 2 dims are ignored + [0.0, -1.0, 0.0, 0.05, 0.05, 0.9, 0.1, 0.4], + ]) + + c = torch.tensor([ + # first 3 dims are a one-hot for the categorical, + # next 2 are for a different categorical that we are not conditioning on + # (continuous values are not stored in this tensor) + [0.0, 0.0, 1.0, 0.0, 0.0], + ]) + + # this indicates that we are conditioning on the first categorical + m = torch.tensor([[1, 0]]) + + result = model._cond_loss(data, c, m) + expected = torch.nn.functional.cross_entropy( + torch.tensor([ + [0.05, 0.05, 0.9], # 3 categories, one hot + ]), + torch.tensor([2]) + ) + + assert (result - expected).abs() < 1e-3 + + def test__validate_discrete_columns(self): + """Test `_validate_discrete_columns` if the discrete column doesn't exist. + + Check the appropriate error is raised if `discrete_columns` is invalid, both + for numpy arrays and dataframes. + + Setup: + - Create dataframe with a discrete column + - Define `discrete_columns` as something not in the dataframe + + Input: + - train_data = 2-dimensional numpy array or a pandas.DataFrame + - discrete_columns = list of strings or integers + + Output: + None + + Side Effects: + - Raises error if the discrete column is invalid. + + Note: + - could create another function for numpy array + """ + data = pd.DataFrame({ + 'discrete': ['a', 'b'] + }) + discrete_columns = ['doesnt exist'] + + ctgan = CTGANSynthesizer(epochs=1) + with pytest.raises(ValueError, match=r'Invalid columns found: {\'doesnt exist\'}'): + ctgan.fit(data, discrete_columns) + + def test_sample(self): + """Test `sample` correctly sets `condition_info` and `global_condition_vec`. + + Tests the first 7 lines of sample by mocking the DataTransformer and DataSampler + and checking that they are being correctly used. + + Setup: + - Create and fit the synthesizer + - Mock DataTransformer, DataSampler + + Input: + - n = integer + - condition_column = string (not None) + - condition_value = string (not None) + + Output: + Not relevant + + Note: + - I'm not sure we need this test + """ + + def test_set_device(self): + """Test 'set_device' if a GPU is available. + + Check that decoder/encoder can successfully be moved to the device. + If the machine doesn't have a GPU, this test shouldn't run. + + Setup: + - Move decoder/encoder to device + + Input: + - device = string + + Output: + None + + Side Effects: + - Set `self._device` to `device` + - Moves `self.decoder` to `self._device` + + Note: + - Need to be careful when checking whether the encoder is actually set + to the right device, since it's not saved (it's only used in fit). + """ diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/synthesizer/test_tvae.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/synthesizer/test_tvae.py new file mode 100644 index 0000000000000000000000000000000000000000..cff981a86078d1689bec7bb5a37856aa903b68e7 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/synthesizer/test_tvae.py @@ -0,0 +1,123 @@ +"""TVAESynthesizer unit testing module.""" + +from unittest import TestCase + + +class TestEncoder(TestCase): + + def test___init__(self): + """Test `__init__` for a generic case. + + Make sure 'self.seq' has same length as 2*`compress_dims`. + + Setup: + - Create Encoder + + Input: + - data_dim = positive integer + - compress_dims = list of integers + - embedding_dim = positive integer + + Output: + - None + + Side Effects: + - Set `self.seq`, `self.fc1` and `self.fc2` + """ + + def test_forward(self): + """Test `test_forward` for a generic case. + + Check that the output shapes are correct and that std is positive. + We can also test that all parameters have a gradient attached to them + by running `encoder.parameters()`. To do that, we just need to use `loss.backward()` + for some loss, like `loss = torch.mean(mu) + torch.mean(std) + torch.mean(logvar)`. + + Setup: + - Create random tensor + + Input: + - input = random tensor of shape (N, data_dim) + + Output: + - Tuple of (mu, std, logvar): + mu - tensor of shape (N, embedding_dim) + std - tensor of shape (N, embedding_dim), non-negative values + logvar - tensor of shape (N, embedding_dim) + """ + + +class TestDecoder(TestCase): + + def test___init__(self): + """Test `__init__` for a generic case. + + Make sure 'self.seq' has same length as 2*`decompress_dims` + 1. + + Setup: + - Create Decoder + + Input: + - data_dim = positive integer + - decompress_dims = list of integers + - embedding_dim = positive integer + + Output: + - None + + Side Effects: + - Set `self.seq`, `self.sigma` + """ + + +class TestLossFunction(TestCase): + + def test__loss_function(self): + """Test `_loss_function`. + + Check loss values = to specific numbers. + + Setup: + Build all the tensors, lists, etc. + + Input: + recon_x = tensor of shape (N, data_dims) + x = tensor of shape (N, data_dims) + sigmas = tensor of shape (N,) + mu = tensor of shape (N,) + logvar = tensor of shape (N,) + output_info = list of SpanInfo objects from the data transformer, + including at least 1 continuous and 1 discrete + factor = scalar + + Output: + reconstruction loss = scalar = f(recon_x, x, sigmas, output_info, factor) + kld loss = scalar = f(logvar, mu) + """ + + +class TestTVAESynthesizer(TestCase): + + def test_set_device(self): + """Test 'set_device' if a GPU is available. + + Check that decoder/encoder can successfully be moved to the device. + If the machine doesn't have a GPU, this test shouldn't run. + + Setup: + - Move decoder/encoder to device + + Input: + - device = string + + Output: + None + + Side Effects: + - Set `self._device` to `device` + - Moves `self.decoder` to `self._device` + + Note: + - Need to be careful when checking whether the encoder is actually set + to the right device, since it's not saved (it's only used in fit). + """ diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/test_data_transformer.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/test_data_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..305f84ee5e57d156348b54400a5c6138d3466b40 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tests/unit/test_data_transformer.py @@ -0,0 +1,473 @@ +"""Data transformer unit testing module.""" + +from unittest import TestCase +from unittest.mock import Mock, patch + +import numpy as np +import pandas as pd + +from ctgan.data_transformer import ColumnTransformInfo, DataTransformer, SpanInfo + + +class TestDataTransformer(TestCase): + + @patch('ctgan.data_transformer.BayesGMMTransformer') + def test___fit_continuous(self, MockBGM): + """Test ``_fit_continuous`` on a simple continuous column. + + A ``BayesGMMTransformer`` will be created and fit with some ``data``. + + Setup: + - Mock the ``BayesGMMTransformer`` with ``valid_component_indicator`` as + ``[True, False, True]``. + - Initialize a ``DataTransformer``. + + Input: + - A dataframe with only one column containing random float values. + + Output: + - A ``ColumnTransformInfo`` object where: + - ``column_name`` matches the column of the data. + - ``transform`` is the ``BayesGMMTransformer`` instance. + - ``output_dimensions`` is 3 (matches size of ``valid_component_indicator``). + - ``output_info`` assigns the correct activation functions. + + Side Effects: + - ``fit`` should be called with the data. + """ + # Setup + bgm_instance = MockBGM.return_value + bgm_instance.valid_component_indicator = [True, False, True] + transformer = DataTransformer() + data = pd.DataFrame(np.random.normal((100, 1)), columns=['column']) + + # Run + info = transformer._fit_continuous(data) + + # Assert + assert info.column_name == 'column' + assert info.transform == bgm_instance + assert info.output_dimensions == 3 + assert info.output_info[0].dim == 1 + assert info.output_info[0].activation_fn == 'tanh' + assert info.output_info[1].dim == 2 + assert info.output_info[1].activation_fn == 'softmax' + + @patch('ctgan.data_transformer.BayesGMMTransformer') + def test__fit_continuous_max_clusters(self, MockBGM): + """Test ``_fit_continuous`` with data that has less than 10 rows. + + Expect that a ``BayesGMMTransformer`` is created with the max number of clusters + set to the length of the data. + + Input: + - Data with less than 10 rows. + + Side Effects: + - A ``BayesGMMTransformer`` is created with the max number of clusters set to the + length of the data. + """ + # Setup + data = pd.DataFrame(np.random.normal((7, 1)), columns=['column']) + transformer = DataTransformer() + + # Run + transformer._fit_continuous(data) + + # Assert + MockBGM.assert_called_once_with(max_clusters=len(data)) + + @patch('ctgan.data_transformer.OneHotEncodingTransformer') + def test___fit_discrete(self, MockOHE): + """Test ``_fit_discrete_`` on a simple discrete column. + + A ``OneHotEncodingTransformer`` will be created and fit with the ``data``. + + Setup: + - Mock the ``OneHotEncodingTransformer``. + - Create ``DataTransformer``. + + Input: + - A dataframe with only one column containing ``['a', 'b']`` values. + + Output: + - A ``ColumnTransformInfo`` object where: + - ``column_name`` matches the column of the data. + - ``transform`` is the ``OneHotEncodingTransformer`` instance. + - ``output_dimensions`` is 2. + - ``output_info`` assigns the correct activation function. + + Side Effects: + - ``fit`` should be called with the data. + """ + # Setup + ohe_instance = MockOHE.return_value + ohe_instance.dummies = ['a', 'b'] + transformer = DataTransformer() + data = pd.DataFrame(np.array(['a', 'b'] * 100), columns=['column']) + + # Run + info = transformer._fit_discrete(data) + + # Assert + assert info.column_name == 'column' + assert info.transform == ohe_instance + assert info.output_dimensions == 2 + assert info.output_info[0].dim == 2 + assert info.output_info[0].activation_fn == 'softmax' + + def test_fit(self): + """Test ``fit`` on a np.ndarray with one continuous and one discrete columns. + + The ``fit`` method should: + - Set ``self.dataframe`` to ``False``. + - Set ``self._column_raw_dtypes`` to the appropirate dtypes. + - Use the appropriate ``_fit`` type for each column. + - Update ``self.output_info_list``, ``self.output_dimensions`` and + ``self._column_transform_info_list`` appropriately. + + Setup: + - Create ``DataTransformer``. + - Mock ``_fit_discrete``. + - Mock ``_fit_continuous``. + + Input: + - A table with one continuous and one discrete columns. + - A list with the name of the discrete column. + + Side Effects: + - ``_fit_discrete`` and ``_fit_continuous`` should each be called once. + - Assigns ``self._column_raw_dtypes`` the appropriate dtypes. + - Assigns ``self.output_info_list`` the appropriate ``output_info``. + - Assigns ``self.output_dimensions`` the appropriate ``output_dimensions``. + - Assigns ``self._column_transform_info_list`` the appropriate + ``column_transform_info``. + """ + # Setup + transformer = DataTransformer() + transformer._fit_continuous = Mock() + transformer._fit_continuous.return_value = ColumnTransformInfo( + column_name='x', column_type='continuous', transform=None, + output_info=[SpanInfo(1, 'tanh'), SpanInfo(3, 'softmax')], + output_dimensions=1 + 3 + ) + + transformer._fit_discrete = Mock() + transformer._fit_discrete.return_value = ColumnTransformInfo( + column_name='y', column_type='discrete', transform=None, + output_info=[SpanInfo(2, 'softmax')], + output_dimensions=2 + ) + + data = pd.DataFrame({ + 'x': np.random.random(size=100), + 'y': np.random.choice(['yes', 'no'], size=100) + }) + + # Run + transformer.fit(data, discrete_columns=['y']) + + # Assert + transformer._fit_discrete.assert_called_once() + transformer._fit_continuous.assert_called_once() + assert transformer.output_dimensions == 6 + + @patch('ctgan.data_transformer.BayesGMMTransformer') + def test__transform_continuous(self, MockBGM): + """Test ``_transform_continuous``. + + Setup: + - Mock the ``BayesGMMTransformer`` with the transform method returning + some dataframe. + - Create ``DataTransformer``. + + Input: + - ``ColumnTransformInfo`` object. + - A dataframe containing a continuous column. + + Output: + - A np.array where the first column contains the normalized part + of the mocked transform, and the other columns are a one hot encoding + representation of the component part of the mocked transform. + """ + # Setup + bgm_instance = MockBGM.return_value + bgm_instance.transform.return_value = pd.DataFrame({ + 'x.normalized': [0.1, 0.2, 0.3], + 'x.component': [0.0, 1.0, 1.0] + }) + + transformer = DataTransformer() + data = pd.DataFrame({'x': np.array([0.1, 0.3, 0.5])}) + column_transform_info = ColumnTransformInfo( + column_name='x', column_type='continuous', transform=bgm_instance, + output_info=[SpanInfo(1, 'tanh'), SpanInfo(3, 'softmax')], + output_dimensions=1 + 3 + ) + + # Run + result = transformer._transform_continuous(column_transform_info, data) + + # Assert + expected = np.array([ + [0.1, 1, 0, 0], + [0.2, 0, 1, 0], + [0.3, 0, 1, 0], + ]) + np.testing.assert_array_equal(result, expected) + + def test_transform(self): + """Test ``transform`` on a dataframe with one continuous and one discrete columns. + + It should use the appropriate ``_transform`` type for each column and should return + them concanenated appropriately. + + Setup: + - Initialize a ``DataTransformer`` with a ``column_transform_info`` detailing + a continuous and a discrete columns. + - Mock the ``_transform_discrete`` and ``_transform_continuous`` methods. + + Input: + - A table with one continuous and one discrete columns. + + Output: + - np.array containing the transformed columns. + + Side Effects: + - ``_transform_discrete`` and ``_transform_continuous`` should each be called once. + """ + # Setup + data = pd.DataFrame({ + 'x': np.array([0.1, 0.3, 0.5]), + 'y': np.array(['yes', 'yes', 'no']) + }) + + transformer = DataTransformer() + transformer._column_transform_info_list = [ + ColumnTransformInfo( + column_name='x', column_type='continuous', transform=None, + output_info=[SpanInfo(1, 'tanh'), SpanInfo(3, 'softmax')], + output_dimensions=1 + 3 + ), + ColumnTransformInfo( + column_name='y', column_type='discrete', transform=None, + output_info=[SpanInfo(2, 'softmax')], + output_dimensions=2 + ) + ] + + transformer._transform_continuous = Mock() + selected_normalized_value = np.array([[0.1], [0.3], [0.5]]) + selected_component_onehot = np.array([ + [1, 0, 0], + [0, 1, 0], + [0, 1, 0], + ]) + return_value = np.concatenate( + (selected_normalized_value, selected_component_onehot), axis=1) + transformer._transform_continuous.return_value = return_value + + transformer._transform_discrete = Mock() + transformer._transform_discrete.return_value = np.array([ + [0, 1], + [0, 1], + [1, 0], + ]) + + # Run + result = transformer.transform(data) + + # Assert + transformer._transform_continuous.assert_called_once() + transformer._transform_discrete.assert_called_once() + + expected = np.array([ + [0.1, 1, 0, 0, 0, 1], + [0.3, 0, 1, 0, 0, 1], + [0.5, 0, 1, 0, 1, 0], + ]) + assert result.shape == (3, 6) + assert (result[:, 0] == expected[:, 0]).all(), 'continuous-cdf' + assert (result[:, 1:4] == expected[:, 1:4]).all(), 'continuous-softmax' + assert (result[:, 4:6] == expected[:, 4:6]).all(), 'discrete' + + @patch('ctgan.data_transformer.BayesGMMTransformer') + def test__inverse_transform_continuous(self, MockBGM): + """Test ``_inverse_transform_continuous``. + + Setup: + - Create ``DataTransformer``. + - Mock the ``BayesGMMTransformer`` where: + - ``get_output_types`` returns the appropriate dictionary. + - ``reverse_transform`` returns some dataframe. + + Input: + - A ``ColumnTransformInfo`` object. + - A np.ndarray where: + - The first column contains the normalized value + - The remaining columns correspond to the one-hot + - sigmas = np.ndarray of floats + - st = index of the sigmas ndarray + + Output: + - Dataframe where the first column are floats and the second is a lable encoding. + + Side Effects: + - The ``reverse_transform`` method should be called with a dataframe + where the first column are floats and the second is a lable encoding. + """ + # Setup + bgm_instance = MockBGM.return_value + bgm_instance.get_output_types.return_value = { + 'x.normalized': 'numerical', + 'x.component': 'numerical' + } + + bgm_instance.reverse_transform.return_value = pd.DataFrame({ + 'x.normalized': [0.1, 0.2, 0.3], + 'x.component': [0.0, 1.0, 1.0] + }) + + transformer = DataTransformer() + column_data = np.array([ + [0.1, 1, 0, 0], + [0.3, 0, 1, 0], + [0.5, 0, 1, 0], + ]) + + column_transform_info = ColumnTransformInfo( + column_name='x', column_type='continuous', transform=bgm_instance, + output_info=[SpanInfo(1, 'tanh'), SpanInfo(3, 'softmax')], + output_dimensions=1 + 3 + ) + + # Run + result = transformer._inverse_transform_continuous( + column_transform_info, column_data, None, None) + + # Assert + expected = pd.DataFrame({ + 'x.normalized': [0.1, 0.2, 0.3], + 'x.component': [0.0, 1.0, 1.0] + }) + + np.testing.assert_array_equal(result, expected) + + expected_data = pd.DataFrame({ + 'x.normalized': [0.1, 0.3, 0.5], + 'x.component': [0, 1, 1] + }) + + pd.testing.assert_frame_equal( + bgm_instance.reverse_transform.call_args[0][0], + expected_data + ) + + def test_inverse_transform(self): + """Test ``inverse_transform`` on a np.ndarray with continuous and discrete columns. + + It should use the appropriate '_fit' type for each column and should return + the corresponding columns. Since we are using the same example as the 'test_transform', + and these two functions are inverse of each other, the returned value here should + match the input of that function. + + Setup: + - Mock _column_transform_info_list + - Mock _inverse_transform_discrete + - Mock _inverse_trarnsform_continuous + + Input: + - column_data = a concatenation of two np.ndarrays + - the first one refers to the continuous values + - the first column contains the normalized values + - the remaining columns correspond to the a one-hot + - the second one refers to the discrete values + - the columns correspond to a one-hot + Output: + - numpy array containing a discrete column and a continuous column + + Side Effects: + - _transform_discrete and _transform_continuous should each be called once. + """ + + def test_convert_column_name_value_to_id(self): + """Test ``convert_column_name_value_to_id`` on a simple ``_column_transform_info_list``. + + Tests that the appropriate indexes are returned when a table of three columns, + discrete, continuous, discrete, is passed as '_column_transform_info_list'. + + Setup: + - Mock ``_column_transform_info_list``. + + Input: + - column_name = the name of a discrete column + - value = the categorical value + + Output: + - dictionary containing: + - ``discrete_column_id`` = the index of the target column, + when considering only discrete columns + - ``column_id`` = the index of the target column + (e.g. 3 = the third column in the data) + - ``value_id`` = the index of the indicator value in the one-hot encoding + """ + # Setup + ohe = Mock() + ohe.transform.return_value = pd.DataFrame([ + [0, 1] # one hot encoding, second dimension + ]) + transformer = DataTransformer() + transformer._column_transform_info_list = [ + ColumnTransformInfo( + column_name='x', column_type='continuous', transform=None, + output_info=[SpanInfo(1, 'tanh'), SpanInfo(3, 'softmax')], + output_dimensions=1 + 3 + ), + ColumnTransformInfo( + column_name='y', column_type='discrete', transform=ohe, + output_info=[SpanInfo(2, 'softmax')], + output_dimensions=2 + ) + ] + + # Run + result = transformer.convert_column_name_value_to_id('y', 'yes') + + # Assert + assert result['column_id'] == 1 # this is the 2nd column + assert result['discrete_column_id'] == 0 # this is the 1st discrete column + assert result['value_id'] == 1 # this is the 2nd dimension in the one hot encoding + + def test_convert_column_name_value_to_id_multiple(self): + """Test ``convert_column_name_value_to_id``.""" + # Setup + ohe = Mock() + ohe.transform.return_value = pd.DataFrame([ + [0, 1, 0] # one hot encoding, second dimension + ]) + transformer = DataTransformer() + transformer._column_transform_info_list = [ + ColumnTransformInfo( + column_name='x', column_type='continuous', transform=None, + output_info=[SpanInfo(1, 'tanh'), SpanInfo(3, 'softmax')], + output_dimensions=1 + 3 + ), + ColumnTransformInfo( + column_name='y', column_type='discrete', transform=ohe, + output_info=[SpanInfo(2, 'softmax')], + output_dimensions=2 + ), + ColumnTransformInfo( + column_name='z', column_type='discrete', transform=ohe, + output_info=[SpanInfo(2, 'softmax')], + output_dimensions=2 + ) + ] + + # Run + result = transformer.convert_column_name_value_to_id('z', 'yes') + + # Assert + assert result['column_id'] == 2 # this is the 3rd column + assert result['discrete_column_id'] == 1 # this is the 2nd discrete column + assert result['value_id'] == 1 # this is the 1st dimension in the one hot encoding diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tox.ini b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tox.ini new file mode 100644 index 0000000000000000000000000000000000000000..5fbffba409c0fee10719de58cf5a5bf4639b374e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/CTGAN/tox.ini @@ -0,0 +1,19 @@ +[tox] +envlist = py38-lint, py3{6,7,8,9}-{unit,integration,readme} + +[testenv] +skipsdist = false +skip_install = false +deps = + invoke + readme: rundoc +extras = + lint: dev + unit: test + integration: test +commands = + lint: invoke lint + unit: invoke unit + integration: invoke integration + readme: invoke readme + invoke rmdir --path {envdir} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/pipeline_tvae.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/pipeline_tvae.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8f0c4afd222607db65aae70fe969385ea4d79b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/pipeline_tvae.py @@ -0,0 +1,80 @@ +import tomli +import shutil +import os +import argparse +from train_sample_tvae import train_tvae, sample_tvae +from scripts.eval_catboost import train_catboost +import zero +import lib + +def load_config(path) : + with open(path, 'rb') as f: + return tomli.load(f) + +def save_file(parent_dir, config_path): + try: + dst = os.path.join(parent_dir) + os.makedirs(os.path.dirname(dst), exist_ok=True) + shutil.copyfile(os.path.abspath(config_path), dst) + except shutil.SameFileError: + pass + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--config', metavar='FILE') + parser.add_argument('--train', action='store_true', default=False) + parser.add_argument('--sample', action='store_true', default=False) + parser.add_argument('--eval', action='store_true', default=False) + parser.add_argument('--change_val', action='store_true', default=False) + + args = parser.parse_args() + raw_config = lib.load_config(args.config) + timer = zero.Timer() + timer.run() + save_file(os.path.join(raw_config['parent_dir'], 'config.toml'), args.config) + ctabgan = None + if args.train: + ctabgan = train_tvae( + parent_dir=raw_config['parent_dir'], + real_data_path=raw_config['real_data_path'], + train_params=raw_config['train_params'], + change_val=args.change_val, + device=raw_config['device'] + ) + if args.sample: + sample_tvae( + synthesizer=ctabgan, + parent_dir=raw_config['parent_dir'], + real_data_path=raw_config['real_data_path'], + num_samples=raw_config['sample']['num_samples'], + train_params=raw_config['train_params'], + change_val=args.change_val, + seed=raw_config['sample']['seed'], + device=raw_config['device'] + ) + + save_file(os.path.join(raw_config['parent_dir'], 'info.json'), os.path.join(raw_config['real_data_path'], 'info.json')) + if args.eval: + if raw_config['eval']['type']['eval_model'] == 'catboost': + train_catboost( + parent_dir=raw_config['parent_dir'], + real_data_path=raw_config['real_data_path'], + eval_type=raw_config['eval']['type']['eval_type'], + T_dict=raw_config['eval']['T'], + seed=raw_config['seed'], + change_val=args.change_val + ) + # elif raw_config['eval']['type']['eval_model'] == 'mlp': + # train_mlp( + # parent_dir=raw_config['parent_dir'], + # real_data_path=raw_config['real_data_path'], + # eval_type=raw_config['eval']['type']['eval_type'], + # T_dict=raw_config['eval']['T'], + # seed=raw_config['seed'], + # change_val=args.change_val + # ) + + print(f'Elapsed time: {str(timer)}') + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/train_sample_tvae.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/train_sample_tvae.py new file mode 100644 index 0000000000000000000000000000000000000000..7db74590f2826edf87938d3707bf12fdcfbf2b5a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/train_sample_tvae.py @@ -0,0 +1,117 @@ +import lib +import os +import numpy as np +import argparse +from CTGAN.ctgan import TVAESynthesizer +from pathlib import Path +import torch +import pickle +import warnings +from sklearn.exceptions import ConvergenceWarning + +warnings.filterwarnings("ignore", category=ConvergenceWarning) + +def train_tvae( + parent_dir, + real_data_path, + train_params = {"batch_size": 512}, + change_val=False, + device = "cpu" +): + real_data_path = Path(real_data_path) + parent_dir = Path(parent_dir) + device = torch.device(device) + + if change_val: + X_num_train, X_cat_train, y_train, _, _, _ = lib.read_changed_val(real_data_path) + else: + X_num_train, X_cat_train, y_train = lib.read_pure_data(real_data_path, 'train') + + X = lib.concat_to_pd(X_num_train, X_cat_train, y_train) + + X.columns = [str(_) for _ in X.columns] + + cat_features = list(map(str, range(X_num_train.shape[1], X_num_train.shape[1]+X_cat_train.shape[1]))) if X_cat_train is not None else [] + if lib.load_json(real_data_path / "info.json")["task_type"] != "regression": + cat_features += ["y"] + + train_params["batch_size"] = min(y_train.shape[0], train_params["batch_size"]) + + print(train_params) + synthesizer = TVAESynthesizer( + **train_params, + device=device + ) + + synthesizer.fit(X, cat_features) + + # save_ctabgan(synthesizer, parent_dir) + with open(parent_dir / "tvae.obj", "wb") as f: + pickle.dump(synthesizer, f) + + return synthesizer + +def sample_tvae( + synthesizer, + parent_dir, + real_data_path, + num_samples, + train_params = {"batch_size": 512}, + change_val=False, + device="cpu", + seed=0 +): + real_data_path = Path(real_data_path) + parent_dir = Path(parent_dir) + device = torch.device(device) + + if change_val: + X_num_train, X_cat_train, y_train, _, _, _ = lib.read_changed_val(real_data_path) + else: + X_num_train, X_cat_train, y_train = lib.read_pure_data(real_data_path, 'train') + + X = lib.concat_to_pd(X_num_train, X_cat_train, y_train) + + X.columns = [str(_) for _ in X.columns] + + + cat_features = list(map(str, range(X_num_train.shape[1], X_num_train.shape[1]+X_cat_train.shape[1]))) if X_cat_train is not None else [] + if lib.load_json(real_data_path / "info.json")["task_type"] != "regression": + cat_features += ["y"] + + with open(parent_dir / "tvae.obj", 'rb') as f: + synthesizer = pickle.load(f) + synthesizer.decoder = synthesizer.decoder.to(device) + + gen_data = synthesizer.sample(num_samples, seed) + + y = gen_data['y'].values + if len(np.unique(y)) == 1: + y[0] = 0 + y[1] = 1 + + X_cat = gen_data[cat_features].drop('y', axis=1, errors="ignore").values if len(cat_features) else None + X_num = gen_data.values[:, :X_num_train.shape[1]] if X_num_train is not None else None + + if X_num_train is not None: + np.save(parent_dir / 'X_num_train', X_num.astype(float)) + if X_cat_train is not None: + np.save(parent_dir / 'X_cat_train', X_cat.astype(str)) + y = y.astype(float) + if lib.load_json(real_data_path / "info.json")["task_type"] != "regression": + y = y.astype(int) + np.save(parent_dir / 'y_train', y) # only clf !!! + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('real_data_path', type=str) + parser.add_argument('parent_dir', type=str) + parser.add_argument('train_size', type=int) + args = parser.parse_args() + + ctabgan = train_tvae(args.parent_dir, args.real_data_path, change_val=True) + sample_tvae(ctabgan, args.parent_dir, args.real_data_path, args.train_size, change_val=True) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/tune_tvae.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/tune_tvae.py new file mode 100644 index 0000000000000000000000000000000000000000..0b6558c024ea45bdc43b09025ba4f233417de999 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/CTGAN/tune_tvae.py @@ -0,0 +1,153 @@ +from multiprocessing.sharedctypes import RawValue +import tempfile +import subprocess +import lib +import os +import optuna +import argparse +from pathlib import Path +from train_sample_tvae import train_tvae, sample_tvae +from scripts.eval_catboost import train_catboost + +parser = argparse.ArgumentParser() +parser.add_argument('data_path', type=str) +parser.add_argument('train_size', type=int) +parser.add_argument('eval_type', type=str) +parser.add_argument('device', type=str) + +args = parser.parse_args() +real_data_path = args.data_path +eval_type = args.eval_type +train_size = args.train_size +device = args.device +assert eval_type in ('merged', 'synthetic') + +def objective(trial): + + lr = trial.suggest_loguniform('lr', 0.00001, 0.003) + + def suggest_dim(name): + t = trial.suggest_int(name, d_min, d_max) + return 2 ** t + + # construct model + min_n_layers, max_n_layers, d_min, d_max = 1, 3, 6, 9 + n_layers = 2 * trial.suggest_int('n_layers', min_n_layers, max_n_layers) + d_first = [suggest_dim('d_first')] if n_layers else [] + d_middle = ( + [suggest_dim('d_middle')] * (n_layers - 2) + if n_layers > 2 + else [] + ) + d_last = [suggest_dim('d_last')] if n_layers > 1 else [] + d_layers = d_first + d_middle + d_last + #### + + steps = trial.suggest_categorical('steps', [5000, 20000, 30000]) + # steps = trial.suggest_categorical('steps', [1000]) + batch_size = trial.suggest_categorical('batch_size', [256, 4096]) + + num_samples = int(train_size * (2 ** trial.suggest_int('frac_samples', -2, 3))) + embedding_dim = 2 ** trial.suggest_int('embedding_dim', 6, 10) + loss_factor = trial.suggest_loguniform('loss_factor', 0.001, 10) + + + train_params = { + "lr": lr, + "epochs": steps, + "embedding_dim": embedding_dim, + "batch_size": batch_size, + "loss_factor": loss_factor, + "compress_dims": d_layers, + "decompress_dims": d_layers + } + + trial.set_user_attr("train_params", train_params) + trial.set_user_attr("num_samples", num_samples) + + score = 0.0 + with tempfile.TemporaryDirectory() as dir_: + dir_ = Path(dir_) + ctabgan = train_tvae( + parent_dir=dir_, + real_data_path=real_data_path, + train_params=train_params, + change_val=True, + device=device + ) + + for sample_seed in range(5): + sample_tvae( + ctabgan, + parent_dir=dir_, + real_data_path=real_data_path, + num_samples=num_samples, + train_params=train_params, + change_val=True, + seed=sample_seed, + device=device + ) + + T_dict = { + "seed": 0, + "normalization": None, + "num_nan_policy": None, + "cat_nan_policy": None, + "cat_min_frequency": None, + "cat_encoding": None, + "y_policy": "default" + } + metrics = train_catboost( + parent_dir=dir_, + real_data_path=real_data_path, + eval_type=eval_type, + T_dict=T_dict, + change_val=True, + seed = 0 + ) + + score += metrics.get_val_score() + return score / 5 + + +study = optuna.create_study( + direction='maximize', + sampler=optuna.samplers.TPESampler(seed=0), +) + +study.optimize(objective, n_trials=50, show_progress_bar=True) + +os.makedirs(f"exp/{Path(real_data_path).name}/tvae/", exist_ok=True) +config = { + "parent_dir": f"exp/{Path(real_data_path).name}/tvae/", + "real_data_path": real_data_path, + "seed": 0, + "device": args.device, + "train_params": study.best_trial.user_attrs["train_params"], + "sample": {"seed": 0, "num_samples": study.best_trial.user_attrs["num_samples"]}, + "eval": { + "type": {"eval_model": "catboost", "eval_type": eval_type}, + "T": { + "seed": 0, + "normalization": None, + "num_nan_policy": None, + "cat_nan_policy": None, + "cat_min_frequency": None, + "cat_encoding": None, + "y_policy": "default" + }, + } +} + +train_tvae( + parent_dir=f"exp/{Path(real_data_path).name}/tvae/", + real_data_path=real_data_path, + train_params=study.best_trial.user_attrs["train_params"], + change_val=False, + device=device +) + +lib.dump_config(config, config["parent_dir"]+"config.toml") + +subprocess.run(['python3.9', "scripts/eval_seeds.py", '--config', f'{config["parent_dir"]+"config.toml"}', + '10', "tvae", eval_type, "catboost", "5"], check=True) \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/LICENSE.md b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/LICENSE.md new file mode 100644 index 0000000000000000000000000000000000000000..3c105473f782136fd5659e03d454cfc3ba31252e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/LICENSE.md @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Akim Kotelnikov + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/README.md b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/README.md new file mode 100644 index 0000000000000000000000000000000000000000..517e9aa2f8024a8412a9028529e7fe88f6c57dec --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/README.md @@ -0,0 +1,99 @@ +# TabDDPM: Modelling Tabular Data with Diffusion Models +This is the official code for our paper "TabDDPM: Modelling Tabular Data with Diffusion Models" ([paper](https://arxiv.org/abs/2209.15421)) + + + +## Setup the environment +1. Install [conda](https://docs.conda.io/en/latest/miniconda.html) (just to manage the env). +2. Run the following commands + ```bash + export REPO_DIR=/path/to/the/code + cd $REPO_DIR + + conda create -n tddpm python=3.9.7 + conda activate tddpm + + pip install torch==1.10.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html + pip install -r requirements.txt + + # if the following commands do not succeed, update conda + conda env config vars set PYTHONPATH=${PYTHONPATH}:${REPO_DIR} + conda env config vars set PROJECT_DIR=${REPO_DIR} + + conda deactivate + conda activate tddpm + ``` + +## Running the experiments + +Here we describe the neccesary info for reproducing the experimental results. +Use `agg_results.ipynb` to print results for all dataset and all methods. + +### Datasets + +We upload the datasets used in the paper with our train/val/test splits (link below). We do not impose additional restrictions to the original dataset licenses, the sources of the data are listed in the paper appendix. + +You could load the datasets with the following commands: + +``` bash +conda activate tddpm +cd $PROJECT_DIR +wget "https://www.dropbox.com/s/rpckvcs3vx7j605/data.tar?dl=0" -O data.tar +tar -xvf data.tar +``` + +### File structure +`tab-ddpm/` -- implementation of the proposed method +`tuned_models/` -- tuned hyperparameters of evaluation model (CatBoost or MLP) + +All main scripts are in `scripts/` folder: + +- `scripts/pipeline.py` are used to train, sample and eval TabDDPM using a given config +- `scripts/tune_ddpm.py` -- tune hyperparameters of TabDDPM +- `scripts/eval_[catboost|mlp|simple].py` -- evaluate synthetic data using a tuned evaluation model or simple models +- `scripts/eval_seeds.py` -- eval using multiple sampling and multuple eval seeds +- `scripts/eval_seeds_simple.py` -- eval using multiple sampling and multuple eval seeds (for simple models) +- `scripts/tune_evaluation_model.py` -- tune hyperparameters of eval model (CatBoost or MLP) +- `scripts/resample_privacy.py` -- privacy calculation + +Experiments folder (`exp/`): +- All results and synthetic data are stored in `exp/[ds_name]/[exp_name]/` folder +- `exp/[ds_name]/config.toml` is a base config for tuning TabDDPM +- `exp/[ds_name]/eval_[catboost|mlp].json` stores results of evaluation (`scripts/eval_seeds.py`) + +To understand the structure of `config.toml` file, read `CONFIG_DESCRIPTION.md`. + +Baselines: +- `smote/` +- `CTGAN/` -- TVAE [official repo](https://github.com/sdv-dev/CTGAN) +- `CTAB-GAN/` -- [official repo](https://github.com/Team-TUD/CTAB-GAN) +- `CTAB-GAN-Plus/` -- [official repo](https://github.com/Team-TUD/CTAB-GAN-Plus) + +### Examples + +Run TabDDPM tuning. + +Template and example (`--eval_seeds` is optional): +```bash +python scripts/tune_ddpm.py [ds_name] [train_size] synthetic [catboost|mlp] [exp_name] --eval_seeds +python scripts/tune_ddpm.py churn2 6500 synthetic catboost ddpm_tune --eval_seeds +``` + +Run TabDDPM pipeline. + +Template and example (`--train`, `--sample`, `--eval` are optional): +```bash +python scripts/pipeline.py --config [path_to_your_config] --train --sample --eval +python scripts/pipeline.py --config exp/churn2/ddpm_cb_best/config.toml --train --sample +``` +It takes approximately 7min to run the script above (NVIDIA GeForce RTX 2080 Ti). + +Run evaluation over seeds +Before running evaluation, you have to train the model with the given hyperparameters (the example above). + +Template and example: +```bash +python scripts/eval_seeds.py --config [path_to_your_config] [n_eval_seeds] [ddpm|smote|ctabgan|ctabgan-plus|tvae] synthetic [catboost|mlp] [n_sample_seeds] +python scripts/eval_seeds.py --config exp/churn2/ddpm_cb_best/config.toml 10 ddpm synthetic catboost 5 +``` \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/_compat_run.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/_compat_run.py new file mode 100644 index 0000000000000000000000000000000000000000..77f1230651645a74d0f38b3ff44204cbe28e07ec --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/_compat_run.py @@ -0,0 +1,6 @@ +import collections, collections.abc +for _a in ('Sequence','MutableSequence','MutableMapping','Mapping','MutableSet','Set','Callable','Iterable','Iterator'): + if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None)) +import sys, runpy +sys.argv = sys.argv[1:] +runpy.run_path(sys.argv[0], run_name='__main__') diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/agg_results.ipynb b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/agg_results.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b2265036321a1b9a52a99daf500759ef4b03d60a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/agg_results.ipynb @@ -0,0 +1,315 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aggregating results to DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import lib\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "DATASETS = [\n", + " \"abalone\",\n", + " \"adult\",\n", + " \"buddy\",\n", + " \"california\",\n", + " \"cardio\",\n", + " \"churn2\",\n", + " \"default\",\n", + " \"diabetes\",\n", + " \"fb-comments\",\n", + " \"gesture\",\n", + " \"higgs-small\",\n", + " \"house\",\n", + " \"insurance\",\n", + " \"king\",\n", + " \"miniboone\",\n", + " \"wilt\"\n", + "]\n", + "\n", + "_REGRESSION = [\n", + " \"abalone\",\n", + " \"california\",\n", + " \"fb-comments\",\n", + " \"house\",\n", + " \"insurance\",\n", + " \"king\",\n", + "]\n", + "\n", + "\n", + "method2exp = {\n", + " \"real\": \"exp/{}/ddpm_cb_best/\",\n", + " \"tab-ddpm\": \"exp/{}/ddpm_cb_best/\",\n", + " \"smote\": \"exp/{}/smote/\",\n", + " \"ctabgan+\": \"exp/{}/ctabgan-plus/\",\n", + " \"ctabgan\": \"exp/{}/ctabgan/\",\n", + " \"tvae\": \"exp/{}/tvae/\"\n", + "}\n", + "\n", + "eval_file = \"eval_catboost.json\"\n", + "show_std = False\n", + "df = pd.DataFrame(columns=[\"method\"] + [_[:3].upper() for _ in DATASETS])\n", + "\n", + "for algo in method2exp: \n", + " algo_res = []\n", + " for ds in DATASETS:\n", + " if not os.path.exists(os.path.join(method2exp[algo].format(ds), eval_file)):\n", + " algo_res.append(\"--\")\n", + " continue\n", + " metric = \"r2\" if ds in _REGRESSION else \"f1\"\n", + " res_dict = lib.load_json(os.path.join(method2exp[algo].format(ds), eval_file))\n", + "\n", + " if algo == \"real\":\n", + " res = f'{res_dict[\"real\"][\"test\"][metric + \"-mean\"]:.4f}' \n", + " if show_std: res += f'+-{res_dict[\"real\"][\"test\"][metric + \"-std\"]:.4f}'\n", + " else:\n", + " res = f'{res_dict[\"synthetic\"][\"test\"][metric + \"-mean\"]:.4f}'\n", + " if show_std: res += f'+-{res_dict[\"synthetic\"][\"test\"][metric + \"-std\"]:.4f}'\n", + "\n", + " algo_res.append(res)\n", + " df.loc[len(df)] = [algo] + algo_res" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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methodABAADUBUDCALCARCHUDEFDIAFB-GESHIGHOUINSKINMINWIL
0real0.55620.81520.90630.85680.73790.74030.68800.78490.83710.63650.72380.66160.81370.90700.93420.8982
1tab-ddpm0.54990.79510.90570.83620.73740.75480.69100.73980.71280.59670.72180.67660.80920.83310.93620.9045
2smote0.54860.79120.89060.83970.73230.74320.69300.68350.80350.65790.72190.66250.81190.84160.93230.9127
3ctabgan+0.46720.77240.88440.52470.73270.70240.68650.73390.50880.40550.66390.50400.79660.44380.89200.7983
4ctabgan--0.78310.8552--0.71710.68750.64370.7310--0.39220.5748------0.88920.9060
5tvae0.43280.78100.86380.75180.71740.73170.65640.71360.68530.43400.63780.49260.78420.82380.91250.5006
\n", + "
" + ], + "text/plain": [ + " method ABA ADU BUD CAL CAR CHU DEF DIA \\\n", + "0 real 0.5562 0.8152 0.9063 0.8568 0.7379 0.7403 0.6880 0.7849 \n", + "1 tab-ddpm 0.5499 0.7951 0.9057 0.8362 0.7374 0.7548 0.6910 0.7398 \n", + "2 smote 0.5486 0.7912 0.8906 0.8397 0.7323 0.7432 0.6930 0.6835 \n", + "3 ctabgan+ 0.4672 0.7724 0.8844 0.5247 0.7327 0.7024 0.6865 0.7339 \n", + "4 ctabgan -- 0.7831 0.8552 -- 0.7171 0.6875 0.6437 0.7310 \n", + "5 tvae 0.4328 0.7810 0.8638 0.7518 0.7174 0.7317 0.6564 0.7136 \n", + "\n", + " FB- GES HIG HOU INS KIN MIN WIL \n", + "0 0.8371 0.6365 0.7238 0.6616 0.8137 0.9070 0.9342 0.8982 \n", + "1 0.7128 0.5967 0.7218 0.6766 0.8092 0.8331 0.9362 0.9045 \n", + "2 0.8035 0.6579 0.7219 0.6625 0.8119 0.8416 0.9323 0.9127 \n", + "3 0.5088 0.4055 0.6639 0.5040 0.7966 0.4438 0.8920 0.7983 \n", + "4 -- 0.3922 0.5748 -- -- -- 0.8892 0.9060 \n", + "5 0.6853 0.4340 0.6378 0.4926 0.7842 0.8238 0.9125 0.5006 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.7 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "a06af253165e97d0c1e75e8bf6d3252013856f30b8177e11b02d3fa36c37333d" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/Tab-Cate-1/ddpm_mlp_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/Tab-Cate-1/ddpm_mlp_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..44fce1497d520865c2dc60dac59e2ef6126fe843 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/Tab-Cate-1/ddpm_mlp_best/config.toml @@ -0,0 +1,55 @@ +seed = 0 +parent_dir = "exp/Tab-Cate-1/ddpm_mlp_best" +real_data_path = "data/Tab-Cate-1/" +num_numerical_features = 10 +model_type = "mlp" +device = "cuda:1" + +[model_params] +d_in = 26 +is_y_cond = false +num_classes = 0 + +[model_params.rtdl_params] +d_layers = [ + 512, + 512, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.0006774069976719967 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 27600 +batch_size = 20000 +seed = 0 + +[eval.type] +eval_model = "mlp" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "one-hot" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/Tab-Cate-1/ddpm_mlp_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/Tab-Cate-1/ddpm_mlp_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..45c9e2aa7e7c863b8630ec46d11b541348b66c03 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/Tab-Cate-1/ddpm_mlp_best/info.json @@ -0,0 +1,17 @@ +{ + "task_type": "regression", + "n_num_features": 10, + "n_cat_features": 3, + "cat_sizes": [ + 6, + 6, + 4 + ], + "y_dtype": "int64", + "y_unique_count": 1, + "y_unique_head": [ + 0 + ], + "is_classification_like": true, + "num_classes_like": 1 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..dc4dc5d37b67b14e2a48fc83b54a40039c12ab63 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/config.toml @@ -0,0 +1,56 @@ +seed = 0 +parent_dir = "exp/abalone/check" +real_data_path = "data/abalone/" +model_type = "mlp" +num_numerical_features = 7 +device = "cuda:0" + +[model_params] +is_y_cond = false +d_in = 11 +num_classes = 0 + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 20800 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ctabgan-plus/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ctabgan-plus/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..1815b9bfcf0342319933633c75bbcb6aeddf38af --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ctabgan-plus/config.toml @@ -0,0 +1,34 @@ +parent_dir = "exp/abalone/ctabgan-plus" +real_data_path = "data/abalone/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.0009271475289053036 +epochs = 5000 +class_dim = [ + 64, + 128, + 128, + 64, +] +batch_size = 2048 +random_dim = 16 +num_channels = 32 + +[sample] +seed = 0 +num_samples = 21600 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ctabgan-plus/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ctabgan-plus/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..836486aa97e32709db0a8b59759819ca965f8820 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ctabgan-plus/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.5152, + "r2-std": 0.0074, + "rmse-count": 50.0, + "rmse-mean": 2.1752, + "rmse-std": 0.0166 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.4672, + "r2-std": 0.0045, + "rmse-count": 50.0, + "rmse-mean": 2.2017, + "rmse-std": 0.0094 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ctabgan-plus/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ctabgan-plus/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..401db1276ff4cb9eb06b9cf181ceff6ec089cd60 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ctabgan-plus/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 50.0, + "r2-mean": -0.0148, + "r2-std": 0.0747, + "rmse-count": 50.0, + "rmse-mean": 3.1451, + "rmse-std": 0.1149 + }, + "test": { + "r2-count": 50.0, + "r2-mean": -0.093, + "r2-std": 0.0589, + "rmse-count": 50.0, + "rmse-mean": 3.1524, + "rmse-std": 0.0854 + } + }, + "lr": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.3929, + "r2-std": 0.0096, + "rmse-count": 50.0, + "rmse-mean": 2.4342, + "rmse-std": 0.0192 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.4364, + "r2-std": 0.0027, + "rmse-count": 50.0, + "rmse-mean": 2.2645, + "rmse-std": 0.0053 + } + }, + "rf": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.5061, + "r2-std": 0.01, + "rmse-count": 50.0, + "rmse-mean": 2.1955, + "rmse-std": 0.0221 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.4467, + "r2-std": 0.0106, + "rmse-count": 50.0, + "rmse-mean": 2.2437, + "rmse-std": 0.0214 + } + }, + "mlp": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.4727, + "r2-std": 0.0206, + "rmse-count": 50.0, + "rmse-mean": 2.2681, + "rmse-std": 0.0443 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.4757, + "r2-std": 0.0083, + "rmse-count": 50.0, + "rmse-mean": 2.1842, + "rmse-std": 0.0173 + } + }, + "avg": { + "val": { + "r2-mean": 0.3392, + "rmse-mean": 2.5107, + "r2-std": 0.0287 + }, + "test": { + "r2-mean": 0.3164, + "rmse-mean": 2.4612, + "r2-std": 0.0244 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ctabgan-plus/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ctabgan-plus/info.json new file mode 100644 index 0000000000000000000000000000000000000000..d548b4b0763b5464fefa32a78ae8a2d9323609c2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ctabgan-plus/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "regression", + "name": "Abalone", + "id": "abalone--id", + "train_size": 2672, + "val_size": 669, + "test_size": 836, + "n_num_features": 7, + "n_cat_features": 1 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..6f3acbc6c568067fb830b4af3ae6c0990da4a61a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/config.toml @@ -0,0 +1,55 @@ +seed = 0 +parent_dir = "exp/abalone/ddpm_cb_best" +real_data_path = "data/abalone/" +model_type = "mlp" +num_numerical_features = 7 +device = "cuda:0" + +[model_params] +is_y_cond = false +d_in = 11 +num_classes = 0 + +[model_params.rtdl_params] +d_layers = [ + 512, + 128, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 100 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.0003442062504112041 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 20800 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..9144e7b476a018f7f21d63d7bafa8fbb49e5e59a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/eval_catboost.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.5666, + "r2-std": 0.0029, + "rmse-count": 50.0, + "rmse-mean": 2.0566, + "rmse-std": 0.0069 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.5499, + "r2-std": 0.0103, + "rmse-count": 50.0, + "rmse-mean": 2.0237, + "rmse-std": 0.0233 + } + }, + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.5616, + "r2-std": 0.0017, + "rmse-count": 10.0, + "rmse-mean": 2.0685, + "rmse-std": 0.004 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.5562, + "r2-std": 0.0038, + "rmse-count": 10.0, + "rmse-mean": 2.0096, + "rmse-std": 0.0087 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..6cf592c8acd682a9709e392fe3d3925a80832a0f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/eval_mlp.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.5573, + "r2-std": 0.0076, + "rmse-count": 50.0, + "rmse-mean": 2.0785, + "rmse-std": 0.0177 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.5694, + "r2-std": 0.0097, + "rmse-count": 50.0, + "rmse-mean": 1.9793, + "rmse-std": 0.0222 + } + }, + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.5664, + "r2-std": 0.0042, + "rmse-count": 10.0, + "rmse-mean": 2.0573, + "rmse-std": 0.0099 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.5809, + "r2-std": 0.0049, + "rmse-count": 10.0, + "rmse-mean": 1.9527, + "rmse-std": 0.0113 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..71a1bc39afde38be8befe879401b516047c0b31c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/eval_simple.json @@ -0,0 +1,174 @@ +{ + "real": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.0984, + "r2-std": 0.0426, + "rmse-count": 10.0, + "rmse-mean": 2.9656, + "rmse-std": 0.0705 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.1425, + "r2-std": 0.0202, + "rmse-count": 10.0, + "rmse-mean": 2.7931, + "rmse-std": 0.033 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.4805, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 2.2517, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.4829, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 2.1692, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.528, + "r2-std": 0.0034, + "rmse-count": 10.0, + "rmse-mean": 2.1463, + "rmse-std": 0.0076 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.5513, + "r2-std": 0.0045, + "rmse-count": 10.0, + "rmse-mean": 2.0206, + "rmse-std": 0.0102 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.5252, + "r2-std": 0.006, + "rmse-count": 10.0, + "rmse-mean": 2.1527, + "rmse-std": 0.0136 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.5139, + "r2-std": 0.0032, + "rmse-count": 10.0, + "rmse-mean": 2.1031, + "rmse-std": 0.0068 + } + }, + "avg": { + "val": { + "r2-mean": 0.408, + "rmse-mean": 2.3791, + "r2-std": 0.013 + }, + "test": { + "r2-mean": 0.4226, + "rmse-mean": 2.2715, + "r2-std": 0.0095 + } + } + }, + "synthetic": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.1274, + "r2-std": 0.0235, + "rmse-count": 10.0, + "rmse-mean": 2.9181, + "rmse-std": 0.0393 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.0052, + "r2-std": 0.0107, + "rmse-count": 10.0, + "rmse-mean": 3.0086, + "rmse-std": 0.0161 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.4126, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 2.3944, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.4838, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 2.1671, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.5352, + "r2-std": 0.0033, + "rmse-count": 10.0, + "rmse-mean": 2.1299, + "rmse-std": 0.0075 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.5331, + "r2-std": 0.0044, + "rmse-count": 10.0, + "rmse-mean": 2.061, + "rmse-std": 0.0096 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.5395, + "r2-std": 0.007, + "rmse-count": 10.0, + "rmse-mean": 2.12, + "rmse-std": 0.0163 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.5462, + "r2-std": 0.0069, + "rmse-count": 10.0, + "rmse-mean": 2.0319, + "rmse-std": 0.0154 + } + }, + "avg": { + "val": { + "r2-mean": 0.4037, + "rmse-mean": 2.3906, + "r2-std": 0.0107 + }, + "test": { + "r2-mean": 0.3921, + "rmse-mean": 2.3172, + "r2-std": 0.0085 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/eval_simple_ada.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/eval_simple_ada.json new file mode 100644 index 0000000000000000000000000000000000000000..b03431e4d61691d97835a153eef465236a99e046 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/eval_simple_ada.json @@ -0,0 +1,20 @@ +{ + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.1525, + "r2-std": 0.0839, + "rmse-count": 10.0, + "rmse-mean": 2.8725, + "rmse-std": 0.1449 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.1219, + "r2-std": 0.0922, + "rmse-count": 10.0, + "rmse-mean": 2.8226, + "rmse-std": 0.1508 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/eval_simple_lr.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/eval_simple_lr.json new file mode 100644 index 0000000000000000000000000000000000000000..8ccbdd72746080fb7e637346426b4481e74182fd --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/eval_simple_lr.json @@ -0,0 +1,20 @@ +{ + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.4722, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 2.2696, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.4952, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 2.1431, + "rmse-std": 0.0 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..f7bccab60c2d2e22942e6f43f3ef29aca068cbee --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/importance.json @@ -0,0 +1,10 @@ +{ + "n_layers": 0.518295105346543, + "lr": 0.35275257630010814, + "d_last": 0.04009292894740083, + "num_timesteps": 0.03288596344345562, + "num_samples": 0.025727956001032996, + "steps": 0.021123385266617178, + "d_first": 0.009063469157252533, + "batch_size": 5.861553758964502e-05 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..d548b4b0763b5464fefa32a78ae8a2d9323609c2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "regression", + "name": "Abalone", + "id": "abalone--id", + "train_size": 2672, + "val_size": 669, + "test_size": 836, + "n_num_features": 7, + "n_cat_features": 1 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..d26af24da0dc0ddd0df3253aeef4001769f26844 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.5499, + 0.5722, + 0.5737, + 0.5511, + 0.5194 + ], + "privacies": [ + 0.05026405426245476, + 0.0526, + 0.0555, + 0.0585, + 0.062 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..ad98845c59caca1796d47431e6b91d75071ffe22 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_cb_best/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.04990887224573338 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_mlp_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_mlp_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..1f88acaa1470dc07e989c2da6cf880803d61e74e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_mlp_best/config.toml @@ -0,0 +1,55 @@ +seed = 0 +parent_dir = "exp/abalone/ddpm_mlp_best" +real_data_path = "data/abalone/" +model_type = "mlp" +num_numerical_features = 7 +device = "cuda:0" + +[model_params] +is_y_cond = false +d_in = 11 +num_classes = 0 + +[model_params.rtdl_params] +d_layers = [ + 256, + 128, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.00027761965839603165 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 5400 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "mlp" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "one-hot" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_mlp_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_mlp_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..b8ce4b87e65c1ffa2ee2338e0c8a8916a32f942e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_mlp_best/eval_catboost.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.5574, + "r2-std": 0.0069, + "rmse-count": 50.0, + "rmse-mean": 2.0784, + "rmse-std": 0.0163 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.5266, + "r2-std": 0.0058, + "rmse-count": 50.0, + "rmse-mean": 2.0754, + "rmse-std": 0.0127 + } + }, + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.5631, + "r2-std": 0.0022, + "rmse-count": 10.0, + "rmse-mean": 2.065, + "rmse-std": 0.0053 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.5544, + "r2-std": 0.005, + "rmse-count": 10.0, + "rmse-mean": 2.0135, + "rmse-std": 0.0113 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_mlp_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_mlp_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..8aa8f36d1c33f56af4005e51f49ff5c887577a2e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_mlp_best/eval_mlp.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.5626, + "r2-std": 0.0086, + "rmse-count": 50.0, + "rmse-mean": 2.066, + "rmse-std": 0.0201 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.559, + "r2-std": 0.0086, + "rmse-count": 50.0, + "rmse-mean": 2.0031, + "rmse-std": 0.0193 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_mlp_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_mlp_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..1a09255f469961dff9546ccd058f72893662653a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_mlp_best/importance.json @@ -0,0 +1,10 @@ +{ + "n_layers": 0.5799431434016662, + "lr": 0.2374329612242399, + "d_first": 0.04615011425954773, + "num_samples": 0.04319358985725551, + "batch_size": 0.03565044128429545, + "steps": 0.025586591975204628, + "num_timesteps": 0.017389941109874678, + "d_last": 0.01465321688791608 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_mlp_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_mlp_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..d548b4b0763b5464fefa32a78ae8a2d9323609c2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/ddpm_mlp_best/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "regression", + "name": "Abalone", + "id": "abalone--id", + "train_size": 2672, + "val_size": 669, + "test_size": 836, + "n_num_features": 7, + "n_cat_features": 1 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..3d8b1d36668fb900b29e07a56dfa7f795faeac4f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/config.toml @@ -0,0 +1,24 @@ +parent_dir = "exp/abalone/smote" +real_data_path = "data/abalone/" +seed = 0 + +[smote_params] +k_neighbours = 5 +frac_samples = 8 +frac_lam_del = 0.65 + +[sample] +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..2b067589007b90aa7fc1702fe47c0bd9c0d800e2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.5555, + "r2-std": 0.0027, + "rmse-count": 50.0, + "rmse-mean": 2.0828, + "rmse-std": 0.0062 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.5486, + "r2-std": 0.0051, + "rmse-count": 50.0, + "rmse-mean": 2.0267, + "rmse-std": 0.0114 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..c9bc20467c679892b765b6e1c45cac78050bbaef --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/eval_mlp.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.5357, + "r2-std": 0.0076, + "rmse-count": 50.0, + "rmse-mean": 2.1286, + "rmse-std": 0.0176 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.5517, + "r2-std": 0.0085, + "rmse-count": 50.0, + "rmse-mean": 2.0195, + "rmse-std": 0.0191 + } + }, + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.5664, + "r2-std": 0.0042, + "rmse-count": 10.0, + "rmse-mean": 2.0573, + "rmse-std": 0.0099 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.5809, + "r2-std": 0.0049, + "rmse-count": 10.0, + "rmse-mean": 1.9527, + "rmse-std": 0.0113 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..e5ffe794cf8cda004ef939b22379481565341e47 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.0958, + "r2-std": 0.0137, + "rmse-count": 10.0, + "rmse-mean": 2.9706, + "rmse-std": 0.0224 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.0651, + "r2-std": 0.0235, + "rmse-count": 10.0, + "rmse-mean": 2.9165, + "rmse-std": 0.0366 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.4161, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 2.3873, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.493, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 2.1478, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.4561, + "r2-std": 0.0028, + "rmse-count": 10.0, + "rmse-mean": 2.304, + "rmse-std": 0.006 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.4981, + "r2-std": 0.005, + "rmse-count": 10.0, + "rmse-mean": 2.137, + "rmse-std": 0.0107 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.5112, + "r2-std": 0.0239, + "rmse-count": 10.0, + "rmse-mean": 2.1836, + "rmse-std": 0.0529 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.543, + "r2-std": 0.0037, + "rmse-count": 10.0, + "rmse-mean": 2.0391, + "rmse-std": 0.0082 + } + }, + "avg": { + "val": { + "r2-mean": 0.3698, + "rmse-mean": 2.4614, + "r2-std": 0.0101 + }, + "test": { + "r2-mean": 0.3998, + "rmse-mean": 2.3101, + "r2-std": 0.0091 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/info.json new file mode 100644 index 0000000000000000000000000000000000000000..d548b4b0763b5464fefa32a78ae8a2d9323609c2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "regression", + "name": "Abalone", + "id": "abalone--id", + "train_size": 2672, + "val_size": 669, + "test_size": 836, + "n_num_features": 7, + "n_cat_features": 1 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..7ac9e76d9594dfa60e2e3e60e45c9caa6d1671d5 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.5486, + 0.5448, + 0.5438, + 0.5354, + 0.5393 + ], + "privacies": [ + 0.013805983502423515, + 0.015, + 0.0164, + 0.018, + 0.0208 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..e117638adb56a4292bb2b0abfa8e2450b50d963a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/smote/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.013805983502423515 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/tvae/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/tvae/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..eb58218d1e3115740e5d6ea50ffc0562aafaf0c6 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/tvae/config.toml @@ -0,0 +1,36 @@ +parent_dir = "exp/abalone/tvae" +real_data_path = "data/abalone/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.0006509130949439269 +epochs = 30000 +embedding_dim = 128 +batch_size = 2672 +loss_factor = 0.8306279147963365 +compress_dims = [ + 256, + 256, +] +decompress_dims = [ + 256, + 256, +] + +[sample] +seed = 0 +num_samples = 21600 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/tvae/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/tvae/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..7afbed18f7106e8a731e1d9e8f67f32f9a08a4ed --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/tvae/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.492, + "r2-std": 0.0073, + "rmse-count": 50.0, + "rmse-mean": 2.2266, + "rmse-std": 0.016 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.4328, + "r2-std": 0.0083, + "rmse-count": 50.0, + "rmse-mean": 2.2718, + "rmse-std": 0.0165 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/tvae/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/tvae/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..6af0e3e9107c1bb68180b581afe3440106adb57a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/tvae/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": -0.2407, + "r2-std": 0.0393, + "rmse-count": 10.0, + "rmse-mean": 3.4794, + "rmse-std": 0.0551 + }, + "test": { + "r2-count": 10.0, + "r2-mean": -0.2464, + "r2-std": 0.0283, + "rmse-count": 10.0, + "rmse-mean": 3.3674, + "rmse-std": 0.0381 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.2479, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 2.7094, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.3877, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 2.3604, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.4304, + "r2-std": 0.0058, + "rmse-count": 10.0, + "rmse-mean": 2.3577, + "rmse-std": 0.012 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.4048, + "r2-std": 0.0049, + "rmse-count": 10.0, + "rmse-mean": 2.3271, + "rmse-std": 0.0097 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.4139, + "r2-std": 0.0116, + "rmse-count": 10.0, + "rmse-mean": 2.3917, + "rmse-std": 0.0236 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.4058, + "r2-std": 0.0068, + "rmse-count": 10.0, + "rmse-mean": 2.3252, + "rmse-std": 0.0132 + } + }, + "avg": { + "val": { + "r2-mean": 0.2129, + "rmse-mean": 2.7346, + "r2-std": 0.0142 + }, + "test": { + "r2-mean": 0.238, + "rmse-mean": 2.595, + "r2-std": 0.0121 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/tvae/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/tvae/info.json new file mode 100644 index 0000000000000000000000000000000000000000..d548b4b0763b5464fefa32a78ae8a2d9323609c2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/abalone/tvae/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "regression", + "name": "Abalone", + "id": "abalone--id", + "train_size": 2672, + "val_size": 669, + "test_size": 836, + "n_num_features": 7, + "n_cat_features": 1 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..08bf6244e607ff527f4a13b18a9c3e1ceb55a86f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/config.toml @@ -0,0 +1,55 @@ +parent_dir = "exp/adult/check" +real_data_path = "data/adult/" +num_numerical_features = 6 +model_type = "mlp" +seed = 0 +device = "cuda:0" + +[model_params] +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 216000 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan-plus/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan-plus/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..d4b5a9b673137841768670d7da8deb5bdc73a919 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan-plus/config.toml @@ -0,0 +1,33 @@ +parent_dir = "exp/adult/ctabgan-plus" +real_data_path = "data/adult/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.0002288113668475521 +epochs = 10000 +class_dim = [ + 128, + 128, + 128, +] +batch_size = 2048 +random_dim = 32 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 54000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan-plus/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan-plus/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..584ed7d00350a6cb1ff31de57b7fd44e8cda10e9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan-plus/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8523, + "acc-std": 0.0008, + "f1-count": 50.0, + "f1-mean": 0.7733, + "f1-std": 0.0016, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9077, + "roc_auc-std": 0.0015 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8534, + "acc-std": 0.002, + "f1-count": 50.0, + "f1-mean": 0.7724, + "f1-std": 0.003, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9064, + "roc_auc-std": 0.0022 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan-plus/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan-plus/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..ed5f9631b59ef42e6b4c280344bf37a21893f371 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan-plus/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7906, + "acc-std": 0.0042, + "f1-count": 50.0, + "f1-mean": 0.7052, + "f1-std": 0.0065, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7108, + "roc_auc-std": 0.0089 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.792, + "acc-std": 0.0028, + "f1-count": 50.0, + "f1-mean": 0.7042, + "f1-std": 0.0047, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7098, + "roc_auc-std": 0.0062 + } + }, + "lr": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8157, + "acc-std": 0.0016, + "f1-count": 50.0, + "f1-mean": 0.7051, + "f1-std": 0.0033, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8478, + "roc_auc-std": 0.0008 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8151, + "acc-std": 0.0012, + "f1-count": 50.0, + "f1-mean": 0.7001, + "f1-std": 0.0024, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8448, + "roc_auc-std": 0.0008 + } + }, + "rf": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8433, + "acc-std": 0.0021, + "f1-count": 50.0, + "f1-mean": 0.7594, + "f1-std": 0.0043, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8905, + "roc_auc-std": 0.0008 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.844, + "acc-std": 0.0014, + "f1-count": 50.0, + "f1-mean": 0.7578, + "f1-std": 0.003, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8892, + "roc_auc-std": 0.0009 + } + }, + "mlp": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8426, + "acc-std": 0.0033, + "f1-count": 50.0, + "f1-mean": 0.7623, + "f1-std": 0.0147, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8968, + "roc_auc-std": 0.0014 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8433, + "acc-std": 0.0031, + "f1-count": 50.0, + "f1-mean": 0.7591, + "f1-std": 0.0159, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8962, + "roc_auc-std": 0.0013 + } + }, + "avg": { + "val": { + "f1-mean": 0.733, + "acc-mean": 0.823, + "f1-std": 0.0072 + }, + "test": { + "f1-mean": 0.7303, + "acc-mean": 0.8236, + "f1-std": 0.0068 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan-plus/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan-plus/info.json new file mode 100644 index 0000000000000000000000000000000000000000..547394c85218b5195cf9d82cc6a0968bb0286e03 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan-plus/info.json @@ -0,0 +1,10 @@ +{ + "name": "Adult", + "id": "adult--default", + "task_type": "binclass", + "n_num_features": 6, + "n_cat_features": 8, + "test_size": 16281, + "train_size": 26048, + "val_size": 6513 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..3594b5b5b55d6c08639d58feeb8b2772baf076cd --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan/config.toml @@ -0,0 +1,36 @@ +parent_dir = "exp/adult/ctabgan" +real_data_path = "data/adult/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.00016533438465187033 +epochs = 10000 +class_dim = [ + 64, + 256, + 256, + 256, + 256, + 512, +] +batch_size = 2048 +random_dim = 128 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 54000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..1b3c9439118507e7098aa5ffff9155c59e230aee --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8519, + "acc-std": 0.0011, + "f1-count": 50.0, + "f1-mean": 0.7832, + "f1-std": 0.0016, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9048, + "roc_auc-std": 0.0008 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8536, + "acc-std": 0.001, + "f1-count": 50.0, + "f1-mean": 0.7831, + "f1-std": 0.0015, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9038, + "roc_auc-std": 0.0008 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..1b7011bd976fd87bb08060e58aa370fdf948e9e9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7938, + "acc-std": 0.0036, + "f1-count": 10.0, + "f1-mean": 0.717, + "f1-std": 0.0049, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7183, + "roc_auc-std": 0.0049 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7937, + "acc-std": 0.003, + "f1-count": 10.0, + "f1-mean": 0.715, + "f1-std": 0.0031, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7177, + "roc_auc-std": 0.0024 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8214, + "acc-std": 0.0007, + "f1-count": 10.0, + "f1-mean": 0.7092, + "f1-std": 0.0012, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8504, + "roc_auc-std": 0.0005 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8179, + "acc-std": 0.0005, + "f1-count": 10.0, + "f1-mean": 0.7006, + "f1-std": 0.0014, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8461, + "roc_auc-std": 0.0004 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8386, + "acc-std": 0.0023, + "f1-count": 10.0, + "f1-mean": 0.7649, + "f1-std": 0.0033, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8919, + "roc_auc-std": 0.0014 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8447, + "acc-std": 0.0022, + "f1-count": 10.0, + "f1-mean": 0.7707, + "f1-std": 0.0036, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8904, + "roc_auc-std": 0.0014 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8348, + "acc-std": 0.0051, + "f1-count": 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"Adult", + "id": "adult--default", + "task_type": "binclass", + "n_num_features": 6, + "n_cat_features": 8, + "test_size": 16281, + "train_size": 26048, + "val_size": 6513 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..2a23a8ec8727a4d63cb68250eae7c156ba11509b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ctabgan/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.7831, + 0.7863, + 0.7837, + 0.7846, + 0.784 + ], + "privacies": [ + 0.20886260991533906, + 0.2088, + 0.2077, + 0.2048, + 0.2059 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..9099c8e34e60162c48d94c3c763abc7002b7365c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/config.toml @@ -0,0 +1,58 @@ +parent_dir = "exp/adult/ddpm_cb_best" +real_data_path = "data/adult/" +num_numerical_features = 6 +model_type = "mlp" +seed = 0 +device = "cuda:0" + +[model_params] +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 256, + 1024, + 1024, + 1024, + 1024, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 100 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.0020099410620098234 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 216000 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..a0ca781c3a04442bc70c4a866265950dd09d5233 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/eval_catboost.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8592, + "acc-std": 0.0011, + "f1-count": 50.0, + 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b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..54c85f402396cacaeefb04085b34059eeeb16574 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/eval_mlp.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.855, + "acc-std": 0.0021, + "f1-count": 50.0, + "f1-mean": 0.7974, + "f1-std": 0.0032, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9144, + "roc_auc-std": 0.0007 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8551, + "acc-std": 0.0024, + "f1-count": 50.0, + "f1-mean": 0.7945, + "f1-std": 0.002, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9103, + "roc_auc-std": 0.0008 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.851, + "acc-std": 0.0029, + "f1-count": 10.0, + "f1-mean": 0.8004, + "f1-std": 0.002, + "roc_auc-count": 10.0, + 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0000000000000000000000000000000000000000..39125ec6e12e970cd38927a52c3a78294e1810f5 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/importance.json @@ -0,0 +1,10 @@ +{ + "steps": 0.3526892424947137, + "n_layers": 0.2811210761704287, + "batch_size": 0.13140765664855633, + "lr": 0.09429216051657473, + "d_last": 0.04498829462823654, + "d_first": 0.038735153969051944, + "num_timesteps": 0.03538066147798376, + "num_samples": 0.02138575409445434 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..547394c85218b5195cf9d82cc6a0968bb0286e03 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/info.json @@ -0,0 +1,10 @@ +{ + "name": "Adult", + "id": "adult--default", + "task_type": "binclass", + "n_num_features": 6, + "n_cat_features": 8, + "test_size": 16281, + "train_size": 26048, + "val_size": 6513 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..2c7ba9d9c3819cb794c13d9c7ff888bfab6e57ae --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.7951, + 0.797, + 0.7965, + 0.7964, + 0.7958 + ], + "privacies": [ + 0.10487634521322395, + 0.1057, + 0.1051, + 0.1086, + 0.1124 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..b686e804afe672765d6fde8fb0e0b67096d24dad --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_cb_best/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.1043782482006405 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_mlp_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_mlp_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..792c3d14f4ecff4410adb698aa97785ecc211ae6 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_mlp_best/config.toml @@ -0,0 +1,54 @@ +parent_dir = "exp/adult/ddpm_mlp_best" +real_data_path = "data/adult/" +num_numerical_features = 6 +model_type = "mlp" +seed = 0 +device = "cuda:0" + +[model_params] +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 1024, + 512, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.001809824563637657 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 54000 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "mlp" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "one-hot" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_mlp_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_mlp_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..17edd1783830c337401486dd1b79ee257f03230e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_mlp_best/eval_mlp.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8474, + "acc-std": 0.0035, + "f1-count": 50.0, + "f1-mean": 0.7943, + "f1-std": 0.0026, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9089, + "roc_auc-std": 0.0012 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8479, + "acc-std": 0.003, + "f1-count": 50.0, + "f1-mean": 0.7919, + "f1-std": 0.0016, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9069, + "roc_auc-std": 0.0007 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_mlp_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_mlp_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..3dc9b090ce2d3b346fc7e267247b29009d497920 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_mlp_best/importance.json @@ -0,0 +1,10 @@ +{ + "d_last": 0.2668014691210104, + "steps": 0.24230358134761998, + "batch_size": 0.1481496302103072, + "lr": 0.1418805253996675, + "n_layers": 0.10909427795635407, + "d_first": 0.048134894120944606, + "num_samples": 0.03517841611166816, + "num_timesteps": 0.00845720573242804 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_mlp_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_mlp_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..547394c85218b5195cf9d82cc6a0968bb0286e03 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/ddpm_mlp_best/info.json @@ -0,0 +1,10 @@ +{ + "name": "Adult", + "id": "adult--default", + "task_type": "binclass", + "n_num_features": 6, + "n_cat_features": 8, + "test_size": 16281, + "train_size": 26048, + "val_size": 6513 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..b5818d48418bce9086bfd40c926da0f43213f1b2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/config.toml @@ -0,0 +1,24 @@ +parent_dir = "exp/adult/smote" +real_data_path = "data/adult/" +seed = 0 + +[smote_params] +k_neighbours = 5 +frac_samples = 1 +frac_lam_del = 0.65 + +[sample] +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..ac19ad2c5fc69ea48f24d148f3ff97c0b4b2e3a1 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8584, + "acc-std": 0.0008, + "f1-count": 50.0, + "f1-mean": 0.7937, + "f1-std": 0.0009, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9106, + "roc_auc-std": 0.0007 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8582, + "acc-std": 0.0009, + "f1-count": 50.0, + "f1-mean": 0.7912, + "f1-std": 0.0016, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9104, + "roc_auc-std": 0.0009 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..c9eca3059a59f8ec651f351816519c3f692ba7d5 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8132, + "acc-std": 0.0012, + "f1-count": 10.0, + "f1-mean": 0.7461, + "f1-std": 0.0015, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7484, + "roc_auc-std": 0.0016 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8131, + "acc-std": 0.0011, + "f1-count": 10.0, + "f1-mean": 0.7429, + "f1-std": 0.0014, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7453, + "roc_auc-std": 0.0014 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8245, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.7221, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.853, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8232, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.7156, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8521, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8504, + "acc-std": 0.0011, + "f1-count": 10.0, + "f1-mean": 0.7852, + "f1-std": 0.0017, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8985, + "roc_auc-std": 0.0007 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8483, + "acc-std": 0.0007, + "f1-count": 10.0, + "f1-mean": 0.7786, + "f1-std": 0.0011, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8983, + "roc_auc-std": 0.0005 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8412, + "acc-std": 0.0026, + "f1-count": 10.0, + "f1-mean": 0.7632, + "f1-std": 0.0143, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8844, + "roc_auc-std": 0.0019 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8438, + "acc-std": 0.0017, + "f1-count": 10.0, + "f1-mean": 0.7635, + "f1-std": 0.0128, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8865, + "roc_auc-std": 0.0014 + } + }, + "avg": { + "val": { + "f1-mean": 0.7542, + "acc-mean": 0.8323, + "f1-std": 0.0044 + }, + "test": { + "f1-mean": 0.7502, + "acc-mean": 0.8321, + "f1-std": 0.0041 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/info.json new file mode 100644 index 0000000000000000000000000000000000000000..547394c85218b5195cf9d82cc6a0968bb0286e03 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/info.json @@ -0,0 +1,10 @@ +{ + "name": "Adult", + "id": "adult--default", + "task_type": "binclass", + "n_num_features": 6, + "n_cat_features": 8, + "test_size": 16281, + "train_size": 26048, + "val_size": 6513 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..f65ea32e88bfa9608f98df07ffd615623e346469 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.7912, + 0.7837, + 0.7737, + 0.7642, + 0.7501 + ], + "privacies": [ + 0.02446612554664042, + 0.0282, + 0.0332, + 0.0391, + 0.0472 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..22cdba0581be368a2cabb9f416f21a0ae22b4ea0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/smote/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.02446612554664042 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/tvae/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/tvae/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..b5d7ad6a59a09b871536e8c5d3abd1a05d766e80 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/tvae/config.toml @@ -0,0 +1,36 @@ +parent_dir = "exp/adult/tvae" +real_data_path = "data/adult/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.00032660626928874973 +epochs = 30000 +embedding_dim = 512 +batch_size = 4096 +loss_factor = 1.1675626917524105 +compress_dims = [ + 256, + 512, +] +decompress_dims = [ + 256, + 512, +] + +[sample] +seed = 0 +num_samples = 110400 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/tvae/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/tvae/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..4d775fe451d207e5c6371fa828eb6bdb4d4ec790 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/tvae/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8508, + "acc-std": 0.0022, + "f1-count": 50.0, + "f1-mean": 0.7842, + "f1-std": 0.0022, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9032, + "roc_auc-std": 0.0007 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.85, + "acc-std": 0.0028, + "f1-count": 50.0, + "f1-mean": 0.781, + "f1-std": 0.0021, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9017, + "roc_auc-std": 0.0011 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/tvae/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/tvae/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..3a7d8ba0a2377a155535237c34bf2f2b9d68f02d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/tvae/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7941, + "acc-std": 0.0013, + "f1-count": 10.0, + "f1-mean": 0.7221, + "f1-std": 0.0023, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7269, + "roc_auc-std": 0.0029 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7895, + "acc-std": 0.0016, + "f1-count": 10.0, + "f1-mean": 0.7148, + "f1-std": 0.0014, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7221, + "roc_auc-std": 0.0012 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8053, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.7245, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8409, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8014, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.7185, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8326, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8359, + "acc-std": 0.001, + "f1-count": 10.0, + "f1-mean": 0.7717, + "f1-std": 0.0013, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8942, + "roc_auc-std": 0.0006 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8346, + "acc-std": 0.0011, + "f1-count": 10.0, + "f1-mean": 0.7673, + "f1-std": 0.0015, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8867, + "roc_auc-std": 0.0006 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8291, + "acc-std": 0.0041, + "f1-count": 10.0, + "f1-mean": 0.767, + "f1-std": 0.0024, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8901, + "roc_auc-std": 0.0013 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8321, + "acc-std": 0.0031, + "f1-count": 10.0, + "f1-mean": 0.7685, + "f1-std": 0.002, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8883, + "roc_auc-std": 0.0014 + } + }, + "avg": { + "val": { + "f1-mean": 0.7463, + "acc-mean": 0.8161, + "f1-std": 0.0015 + }, + "test": { + "f1-mean": 0.7423, + "acc-mean": 0.8144, + "f1-std": 0.0014 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/tvae/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/tvae/info.json new file mode 100644 index 0000000000000000000000000000000000000000..547394c85218b5195cf9d82cc6a0968bb0286e03 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/adult/tvae/info.json @@ -0,0 +1,10 @@ +{ + "name": "Adult", + "id": "adult--default", + "task_type": "binclass", + "n_num_features": 6, + "n_cat_features": 8, + "test_size": 16281, + "train_size": 26048, + "val_size": 6513 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..d0d7d7b7c14f571276c0453bac6ec3a18a70144b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/config.toml @@ -0,0 +1,56 @@ +seed = 0 +parent_dir = "exp/buddy/check" +real_data_path = "data/buddy/" +model_type = "mlp" +num_numerical_features = 4 +device = "cuda:0" + +[model_params] +num_classes = 3 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 48000 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" +model = "catboost" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan-plus/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan-plus/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..02a9c71cc999a41681035ab5c6afed39b0d384b8 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan-plus/config.toml @@ -0,0 +1,34 @@ +parent_dir = "exp/buddy/ctabgan-plus" +real_data_path = "data/buddy/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.0002140849623300361 +epochs = 10000 +class_dim = [ + 64, + 64, + 64, + 128, +] +batch_size = 2048 +random_dim = 32 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 48000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan-plus/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan-plus/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..c7da2191d1890242ce521eebf526d8a2681ef8c9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan-plus/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8902, + "acc-std": 0.0013, + "f1-count": 50.0, + "f1-mean": 0.8909, + "f1-std": 0.0025 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8831, + "acc-std": 0.0029, + "f1-count": 50.0, + "f1-mean": 0.8844, + "f1-std": 0.0049 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan-plus/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan-plus/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..fa5a44aa9d0d671ddeb73314ba6ff647a2072b5d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan-plus/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8626, + "acc-std": 0.0037, + "f1-count": 50.0, + "f1-mean": 0.849, + "f1-std": 0.0049 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.863, + "acc-std": 0.0039, + "f1-count": 50.0, + "f1-mean": 0.8559, + "f1-std": 0.0046 + } + }, + "lr": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7773, + "acc-std": 0.0029, + "f1-count": 50.0, + "f1-mean": 0.7294, + "f1-std": 0.006 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7766, + "acc-std": 0.0027, + "f1-count": 50.0, + "f1-mean": 0.7419, + "f1-std": 0.0045 + } + }, + "rf": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.882, + "acc-std": 0.0039, + "f1-count": 50.0, + "f1-mean": 0.8859, + "f1-std": 0.0057 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8805, + "acc-std": 0.0045, + "f1-count": 50.0, + "f1-mean": 0.8822, + "f1-std": 0.0047 + } + }, + "mlp": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8678, + "acc-std": 0.005, + "f1-count": 50.0, + "f1-mean": 0.8644, + "f1-std": 0.0075 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8722, + "acc-std": 0.0058, + "f1-count": 50.0, + "f1-mean": 0.8691, + "f1-std": 0.0072 + } + }, + "avg": { + "val": { + "f1-mean": 0.8322, + "acc-mean": 0.8474, + "f1-std": 0.006 + }, + "test": { + "f1-mean": 0.8373, + "acc-mean": 0.8481, + "f1-std": 0.0056 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan-plus/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan-plus/info.json new file mode 100644 index 0000000000000000000000000000000000000000..cc38a53a90b267fd7a3070ba9c6e8fffe316566a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan-plus/info.json @@ -0,0 +1,11 @@ +{ + "task_type": "multiclass", + "name": "buddy", + "id": "buddy--id", + "n_classes": 3, + "train_size": 12053, + "val_size": 3014, + "test_size": 3767, + "n_num_features": 4, + "n_cat_features": 5 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..3e0616429579cbb75f15467fd572c2b0a300bb9e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan/config.toml @@ -0,0 +1,32 @@ +parent_dir = "exp/buddy/ctabgan" +real_data_path = "data/buddy/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 8.278523938127701e-05 +epochs = 10000 +class_dim = [ + 128, + 512, +] +batch_size = 2048 +random_dim = 128 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 96000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..36cfb305cc01c3f0c21bcc849f220f4f8c69a66c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8732, + "acc-std": 0.0037, + "f1-count": 50.0, + "f1-mean": 0.847, + "f1-std": 0.0092 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8724, + "acc-std": 0.002, + "f1-count": 50.0, + "f1-mean": 0.8552, + "f1-std": 0.0053 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..7d1c2c29f22e4b9f0af6788545ce80a2bd93a77e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8261, + "acc-std": 0.006, + "f1-count": 10.0, + "f1-mean": 0.7786, + "f1-std": 0.0116 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.823, + "acc-std": 0.0081, + "f1-count": 10.0, + "f1-mean": 0.7846, + "f1-std": 0.0116 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7118, + "acc-std": 0.0033, + "f1-count": 10.0, + "f1-mean": 0.6647, + "f1-std": 0.0036 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7073, + "acc-std": 0.0032, + "f1-count": 10.0, + "f1-mean": 0.6746, + "f1-std": 0.0039 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8595, + "acc-std": 0.0041, + "f1-count": 10.0, + "f1-mean": 0.8219, + "f1-std": 0.0074 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8626, + "acc-std": 0.0043, + "f1-count": 10.0, + "f1-mean": 0.8373, + "f1-std": 0.0069 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8609, + "acc-std": 0.004, + "f1-count": 10.0, + "f1-mean": 0.8319, + "f1-std": 0.0136 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8637, + "acc-std": 0.0028, + "f1-count": 10.0, + "f1-mean": 0.8464, + "f1-std": 0.0056 + } + }, + "avg": { + "val": { + "f1-mean": 0.7743, + "acc-mean": 0.8146, + "f1-std": 0.009 + }, + "test": { + "f1-mean": 0.7857, + "acc-mean": 0.8142, + "f1-std": 0.008 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan/info.json new file mode 100644 index 0000000000000000000000000000000000000000..cc38a53a90b267fd7a3070ba9c6e8fffe316566a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan/info.json @@ -0,0 +1,11 @@ +{ + "task_type": "multiclass", + "name": "buddy", + "id": "buddy--id", + "n_classes": 3, + "train_size": 12053, + "val_size": 3014, + "test_size": 3767, + "n_num_features": 4, + "n_cat_features": 5 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..9ad62a317a48ba07d028fe93b379d00b09807c1e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ctabgan/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.8552, + 0.852, + 0.8542, + 0.853, + 0.8539 + ], + "privacies": [ + 0.23691120816139127, + 0.2348, + 0.2335, + 0.2338, + 0.2342 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..0d123eefe92fe8aa0cd8737d19cfc01e5f0ed167 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/config.toml @@ -0,0 +1,56 @@ +seed = 0 +parent_dir = "exp/buddy/ddpm_cb_best" +real_data_path = "data/buddy/" +model_type = "mlp" +num_numerical_features = 4 +device = "cuda:0" + +[model_params] +num_classes = 3 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 1024, + 512, + 512, + 512, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.0004168262823315286 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 48000 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..89bbcd985e1d8f5191b9b86c0b2ef28b1d3205e2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/eval_catboost.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.9025, + "acc-std": 0.0017, + "f1-count": 50.0, + "f1-mean": 0.913, + "f1-std": 0.0026 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8944, + "acc-std": 0.002, + "f1-count": 50.0, + "f1-mean": 0.9057, + "f1-std": 0.0029 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9034, + "acc-std": 0.0012, + "f1-count": 10.0, + "f1-mean": 0.9164, + "f1-std": 0.0014 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8922, + "acc-std": 0.0022, + "f1-count": 10.0, + "f1-mean": 0.9063, + "f1-std": 0.0018 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..6210a32e3b321ff7862e6d5d58f1ffbbefe6ec6e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/eval_mlp.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8965, + "acc-std": 0.0028, + "f1-count": 50.0, + "f1-mean": 0.9057, + "f1-std": 0.003 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.891, + "acc-std": 0.0026, + "f1-count": 50.0, + "f1-mean": 0.9035, + "f1-std": 0.0027 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8948, + "acc-std": 0.0033, + "f1-count": 10.0, + "f1-mean": 0.9033, + "f1-std": 0.0035 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8946, + "acc-std": 0.0023, + "f1-count": 10.0, + "f1-mean": 0.9048, + "f1-std": 0.0025 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..289f63264daecf8908d2fe390e8aea8f1eb0e0e7 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/eval_simple.json @@ -0,0 +1,174 @@ +{ + "real": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8823, + "acc-std": 0.0012, + "f1-count": 10.0, + "f1-mean": 0.8925, + "f1-std": 0.001 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.878, + "acc-std": 0.0014, + "f1-count": 10.0, + "f1-mean": 0.8899, + "f1-std": 0.0015 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7571, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6977, + "f1-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7582, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.7173, + "f1-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8942, + "acc-std": 0.0023, + "f1-count": 10.0, + "f1-mean": 0.9079, + "f1-std": 0.0016 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8905, + "acc-std": 0.0019, + "f1-count": 10.0, + "f1-mean": 0.9023, + "f1-std": 0.0021 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8782, + "acc-std": 0.006, + "f1-count": 10.0, + "f1-mean": 0.8691, + "f1-std": 0.0196 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8759, + "acc-std": 0.0048, + "f1-count": 10.0, + "f1-mean": 0.8712, + "f1-std": 0.0122 + } + }, + "avg": { + "val": { + "f1-mean": 0.8418, + "acc-mean": 0.853, + "f1-std": 0.0055 + }, + "test": { + "f1-mean": 0.8452, + "acc-mean": 0.8506, + "f1-std": 0.0037 + } + } + }, + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8782, + "acc-std": 0.0014, + "f1-count": 10.0, + "f1-mean": 0.8947, + "f1-std": 0.0011 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.876, + "acc-std": 0.0007, + "f1-count": 10.0, + "f1-mean": 0.8852, + "f1-std": 0.0007 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7601, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.7137, + "f1-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.759, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.7267, + "f1-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8919, + "acc-std": 0.0013, + "f1-count": 10.0, + "f1-mean": 0.906, + "f1-std": 0.0019 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8915, + "acc-std": 0.0018, + "f1-count": 10.0, + "f1-mean": 0.9037, + "f1-std": 0.0019 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8833, + "acc-std": 0.0025, + "f1-count": 10.0, + "f1-mean": 0.8866, + "f1-std": 0.0035 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8823, + "acc-std": 0.0045, + "f1-count": 10.0, + "f1-mean": 0.8882, + "f1-std": 0.0038 + } + }, + "avg": { + "val": { + "f1-mean": 0.8502, + "acc-mean": 0.8534, + "f1-std": 0.0036 + }, + "test": { + "f1-mean": 0.851, + "acc-mean": 0.8522, + "f1-std": 0.0032 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..8209ce7c6b09c363298deeca385c95672b458944 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/importance.json @@ -0,0 +1,10 @@ +{ + "lr": 0.9595029501329746, + "num_samples": 0.02010229079026811, + "n_layers": 0.008782069012607469, + "steps": 0.007199003463528995, + "batch_size": 0.003656637013729657, + "d_first": 0.0003368742113143316, + "num_timesteps": 0.00025998090481841766, + "d_last": 0.00016019447075843346 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..cc38a53a90b267fd7a3070ba9c6e8fffe316566a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/info.json @@ -0,0 +1,11 @@ +{ + "task_type": "multiclass", + "name": "buddy", + "id": "buddy--id", + "n_classes": 3, + "train_size": 12053, + "val_size": 3014, + "test_size": 3767, + "n_num_features": 4, + "n_cat_features": 5 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..5ad3a8be0a3c1c1d38f134d0dcd986438da94b45 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.9057, + 0.9073, + 0.9056, + 0.9062, + 0.9037 + ], + "privacies": [ + 0.14252413095758615, + 0.1513, + 0.162, + 0.1749, + 0.1886 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..92bc3d8331ace74b0d9b5299224038e9fa26ccd9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_cb_best/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.1429755682714082 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_mlp_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_mlp_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..1d9ad8d8b06a469d9597771c65143005eb8bf67b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_mlp_best/config.toml @@ -0,0 +1,60 @@ +seed = 0 +parent_dir = "exp/buddy/ddpm_mlp_best" +real_data_path = "data/buddy/" +model_type = "mlp" +num_numerical_features = 4 +device = "cuda:0" + +[model_params] +num_classes = 3 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 1024, + 1024, + 1024, + 1024, + 1024, + 1024, + 1024, + 128, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.0002795654837714865 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 24000 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "mlp" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "one-hot" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_mlp_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_mlp_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..94bf8780fac6486c117a880fceaa9881e49ce0d2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_mlp_best/eval_mlp.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8936, + "acc-std": 0.0036, + "f1-count": 50.0, + "f1-mean": 0.9023, + "f1-std": 0.0031 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8908, + "acc-std": 0.0024, + "f1-count": 50.0, + "f1-mean": 0.9007, + "f1-std": 0.0026 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_mlp_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_mlp_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..bd0eeff304d83cb5448cc8a659a18429140910ef --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_mlp_best/importance.json @@ -0,0 +1,10 @@ +{ + "lr": 0.5136885316189886, + "steps": 0.20326687211830372, + "num_samples": 0.09579295920892607, + "n_layers": 0.09071103137696178, + "num_timesteps": 0.03528353381307014, + "d_last": 0.02565653391459133, + "batch_size": 0.020741174566248285, + "d_first": 0.014859363382910057 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_mlp_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_mlp_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..cc38a53a90b267fd7a3070ba9c6e8fffe316566a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/ddpm_mlp_best/info.json @@ -0,0 +1,11 @@ +{ + "task_type": "multiclass", + "name": "buddy", + "id": "buddy--id", + "n_classes": 3, + "train_size": 12053, + "val_size": 3014, + "test_size": 3767, + "n_num_features": 4, + "n_cat_features": 5 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..be3167897c4ef2cdc9c2e6220335ae39aba98386 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/config.toml @@ -0,0 +1,24 @@ +parent_dir = "exp/buddy/smote" +real_data_path = "data/buddy/" +seed = 0 + +[smote_params] +k_neighbours = 5 +frac_samples = 2 +frac_lam_del = 0.5 + +[sample] +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..646ada992380f0e6b52a13b3b2fdae4b38ec91f3 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.895, + "acc-std": 0.0013, + "f1-count": 50.0, + "f1-mean": 0.9014, + "f1-std": 0.0037 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8863, + "acc-std": 0.0026, + "f1-count": 50.0, + "f1-mean": 0.8906, + "f1-std": 0.0034 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..e58a9f589c9ce8a43856ba7746f1f0855af9cec0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.874, + "acc-std": 0.0015, + "f1-count": 10.0, + "f1-mean": 0.8765, + "f1-std": 0.0019 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8752, + "acc-std": 0.0014, + "f1-count": 10.0, + "f1-mean": 0.8797, + "f1-std": 0.0019 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7575, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.729, + "f1-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7587, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.7421, + "f1-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8822, + "acc-std": 0.0024, + "f1-count": 10.0, + "f1-mean": 0.8867, + "f1-std": 0.0024 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8819, + "acc-std": 0.002, + "f1-count": 10.0, + "f1-mean": 0.885, + "f1-std": 0.0023 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8748, + "acc-std": 0.0039, + "f1-count": 10.0, + "f1-mean": 0.8701, + "f1-std": 0.0063 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8647, + "acc-std": 0.0046, + "f1-count": 10.0, + "f1-mean": 0.8593, + "f1-std": 0.0061 + } + }, + "avg": { + "val": { + "f1-mean": 0.8406, + "acc-mean": 0.8471, + "f1-std": 0.0026 + }, + "test": { + "f1-mean": 0.8415, + "acc-mean": 0.8451, + "f1-std": 0.0026 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/info.json new file mode 100644 index 0000000000000000000000000000000000000000..cc38a53a90b267fd7a3070ba9c6e8fffe316566a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/info.json @@ -0,0 +1,11 @@ +{ + "task_type": "multiclass", + "name": "buddy", + "id": "buddy--id", + "n_classes": 3, + "train_size": 12053, + "val_size": 3014, + "test_size": 3767, + "n_num_features": 4, + "n_cat_features": 5 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..38ef492a5632cea961e543f9080b7f5458e1edce --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.8906, + 0.8962, + 0.8955, + 0.8935, + 0.8948 + ], + "privacies": [ + 0.0544910939310287, + 0.0607, + 0.067, + 0.0739, + 0.0826 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..4a3c8f88f05ed2bf2e025b349e6c2363fd41e2ec --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/smote/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.0544910939310287 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/tvae/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/tvae/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..c919e3ced783950cffe0d35e5eb858987dade403 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/tvae/config.toml @@ -0,0 +1,40 @@ +parent_dir = "exp/buddy/tvae" +real_data_path = "data/buddy/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.0016961264607506202 +epochs = 20000 +embedding_dim = 1024 +batch_size = 256 +loss_factor = 6.998471329724449 +compress_dims = [ + 256, + 64, + 64, + 256, +] +decompress_dims = [ + 256, + 64, + 64, + 256, +] + +[sample] +seed = 0 +num_samples = 6000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/tvae/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/tvae/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..d30c9c5aa4b276345f432ecd7ecba1693c37a648 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/tvae/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8849, + "acc-std": 0.0027, + "f1-count": 50.0, + "f1-mean": 0.8779, + "f1-std": 0.0053 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8714, + "acc-std": 0.0039, + "f1-count": 50.0, + "f1-mean": 0.8638, + "f1-std": 0.0055 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/tvae/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/tvae/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..500aac7c960cc366102dc540287592d58418d2dd --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/tvae/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8688, + "acc-std": 0.0017, + "f1-count": 10.0, + "f1-mean": 0.8604, + "f1-std": 0.0043 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8621, + "acc-std": 0.0012, + "f1-count": 10.0, + "f1-mean": 0.8513, + "f1-std": 0.0021 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7246, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6182, + "f1-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7053, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.5721, + "f1-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8748, + "acc-std": 0.0021, + "f1-count": 10.0, + "f1-mean": 0.8728, + "f1-std": 0.0032 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.868, + "acc-std": 0.0021, + "f1-count": 10.0, + "f1-mean": 0.8666, + "f1-std": 0.0028 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8601, + "acc-std": 0.004, + "f1-count": 10.0, + "f1-mean": 0.8264, + "f1-std": 0.0086 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.851, + "acc-std": 0.0055, + "f1-count": 10.0, + "f1-mean": 0.8263, + "f1-std": 0.0093 + } + }, + "avg": { + "val": { + "f1-mean": 0.7944, + "acc-mean": 0.8321, + "f1-std": 0.004 + }, + "test": { + "f1-mean": 0.7791, + "acc-mean": 0.8216, + "f1-std": 0.0038 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/tvae/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/tvae/info.json new file mode 100644 index 0000000000000000000000000000000000000000..cc38a53a90b267fd7a3070ba9c6e8fffe316566a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/buddy/tvae/info.json @@ -0,0 +1,11 @@ +{ + "task_type": "multiclass", + "name": "buddy", + "id": "buddy--id", + "n_classes": 3, + "train_size": 12053, + "val_size": 3014, + "test_size": 3767, + "n_num_features": 4, + "n_cat_features": 5 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..7627464f8886fda49c913a8ad9e511b5f1c87917 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/config.toml @@ -0,0 +1,56 @@ +seed = 0 +parent_dir = "exp/california/check" +real_data_path = "data/california/" +num_numerical_features = 8 +model_type = "mlp" +device = "cuda:1" + +[model_params] +d_in = 9 +num_classes = 0 +is_y_cond = false + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 52800 +batch_size = 8192 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ctabgan-plus/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ctabgan-plus/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..1dbb8e9927bcd4aff4960ed3563b90f7a6fdb114 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ctabgan-plus/config.toml @@ -0,0 +1,33 @@ +parent_dir = "exp/california/ctabgan-plus" +real_data_path = "data/california/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.0002455666078405669 +epochs = 10000 +class_dim = [ + 64, + 64, + 64, +] +batch_size = 2048 +random_dim = 32 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 26400 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ctabgan-plus/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ctabgan-plus/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..1a0f7615d9f340620c397e17d12b874cc5e4f48b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ctabgan-plus/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.5262, + "r2-std": 0.0028, + "rmse-count": 50.0, + "rmse-mean": 0.786, + "rmse-std": 0.0023 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.5247, + "r2-std": 0.0044, + "rmse-count": 50.0, + "rmse-mean": 0.7873, + "rmse-std": 0.0036 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ctabgan-plus/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ctabgan-plus/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..e0e474251ac62ca7ca20af12f3ca915cce657d46 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ctabgan-plus/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.232, + "r2-std": 0.0186, + "rmse-count": 50.0, + "rmse-mean": 1.0006, + "rmse-std": 0.0121 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.2299, + "r2-std": 0.0123, + "rmse-count": 50.0, + "rmse-mean": 1.002, + "rmse-std": 0.008 + } + }, + "lr": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.3709, + "r2-std": 0.0066, + "rmse-count": 50.0, + "rmse-mean": 0.9057, + "rmse-std": 0.0048 + }, + "test": { + "r2-count": 50.0, + "r2-mean": -26.1383, + "r2-std": 0.6044, + "rmse-count": 50.0, + "rmse-mean": 5.9483, + "rmse-std": 0.0664 + } + }, + "rf": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.5114, + "r2-std": 0.0052, + "rmse-count": 50.0, + "rmse-mean": 0.7981, + "rmse-std": 0.0042 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.5076, + "r2-std": 0.0035, + "rmse-count": 50.0, + "rmse-mean": 0.8013, + "rmse-std": 0.0028 + } + }, + "mlp": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.4666, + "r2-std": 0.0592, + "rmse-count": 50.0, + "rmse-mean": 0.8327, + "rmse-std": 0.0458 + }, + "test": { + "r2-count": 50.0, + "r2-mean": -4.9554, + "r2-std": 4.4482, + "rmse-count": 50.0, + "rmse-mean": 2.5815, + "rmse-std": 1.0494 + } + }, + "avg": { + "val": { + "r2-mean": 0.3952, + "rmse-mean": 0.8843, + "r2-std": 0.0224 + }, + "test": { + "r2-mean": -7.589, + "rmse-mean": 2.5833, + "r2-std": 0.6447 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ctabgan-plus/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ctabgan-plus/info.json new file mode 100644 index 0000000000000000000000000000000000000000..627ecbc198b3283c98199cb9117e6613d46193bc --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ctabgan-plus/info.json @@ -0,0 +1,10 @@ +{ + "name": "California Housing", + "id": "california--default", + "task_type": "regression", + "n_num_features": 8, + "n_cat_features": 0, + "train_size": 13209, + "val_size": 3303, + "test_size": 4128 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..801a2a944aa9acc55f12517588fd99076a059495 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/config.toml @@ -0,0 +1,59 @@ +seed = 0 +parent_dir = "exp/california/ddpm_cb_best" +real_data_path = "data/california/" +num_numerical_features = 8 +model_type = "mlp" +device = "cuda:1" + +[model_params] +d_in = 9 +num_classes = 0 +is_y_cond = false + +[model_params.rtdl_params] +d_layers = [ + 512, + 256, + 256, + 256, + 256, + 128, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.0013275991211473216 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 52800 +batch_size = 8192 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..b329aa34e561ba1f47bc974d8084d6f1e2a4df6e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/eval_catboost.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8304, + "r2-std": 0.0016, + "rmse-count": 50.0, + "rmse-mean": 0.4702, + "rmse-std": 0.0022 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.8362, + "r2-std": 0.0015, + "rmse-count": 50.0, + "rmse-mean": 0.4621, + "rmse-std": 0.0021 + } + }, + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8535, + "r2-std": 0.0013, + "rmse-count": 10.0, + "rmse-mean": 0.437, + "rmse-std": 0.002 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.8568, + "r2-std": 0.0013, + "rmse-count": 10.0, + "rmse-mean": 0.4321, + "rmse-std": 0.002 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..38ed62c608a69c0b8276919ffce11a14a0f7974e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/eval_mlp.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8128, + "r2-std": 0.003, + "rmse-count": 50.0, + "rmse-mean": 0.494, + "rmse-std": 0.004 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.8087, + "r2-std": 0.0031, + "rmse-count": 50.0, + "rmse-mean": 0.4994, + "rmse-std": 0.004 + } + }, + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8115, + "r2-std": 0.0025, + "rmse-count": 10.0, + "rmse-mean": 0.4957, + "rmse-std": 0.0033 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.8078, + "r2-std": 0.0017, + "rmse-count": 10.0, + "rmse-mean": 0.5007, + "rmse-std": 0.0022 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..22812fbf730ace63fb101800532a9fe3ef0d113c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/eval_simple.json @@ -0,0 +1,174 @@ +{ + "real": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.6093, + "r2-std": 0.0046, + "rmse-count": 10.0, + "rmse-mean": 0.7137, + "rmse-std": 0.0042 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.5745, + "r2-std": 0.0056, + "rmse-count": 10.0, + "rmse-mean": 0.7449, + "rmse-std": 0.0049 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.6168, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 0.7069, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.5911, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 0.7302, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8055, + "r2-std": 0.0014, + "rmse-count": 10.0, + "rmse-mean": 0.5036, + "rmse-std": 0.0018 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.7928, + "r2-std": 0.0007, + "rmse-count": 10.0, + "rmse-mean": 0.5198, + "rmse-std": 0.0009 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.6971, + "r2-std": 0.0048, + "rmse-count": 10.0, + "rmse-mean": 0.6285, + "rmse-std": 0.005 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.6928, + "r2-std": 0.0037, + "rmse-count": 10.0, + "rmse-mean": 0.6329, + "rmse-std": 0.0038 + } + }, + "avg": { + "val": { + "r2-mean": 0.6822, + "rmse-mean": 0.6382, + "r2-std": 0.0027 + }, + "test": { + "r2-mean": 0.6628, + "rmse-mean": 0.6569, + "r2-std": 0.0024 + } + } + }, + "synthetic": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.6622, + "r2-std": 0.0027, + "rmse-count": 10.0, + "rmse-mean": 0.6637, + "rmse-std": 0.0027 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.627, + "r2-std": 0.0031, + "rmse-count": 10.0, + "rmse-mean": 0.6974, + "rmse-std": 0.0029 + } + }, + "lr": { + "val": { + 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"rmse-mean": 0.6236, + "r2-std": 0.0024 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..b2656793632f8ac4c73da626db6d32a11078345e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/importance.json @@ -0,0 +1,10 @@ +{ + "batch_size": 0.32456210174469097, + "steps": 0.23822242784658107, + "lr": 0.22000148541282327, + "num_samples": 0.07117122783203195, + "d_first": 0.0625980215283031, + "n_layers": 0.034560949247648, + "num_timesteps": 0.031103380463402802, + "d_last": 0.01778040592451887 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..627ecbc198b3283c98199cb9117e6613d46193bc --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/info.json @@ -0,0 +1,10 @@ +{ + "name": "California Housing", + "id": "california--default", + "task_type": "regression", + "n_num_features": 8, + "n_cat_features": 0, + "train_size": 13209, + "val_size": 3303, + "test_size": 4128 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..69ccccc9254fe88b1a547a83c518c6335c22c049 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.8362, + 0.8361, + 0.8367, + 0.8362, + 0.8387 + ], + "privacies": [ + 0.04049266142086302, + 0.0408, + 0.0415, + 0.043, + 0.0451 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..b0052308547989f154f0fe7ec24f1bc09d7bdd59 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_cb_best/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.04060183504124948 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_mlp_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_mlp_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..bcdb2668d23fabec59b8ab0b66055581cb04131f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_mlp_best/config.toml @@ -0,0 +1,61 @@ +seed = 0 +parent_dir = "exp/california/ddpm_mlp_best" +real_data_path = "data/california/" +num_numerical_features = 8 +model_type = "mlp" +device = "cuda:1" + +[model_params] +d_in = 9 +num_classes = 0 +is_y_cond = false + +[model_params.rtdl_params] +d_layers = [ + 128, + 256, + 256, + 256, + 256, + 256, + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 20000 +lr = 0.0028386514632022476 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 26400 +batch_size = 8192 +seed = 0 + +[eval.type] +eval_model = "mlp" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "one-hot" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_mlp_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_mlp_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..4c9b7c755e36d144d209242758c37104bc62b061 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_mlp_best/eval_catboost.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8307, + "r2-std": 0.0016, + "rmse-count": 50.0, + "rmse-mean": 0.4699, + "rmse-std": 0.0022 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.8323, + "r2-std": 0.0024, + "rmse-count": 50.0, + "rmse-mean": 0.4675, + "rmse-std": 0.0033 + } + }, + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8536, + "r2-std": 0.0011, + "rmse-count": 10.0, + "rmse-mean": 0.4368, + "rmse-std": 0.0016 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.8568, + "r2-std": 0.0012, + "rmse-count": 10.0, + "rmse-mean": 0.4322, + "rmse-std": 0.0018 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_mlp_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_mlp_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..0d59cc764bb99ce02d2706a0ff1a75b3a56b9712 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_mlp_best/eval_mlp.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8078, + "r2-std": 0.0032, + "rmse-count": 50.0, + "rmse-mean": 0.5006, + "rmse-std": 0.0041 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.8032, + "r2-std": 0.004, + "rmse-count": 50.0, + "rmse-mean": 0.5066, + "rmse-std": 0.0051 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_mlp_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_mlp_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..6211083594e5e471d57d24a982cc8f6484a890ee --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_mlp_best/importance.json @@ -0,0 +1,10 @@ +{ + "num_samples": 0.3549933804851517, + "steps": 0.3493652949676025, + "lr": 0.15943043925612294, + "num_timesteps": 0.043284372644798244, + "n_layers": 0.024830901137310634, + "d_last": 0.023923706887466343, + "d_first": 0.022267430752231445, + "batch_size": 0.021904473869316265 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_mlp_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_mlp_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..627ecbc198b3283c98199cb9117e6613d46193bc --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/ddpm_mlp_best/info.json @@ -0,0 +1,10 @@ +{ + "name": "California Housing", + "id": "california--default", + "task_type": "regression", + "n_num_features": 8, + "n_cat_features": 0, + "train_size": 13209, + "val_size": 3303, + "test_size": 4128 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..fd1ea171f5a0f55e499e68dfecb0ad46bcaf98ea --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/config.toml @@ -0,0 +1,24 @@ +parent_dir = "exp/california/smote" +real_data_path = "data/california/" +seed = 0 + +[smote_params] +k_neighbours = 8 +frac_samples = 8 +frac_lam_del = 0.0 + +[sample] +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..92d31871c953c8570f07939b179598eb576d109e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8366, + "r2-std": 0.0013, + "rmse-count": 50.0, + "rmse-mean": 0.4616, + "rmse-std": 0.0018 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.8397, + "r2-std": 0.0011, + "rmse-count": 50.0, + "rmse-mean": 0.4572, + "rmse-std": 0.0015 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..21c534410a4819f67fbec1202aed4cc0470708d5 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/eval_mlp.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8103, + "r2-std": 0.0028, + "rmse-count": 50.0, + "rmse-mean": 0.4973, + "rmse-std": 0.0037 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.8028, + "r2-std": 0.0037, + "rmse-count": 50.0, + "rmse-mean": 0.507, + "rmse-std": 0.0047 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..8117d205aed018ebd18747c4745aec0422003c07 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.6119, + "r2-std": 0.005, + "rmse-count": 10.0, + "rmse-mean": 0.7114, + "rmse-std": 0.0046 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.5911, + "r2-std": 0.0051, + "rmse-count": 10.0, + "rmse-mean": 0.7302, + "rmse-std": 0.0046 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.604, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 0.7186, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.5275, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 0.7849, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7935, + "r2-std": 0.0007, + "rmse-count": 10.0, + "rmse-mean": 0.5189, + "rmse-std": 0.0009 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.7894, + "r2-std": 0.0011, + "rmse-count": 10.0, + "rmse-mean": 0.524, + "rmse-std": 0.0013 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7431, + "r2-std": 0.0243, + "rmse-count": 10.0, + "rmse-mean": 0.5781, + "rmse-std": 0.0275 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.7613, + "r2-std": 0.0103, + "rmse-count": 10.0, + "rmse-mean": 0.5578, + "rmse-std": 0.012 + } + }, + "avg": { + "val": { + "r2-mean": 0.6881, + "rmse-mean": 0.6318, + "r2-std": 0.0075 + }, + "test": { + "r2-mean": 0.6673, + "rmse-mean": 0.6492, + "r2-std": 0.0058 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/info.json new file mode 100644 index 0000000000000000000000000000000000000000..627ecbc198b3283c98199cb9117e6613d46193bc --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/info.json @@ -0,0 +1,10 @@ +{ + "name": "California Housing", + "id": "california--default", + "task_type": "regression", + "n_num_features": 8, + "n_cat_features": 0, + "train_size": 13209, + "val_size": 3303, + "test_size": 4128 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..8099c0bbfef3fa8bf3a3ca59a915375f8c461a8a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.8397, + 0.8395, + 0.8394, + 0.8402, + 0.8397 + ], + "privacies": [ + 0.013630221409740444, + 0.0135, + 0.0135, + 0.0136, + 0.0136 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..fd52dc120d8890b9ab89509156f6841a4957434d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/smote/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.013608826080114319 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/tvae/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/tvae/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..b09858703b891abf43ab565b34c4f989c2c929c4 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/tvae/config.toml @@ -0,0 +1,40 @@ +parent_dir = "exp/california/tvae" +real_data_path = "data/california/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.0011942868314382704 +epochs = 20000 +embedding_dim = 128 +batch_size = 256 +loss_factor = 3.3878447780232825 +compress_dims = [ + 512, + 512, + 512, + 512, +] +decompress_dims = [ + 512, + 512, + 512, + 512, +] + +[sample] +seed = 0 +num_samples = 26400 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/tvae/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/tvae/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..3f591ebe45c0bf5d4d81178573914f11a2179813 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/tvae/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.7531, + "r2-std": 0.0022, + "rmse-count": 50.0, + "rmse-mean": 0.5674, + "rmse-std": 0.0026 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.7518, + "r2-std": 0.0012, + "rmse-count": 50.0, + "rmse-mean": 0.5689, + "rmse-std": 0.0014 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/tvae/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/tvae/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..6e621e240ea0a5a168b9a377df360e2d1d02f2c1 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/tvae/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.4293, + "r2-std": 0.0123, + "rmse-count": 50.0, + "rmse-mean": 0.8625, + "rmse-std": 0.0093 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.4113, + "r2-std": 0.0148, + "rmse-count": 50.0, + "rmse-mean": 0.8761, + "rmse-std": 0.0109 + } + }, + "lr": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.606, + "r2-std": 0.0019, + "rmse-count": 50.0, + "rmse-mean": 0.7167, + "rmse-std": 0.0018 + }, + "test": { + "r2-count": 50.0, + "r2-mean": -45.6459, + "r2-std": 1.5105, + "rmse-count": 50.0, + "rmse-mean": 7.798, + "rmse-std": 0.1254 + } + }, + "rf": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.738, + "r2-std": 0.0035, + "rmse-count": 50.0, + "rmse-mean": 0.5845, + "rmse-std": 0.0039 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.7308, + "r2-std": 0.0032, + "rmse-count": 50.0, + "rmse-mean": 0.5925, + "rmse-std": 0.0035 + } + }, + "mlp": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.5813, + "r2-std": 0.0734, + "rmse-count": 50.0, + "rmse-mean": 0.7361, + "rmse-std": 0.0641 + }, + "test": { + "r2-count": 50.0, + "r2-mean": -7.7172, + "r2-std": 10.4556, + "rmse-count": 50.0, + "rmse-mean": 2.8385, + "rmse-std": 1.8193 + } + }, + "avg": { + "val": { + "r2-mean": 0.5886, + "rmse-mean": 0.725, + "r2-std": 0.0228 + }, + "test": { + "r2-mean": -13.0552, + "rmse-mean": 3.0263, + "r2-std": 1.5094 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/tvae/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/tvae/info.json new file mode 100644 index 0000000000000000000000000000000000000000..627ecbc198b3283c98199cb9117e6613d46193bc --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/california/tvae/info.json @@ -0,0 +1,10 @@ +{ + "name": "California Housing", + "id": "california--default", + "task_type": "regression", + "n_num_features": 8, + "n_cat_features": 0, + "train_size": 13209, + "val_size": 3303, + "test_size": 4128 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..cd31ab0f8d4f3734f0285df9ce913855a7a2ede0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/config.toml @@ -0,0 +1,55 @@ +parent_dir = "exp/cardio/check" +real_data_path = "data/cardio/" +num_numerical_features = 5 +device = "cuda:0" +model_type = "mlp" +seed = 0 + +[model_params] +is_y_cond = true +num_classes = 2 + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 360000 +batch_size = 20000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan-plus/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan-plus/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..01d7b07b7cc50c178704eb68ea4c052fca2978c2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan-plus/config.toml @@ -0,0 +1,33 @@ +parent_dir = "exp/cardio/ctabgan-plus" +real_data_path = "data/cardio/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.00026249016506081624 +epochs = 10000 +class_dim = [ + 64, + 128, + 128, +] +batch_size = 2048 +random_dim = 32 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 90000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan-plus/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan-plus/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..cfb0b0eb4f61b8466f443aeafd71481ded5cdf37 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan-plus/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7337, + "acc-std": 0.0008, + "f1-count": 50.0, + "f1-mean": 0.7333, + "f1-std": 0.0009, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7979, + "roc_auc-std": 0.0008 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7331, + "acc-std": 0.0012, + "f1-count": 50.0, + "f1-mean": 0.7327, + "f1-std": 0.0012, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8018, + "roc_auc-std": 0.0009 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan-plus/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan-plus/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..ac9d60074dc6fc0f47d76b538e33557f0aaa7e58 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan-plus/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.6449, + "acc-std": 0.0052, + "f1-count": 50.0, + "f1-mean": 0.6446, + "f1-std": 0.0052, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6358, + "roc_auc-std": 0.0072 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.6489, + "acc-std": 0.0042, + "f1-count": 50.0, + "f1-mean": 0.6487, + "f1-std": 0.0042, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6396, + "roc_auc-std": 0.0039 + } + }, + "lr": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7231, + "acc-std": 0.0004, + "f1-count": 50.0, + "f1-mean": 0.7223, + "f1-std": 0.0004, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.788, + "roc_auc-std": 0.0002 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7279, + "acc-std": 0.0003, + "f1-count": 50.0, + "f1-mean": 0.7271, + "f1-std": 0.0003, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7926, + "roc_auc-std": 0.0003 + } + }, + "rf": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7185, + "acc-std": 0.0031, + "f1-count": 50.0, + "f1-mean": 0.7182, + "f1-std": 0.0031, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7817, + "roc_auc-std": 0.0015 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7211, + "acc-std": 0.0029, + "f1-count": 50.0, + "f1-mean": 0.7208, + "f1-std": 0.0028, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7844, + "roc_auc-std": 0.0017 + } + }, + "mlp": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7312, + "acc-std": 0.0013, + "f1-count": 50.0, + "f1-mean": 0.7309, + "f1-std": 0.0014, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7956, + "roc_auc-std": 0.0007 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7339, + "acc-std": 0.0015, + "f1-count": 50.0, + "f1-mean": 0.7335, + "f1-std": 0.0016, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7989, + "roc_auc-std": 0.0006 + } + }, + "avg": { + "val": { + "f1-mean": 0.704, + "acc-mean": 0.7044, + "f1-std": 0.0025 + }, + "test": { + "f1-mean": 0.7075, + "acc-mean": 0.708, + "f1-std": 0.0024 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan-plus/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan-plus/info.json new file mode 100644 index 0000000000000000000000000000000000000000..3e0e85a4920766ee7325ebfe8ac651cb032cec70 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan-plus/info.json @@ -0,0 +1,10 @@ +{ + "name": "Cardio", + "id": "cardio--default", + "task_type": "binclass", + "n_num_features": 5, + "n_cat_features": 6, + "test_size": 11200, + "train_size": 44800, + "val_size": 14000 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..ff3808914c81dd43440b46c96f9e3f74886af092 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan/config.toml @@ -0,0 +1,34 @@ +parent_dir = "exp/cardio/ctabgan" +real_data_path = "data/cardio/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.0006525158632656171 +epochs = 10000 +class_dim = [ + 512, + 512, + 512, + 128, +] +batch_size = 2048 +random_dim = 128 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 180000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..8c87e1040831f2951934b514548b9cf25f99d70a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7211, + "acc-std": 0.0007, + "f1-count": 50.0, + "f1-mean": 0.7207, + "f1-std": 0.0007, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7781, + "roc_auc-std": 0.0009 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7176, + "acc-std": 0.0008, + "f1-count": 50.0, + "f1-mean": 0.7171, + "f1-std": 0.0009, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7777, + "roc_auc-std": 0.0008 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..695076ae4830e0cc4ba6e958dd35a276c6e0f937 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6095, + "acc-std": 0.0044, + "f1-count": 10.0, + "f1-mean": 0.6093, + "f1-std": 0.0045, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6108, + "roc_auc-std": 0.0036 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6105, + "acc-std": 0.0065, + "f1-count": 10.0, + "f1-mean": 0.6102, + "f1-std": 0.0066, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6117, + "roc_auc-std": 0.0061 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7219, + "acc-std": 0.0003, + "f1-count": 10.0, + "f1-mean": 0.7219, + "f1-std": 0.0003, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7829, + "roc_auc-std": 0.0005 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7234, + "acc-std": 0.0006, + "f1-count": 10.0, + "f1-mean": 0.7234, + "f1-std": 0.0006, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7834, + "roc_auc-std": 0.0005 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6889, + "acc-std": 0.0037, + "f1-count": 10.0, + "f1-mean": 0.6889, + "f1-std": 0.0037, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7524, + "roc_auc-std": 0.0027 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6891, + "acc-std": 0.0051, + "f1-count": 10.0, + "f1-mean": 0.6891, + "f1-std": 0.0051, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7512, + "roc_auc-std": 0.0021 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7157, + "acc-std": 0.0016, + "f1-count": 10.0, + "f1-mean": 0.7146, + "f1-std": 0.002, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7765, + "roc_auc-std": 0.0018 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7147, + "acc-std": 0.0018, + "f1-count": 10.0, + "f1-mean": 0.7136, + "f1-std": 0.0021, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.777, + "roc_auc-std": 0.0017 + } + }, + "avg": { + "val": { + "f1-mean": 0.6837, + "acc-mean": 0.684, + "f1-std": 0.0026 + }, + "test": { + "f1-mean": 0.6841, + "acc-mean": 0.6844, + "f1-std": 0.0031 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan/info.json new file mode 100644 index 0000000000000000000000000000000000000000..3e0e85a4920766ee7325ebfe8ac651cb032cec70 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ctabgan/info.json @@ -0,0 +1,10 @@ +{ + "name": "Cardio", + "id": "cardio--default", + "task_type": "binclass", + "n_num_features": 5, + "n_cat_features": 6, + "test_size": 11200, + "train_size": 44800, + "val_size": 14000 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..6de24beb55e5f746b9a8462ee1c172fdc8c65070 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/config.toml @@ -0,0 +1,58 @@ +parent_dir = "exp/cardio/ddpm_cb_best" +real_data_path = "data/cardio/" +num_numerical_features = 5 +device = "cuda:0" +model_type = "mlp" +seed = 0 + +[model_params] +is_y_cond = true +num_classes = 2 + +[model_params.rtdl_params] +d_layers = [ + 512, + 1024, + 1024, + 1024, + 1024, + 1024, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.0005642428536156398 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 360000 +batch_size = 20000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..737e744881d8aa4ccf22f0a3b5e649f3464b9150 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/eval_catboost.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7361, + "acc-std": 0.0006, + "f1-count": 50.0, + "f1-mean": 0.7358, + "f1-std": 0.0006, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8026, + "roc_auc-std": 0.0007 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7378, + "acc-std": 0.0011, + "f1-count": 50.0, + "f1-mean": 0.7374, + "f1-std": 0.0011, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8034, + "roc_auc-std": 0.0008 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7373, + "acc-std": 0.0005, + "f1-count": 10.0, + "f1-mean": 0.737, + "f1-std": 0.0006, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8043, + "roc_auc-std": 0.0004 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7382, + "acc-std": 0.0008, + "f1-count": 10.0, + "f1-mean": 0.7379, + "f1-std": 0.0008, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8053, + "roc_auc-std": 0.0004 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..2c08b5829c6ad2472a86a03935db23f1e6d1408d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/eval_mlp.json @@ -0,0 +1,50 @@ +{ + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7351, + "acc-std": 0.0006, + "f1-count": 10.0, + "f1-mean": 0.7349, + "f1-std": 0.0005, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.803, + "roc_auc-std": 0.0003 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7389, + "acc-std": 0.0007, + "f1-count": 10.0, + "f1-mean": 0.7386, + "f1-std": 0.0007, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8045, + "roc_auc-std": 0.0002 + } + }, + "synthetic": { 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b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..e038295122d13bd3334fa73f61a06838a65ea214 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/importance.json @@ -0,0 +1,10 @@ +{ + "batch_size": 0.31311023998469506, + "lr": 0.2960602744043888, + "steps": 0.16537848701056174, + "n_layers": 0.11167205004916561, + "num_samples": 0.07221163469300777, + "d_first": 0.03474485611825957, + "d_last": 0.005025464701109562, + "num_timesteps": 0.0017969930388117056 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..3e0e85a4920766ee7325ebfe8ac651cb032cec70 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/info.json @@ -0,0 +1,10 @@ +{ + "name": "Cardio", + "id": "cardio--default", + "task_type": "binclass", + "n_num_features": 5, + "n_cat_features": 6, + "test_size": 11200, + "train_size": 44800, + "val_size": 14000 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..ccba95bb304d93c8197886064063a5b8ffea21ef --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.7374, + 0.7372, + 0.7378, + 0.7373, + 0.7372 + ], + "privacies": [ + 0.011005439757607312, + 0.011, + 0.0111, + 0.0112, + 0.0114 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..cf37ad47d59363ccca482c09bc8ac63a9a30d261 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_cb_best/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.0121709921531676 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_mlp_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_mlp_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..0186646f8470c7901f5aea6dc40c891d013c753b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_mlp_best/config.toml @@ -0,0 +1,58 @@ +parent_dir = "exp/cardio/ddpm_mlp_best" +real_data_path = "data/cardio/" +num_numerical_features = 5 +device = "cuda:0" +model_type = "mlp" +seed = 0 + +[model_params] +is_y_cond = true +num_classes = 2 + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, + 256, + 256, + 256, + 512, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.0005666918237189449 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 179200 +batch_size = 20000 +seed = 0 + +[eval.type] +eval_model = "mlp" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "one-hot" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_mlp_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_mlp_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..18b2e81ab50bdf0c25b6bf951690e7d510173715 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_mlp_best/eval_mlp.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7351, + "acc-std": 0.0008, + "f1-count": 50.0, + "f1-mean": 0.7348, + "f1-std": 0.0008, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8028, + "roc_auc-std": 0.0004 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7375, + "acc-std": 0.001, + "f1-count": 50.0, + "f1-mean": 0.737, + "f1-std": 0.001, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8038, + "roc_auc-std": 0.0004 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_mlp_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_mlp_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..61dd7c21e21e97cfe3136d5915cb2f35e092100a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_mlp_best/importance.json @@ -0,0 +1,10 @@ +{ + "lr": 0.7944940198607096, + "batch_size": 0.06590168947999744, + "steps": 0.06471892898106565, + "num_samples": 0.03685547467036078, + "n_layers": 0.021375256446117548, + "d_first": 0.010046463206928626, + "num_timesteps": 0.004583602148597543, + "d_last": 0.0020245652062229344 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_mlp_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_mlp_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..3e0e85a4920766ee7325ebfe8ac651cb032cec70 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/ddpm_mlp_best/info.json @@ -0,0 +1,10 @@ +{ + "name": "Cardio", + "id": "cardio--default", + "task_type": "binclass", + "n_num_features": 5, + "n_cat_features": 6, + "test_size": 11200, + "train_size": 44800, + "val_size": 14000 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..77b5aea7899211f7d086167f0b204e491e0f0423 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/config.toml @@ -0,0 +1,24 @@ +parent_dir = "exp/cardio/smote" +real_data_path = "data/cardio/" +seed = 0 + +[smote_params] +k_neighbours = 18 +frac_samples = 4 +frac_lam_del = 0.15000000000000002 + +[sample] +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..eb2a0491d6afa6627b8e433da07abcdd9dde5699 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7345, + "acc-std": 0.0009, + "f1-count": 50.0, + "f1-mean": 0.7337, + "f1-std": 0.001, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7954, + "roc_auc-std": 0.0003 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7331, + "acc-std": 0.0013, + "f1-count": 50.0, + "f1-mean": 0.7323, + "f1-std": 0.0013, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7992, + "roc_auc-std": 0.0003 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..7507b998ed1676f93b4befc5186a64fcb7316b0a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6623, + "acc-std": 0.001, + "f1-count": 10.0, + "f1-mean": 0.6623, + "f1-std": 0.001, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6577, + "roc_auc-std": 0.0011 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6685, + "acc-std": 0.0013, + "f1-count": 10.0, + "f1-mean": 0.6685, + "f1-std": 0.0013, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6638, + "roc_auc-std": 0.0014 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6434, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6432, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6994, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6547, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6544, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7085, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7213, + "acc-std": 0.0012, + "f1-count": 10.0, + "f1-mean": 0.7212, + "f1-std": 0.0012, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7807, + "roc_auc-std": 0.0005 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7255, + "acc-std": 0.0011, + "f1-count": 10.0, + "f1-mean": 0.7253, + "f1-std": 0.0011, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7865, + "roc_auc-std": 0.0007 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7198, + "acc-std": 0.0019, + "f1-count": 10.0, + "f1-mean": 0.7194, + "f1-std": 0.0018, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7742, + "roc_auc-std": 0.0015 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7246, + "acc-std": 0.0025, + "f1-count": 10.0, + "f1-mean": 0.7243, + "f1-std": 0.0024, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7816, + "roc_auc-std": 0.0016 + } + }, + "avg": { + "val": { + "f1-mean": 0.6865, + "acc-mean": 0.6867, + "f1-std": 0.001 + }, + "test": { + "f1-mean": 0.6931, + "acc-mean": 0.6933, + "f1-std": 0.0011 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/info.json new file mode 100644 index 0000000000000000000000000000000000000000..3e0e85a4920766ee7325ebfe8ac651cb032cec70 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/info.json @@ -0,0 +1,10 @@ +{ + "name": "Cardio", + "id": "cardio--default", + "task_type": "binclass", + "n_num_features": 5, + "n_cat_features": 6, + "test_size": 11200, + "train_size": 44800, + "val_size": 14000 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..77bdb501e388b5415b943b504a5b84de8073c3ab --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.7323, + 0.7329, + 0.7326, + 0.7329, + 0.7328 + ], + "privacies": [ + 0.006556992145314727, + 0.0074, + 0.0072, + 0.0073, + 0.0073 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..77ed9f6d27105099ca4b0ead21e815675ec3bc59 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/smote/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.006556992145314727 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/tvae/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/tvae/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..69a3427884e6258367160aca96e894ce7a8983c9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/tvae/config.toml @@ -0,0 +1,44 @@ +parent_dir = "exp/cardio/tvae" +real_data_path = "data/cardio/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.0012012873947818063 +epochs = 30000 +embedding_dim = 64 +batch_size = 256 +loss_factor = 8.748421768542762 +compress_dims = [ + 256, + 256, + 256, + 256, + 256, + 512, +] +decompress_dims = [ + 256, + 256, + 256, + 256, + 256, + 512, +] + +[sample] +seed = 0 +num_samples = 45000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/tvae/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/tvae/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..9796d7eabad71036dafe4f1f3bf68afb3d29ce17 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/tvae/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7221, + "acc-std": 0.0012, + "f1-count": 50.0, + "f1-mean": 0.7219, + "f1-std": 0.0012, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7759, + "roc_auc-std": 0.0054 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7176, + "acc-std": 0.0009, + "f1-count": 50.0, + "f1-mean": 0.7174, + "f1-std": 0.001, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7734, + "roc_auc-std": 0.005 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/tvae/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/tvae/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..bd67df4eabd38adbfbb9d08e91d6f3268abe2fc0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/tvae/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7033, + "acc-std": 0.0022, + "f1-count": 10.0, + "f1-mean": 0.7017, + "f1-std": 0.0027, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7036, + "roc_auc-std": 0.0022 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7018, + "acc-std": 0.0023, + "f1-count": 10.0, + "f1-mean": 0.7001, + "f1-std": 0.0026, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7021, + "roc_auc-std": 0.0022 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6774, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6728, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7521, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6797, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6749, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.752, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7178, + "acc-std": 0.0013, + "f1-count": 10.0, + "f1-mean": 0.7173, + "f1-std": 0.0014, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.769, + "roc_auc-std": 0.0008 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7218, + "acc-std": 0.0012, + "f1-count": 10.0, + "f1-mean": 0.7212, + "f1-std": 0.0012, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7707, + "roc_auc-std": 0.0008 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6805, + "acc-std": 0.0042, + "f1-count": 10.0, + "f1-mean": 0.6789, + "f1-std": 0.0047, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7442, + "roc_auc-std": 0.0024 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6784, + "acc-std": 0.0052, + "f1-count": 10.0, + "f1-mean": 0.6769, + "f1-std": 0.0057, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7433, + "roc_auc-std": 0.0027 + } + }, + "avg": { + "val": { + "f1-mean": 0.6927, + "acc-mean": 0.6948, + "f1-std": 0.0022 + }, + "test": { + "f1-mean": 0.6933, + "acc-mean": 0.6954, + "f1-std": 0.0023 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/tvae/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/tvae/info.json new file mode 100644 index 0000000000000000000000000000000000000000..3e0e85a4920766ee7325ebfe8ac651cb032cec70 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/cardio/tvae/info.json @@ -0,0 +1,10 @@ +{ + "name": "Cardio", + "id": "cardio--default", + "task_type": "binclass", + "n_num_features": 5, + "n_cat_features": 6, + "test_size": 11200, + "train_size": 44800, + "val_size": 14000 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..a7bc9acea8049c82d164e7236ebc03e51e5aa171 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/config.toml @@ -0,0 +1,55 @@ +seed = 1 +parent_dir = "exp/churn2/check" +real_data_path = "data/churn2/" +model_type = "mlp" +num_numerical_features = 7 +device = "cuda:1" + +[model_params] +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 26000 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "simple" +eval_type = "real" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan-plus/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan-plus/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..3b758f5918e0230a9fb60c8a055ad365b469cd14 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan-plus/config.toml @@ -0,0 +1,31 @@ +parent_dir = "exp/churn2/ctabgan-plus" +real_data_path = "data/churn2/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.00045004551479319233 +epochs = 10000 +class_dim = [ + 256, +] +batch_size = 512 +random_dim = 128 +num_channels = 16 + +[sample] +seed = 0 +num_samples = 26000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan-plus/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan-plus/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..a39e42cc95303b9124115955d57ba9bb2c6536de --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan-plus/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8237, + "acc-std": 0.0135, + "f1-count": 50.0, + "f1-mean": 0.7303, + "f1-std": 0.012, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8243, + "roc_auc-std": 0.0103 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8071, + "acc-std": 0.0182, + "f1-count": 50.0, + "f1-mean": 0.7024, + "f1-std": 0.0118, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8111, + "roc_auc-std": 0.0159 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan-plus/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan-plus/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..27d13a7384806daf0dabf0121f0fd17af19c4e8c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan-plus/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7297, + "acc-std": 0.0102, + "f1-count": 50.0, + "f1-mean": 0.644, + "f1-std": 0.0087, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6762, + "roc_auc-std": 0.0086 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7414, + "acc-std": 0.0137, + "f1-count": 50.0, + "f1-mean": 0.6515, + "f1-std": 0.0149, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6783, + "roc_auc-std": 0.0144 + } + }, + "lr": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8131, + "acc-std": 0.0007, + "f1-count": 50.0, + "f1-mean": 0.539, + "f1-std": 0.0022, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.5759, + "roc_auc-std": 0.0066 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8178, + "acc-std": 0.0008, + "f1-count": 50.0, + "f1-mean": 0.5521, + "f1-std": 0.0011, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.5939, + "roc_auc-std": 0.0068 + } + }, + "rf": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.809, + "acc-std": 0.0113, + "f1-count": 50.0, + "f1-mean": 0.7228, + "f1-std": 0.0079, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8242, + "roc_auc-std": 0.0065 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8007, + "acc-std": 0.0107, + "f1-count": 50.0, + "f1-mean": 0.7059, + "f1-std": 0.0082, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8148, + "roc_auc-std": 0.0055 + } + }, + "mlp": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7871, + "acc-std": 0.0175, + "f1-count": 50.0, + "f1-mean": 0.7095, + "f1-std": 0.0109, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8212, + "roc_auc-std": 0.0039 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7753, + "acc-std": 0.0164, + "f1-count": 50.0, + "f1-mean": 0.6915, + "f1-std": 0.0105, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8205, + "roc_auc-std": 0.0047 + } + }, + "avg": { + "val": { + "f1-mean": 0.6538, + "acc-mean": 0.7847, + "f1-std": 0.0074 + }, + "test": { + "f1-mean": 0.6502, + "acc-mean": 0.7838, + "f1-std": 0.008 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan-plus/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan-plus/info.json new file mode 100644 index 0000000000000000000000000000000000000000..003f89d74f6ac5bb79e0dc99e93ea0d9ee51ffb9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan-plus/info.json @@ -0,0 +1,10 @@ +{ + "name": "Churn Modelling", + "id": "churn--default", + "task_type": "binclass", + "n_num_features": 7, + "n_cat_features": 4, + "train_size": 6400, + "val_size": 1600, + "test_size": 2000 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..0dac132ad66dd3e10d216891faeb953513869281 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/config.toml @@ -0,0 +1,32 @@ +parent_dir = "exp/churn2/ctabgan" +real_data_path = "data/churn2/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.0003218899030093514 +epochs = 10000 +class_dim = [ + 512, + 64, +] +batch_size = 1024 +random_dim = 128 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 52000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..499897bedcf5f30a5754b50ebce4af948068d9e3 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8448, + "acc-std": 0.0036, + "f1-count": 50.0, + "f1-mean": 0.7079, + "f1-std": 0.0057, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8159, + "roc_auc-std": 0.0082 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.836, + "acc-std": 0.0047, + "f1-count": 50.0, + "f1-mean": 0.6875, + "f1-std": 0.0065, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8169, + "roc_auc-std": 0.0094 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..357efbd4ae2b7483754569d19e6eed5f49b7ffae --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7425, + "acc-std": 0.0085, + "f1-count": 10.0, + "f1-mean": 0.6119, + "f1-std": 0.0136, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6148, + "roc_auc-std": 0.0151 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7445, + "acc-std": 0.0099, + "f1-count": 10.0, + "f1-mean": 0.6151, + "f1-std": 0.012, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.618, + "roc_auc-std": 0.0121 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8099, + "acc-std": 0.0019, + "f1-count": 10.0, + "f1-mean": 0.5888, + "f1-std": 0.0082, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7221, + "roc_auc-std": 0.004 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8059, + "acc-std": 0.0024, + 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"roc_auc-std": 0.004 + } + }, + "avg": { + "val": { + "f1-mean": 0.635, + "acc-mean": 0.8016, + "f1-std": 0.0122 + }, + "test": { + "f1-mean": 0.6361, + "acc-mean": 0.8008, + "f1-std": 0.0104 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/info.json new file mode 100644 index 0000000000000000000000000000000000000000..003f89d74f6ac5bb79e0dc99e93ea0d9ee51ffb9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/info.json @@ -0,0 +1,10 @@ +{ + "name": "Churn Modelling", + "id": "churn--default", + "task_type": "binclass", + "n_num_features": 7, + "n_cat_features": 4, + "train_size": 6400, + "val_size": 1600, + "test_size": 2000 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..09fbac565725f01e4339fc4e7f46498948739de0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.6875, + 0.6889, + 0.6874, + 0.6829, + 0.6837 + ], + "privacies": [ + 0.22659042216389888, + 0.2268, + 0.2271, + 0.2264, + 0.2273 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..4b2419b3b0af9bc2341106376290aa6009c71278 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ctabgan/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.22753493540777897 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..2836e797bacef80744a4b847aaa2dd39f083e254 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/config.toml @@ -0,0 +1,59 @@ +seed = 1 +parent_dir = "exp/churn2/ddpm_cb_best" +real_data_path = "data/churn2/" +model_type = "mlp" +num_numerical_features = 7 +device = "cuda:1" + +[model_params] +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 512, + 1024, + 1024, + 1024, + 1024, + 512, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 100 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 30000 +lr = 0.0008057094292475385 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 26000 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..9a7d72d9d184561fd8c23244e19c582dc25f0e2c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/eval_catboost.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8696, + "acc-std": 0.0025, + "f1-count": 50.0, + "f1-mean": 0.7725, + "f1-std": 0.0041, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8669, + "roc_auc-std": 0.0038 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8604, + "acc-std": 0.0028, + "f1-count": 50.0, + "f1-mean": 0.7548, + "f1-std": 0.0059, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8564, + "roc_auc-std": 0.0033 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8716, + "acc-std": 0.0022, + "f1-count": 10.0, + "f1-mean": 0.7656, + "f1-std": 0.0038, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8672, + "roc_auc-std": 0.0019 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8593, + "acc-std": 0.0024, + 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b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/eval_simple.json @@ -0,0 +1,222 @@ +{ + "real": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7962, + "acc-std": 0.0026, + "f1-count": 10.0, + "f1-mean": 0.6909, + "f1-std": 0.0035, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6941, + "roc_auc-std": 0.004 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7968, + "acc-std": 0.003, + "f1-count": 10.0, + "f1-mean": 0.6913, + "f1-std": 0.0034, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6943, + "roc_auc-std": 0.003 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.815, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.5911, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7449, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8035, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.5719, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7542, + 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"f1-mean": 0.6933, + "acc-mean": 0.8236, + "f1-std": 0.0031 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..1fbb9c7a5f6f73deb6059538ea657c592b80396a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/importance.json @@ -0,0 +1,10 @@ +{ + "lr": 0.6326973146539404, + "num_samples": 0.2053985996312392, + "n_layers": 0.09043572080084922, + "steps": 0.039134120741514324, + "num_timesteps": 0.009869368135874187, + "batch_size": 0.009381102679319969, + "d_last": 0.007097304726689507, + "d_first": 0.005986468630573244 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..003f89d74f6ac5bb79e0dc99e93ea0d9ee51ffb9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/info.json @@ -0,0 +1,10 @@ +{ + "name": "Churn Modelling", + "id": "churn--default", + "task_type": "binclass", + "n_num_features": 7, + "n_cat_features": 4, + "train_size": 6400, + "val_size": 1600, + "test_size": 2000 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..672c29afbeb160c7b4d05c758fd0fc0355c6dd55 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.7548, + 0.7535, + 0.7527, + 0.7545, + 0.7509 + ], + "privacies": [ + 0.15773408235806635, + 0.1653, + 0.1741, + 0.1834, + 0.1945 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..2efbc4e37fb65de4548820bd6330219082349751 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_cb_best/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.15735060469196344 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_mlp_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_mlp_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..67eb2f2345650c05111f15bf5d55b9eec020b127 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_mlp_best/config.toml @@ -0,0 +1,59 @@ +seed = 0 +parent_dir = "exp/churn2/ddpm_mlp_best" +real_data_path = "data/churn2/" +model_type = "mlp" +num_numerical_features = 7 +device = "cuda:1" + +[model_params] +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 512, + 1024, + 1024, + 1024, + 1024, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 100 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 30000 +lr = 0.00015044197980173247 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 52000 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "mlp" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "one-hot" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_mlp_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_mlp_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..f467a3741a5d66b56f541977b9e6829078e431f9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_mlp_best/eval_catboost.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8692, + "acc-std": 0.002, + "f1-count": 50.0, + "f1-mean": 0.7726, + "f1-std": 0.0038, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8662, + "roc_auc-std": 0.0028 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8601, + "acc-std": 0.0024, + "f1-count": 50.0, + "f1-mean": 0.7541, + "f1-std": 0.0065, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.858, + "roc_auc-std": 0.0027 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8709, + "acc-std": 0.002, + "f1-count": 10.0, + "f1-mean": 0.7643, + "f1-std": 0.004, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8673, + "roc_auc-std": 0.0017 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.86, + "acc-std": 0.0026, + "f1-count": 10.0, + "f1-mean": 0.7416, + "f1-std": 0.0063, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8596, + "roc_auc-std": 0.0021 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_mlp_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_mlp_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..8d183d36d259df0af4b9e7956f1cf7e94539af3f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_mlp_best/eval_mlp.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8604, + "acc-std": 0.0024, + "f1-count": 50.0, + "f1-mean": 0.7586, + "f1-std": 0.0062, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8638, + "roc_auc-std": 0.0027 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8562, + "acc-std": 0.0026, + "f1-count": 50.0, + "f1-mean": 0.7491, + "f1-std": 0.0064, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8549, + "roc_auc-std": 0.0027 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_mlp_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_mlp_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..1c334a602c34968034cefb77b076ee0df6a2dc9c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_mlp_best/importance.json @@ -0,0 +1,10 @@ +{ + "num_samples": 0.452700767426159, + "n_layers": 0.31801488176181864, + "lr": 0.09492545493567499, + "steps": 0.03790295273237544, + "d_last": 0.028774396778319253, + "num_timesteps": 0.027652752485737776, + "d_first": 0.020601581874087914, + "batch_size": 0.019427212005826835 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_mlp_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_mlp_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..003f89d74f6ac5bb79e0dc99e93ea0d9ee51ffb9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/ddpm_mlp_best/info.json @@ -0,0 +1,10 @@ +{ + "name": "Churn Modelling", + "id": "churn--default", + "task_type": "binclass", + "n_num_features": 7, + "n_cat_features": 4, + "train_size": 6400, + "val_size": 1600, + "test_size": 2000 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..09f06d9e706de49b21d2c372c32dc893299581ef --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/config.toml @@ -0,0 +1,24 @@ +parent_dir = "exp/churn2/smote" +real_data_path = "data/churn2/" +seed = 0 + +[smote_params] +k_neighbours = 6 +frac_samples = 8 +frac_lam_del = 0.2 + +[sample] +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..72187748b32e320271ffef1525307e77cb1f09eb --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8672, + "acc-std": 0.0014, + "f1-count": 50.0, + "f1-mean": 0.7713, + "f1-std": 0.0022, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8649, + "roc_auc-std": 0.0026 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.852, + "acc-std": 0.0031, + "f1-count": 50.0, + "f1-mean": 0.7432, + "f1-std": 0.0046, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8492, + "roc_auc-std": 0.0021 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..6e0a6c34b62a9af3eb8c0bdf21625cf144dd93ef --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/eval_mlp.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8512, + "acc-std": 0.0032, + "f1-count": 50.0, + "f1-mean": 0.7473, + "f1-std": 0.0072, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8509, + "roc_auc-std": 0.0022 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.846, + "acc-std": 0.0036, + "f1-count": 50.0, + "f1-mean": 0.7365, + "f1-std": 0.0039, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.843, + "roc_auc-std": 0.0016 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8627, + "acc-std": 0.0023, + "f1-count": 10.0, + "f1-mean": 0.7566, + "f1-std": 0.0079, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8627, + "roc_auc-std": 0.002 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8552, + "acc-std": 0.0016, + "f1-count": 10.0, + "f1-mean": 0.7412, + "f1-std": 0.0058, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8517, + "roc_auc-std": 0.0024 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..85c8d87a169e5898762e921a790f67c60db4bb5b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8052, + "acc-std": 0.0034, + "f1-count": 10.0, + "f1-mean": 0.7018, + "f1-std": 0.0042, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7031, + "roc_auc-std": 0.0036 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7909, + "acc-std": 0.0016, + "f1-count": 10.0, + "f1-mean": 0.683, + "f1-std": 0.001, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6863, + "roc_auc-std": 0.0013 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8106, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6318, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7454, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8005, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6197, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7501, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.853, + "acc-std": 0.0021, + "f1-count": 10.0, + "f1-mean": 0.7497, + "f1-std": 0.0038, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8447, + "roc_auc-std": 0.0018 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8427, + "acc-std": 0.0024, + "f1-count": 10.0, + "f1-mean": 0.7274, + "f1-std": 0.0034, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8308, + "roc_auc-std": 0.0025 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8477, + "acc-std": 0.0068, + "f1-count": 10.0, + "f1-mean": 0.7416, + "f1-std": 0.0092, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8429, + "roc_auc-std": 0.0045 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8438, + "acc-std": 0.004, + "f1-count": 10.0, + "f1-mean": 0.7303, + "f1-std": 0.0059, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8416, + "roc_auc-std": 0.0031 + } + }, + "avg": { + "val": { + "f1-mean": 0.7062, + "acc-mean": 0.8291, + "f1-std": 0.0043 + }, + "test": { + "f1-mean": 0.6901, + "acc-mean": 0.8195, + "f1-std": 0.0034 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/info.json new file mode 100644 index 0000000000000000000000000000000000000000..003f89d74f6ac5bb79e0dc99e93ea0d9ee51ffb9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/info.json @@ -0,0 +1,10 @@ +{ + "name": "Churn Modelling", + "id": "churn--default", + "task_type": "binclass", + "n_num_features": 7, + "n_cat_features": 4, + "train_size": 6400, + "val_size": 1600, + "test_size": 2000 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..f93711cd5e019e19875b12e6470aeb072eaef40e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.7432, + 0.7443, + 0.7463, + 0.7435, + 0.7432 + ], + "privacies": [ + 0.07670222725818532, + 0.0759, + 0.0756, + 0.0765, + 0.0768 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..4595a38666280afed1d33be14a92ed057fd2c813 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/smote/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.07670222725818532 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/tvae/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/tvae/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..79477a5c7655e81fde70d91334cb4a152e85cd24 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/tvae/config.toml @@ -0,0 +1,36 @@ +parent_dir = "exp/churn2/tvae" +real_data_path = "data/churn2/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.00047756638508070437 +epochs = 30000 +embedding_dim = 256 +batch_size = 256 +loss_factor = 2.4301590023300905 +compress_dims = [ + 256, + 512, +] +decompress_dims = [ + 256, + 512, +] + +[sample] +seed = 0 +num_samples = 52000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/tvae/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/tvae/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..c0c5dd741a819f522e76af09c06ebbbcb883494d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/tvae/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.863, + "acc-std": 0.0016, + "f1-count": 50.0, + "f1-mean": 0.7558, + "f1-std": 0.0028, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8464, + "roc_auc-std": 0.0027 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8501, + "acc-std": 0.0029, + "f1-count": 50.0, + "f1-mean": 0.7317, + "f1-std": 0.0057, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8313, + "roc_auc-std": 0.0027 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/tvae/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/tvae/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..f3a24240f5382ba6cc8ff4bcba1a75f22a253aa8 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/tvae/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7988, + "acc-std": 0.0026, + "f1-count": 10.0, + "f1-mean": 0.6906, + "f1-std": 0.0044, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6916, + "roc_auc-std": 0.0053 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7742, + "acc-std": 0.0024, + "f1-count": 10.0, + "f1-mean": 0.6566, + "f1-std": 0.0039, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6603, + "roc_auc-std": 0.0043 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8112, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.626, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7429, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.807, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6174, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7463, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8517, + "acc-std": 0.0023, + "f1-count": 10.0, + "f1-mean": 0.7436, + "f1-std": 0.0046, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8246, + "roc_auc-std": 0.0019 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8486, + "acc-std": 0.0023, + "f1-count": 10.0, + "f1-mean": 0.7293, + "f1-std": 0.0044, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.809, + "roc_auc-std": 0.002 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8529, + "acc-std": 0.0019, + "f1-count": 10.0, + "f1-mean": 0.746, + "f1-std": 0.0056, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.828, + "roc_auc-std": 0.0027 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.847, + "acc-std": 0.0034, + "f1-count": 10.0, + "f1-mean": 0.7321, + "f1-std": 0.0037, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8218, + "roc_auc-std": 0.0039 + } + }, + "avg": { + "val": { + "f1-mean": 0.7016, + "acc-mean": 0.8286, + "f1-std": 0.0037 + }, + "test": { + "f1-mean": 0.6838, + "acc-mean": 0.8192, + "f1-std": 0.0033 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/tvae/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/tvae/info.json new file mode 100644 index 0000000000000000000000000000000000000000..003f89d74f6ac5bb79e0dc99e93ea0d9ee51ffb9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/churn2/tvae/info.json @@ -0,0 +1,10 @@ +{ + "name": "Churn Modelling", + "id": "churn--default", + "task_type": "binclass", + "n_num_features": 7, + "n_cat_features": 4, + "train_size": 6400, + "val_size": 1600, + "test_size": 2000 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..e88c3adb38a212e6c5b005d5a7aea75e2ab4a855 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/config.toml @@ -0,0 +1,56 @@ +seed = 0 +parent_dir = "exp/default/check" +real_data_path = "data/default/" +model_type = "mlp" +num_numerical_features = 20 +device = "cuda:1" + +[model_params] +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "minmax" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 76800 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" +model = "catboost" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan-plus/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan-plus/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..c4d7a3e053c71073160a05fa1734c85ea14ff924 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan-plus/config.toml @@ -0,0 +1,32 @@ +parent_dir = "exp/default/ctabgan-plus" +real_data_path = "data/default/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.0008404996901306582 +epochs = 10000 +class_dim = [ + 64, + 256, +] +batch_size = 2048 +random_dim = 128 +num_channels = 32 + +[sample] +seed = 0 +num_samples = 76800 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "minmax" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan-plus/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan-plus/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..0184fce479fe8e5e7f51214653e2181de2ca2fec --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan-plus/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8205, + "acc-std": 0.0013, + "f1-count": 50.0, + "f1-mean": 0.6862, + "f1-std": 0.0026, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7729, + "roc_auc-std": 0.0059 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8192, + "acc-std": 0.0028, + "f1-count": 50.0, + "f1-mean": 0.6865, + "f1-std": 0.0038, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7657, + "roc_auc-std": 0.0053 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan-plus/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan-plus/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..c8d5b42cf585820b133b51094e1d1e31da6c7167 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan-plus/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.6966, + "acc-std": 0.0174, + "f1-count": 50.0, + "f1-mean": 0.5856, + "f1-std": 0.0139, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.5546, + "roc_auc-std": 0.0167 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.6974, + "acc-std": 0.0169, + "f1-count": 50.0, + "f1-mean": 0.5869, + "f1-std": 0.012, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.5554, + "roc_auc-std": 0.0154 + } + }, + "lr": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.816, + "acc-std": 0.0005, + "f1-count": 50.0, + "f1-mean": 0.6522, + "f1-std": 0.0037, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7077, + "roc_auc-std": 0.0017 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8183, + "acc-std": 0.001, + "f1-count": 50.0, + "f1-mean": 0.6565, + "f1-std": 0.0047, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7026, + "roc_auc-std": 0.0016 + } + }, + "rf": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8064, + "acc-std": 0.0049, + "f1-count": 50.0, + "f1-mean": 0.6718, + "f1-std": 0.0072, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7538, + "roc_auc-std": 0.0032 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8065, + "acc-std": 0.0051, + "f1-count": 50.0, + "f1-mean": 0.6732, + "f1-std": 0.0049, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7564, + "roc_auc-std": 0.002 + } + }, + "mlp": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7795, + "acc-std": 0.011, + "f1-count": 50.0, + "f1-mean": 0.667, + "f1-std": 0.0086, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7522, + "roc_auc-std": 0.0066 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7865, + "acc-std": 0.0107, + "f1-count": 50.0, + "f1-mean": 0.6769, + "f1-std": 0.0079, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7475, + "roc_auc-std": 0.0053 + } + }, + "avg": { + "val": { + "f1-mean": 0.6442, + "acc-mean": 0.7746, + "f1-std": 0.0084 + }, + "test": { + "f1-mean": 0.6484, + "acc-mean": 0.7772, + "f1-std": 0.0079 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan-plus/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan-plus/info.json new file mode 100644 index 0000000000000000000000000000000000000000..34f091fe010a94800bf9d8d3b26ba0b9ed4113ab --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan-plus/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "default", + "id": "default--id", + "train_size": 19200, + "val_size": 4800, + "test_size": 6000, + "n_num_features": 20, + "n_cat_features": 3 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..91c4a7604119ef9452eea00ce70f9dfab2441ee8 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan/config.toml @@ -0,0 +1,32 @@ +parent_dir = "exp/default/ctabgan" +real_data_path = "data/default/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.00011098666649406564 +epochs = 5000 +class_dim = [ + 64, + 256, +] +batch_size = 1024 +random_dim = 128 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 153600 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "minmax" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..8c219da9d1c535ffc5d1e6b5fb44da6efb38ea9d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8035, + "acc-std": 0.0051, + "f1-count": 50.0, + "f1-mean": 0.637, + "f1-std": 0.0094, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6833, + "roc_auc-std": 0.0098 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8075, + "acc-std": 0.0056, + "f1-count": 50.0, + "f1-mean": 0.6437, + "f1-std": 0.0109, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6865, + "roc_auc-std": 0.0099 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..57b44e947a31bd54679be2570cbaae235b626187 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7054, + "acc-std": 0.0107, + "f1-count": 10.0, + "f1-mean": 0.5691, + "f1-std": 0.0098, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.5649, + "roc_auc-std": 0.009 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7103, + "acc-std": 0.0111, + "f1-count": 10.0, + "f1-mean": 0.5761, + "f1-std": 0.0092, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.5719, + "roc_auc-std": 0.0082 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7973, + "acc-std": 0.002, + "f1-count": 10.0, + "f1-mean": 0.617, + "f1-std": 0.005, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6522, + "roc_auc-std": 0.0024 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7994, + "acc-std": 0.0019, + "f1-count": 10.0, + "f1-mean": 0.6219, + "f1-std": 0.0051, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6558, + "roc_auc-std": 0.0025 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7885, + "acc-std": 0.0017, + "f1-count": 10.0, + "f1-mean": 0.6106, + "f1-std": 0.0035, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6688, + "roc_auc-std": 0.0051 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7936, + "acc-std": 0.002, + "f1-count": 10.0, + "f1-mean": 0.6187, + "f1-std": 0.0046, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6819, + "roc_auc-std": 0.0056 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7904, + "acc-std": 0.0032, + "f1-count": 10.0, + "f1-mean": 0.6255, + "f1-std": 0.0089, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6652, + "roc_auc-std": 0.005 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7968, + "acc-std": 0.0024, + "f1-count": 10.0, + "f1-mean": 0.6372, + "f1-std": 0.0077, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6706, + "roc_auc-std": 0.0054 + } + }, + "avg": { + "val": { + "f1-mean": 0.6056, + "acc-mean": 0.7704, + "f1-std": 0.0068 + }, + "test": { + "f1-mean": 0.6135, + "acc-mean": 0.775, + "f1-std": 0.0067 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan/info.json new file mode 100644 index 0000000000000000000000000000000000000000..34f091fe010a94800bf9d8d3b26ba0b9ed4113ab --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "default", + "id": "default--id", + "train_size": 19200, + "val_size": 4800, + "test_size": 6000, + "n_num_features": 20, + "n_cat_features": 3 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..b77fc71f5363eb7811704f3fa3d9713ebc4fa3d3 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ctabgan/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.6437, + 0.647, + 0.6404, + 0.6466, + 0.6436 + ], + "privacies": [ + 0.14756816728852223, + 0.1475, + 0.1471, + 0.1472, + 0.148 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..98cf6e7ef752c4c732bb5bb9b1085ff2418918ee --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/config.toml @@ -0,0 +1,59 @@ +seed = 0 +parent_dir = "exp/default/ddpm_cb_best" +real_data_path = "data/default/" +model_type = "mlp" +num_numerical_features = 20 +device = "cuda:1" + +[model_params] +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 256, + 1024, + 1024, + 1024, + 1024, + 512, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.00046818967784044777 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "minmax" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 76800 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" +model = "catboost" + +[eval.T] +seed = 0 +normalization = "minmax" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..b56bc6a330f83384adcc4f50d6439f6417b39220 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/eval_catboost.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8218, + "acc-std": 0.0008, + "f1-count": 50.0, + "f1-mean": 0.688, + "f1-std": 0.0029, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7707, + "roc_auc-std": 0.0066 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8227, + "acc-std": 0.0018, + "f1-count": 50.0, + "f1-mean": 0.691, + "f1-std": 0.004, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7672, + "roc_auc-std": 0.0053 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8218, + "acc-std": 0.0012, + "f1-count": 10.0, + "f1-mean": 0.686, + "f1-std": 0.0012, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7799, + "roc_auc-std": 0.0044 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8223, + "acc-std": 0.0024, + "f1-count": 10.0, + "f1-mean": 0.688, + "f1-std": 0.0028, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7789, + "roc_auc-std": 0.004 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..b600f195941755cf4c5fb33dd9c92bde87ccb831 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/eval_mlp.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8179, + "acc-std": 0.002, + "f1-count": 50.0, + "f1-mean": 0.6794, + "f1-std": 0.0073, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7648, + "roc_auc-std": 0.0034 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8185, + "acc-std": 0.0019, + "f1-count": 50.0, + "f1-mean": 0.6794, + "f1-std": 0.0079, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7592, + "roc_auc-std": 0.003 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8155, + "acc-std": 0.0024, + "f1-count": 10.0, + "f1-mean": 0.6831, + "f1-std": 0.0073, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7732, + "roc_auc-std": 0.0039 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8162, + "acc-std": 0.0037, + "f1-count": 10.0, + "f1-mean": 0.6878, + "f1-std": 0.0037, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7712, + "roc_auc-std": 0.0025 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..cd354b96038b8743934e9efcd53af79afbd22afa --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/eval_simple.json @@ -0,0 +1,222 @@ +{ + "real": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7279, + "acc-std": 0.0017, + "f1-count": 10.0, + "f1-mean": 0.6114, + "f1-std": 0.0022, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6103, + "roc_auc-std": 0.0021 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7334, + "acc-std": 0.0018, + "f1-count": 10.0, + "f1-mean": 0.6172, + "f1-std": 0.0021, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.615, + "roc_auc-std": 0.0023 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8112, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6171, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7175, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8108, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.618, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7123, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8143, + "acc-std": 0.0021, + "f1-count": 10.0, + "f1-mean": 0.6752, + "f1-std": 0.0041, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7588, + "roc_auc-std": 0.002 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8162, + "acc-std": 0.0009, + "f1-count": 10.0, + "f1-mean": 0.6796, + "f1-std": 0.0012, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7647, + "roc_auc-std": 0.0012 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8183, + "acc-std": 0.0013, + "f1-count": 10.0, + "f1-mean": 0.6715, + "f1-std": 0.0096, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7733, + "roc_auc-std": 0.002 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8202, + "acc-std": 0.0005, + "f1-count": 10.0, + "f1-mean": 0.6787, + "f1-std": 0.0072, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7671, + "roc_auc-std": 0.0018 + } + }, + "avg": { + "val": { + "f1-mean": 0.6438, + "acc-mean": 0.7929, + "f1-std": 0.004 + }, + "test": { + "f1-mean": 0.6484, + "acc-mean": 0.7952, + "f1-std": 0.0031 + } + } + }, + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7751, + "acc-std": 0.0025, + "f1-count": 10.0, + "f1-mean": 0.6412, + "f1-std": 0.0036, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.599, + "roc_auc-std": 0.0048 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7799, + "acc-std": 0.0017, + "f1-count": 10.0, + "f1-mean": 0.6446, + "f1-std": 0.0028, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.5982, + "roc_auc-std": 0.0047 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8083, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6055, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.698, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.811, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6126, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7038, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.821, + "acc-std": 0.0011, + "f1-count": 10.0, + "f1-mean": 0.6904, + "f1-std": 0.0017, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7546, + "roc_auc-std": 0.0025 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8194, + "acc-std": 0.0007, + "f1-count": 10.0, + "f1-mean": 0.6878, + "f1-std": 0.0012, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7573, + "roc_auc-std": 0.0013 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8181, + "acc-std": 0.0015, + "f1-count": 10.0, + "f1-mean": 0.686, + "f1-std": 0.006, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7455, + "roc_auc-std": 0.0026 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8199, + "acc-std": 0.002, + "f1-count": 10.0, + "f1-mean": 0.6914, + "f1-std": 0.0044, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7492, + "roc_auc-std": 0.0019 + } + }, + "avg": { + "val": { + "f1-mean": 0.6558, + "acc-mean": 0.8056, + "f1-std": 0.0034 + }, + "test": { + "f1-mean": 0.6591, + "acc-mean": 0.8076, + "f1-std": 0.0029 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..3f91ceddd7b50d291f9dfc545a95b565b156eca9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/importance.json @@ -0,0 +1,9 @@ +{ + "lr": 0.749159477993559, + "n_layers": 0.08825564331170509, + "d_last": 0.06278259601434903, + "num_samples": 0.06090500072033674, + "steps": 0.027803959705527428, + "d_first": 0.006311781155453894, + "num_timesteps": 0.004781541099068782 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..34f091fe010a94800bf9d8d3b26ba0b9ed4113ab --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "default", + "id": "default--id", + "train_size": 19200, + "val_size": 4800, + "test_size": 6000, + "n_num_features": 20, + "n_cat_features": 3 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..fb1984715359c198143c90e90ba49afab983f397 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.691, + 0.6902, + 0.6906, + 0.6934, + 0.6894 + ], + "privacies": [ + 0.11228128278179914, + 0.1131, + 0.1145, + 0.1181, + 0.1247 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..03cb2f0b6033162117611940f571fd52d00592e0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_cb_best/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.11229893083908625 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_mlp_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_mlp_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..47a0b35c7754683f5006ce696f669ab98e532d5c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_mlp_best/config.toml @@ -0,0 +1,54 @@ +seed = 0 +parent_dir = "exp/default/ddpm_mlp_best" +real_data_path = "data/default/" +model_type = "mlp" +num_numerical_features = 20 +device = "cuda:1" + +[model_params] +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 256, + 1024, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.0005500256899682464 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "minmax" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 76800 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "mlp" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "one-hot" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_mlp_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_mlp_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..b76c8f71998f78a42eb3e91af5943acc0fd66bcd --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_mlp_best/eval_mlp.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8108, + "acc-std": 0.004, + "f1-count": 50.0, + "f1-mean": 0.6721, + "f1-std": 0.0105, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7553, + "roc_auc-std": 0.0044 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8111, + "acc-std": 0.0044, + "f1-count": 50.0, + "f1-mean": 0.6739, + "f1-std": 0.0129, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7526, + "roc_auc-std": 0.003 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_mlp_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_mlp_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..d1a03a96dc8376bc672311fe33db8e75b9b368ee --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_mlp_best/importance.json @@ -0,0 +1,10 @@ +{ + "batch_size": 0.477976772640863, + "steps": 0.19767879595674476, + "n_layers": 0.12357602094706163, + "lr": 0.12062042563838252, + "d_first": 0.027535424618594616, + "num_samples": 0.0223446627283622, + "num_timesteps": 0.017525108184857613, + "d_last": 0.012742789285133541 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_mlp_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_mlp_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..34f091fe010a94800bf9d8d3b26ba0b9ed4113ab --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/ddpm_mlp_best/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "default", + "id": "default--id", + "train_size": 19200, + "val_size": 4800, + "test_size": 6000, + "n_num_features": 20, + "n_cat_features": 3 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..99dba17e7582e512ce649d8e10eac5f473e955b3 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/config.toml @@ -0,0 +1,24 @@ +parent_dir = "exp/default/smote" +real_data_path = "data/default/" +seed = 0 + +[smote_params] +k_neighbours = 6 +frac_samples = 2 +frac_lam_del = 0.1 + +[sample] +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "minmax" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..79af02523b22db110d753e56ca2c0f20ec8b5408 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8199, + "acc-std": 0.0015, + "f1-count": 50.0, + "f1-mean": 0.6901, + "f1-std": 0.0027, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7781, + "roc_auc-std": 0.0028 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8215, + "acc-std": 0.0025, + "f1-count": 50.0, + "f1-mean": 0.693, + "f1-std": 0.0032, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7765, + "roc_auc-std": 0.0036 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..657fbfcf0dd4d7bc3e2dccd4f37a5ff4d4398144 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/eval_mlp.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8068, + "acc-std": 0.0051, + "f1-count": 50.0, + "f1-mean": 0.6763, + "f1-std": 0.0064, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7461, + "roc_auc-std": 0.005 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8056, + "acc-std": 0.0058, + "f1-count": 50.0, + "f1-mean": 0.6756, + "f1-std": 0.0093, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7531, + "roc_auc-std": 0.0035 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..48717d9a4c0d6df8c9135e366f910c3ea030dce7 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7203, + "acc-std": 0.0022, + "f1-count": 10.0, + "f1-mean": 0.6095, + "f1-std": 0.0025, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6035, + "roc_auc-std": 0.0029 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7145, + "acc-std": 0.0019, + "f1-count": 10.0, + "f1-mean": 0.6057, + "f1-std": 0.0022, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6022, + "roc_auc-std": 0.0027 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8135, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6278, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7199, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8163, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6375, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7122, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8089, + "acc-std": 0.0016, + "f1-count": 10.0, + "f1-mean": 0.6735, + "f1-std": 0.0031, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7491, + "roc_auc-std": 0.0021 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8106, + "acc-std": 0.0014, + "f1-count": 10.0, + "f1-mean": 0.6783, + "f1-std": 0.0023, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7641, + "roc_auc-std": 0.0019 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.813, + "acc-std": 0.0027, + "f1-count": 10.0, + "f1-mean": 0.6671, + "f1-std": 0.0072, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7584, + "roc_auc-std": 0.0043 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.814, + "acc-std": 0.002, + "f1-count": 10.0, + "f1-mean": 0.6734, + "f1-std": 0.0086, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7588, + "roc_auc-std": 0.005 + } + }, + "avg": { + "val": { + "f1-mean": 0.6445, + "acc-mean": 0.7889, + "f1-std": 0.0032 + }, + "test": { + "f1-mean": 0.6487, + "acc-mean": 0.7889, + "f1-std": 0.0032 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/info.json new file mode 100644 index 0000000000000000000000000000000000000000..34f091fe010a94800bf9d8d3b26ba0b9ed4113ab --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "default", + "id": "default--id", + "train_size": 19200, + "val_size": 4800, + "test_size": 6000, + "n_num_features": 20, + "n_cat_features": 3 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..ce33888b12383fdca5dd0fbd2ce12d59342f9072 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.693, + 0.6852, + 0.6758, + 0.6701, + 0.6688 + ], + "privacies": [ + 0.026842344268214125, + 0.0318, + 0.0381, + 0.0461, + 0.0568 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..865d1e74a24c6432c0e8aab8e0004f92a747a466 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/smote/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.026871079089105618 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/tvae/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/tvae/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..9baf21a79b90aa7bd0839a3e2fa773cf96abbd88 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/tvae/config.toml @@ -0,0 +1,36 @@ +parent_dir = "exp/default/tvae" +real_data_path = "data/default/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.0004233807442086402 +epochs = 20000 +embedding_dim = 512 +batch_size = 4096 +loss_factor = 1.3948590431463646 +compress_dims = [ + 256, + 512, +] +decompress_dims = [ + 256, + 512, +] + +[sample] +seed = 0 +num_samples = 153600 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "minmax" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/tvae/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/tvae/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..d1c042efb9fe977d8c87b13f6116809760aa661b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/tvae/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.8105, + "acc-std": 0.0023, + "f1-count": 50.0, + "f1-mean": 0.656, + "f1-std": 0.0047, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7504, + "roc_auc-std": 0.0142 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.8118, + "acc-std": 0.0031, + "f1-count": 50.0, + "f1-mean": 0.6564, + "f1-std": 0.0067, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7522, + "roc_auc-std": 0.0136 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/tvae/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/tvae/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..4cc47d812c8fce08f92c73f3b567e5caa0fb593c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/tvae/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7781, + "acc-std": 0.002, + "f1-count": 10.0, + "f1-mean": 0.6377, + "f1-std": 0.0028, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.5814, + "roc_auc-std": 0.0032 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7804, + "acc-std": 0.0011, + "f1-count": 10.0, + "f1-mean": 0.6359, + "f1-std": 0.0028, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.5713, + "roc_auc-std": 0.0038 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8079, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6475, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.722, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8065, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6476, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7022, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7998, + "acc-std": 0.0008, + "f1-count": 10.0, + "f1-mean": 0.6409, + "f1-std": 0.0023, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.752, + "roc_auc-std": 0.002 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8043, + "acc-std": 0.0008, + "f1-count": 10.0, + "f1-mean": 0.6428, + "f1-std": 0.001, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7568, + "roc_auc-std": 0.0023 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7985, + "acc-std": 0.0016, + "f1-count": 10.0, + "f1-mean": 0.6365, + "f1-std": 0.0087, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7569, + "roc_auc-std": 0.0015 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8043, + "acc-std": 0.0011, + "f1-count": 10.0, + "f1-mean": 0.6459, + "f1-std": 0.0094, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7484, + "roc_auc-std": 0.002 + } + }, + "avg": { + "val": { + "f1-mean": 0.6407, + "acc-mean": 0.7961, + "f1-std": 0.0034 + }, + "test": { + "f1-mean": 0.643, + "acc-mean": 0.7989, + "f1-std": 0.0034 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/tvae/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/tvae/info.json new file mode 100644 index 0000000000000000000000000000000000000000..34f091fe010a94800bf9d8d3b26ba0b9ed4113ab --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/default/tvae/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "default", + "id": "default--id", + "train_size": 19200, + "val_size": 4800, + "test_size": 6000, + "n_num_features": 20, + "n_cat_features": 3 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..7aa7e55f4ccab215328ea716c246da7c8b0fcbc1 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/config.toml @@ -0,0 +1,56 @@ +seed = 0 +parent_dir = "exp/diabetes/check" +real_data_path = "data/diabetes/" +model_type = "mlp" +num_numerical_features = 8 +device = "cuda:0" + +[model_params] +d_in = 8 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 500 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan-plus/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan-plus/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..58d72910edabd67fb575ef61eba5cb769e021cbb --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan-plus/config.toml @@ -0,0 +1,33 @@ +parent_dir = "exp/diabetes/ctabgan-plus" +real_data_path = "data/diabetes/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 2.5546195429833966e-05 +epochs = 10000 +class_dim = [ + 64, + 128, + 256, +] +batch_size = 491 +random_dim = 128 +num_channels = 16 + +[sample] +seed = 0 +num_samples = 2000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan-plus/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan-plus/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..d81a06b1c6b014656d99579e7cb6907077aeb045 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan-plus/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7511, + "acc-std": 0.012, + "f1-count": 50.0, + "f1-mean": 0.6973, + "f1-std": 0.0136, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7814, + "roc_auc-std": 0.0115 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.773, + "acc-std": 0.014, + "f1-count": 50.0, + "f1-mean": 0.7339, + "f1-std": 0.0199, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8523, + "roc_auc-std": 0.0069 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan-plus/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan-plus/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..245eef7bb9e70da4a2e0fffa5a7c30b16ec1253f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan-plus/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.6654, + "acc-std": 0.0391, + "f1-count": 50.0, + "f1-mean": 0.6195, + "f1-std": 0.051, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6192, + "roc_auc-std": 0.0519 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7312, + "acc-std": 0.0249, + "f1-count": 50.0, + "f1-mean": 0.6984, + "f1-std": 0.0261, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.695, + "roc_auc-std": 0.0255 + } + }, + "lr": { + "val": { + "acc-count": 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+ "roc_auc-count": 50.0, + "roc_auc-mean": 0.78, + "roc_auc-std": 0.0069 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7806, + "acc-std": 0.01, + "f1-count": 50.0, + "f1-mean": 0.7404, + "f1-std": 0.0109, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8727, + "roc_auc-std": 0.0065 + } + }, + "avg": { + "val": { + "f1-mean": 0.6622, + "acc-mean": 0.7197, + "f1-std": 0.0288 + }, + "test": { + "f1-mean": 0.7267, + "acc-mean": 0.7655, + "f1-std": 0.023 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan-plus/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan-plus/info.json new file mode 100644 index 0000000000000000000000000000000000000000..14ce07b921bf55771a168835b918496e2710548f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan-plus/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "diabetes", + "id": "diabetes--id", + "train_size": 491, + "val_size": 123, + "test_size": 154, + "n_num_features": 8, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..72b1f54ecf93b2aa71cf647108c33e092797d479 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan/config.toml @@ -0,0 +1,36 @@ +parent_dir = "exp/diabetes/ctabgan" +real_data_path = "data/diabetes/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.0026555610849989405 +epochs = 10000 +class_dim = [ + 128, + 512, + 512, + 512, + 512, + 64, +] +batch_size = 491 +random_dim = 128 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 500 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..541e6d3b9b8a456c05c567e4b4924c7e19acffb9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7325, + "acc-std": 0.0168, + "f1-count": 50.0, + "f1-mean": 0.6844, + "f1-std": 0.0276, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7381, + "roc_auc-std": 0.0215 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7681, + "acc-std": 0.0174, + 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"f1-std": 0.0132, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7014, + "roc_auc-std": 0.0128 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6756, + "acc-std": 0.0024, + "f1-count": 10.0, + "f1-mean": 0.5504, + "f1-std": 0.0049, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7143, + "roc_auc-std": 0.0017 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7325, + "acc-std": 0.0026, + "f1-count": 10.0, + "f1-mean": 0.6376, + "f1-std": 0.0022, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8172, + "roc_auc-std": 0.0012 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6951, + "acc-std": 0.0066, + "f1-count": 10.0, + "f1-mean": 0.6226, + "f1-std": 0.009, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7155, + "roc_auc-std": 0.0076 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7578, + "acc-std": 0.0136, + "f1-count": 10.0, + "f1-mean": 0.7084, + "f1-std": 0.018, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8241, + "roc_auc-std": 0.0081 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6374, + "acc-std": 0.0137, + "f1-count": 10.0, + "f1-mean": 0.4979, + "f1-std": 0.0176, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.648, + "roc_auc-std": 0.014 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6909, + "acc-std": 0.0106, + "f1-count": 10.0, + "f1-mean": 0.5612, + "f1-std": 0.0307, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7027, + "roc_auc-std": 0.0185 + } + }, + "avg": { + "val": { + "f1-mean": 0.5602, + "acc-mean": 0.6624, + "f1-std": 0.015 + }, + "test": { + "f1-mean": 0.6549, + "acc-mean": 0.7354, + "f1-std": 0.0155 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan/info.json new file mode 100644 index 0000000000000000000000000000000000000000..14ce07b921bf55771a168835b918496e2710548f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "diabetes", + "id": "diabetes--id", + "train_size": 491, + "val_size": 123, + "test_size": 154, + "n_num_features": 8, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..b098bb3e269020d53ae2f2ecb1c7b25a9137ebb4 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ctabgan/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.731, + 0.722, + 0.7563, + 0.7693, + 0.7719 + ], + "privacies": [ + 0.19966804855497094, + 0.2165, + 0.2298, + 0.2403, + 0.2562 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..ffa1904abd8f70fd28720b0519dfb1938ede41ce --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/config.toml @@ -0,0 +1,55 @@ +seed = 0 +parent_dir = "exp/diabetes/ddpm_cb_best" +real_data_path = "data/diabetes/" +model_type = "mlp" +num_numerical_features = 8 +device = "cuda:0" + +[model_params] +d_in = 8 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 128, + 512, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 1.1510940031144828e-05 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 500 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..70029ae66552a9bf0cfff80a12feaa025b562065 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/eval_catboost.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7753, + "acc-std": 0.0114, + "f1-count": 50.0, + "f1-mean": 0.7309, + "f1-std": 0.0162, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7894, + "roc_auc-std": 0.021 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7755, + "acc-std": 0.0184, + "f1-count": 50.0, + "f1-mean": 0.7398, + "f1-std": 0.0196, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8397, + "roc_auc-std": 0.0257 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7837, + "acc-std": 0.0116, + "f1-count": 10.0, + "f1-mean": 0.7467, + "f1-std": 0.0132, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8186, + "roc_auc-std": 0.0047 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8149, + "acc-std": 0.0106, + "f1-count": 10.0, + "f1-mean": 0.7849, + "f1-std": 0.0129, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8761, + "roc_auc-std": 0.0042 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..186a62dd70062e2cb49582ff889928fc16b838cb --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/eval_mlp.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7286, + "acc-std": 0.0159, + "f1-count": 50.0, + "f1-mean": 0.681, + "f1-std": 0.0192, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.779, + "roc_auc-std": 0.01 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7812, + "acc-std": 0.014, + "f1-count": 50.0, + "f1-mean": 0.7538, + "f1-std": 0.0204, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8607, + "roc_auc-std": 0.0056 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7374, + "acc-std": 0.0109, + "f1-count": 10.0, + "f1-mean": 0.6844, + "f1-std": 0.0169, + "roc_auc-count": 10.0, + 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0000000000000000000000000000000000000000..ad14d197072d0a435603e885bfed4ffcc412ec76 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/importance.json @@ -0,0 +1,10 @@ +{ + "lr": 0.3124235408196611, + "num_samples": 0.19720164839738913, + "steps": 0.18662115324297196, + "batch_size": 0.12471394061762389, + "n_layers": 0.0736509080471688, + "d_last": 0.06659474192106807, + "d_first": 0.03748330165370141, + "num_timesteps": 0.0013107653004155657 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..14ce07b921bf55771a168835b918496e2710548f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "diabetes", + "id": "diabetes--id", + "train_size": 491, + "val_size": 123, + "test_size": 154, + "n_num_features": 8, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..60c18f077d7211c98914297c7206d301a49bf619 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.7361, + 0.7718, + 0.7584, + 0.7573, + 0.7502 + ], + "privacies": [ + 0.20772594997400623, + 0.2154, + 0.2282, + 0.2493, + 0.276 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..d12cf5e32cc96cef5cdd8905b9c9222fc8e42f67 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_cb_best/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.20361711289451803 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_mlp_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_mlp_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..35232f45a5321a4d03334e7dfbeb259da5230da2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_mlp_best/config.toml @@ -0,0 +1,59 @@ +seed = 0 +parent_dir = "exp/diabetes/ddpm_mlp_best" +real_data_path = "data/diabetes/" +model_type = "mlp" +num_numerical_features = 8 +device = "cuda:0" + +[model_params] +d_in = 8 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, + 256, + 256, + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 100 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 1.5402178626529827e-05 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "mlp" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "one-hot" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_mlp_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_mlp_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..1c6ca29f72abe0e89d80ec5b34a2186965066b69 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_mlp_best/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7837, + "acc-std": 0.0054, + "f1-count": 10.0, + "f1-mean": 0.7451, + "f1-std": 0.0085, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8182, + "roc_auc-std": 0.0048 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8071, + "acc-std": 0.0116, + "f1-count": 10.0, + "f1-mean": 0.7759, + "f1-std": 0.0137, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.873, + "roc_auc-std": 0.0056 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_mlp_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_mlp_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..e95b13928e5fcebfb39f13065e36131fb3fc8507 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_mlp_best/eval_mlp.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.719, + "acc-std": 0.0176, + "f1-count": 50.0, + "f1-mean": 0.664, + "f1-std": 0.0309, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7573, + "roc_auc-std": 0.0159 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7719, + "acc-std": 0.0148, + "f1-count": 50.0, + "f1-mean": 0.7408, + "f1-std": 0.018, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.863, + "roc_auc-std": 0.0092 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_mlp_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_mlp_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..65b8244713f918ac5c1e6771f5791d2ac6f0042f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_mlp_best/importance.json @@ -0,0 +1,10 @@ +{ + "num_samples": 0.33926835006327777, + "lr": 0.1750665403341417, + "d_first": 0.16892121041128663, + "steps": 0.11252571759756877, + "d_last": 0.07152052686493506, + "batch_size": 0.05651881677018958, + "num_timesteps": 0.038161111958921225, + "n_layers": 0.03801772599967926 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_mlp_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_mlp_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..14ce07b921bf55771a168835b918496e2710548f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/ddpm_mlp_best/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "diabetes", + "id": "diabetes--id", + "train_size": 491, + "val_size": 123, + "test_size": 154, + "n_num_features": 8, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..573bbb00188cc67437987b85666ad9a6450d80e9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/config.toml @@ -0,0 +1,24 @@ +parent_dir = "exp/diabetes/smote" +real_data_path = "data/diabetes/" +seed = 0 + +[smote_params] +k_neighbours = 8 +frac_samples = 0.25 +frac_lam_del = 0.15000000000000002 + +[sample] +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..647bd4bbf5873f81b70ac6f1da81e0aa11fd5dc8 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7504, + "acc-std": 0.0109, + "f1-count": 50.0, + "f1-mean": 0.6898, + "f1-std": 0.0149, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7705, + "roc_auc-std": 0.0238 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7325, + "acc-std": 0.0341, + "f1-count": 50.0, + "f1-mean": 0.6835, + "f1-std": 0.037, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7935, + "roc_auc-std": 0.051 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..83b626e35e302a02f391efe025ac20782add47ed --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/eval_mlp.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.687, + "acc-std": 0.019, + "f1-count": 50.0, + "f1-mean": 0.6069, + "f1-std": 0.0226, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7155, + "roc_auc-std": 0.0293 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7539, + "acc-std": 0.0074, + "f1-count": 50.0, + "f1-mean": 0.7039, + "f1-std": 0.0126, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.797, + "roc_auc-std": 0.0211 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7374, + "acc-std": 0.0109, + "f1-count": 10.0, + "f1-mean": 0.6844, + "f1-std": 0.0169, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7707, + "roc_auc-std": 0.0062 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7851, + "acc-std": 0.0144, + "f1-count": 10.0, + "f1-mean": 0.7544, + "f1-std": 0.0174, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8696, + "roc_auc-std": 0.003 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..7f8bb46ded9f2c0e3b0405a6851cf3f876b73682 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6846, + "acc-std": 0.0269, + "f1-count": 10.0, + "f1-mean": 0.636, + "f1-std": 0.0236, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6317, + "roc_auc-std": 0.0216 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6974, + "acc-std": 0.0066, + "f1-count": 10.0, + "f1-mean": 0.6743, + "f1-std": 0.0064, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6788, + "roc_auc-std": 0.008 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7317, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6479, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7834, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7532, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6977, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8415, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7366, + "acc-std": 0.0196, + "f1-count": 10.0, + "f1-mean": 0.6675, + "f1-std": 0.0267, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7707, + "roc_auc-std": 0.014 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7455, + "acc-std": 0.0171, + "f1-count": 10.0, + "f1-mean": 0.7014, + "f1-std": 0.0184, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8204, + "roc_auc-std": 0.0086 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7163, + "acc-std": 0.0133, + "f1-count": 10.0, + "f1-mean": 0.6325, + "f1-std": 0.0236, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.768, + "roc_auc-std": 0.0116 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.711, + "acc-std": 0.0073, + "f1-count": 10.0, + "f1-mean": 0.6328, + "f1-std": 0.0058, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7628, + "roc_auc-std": 0.0148 + } + }, + "avg": { + "val": { + "f1-mean": 0.646, + "acc-mean": 0.7173, + "f1-std": 0.0185 + }, + "test": { + "f1-mean": 0.6766, + "acc-mean": 0.7268, + "f1-std": 0.0131 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/info.json new file mode 100644 index 0000000000000000000000000000000000000000..14ce07b921bf55771a168835b918496e2710548f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "diabetes", + "id": "diabetes--id", + "train_size": 491, + "val_size": 123, + "test_size": 154, + "n_num_features": 8, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..c57f2d78665ae93cd39ec9aa1f948c177cebee79 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.6924, + 0.6965, + 0.6928, + 0.6784, + 0.7297 + ], + "privacies": [ + 0.06821187483334126, + 0.0692, + 0.0766, + 0.0815, + 0.0959 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..0087b17505086e09b67f5e68a437825637dd34b5 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/smote/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.06821187483334126 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/tvae/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/tvae/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..f01f18def4332c05f3f4c322432ca88bdf5288ce --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/tvae/config.toml @@ -0,0 +1,40 @@ +parent_dir = "exp/diabetes/tvae" +real_data_path = "data/diabetes/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 5.723631879975606e-05 +epochs = 20000 +embedding_dim = 512 +batch_size = 256 +loss_factor = 6.323198409475003 +compress_dims = [ + 64, + 512, + 512, + 512, +] +decompress_dims = [ + 64, + 512, + 512, + 512, +] + +[sample] +seed = 0 +num_samples = 1000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/tvae/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/tvae/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..cb5f0afc8d2768782e3edf9f10feb1dd16b98f1a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/tvae/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7447, + "acc-std": 0.0167, + "f1-count": 50.0, + "f1-mean": 0.6982, + "f1-std": 0.0277, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.767, + "roc_auc-std": 0.0206 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7512, + "acc-std": 0.0318, + "f1-count": 50.0, + "f1-mean": 0.7136, + "f1-std": 0.0386, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8158, + "roc_auc-std": 0.0415 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/tvae/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/tvae/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..dab1362813d42aca952585431bb647e3d5c44f50 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/tvae/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6821, + "acc-std": 0.0123, + "f1-count": 10.0, + "f1-mean": 0.6594, + "f1-std": 0.0138, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6647, + "roc_auc-std": 0.0147 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7162, + "acc-std": 0.0184, + "f1-count": 10.0, + "f1-mean": 0.6841, + "f1-std": 0.0189, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6818, + "roc_auc-std": 0.0181 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7317, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6923, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7837, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7597, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.7246, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8478, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7293, + "acc-std": 0.0131, + "f1-count": 10.0, + "f1-mean": 0.6793, + "f1-std": 0.0164, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.794, + "roc_auc-std": 0.0083 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7461, + "acc-std": 0.0102, + "f1-count": 10.0, + "f1-mean": 0.7039, + "f1-std": 0.0118, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8284, + "roc_auc-std": 0.0041 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7285, + "acc-std": 0.011, + "f1-count": 10.0, + "f1-mean": 0.6959, + "f1-std": 0.0105, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7976, + "roc_auc-std": 0.0023 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7604, + "acc-std": 0.0054, + "f1-count": 10.0, + "f1-mean": 0.7334, + "f1-std": 0.0078, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8473, + "roc_auc-std": 0.003 + } + }, + "avg": { + "val": { + "f1-mean": 0.6817, + "acc-mean": 0.7179, + "f1-std": 0.0102 + }, + "test": { + "f1-mean": 0.7115, + "acc-mean": 0.7456, + "f1-std": 0.0099 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/tvae/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/tvae/info.json new file mode 100644 index 0000000000000000000000000000000000000000..14ce07b921bf55771a168835b918496e2710548f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/diabetes/tvae/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "diabetes", + "id": "diabetes--id", + "train_size": 491, + "val_size": 123, + "test_size": 154, + "n_num_features": 8, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..9a5b6f3bd9f07a2947f71718509b59078c357c08 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/config.toml @@ -0,0 +1,55 @@ +seed = 0 +parent_dir = "exp/fb-comments/check" +real_data_path = "data/fb-comments/" +model_type = "mlp" +num_numerical_features = 36 +device = "cuda:0" + +[model_params] +num_classes = 0 +is_y_cond = false + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1264000 +batch_size = 150000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ctabgan-plus/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ctabgan-plus/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..43f8f24067f688ef1f4c67e7433051a60846b7a8 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ctabgan-plus/config.toml @@ -0,0 +1,33 @@ +parent_dir = "exp/fb-comments/ctabgan-plus" +real_data_path = "data/fb-comments/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.0002484017612698457 +epochs = 10000 +class_dim = [ + 128, + 64, + 64, +] +batch_size = 2048 +random_dim = 128 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 158000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ctabgan-plus/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ctabgan-plus/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..66a4c6e0ffa2e085310d30cca10b96b4d5106e85 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ctabgan-plus/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.4956, + "r2-std": 0.0113, + "rmse-count": 50.0, + "rmse-mean": 9.1426, + "rmse-std": 0.1023 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.5088, + "r2-std": 0.0109, + "rmse-count": 50.0, + "rmse-mean": 9.215, + "rmse-std": 0.1024 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ctabgan-plus/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ctabgan-plus/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..34695beea60110f75b824b8ab906353c560c78c3 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ctabgan-plus/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 50.0, + "r2-mean": -4.6372, + "r2-std": 1.7216, + "rmse-count": 20.0, + "rmse-mean": 30.1841, + "rmse-std": 4.8224 + }, + "test": { + "r2-count": 50.0, + "r2-mean": -4.3999, + "r2-std": 1.7017, + "rmse-count": 20.0, + "rmse-mean": 30.1551, + "rmse-std": 4.9365 + } + }, + "lr": { + "val": { + "r2-count": 50.0, + "r2-mean": -7439.1183, + "r2-std": 33.0382, + "rmse-count": 20.0, + "rmse-mean": 1110.4749, + "rmse-std": 2.4656 + }, + "test": { + "r2-count": 50.0, + "r2-mean": -3107.3394, + "r2-std": 12.2158, + "rmse-count": 20.0, + "rmse-mean": 733.1204, + "rmse-std": 1.4406 + } + }, + "rf": { + "val": { + "r2-count": 50.0, + "r2-mean": -1.6904, + "r2-std": 0.1759, + "rmse-count": 20.0, + "rmse-mean": 21.1056, + "rmse-std": 0.6926 + }, + "test": { + "r2-count": 50.0, + "r2-mean": -1.568, + "r2-std": 0.1728, + "rmse-count": 20.0, + "rmse-mean": 21.0602, + "rmse-std": 0.7117 + } + }, + "mlp": { + "val": { + "r2-count": 50.0, + "r2-mean": -545076.5941, + "r2-std": 467478.2668, + "rmse-count": 20.0, + "rmse-mean": 8506.8654, + "rmse-std": 4239.9284 + }, + "test": { + "r2-count": 50.0, + "r2-mean": -248824.8797, + "r2-std": 202723.2101, + "rmse-count": 20.0, + "rmse-mean": 5909.5186, + "rmse-std": 2846.4783 + } + }, + "avg": { + "val": { + "r2-mean": -138130.51, + "rmse-mean": 2417.1575, + "r2-std": 116878.3006 + }, + "test": { + "r2-mean": -62984.5468, + "rmse-mean": 1673.4636, + "r2-std": 83781.3129 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ctabgan-plus/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ctabgan-plus/info.json new file mode 100644 index 0000000000000000000000000000000000000000..b2c41b332bf4c4a3a99bd4f8249275a75d1da820 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ctabgan-plus/info.json @@ -0,0 +1,10 @@ +{ + "name": "Facebook Comments Volume", + "id": "fb-comments--default", + "task_type": "regression", + "n_num_features": 36, + "n_cat_features": 15, + "train_size": 157638, + "test_size": 19720, + "val_size": 19722 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..c500df2d52155cad6c6ee9da4c60abdd4a5be322 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/config.toml @@ -0,0 +1,54 @@ +seed = 0 +parent_dir = "exp/fb-comments/ddpm_cb_best" +real_data_path = "data/fb-comments/" +model_type = "mlp" +num_numerical_features = 36 +device = "cuda:0" + +[model_params] +num_classes = 0 +is_y_cond = false + +[model_params.rtdl_params] +d_layers = [ + 512, + 1024, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.0006338119731278414 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1264000 +batch_size = 150000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..efe4b84e5c1ec0e2ac4b5fea8699698a63289a56 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/eval_catboost.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.7133, + "r2-std": 0.0015, + "rmse-count": 50.0, + "rmse-mean": 6.8935, + "rmse-std": 0.018 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.7128, + "r2-std": 0.0021, + "rmse-count": 50.0, + "rmse-mean": 7.047, + "rmse-std": 0.026 + } + }, + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8383, + "r2-std": 0.0016, + "rmse-count": 10.0, + "rmse-mean": 5.1771, + "rmse-std": 0.0263 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.8371, + "r2-std": 0.0012, + "rmse-count": 10.0, + "rmse-mean": 5.3075, + "rmse-std": 0.0195 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..e56926d08f217526c1dcbd931867e87ec7fecc5b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/eval_simple.json @@ -0,0 +1,174 @@ +{ + "real": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.6026, + "r2-std": 0.0051, + "rmse-count": 10.0, + "rmse-mean": 8.1158, + "rmse-std": 0.0523 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.5956, + "r2-std": 0.005, + "rmse-count": 10.0, + "rmse-mean": 8.3618, + "rmse-std": 0.0512 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.4765, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 9.3149, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.4634, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 9.6328, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7925, + "r2-std": 0.0012, + "rmse-count": 10.0, + "rmse-mean": 5.8638, + "rmse-std": 0.0166 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.798, + "r2-std": 0.0011, + "rmse-count": 10.0, + "rmse-mean": 5.9097, + "rmse-std": 0.0167 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7203, + "r2-std": 0.0096, + "rmse-count": 10.0, + "rmse-mean": 6.8072, + "rmse-std": 0.117 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.7219, + "r2-std": 0.0115, + "rmse-count": 10.0, + "rmse-mean": 6.9325, + "rmse-std": 0.1431 + } + }, + "avg": { + "val": { + "r2-mean": 0.648, + "rmse-mean": 7.5254, + "r2-std": 0.004 + }, + "test": { + "r2-mean": 0.6447, + "rmse-mean": 7.7092, + "r2-std": 0.0048 + } + } + }, + "synthetic": { + "tree": { + "val": { + "r2-count": 6.0, + "r2-mean": 0.4676, + "r2-std": 0.0144, + "rmse-count": 6.0, + "rmse-mean": 9.3929, + "rmse-std": 0.1274 + }, + "test": { + "r2-count": 6.0, + "r2-mean": 0.4895, + "r2-std": 0.0134, + "rmse-count": 6.0, + "rmse-mean": 9.3943, + "rmse-std": 0.123 + } + }, + "lr": { + "val": { + "r2-count": 6.0, + "r2-mean": 0.2935, + "r2-std": 0.0008, + "rmse-count": 6.0, + "rmse-mean": 10.8209, + "rmse-std": 0.0058 + }, + "test": { + "r2-count": 6.0, + "r2-mean": 0.2855, + "r2-std": 0.0009, + "rmse-count": 6.0, + "rmse-mean": 11.1148, + "rmse-std": 0.0068 + } + }, + "rf": { + "val": { + "r2-count": 6.0, + "r2-mean": 0.6965, + "r2-std": 0.0015, + "rmse-count": 6.0, + "rmse-mean": 7.093, + "rmse-std": 0.0171 + }, + "test": { + "r2-count": 6.0, + "r2-mean": 0.69, + "r2-std": 0.0024, + "rmse-count": 6.0, + "rmse-mean": 7.3218, + "rmse-std": 0.028 + } + }, + "mlp": { + "val": { + "r2-count": 6.0, + "r2-mean": 0.6464, + "r2-std": 0.0076, + "rmse-count": 6.0, + "rmse-mean": 7.6548, + "rmse-std": 0.0811 + }, + "test": { + "r2-count": 6.0, + "r2-mean": 0.644, + "r2-std": 0.007, + "rmse-count": 6.0, + "rmse-mean": 7.8451, + "rmse-std": 0.077 + } + }, + "avg": { + "val": { + "r2-mean": 0.526, + "rmse-mean": 8.7404, + "r2-std": 0.005 + }, + "test": { + "r2-mean": 0.5272, + "rmse-mean": 8.919, + "r2-std": 0.0051 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..e86f1faf9678efc367876e70d75a8774062dc023 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/importance.json @@ -0,0 +1,10 @@ +{ + "lr": 0.45979431924119857, + "batch_size": 0.10705980672075646, + "d_first": 0.10519161967811953, + "n_layers": 0.09631153938381083, + "num_samples": 0.085462199039541, + "d_last": 0.07520260391408216, + "steps": 0.041862470084830905, + "num_timesteps": 0.02911544193766049 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..b2c41b332bf4c4a3a99bd4f8249275a75d1da820 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/info.json @@ -0,0 +1,10 @@ +{ + "name": "Facebook Comments Volume", + "id": "fb-comments--default", + "task_type": "regression", + "n_num_features": 36, + "n_cat_features": 15, + "train_size": 157638, + "test_size": 19720, + "val_size": 19722 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..deabb75b35a4514b097a87d5717ac812998687c1 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/ddpm_cb_best/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.11151094102732073 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/smote/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/smote/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..22b7e1bee7d9cb7d575b135e697b1bc606875584 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/smote/config.toml @@ -0,0 +1,24 @@ +parent_dir = "exp/fb-comments/smote" +real_data_path = "data/fb-comments/" +seed = 0 + +[smote_params] +k_neighbours = 6 +frac_samples = 8 +frac_lam_del = 0.05 + +[sample] +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/smote/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/smote/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..a32600be508547de2971be8aa0748c86beb13c40 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/smote/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 20.0, + "r2-mean": 0.8076, + "r2-std": 0.0014, + "rmse-count": 20.0, + "rmse-mean": 5.6464, + "rmse-std": 0.0207 + }, + "test": { + "r2-count": 20.0, + "r2-mean": 0.8035, + "r2-std": 0.0015, + "rmse-count": 20.0, + "rmse-mean": 5.8294, + "rmse-std": 0.0223 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/smote/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/smote/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..ec8e364ebe91a3ae8c83a25ed71a46d8b8a3720c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/smote/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 6.0, + "r2-mean": 0.5939, + "r2-std": 0.0008, + "rmse-count": 6.0, + "rmse-mean": 8.2037, + "rmse-std": 0.0076 + }, + "test": { + "r2-count": 6.0, + "r2-mean": 0.6501, + "r2-std": 0.0049, + "rmse-count": 6.0, + "rmse-mean": 7.7783, + "rmse-std": 0.0542 + } + }, + "lr": { + "val": { + "r2-count": 6.0, + "r2-mean": 0.4619, + "r2-std": 0.0, + "rmse-count": 6.0, + "rmse-mean": 9.4443, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 6.0, + "r2-mean": 0.4448, + "r2-std": 0.0, + "rmse-count": 6.0, + "rmse-mean": 9.7975, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 6.0, + "r2-mean": 0.7739, + "r2-std": 0.0009, + "rmse-count": 6.0, + "rmse-mean": 6.1214, + "rmse-std": 0.0126 + }, + "test": { + "r2-count": 6.0, + "r2-mean": 0.7979, + "r2-std": 0.0013, + "rmse-count": 6.0, + "rmse-mean": 5.9107, + "rmse-std": 0.0186 + } + }, + "mlp": { + "val": { + "r2-count": 6.0, + "r2-mean": 0.7183, + "r2-std": 0.0082, + "rmse-count": 6.0, + "rmse-mean": 6.8328, + "rmse-std": 0.0997 + }, + "test": { + "r2-count": 6.0, + "r2-mean": 0.7132, + "r2-std": 0.0031, + "rmse-count": 6.0, + "rmse-mean": 7.0422, + "rmse-std": 0.0382 + } + }, + "avg": { + "val": { + "r2-mean": 0.637, + "rmse-mean": 7.6506, + "r2-std": 0.0025 + }, + "test": { + "r2-mean": 0.6515, + "rmse-mean": 7.6322, + "r2-std": 0.0024 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/smote/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/smote/info.json new file mode 100644 index 0000000000000000000000000000000000000000..b2c41b332bf4c4a3a99bd4f8249275a75d1da820 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/smote/info.json @@ -0,0 +1,10 @@ +{ + "name": "Facebook Comments Volume", + "id": "fb-comments--default", + "task_type": "regression", + "n_num_features": 36, + "n_cat_features": 15, + "train_size": 157638, + "test_size": 19720, + "val_size": 19722 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/smote/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/smote/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..b4fc8f5ec22ad06b41bb56806236286986a47a51 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/smote/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.027316815741151874 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/tvae/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/tvae/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..5213f552bdab69ba01c9516d7e7899eb227ba046 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/tvae/config.toml @@ -0,0 +1,36 @@ +parent_dir = "exp/fb-comments/tvae" +real_data_path = "data/fb-comments/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.00047756638508070437 +epochs = 30000 +embedding_dim = 256 +batch_size = 256 +loss_factor = 2.4301590023300905 +compress_dims = [ + 256, + 512, +] +decompress_dims = [ + 256, + 512, +] + +[sample] +seed = 0 +num_samples = 1280000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/tvae/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/tvae/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..802ad22df399b47e5cf8e9078741ee64b49c550b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/tvae/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.6901, + "r2-std": 0.0043, + "rmse-count": 50.0, + "rmse-mean": 7.1663, + "rmse-std": 0.0493 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.6853, + "r2-std": 0.0028, + "rmse-count": 50.0, + "rmse-mean": 7.3771, + "rmse-std": 0.0328 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/tvae/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/tvae/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..52f72831f7070f02d4e0f2573cfd462b22f768ea --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/tvae/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.6026, + "r2-std": 0.0051, + "rmse-count": 10.0, + "rmse-mean": 8.1158, + "rmse-std": 0.0523 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.5956, + "r2-std": 0.005, + "rmse-count": 10.0, + "rmse-mean": 8.3618, + "rmse-std": 0.0512 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.4765, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 9.3149, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.4634, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 9.6328, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7925, + "r2-std": 0.0012, + "rmse-count": 10.0, + "rmse-mean": 5.8638, + "rmse-std": 0.0166 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.798, + "r2-std": 0.0011, + "rmse-count": 10.0, + "rmse-mean": 5.9097, + "rmse-std": 0.0167 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7203, + "r2-std": 0.0096, + "rmse-count": 10.0, + "rmse-mean": 6.8072, + "rmse-std": 0.117 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.7219, + "r2-std": 0.0115, + "rmse-count": 10.0, + "rmse-mean": 6.9325, + "rmse-std": 0.1431 + } + }, + "avg": { + "val": { + "r2-mean": 0.648, + "rmse-mean": 7.5254, + "r2-std": 0.004 + }, + "test": { + "r2-mean": 0.6447, + "rmse-mean": 7.7092, + "r2-std": 0.0042 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/tvae/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/tvae/info.json new file mode 100644 index 0000000000000000000000000000000000000000..b2c41b332bf4c4a3a99bd4f8249275a75d1da820 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/fb-comments/tvae/info.json @@ -0,0 +1,10 @@ +{ + "name": "Facebook Comments Volume", + "id": "fb-comments--default", + "task_type": "regression", + "n_num_features": 36, + "n_cat_features": 15, + "train_size": 157638, + "test_size": 19720, + "val_size": 19722 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..e3c314c9b698075d68a79f360f731401da46ef39 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/config.toml @@ -0,0 +1,55 @@ +seed = 0 +parent_dir = "exp/gesture/check" +real_data_path = "data/gesture/" +model_type = "mlp" +num_numerical_features = 32 +device = "cuda:0" + +[model_params] +num_classes = 5 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 52000 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan-plus/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan-plus/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..cc6f1134e2f92645199e63cc62fe820801705f19 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan-plus/config.toml @@ -0,0 +1,34 @@ +parent_dir = "exp/gesture/ctabgan-plus" +real_data_path = "data/gesture/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.000806922832292031 +epochs = 10000 +class_dim = [ + 64, + 64, + 64, + 64, +] +batch_size = 2048 +random_dim = 16 +num_channels = 32 + +[sample] +seed = 0 +num_samples = 25200 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan-plus/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan-plus/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..cfbdfafceca0b6e94ca580b64b69d445d2ebcca2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan-plus/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.5039, + "acc-std": 0.0049, + "f1-count": 50.0, + "f1-mean": 0.4266, + "f1-std": 0.0083 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.4998, + "acc-std": 0.0053, + "f1-count": 50.0, + "f1-mean": 0.4055, + "f1-std": 0.0094 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan-plus/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan-plus/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..172c8f8f8af0b46f3e87bff6d0bdd534ce509a75 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan-plus/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.3702, + "acc-std": 0.0202, + "f1-count": 50.0, + "f1-mean": 0.3268, + "f1-std": 0.014 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.3735, + "acc-std": 0.0167, + "f1-count": 50.0, + "f1-mean": 0.3279, + "f1-std": 0.0089 + } + }, + "lr": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.4654, + "acc-std": 0.0031, + "f1-count": 50.0, + "f1-mean": 0.3665, + "f1-std": 0.0022 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.4717, + "acc-std": 0.0028, + "f1-count": 50.0, + "f1-mean": 0.3589, + "f1-std": 0.0032 + } + }, + "rf": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.4957, + "acc-std": 0.0057, + "f1-count": 50.0, + "f1-mean": 0.4071, + "f1-std": 0.0084 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.4926, + "acc-std": 0.0055, + "f1-count": 50.0, + "f1-mean": 0.3888, + "f1-std": 0.0085 + } + }, + "mlp": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.4856, + "acc-std": 0.0082, + "f1-count": 50.0, + "f1-mean": 0.422, + "f1-std": 0.0127 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.4859, + "acc-std": 0.0067, + "f1-count": 50.0, + "f1-mean": 0.4162, + "f1-std": 0.0138 + } + }, + "avg": { + "val": { + "f1-mean": 0.3806, + "acc-mean": 0.4542, + "f1-std": 0.0093 + }, + "test": { + "f1-mean": 0.373, + "acc-mean": 0.4559, + "f1-std": 0.009 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan-plus/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan-plus/info.json new file mode 100644 index 0000000000000000000000000000000000000000..fd2aaed70351244794cbf5a3727de7beaba9d398 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan-plus/info.json @@ -0,0 +1,11 @@ +{ + "name": "Gesture Phase", + "id": "gesture--default", + "task_type": "multiclass", + "n_num_features": 32, + "n_cat_features": 0, + "train_size": 6318, + "val_size": 1580, + "test_size": 1975, + "n_classes": 5 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..214635cb630b28e1feae39624a920e476e545390 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan/config.toml @@ -0,0 +1,34 @@ +parent_dir = "exp/gesture/ctabgan" +real_data_path = "data/gesture/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.0006991141938395216 +epochs = 10000 +class_dim = [ + 256, + 256, + 256, + 128, +] +batch_size = 2048 +random_dim = 128 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 12600 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..562d560c6fae9801ca77f7f5eabc0f769ff61c10 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.4746, + "acc-std": 0.0085, + "f1-count": 50.0, + "f1-mean": 0.4024, + "f1-std": 0.0076 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.4731, + "acc-std": 0.0135, + "f1-count": 50.0, + "f1-mean": 0.3922, + "f1-std": 0.0064 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..63f259aee6a8738349cbbae9aa89cb52cd3f2c79 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.3408, + "acc-std": 0.0122, + "f1-count": 10.0, + "f1-mean": 0.3052, + "f1-std": 0.014 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.3498, + "acc-std": 0.0209, + "f1-count": 10.0, + "f1-mean": 0.3099, + "f1-std": 0.0146 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.4489, + "acc-std": 0.0027, + "f1-count": 10.0, + "f1-mean": 0.3356, + "f1-std": 0.0047 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.4495, + "acc-std": 0.0036, + "f1-count": 10.0, + "f1-mean": 0.3257, + "f1-std": 0.0044 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.4742, + "acc-std": 0.006, + "f1-count": 10.0, + "f1-mean": 0.3756, + "f1-std": 0.008 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.4818, + "acc-std": 0.0057, + "f1-count": 10.0, + "f1-mean": 0.3726, + "f1-std": 0.0048 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.4581, + "acc-std": 0.0061, + "f1-count": 10.0, + "f1-mean": 0.3571, + "f1-std": 0.0129 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.4599, + "acc-std": 0.0033, + "f1-count": 10.0, + "f1-mean": 0.3483, + "f1-std": 0.0094 + } + }, + "avg": { + "val": { + "f1-mean": 0.3434, + "acc-mean": 0.4305, + "f1-std": 0.0099 + }, + "test": { + "f1-mean": 0.3391, + "acc-mean": 0.4352, + "f1-std": 0.0091 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan/info.json new file mode 100644 index 0000000000000000000000000000000000000000..fd2aaed70351244794cbf5a3727de7beaba9d398 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan/info.json @@ -0,0 +1,11 @@ +{ + "name": "Gesture Phase", + "id": "gesture--default", + "task_type": "multiclass", + "n_num_features": 32, + "n_cat_features": 0, + "train_size": 6318, + "val_size": 1580, + "test_size": 1975, + "n_classes": 5 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..0b87b5ec5fff50e078c81823135dca915c4bf53a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ctabgan/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.3922, + 0.387, + 0.3957, + 0.3944, + 0.3923 + ], + "privacies": [ + 0.14606960767133748, + 0.1602, + 0.1806, + 0.1996, + 0.2202 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..b0e0708185b444a81ba11355fc8b5bd390e1596a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/config.toml @@ -0,0 +1,56 @@ +seed = 0 +parent_dir = "exp/gesture/ddpm_cb_best" +real_data_path = "data/gesture/" +model_type = "mlp" +num_numerical_features = 32 +device = "cuda:0" + +[model_params] +num_classes = 5 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 128, + 512, + 512, + 1024, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.002805595109954435 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 52000 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..06dbc1ea6a66a44b8ef462feebd6aa10df8c971b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/eval_catboost.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.6727, + "acc-std": 0.0045, + "f1-count": 50.0, + "f1-mean": 0.6315, + "f1-std": 0.0054 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.6467, + "acc-std": 0.0052, + "f1-count": 50.0, + "f1-mean": 0.5967, + "f1-std": 0.0062 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.708, + "acc-std": 0.0033, + "f1-count": 10.0, + "f1-mean": 0.667, + "f1-std": 0.0041 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6856, + "acc-std": 0.0056, + "f1-count": 10.0, + "f1-mean": 0.6365, + "f1-std": 0.0073 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..5b13e183203e45bd054c176f26e1bee92a38ada0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/eval_mlp.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.6492, + "acc-std": 0.0081, + "f1-count": 50.0, + "f1-mean": 0.6179, + "f1-std": 0.0089 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.6313, + "acc-std": 0.0061, + "f1-count": 50.0, + "f1-mean": 0.5949, + "f1-std": 0.0062 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6634, + "acc-std": 0.0072, + "f1-count": 10.0, + "f1-mean": 0.635, + "f1-std": 0.008 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6433, + "acc-std": 0.0073, + "f1-count": 10.0, + "f1-mean": 0.6072, + "f1-std": 0.0074 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..5e016e85d15c7ce3d9fa91c45e1fbdcf919c1ca4 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/eval_simple.json @@ -0,0 +1,174 @@ +{ + "real": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.4979, + "acc-std": 0.005, + "f1-count": 10.0, + "f1-mean": 0.4615, + "f1-std": 0.0041 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.4999, + "acc-std": 0.0036, + "f1-count": 10.0, + "f1-mean": 0.4571, + "f1-std": 0.0029 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.4671, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.3107, + "f1-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.478, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.314, + "f1-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6518, + "acc-std": 0.0052, + "f1-count": 10.0, + "f1-mean": 0.5938, + "f1-std": 0.0088 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6489, + "acc-std": 0.0053, + "f1-count": 10.0, + "f1-mean": 0.5932, + "f1-std": 0.0078 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.4915, + "acc-std": 0.0049, + "f1-count": 10.0, + "f1-mean": 0.3724, + "f1-std": 0.0137 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.4879, + "acc-std": 0.0044, + "f1-count": 10.0, + "f1-mean": 0.3612, + "f1-std": 0.013 + } + }, + "avg": { + "val": { + "f1-mean": 0.4346, + "acc-mean": 0.5271, + "f1-std": 0.0066 + }, + "test": { + "f1-mean": 0.4314, + "acc-mean": 0.5287, + "f1-std": 0.0055 + } + } + }, + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.4989, + "acc-std": 0.0038, + "f1-count": 10.0, + "f1-mean": 0.4583, + "f1-std": 0.0047 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.4887, + "acc-std": 0.0066, + "f1-count": 10.0, + "f1-mean": 0.4462, + "f1-std": 0.0074 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.4892, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.3818, + "f1-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.4932, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.3775, + "f1-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6441, + "acc-std": 0.0052, + "f1-count": 10.0, + "f1-mean": 0.5938, + "f1-std": 0.0058 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6339, + "acc-std": 0.0037, + "f1-count": 10.0, + "f1-mean": 0.5754, + "f1-std": 0.005 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.5115, + "acc-std": 0.004, + "f1-count": 10.0, + "f1-mean": 0.4434, + "f1-std": 0.0053 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.5124, + "acc-std": 0.004, + "f1-count": 10.0, + "f1-mean": 0.4501, + "f1-std": 0.0099 + } + }, + "avg": { + "val": { + "f1-mean": 0.4693, + "acc-mean": 0.5359, + "f1-std": 0.0053 + }, + "test": { + "f1-mean": 0.4623, + "acc-mean": 0.532, + "f1-std": 0.0055 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..532905d1cf86ecbace2b01670f27ce64446a16c7 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/importance.json @@ -0,0 +1,10 @@ +{ + "batch_size": 0.3578678746396464, + "lr": 0.17140995374669846, + "steps": 0.16791539819974605, + "num_samples": 0.12343635060826025, + "d_first": 0.08882276836748593, + "n_layers": 0.0414547074793888, + "num_timesteps": 0.033229724548236673, + "d_last": 0.015863222410537503 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..fd2aaed70351244794cbf5a3727de7beaba9d398 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/info.json @@ -0,0 +1,11 @@ +{ + "name": "Gesture Phase", + "id": "gesture--default", + "task_type": "multiclass", + "n_num_features": 32, + "n_cat_features": 0, + "train_size": 6318, + "val_size": 1580, + "test_size": 1975, + "n_classes": 5 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..720df9efcfd40261dd785371643cb71681afd2d0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.5967, + 0.5972, + 0.5969, + 0.5833, + 0.5949 + ], + "privacies": [ + 0.058418279349231675, + 0.0575, + 0.0586, + 0.0583, + 0.0584 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..337989094c1c63d9d6ee29fa6b9dec2eea2c18c7 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/ddpm_cb_best/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.05890877157371753 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..b9bffbae881f3902b3b0f7c10d55da1a7d9c217e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/config.toml @@ -0,0 +1,24 @@ +parent_dir = "exp/gesture/smote" +real_data_path = "data/gesture/" +seed = 0 + +[smote_params] +k_neighbours = 10 +frac_samples = 8 +frac_lam_del = 0.05 + +[sample] +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..a5b24e7793550af2fefb98508435f902e237de9e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.723, + "acc-std": 0.0036, + "f1-count": 50.0, + "f1-mean": 0.6878, + "f1-std": 0.0037 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.6997, + "acc-std": 0.0056, + "f1-count": 50.0, + "f1-mean": 0.6579, + "f1-std": 0.0066 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..47be11d34a3869cb0008a63e6f4147423ab15af5 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.5239, + "acc-std": 0.0058, + "f1-count": 10.0, + "f1-mean": 0.4899, + "f1-std": 0.0068 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.5052, + "acc-std": 0.0032, + "f1-count": 10.0, + "f1-mean": 0.4692, + "f1-std": 0.0039 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.4797, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.3559, + "f1-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.4891, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.354, + "f1-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7016, + "acc-std": 0.0052, + "f1-count": 10.0, + "f1-mean": 0.6657, + "f1-std": 0.0063 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6881, + "acc-std": 0.0041, + "f1-count": 10.0, + "f1-mean": 0.6455, + "f1-std": 0.0066 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.5164, + "acc-std": 0.0068, + "f1-count": 10.0, + "f1-mean": 0.4492, + "f1-std": 0.0113 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.5129, + "acc-std": 0.0042, + "f1-count": 10.0, + "f1-mean": 0.4454, + "f1-std": 0.0088 + } + }, + "avg": { + "val": { + "f1-mean": 0.4902, + "acc-mean": 0.5554, + "f1-std": 0.0061 + }, + "test": { + "f1-mean": 0.4785, + "acc-mean": 0.5488, + "f1-std": 0.0055 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/info.json new file mode 100644 index 0000000000000000000000000000000000000000..fd2aaed70351244794cbf5a3727de7beaba9d398 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/info.json @@ -0,0 +1,11 @@ +{ + "name": "Gesture Phase", + "id": "gesture--default", + "task_type": "multiclass", + "n_num_features": 32, + "n_cat_features": 0, + "train_size": 6318, + "val_size": 1580, + "test_size": 1975, + "n_classes": 5 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..a046f2d6ee70a33c070af045f04a28de22d33581 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.6579, + 0.6534, + 0.6515, + 0.6462, + 0.6486 + ], + "privacies": [ + 0.02315935918583025, + 0.0223, + 0.0219, + 0.0213, + 0.0215 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..8e003e6b5cac2b0fa53127267a5a3a79ad2b7812 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/smote/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.02315935918583025 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/tvae/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/tvae/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..45c11bfbaa7aff6875dbb3a36ac70de78f43125b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/tvae/config.toml @@ -0,0 +1,36 @@ +parent_dir = "exp/gesture/tvae" +real_data_path = "data/gesture/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.00047756638508070437 +epochs = 30000 +embedding_dim = 256 +batch_size = 256 +loss_factor = 2.4301590023300905 +compress_dims = [ + 256, + 512, +] +decompress_dims = [ + 256, + 512, +] + +[sample] +seed = 0 +num_samples = 50400 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/tvae/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/tvae/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..9c299fe391c73bff4356458d27de55ac735bf7a8 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/tvae/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.5237, + "acc-std": 0.005, + "f1-count": 50.0, + "f1-mean": 0.4294, + "f1-std": 0.0067 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.5299, + "acc-std": 0.0035, + "f1-count": 50.0, + "f1-mean": 0.434, + "f1-std": 0.0058 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/tvae/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/tvae/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..89955589abce5f973e30d7f6735cc688d513db4e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/tvae/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.4389, + "acc-std": 0.0051, + "f1-count": 10.0, + "f1-mean": 0.3698, + "f1-std": 0.0061 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.4401, + "acc-std": 0.0046, + "f1-count": 10.0, + "f1-mean": 0.3764, + "f1-std": 0.0046 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.4551, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.3152, + "f1-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.4911, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.3375, + "f1-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.5022, + "acc-std": 0.0046, + "f1-count": 10.0, + "f1-mean": 0.3879, + "f1-std": 0.0052 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.5156, + "acc-std": 0.0031, + "f1-count": 10.0, + "f1-mean": 0.3963, + "f1-std": 0.0045 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.477, + "acc-std": 0.006, + "f1-count": 10.0, + "f1-mean": 0.3724, + "f1-std": 0.0151 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.4887, + "acc-std": 0.0057, + "f1-count": 10.0, + "f1-mean": 0.3784, + "f1-std": 0.0129 + } + }, + "avg": { + "val": { + "f1-mean": 0.3613, + "acc-mean": 0.4683, + "f1-std": 0.0066 + }, + "test": { + "f1-mean": 0.3722, + "acc-mean": 0.4839, + "f1-std": 0.006 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/tvae/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/tvae/info.json new file mode 100644 index 0000000000000000000000000000000000000000..fd2aaed70351244794cbf5a3727de7beaba9d398 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/gesture/tvae/info.json @@ -0,0 +1,11 @@ +{ + "name": "Gesture Phase", + "id": "gesture--default", + "task_type": "multiclass", + "n_num_features": 32, + "n_cat_features": 0, + "train_size": 6318, + "val_size": 1580, + "test_size": 1975, + "n_classes": 5 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..5c4345ef15c20e491cb7977c18fc63b8bf50f900 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/config.toml @@ -0,0 +1,57 @@ +seed = 0 +parent_dir = "exp/higgs-small/check" +real_data_path = "data/higgs-small/" +model_type = "mlp" +num_numerical_features = 28 +device = "cuda:1" + +[model_params] +d_in = 28 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 502000 +batch_size = 60000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" +model = "catboost" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan-plus/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan-plus/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..8ad70d066a897715c43e15a0543d7c075b4b0c60 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan-plus/config.toml @@ -0,0 +1,33 @@ +parent_dir = "exp/higgs-small/ctabgan-plus" +real_data_path = "data/higgs-small/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.0002288113668475521 +epochs = 10000 +class_dim = [ + 128, + 128, + 128, +] +batch_size = 2048 +random_dim = 32 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 125400 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan-plus/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan-plus/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..fc63aa9f4a182478bc98405132827b136efa6f9a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan-plus/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.6755, + "acc-std": 0.0015, + "f1-count": 50.0, + "f1-mean": 0.6749, + "f1-std": 0.0015, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7347, + "roc_auc-std": 0.0012 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.6643, + "acc-std": 0.0019, + "f1-count": 50.0, + "f1-mean": 0.6639, + "f1-std": 0.0019, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7257, + "roc_auc-std": 0.0014 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan-plus/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan-plus/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..609269dba441d5c030495f79bb94acb4786f3b7d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan-plus/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.5302, + "acc-std": 0.0068, + "f1-count": 50.0, + "f1-mean": 0.5301, + "f1-std": 0.0068, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.5276, + "roc_auc-std": 0.0076 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.5317, + "acc-std": 0.0064, + "f1-count": 50.0, + "f1-mean": 0.5316, + "f1-std": 0.0063, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.5291, + "roc_auc-std": 0.0072 + } + }, + "lr": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.6065, + "acc-std": 0.0014, + "f1-count": 50.0, + "f1-mean": 0.5981, + "f1-std": 0.0016, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6388, + "roc_auc-std": 0.001 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.599, + "acc-std": 0.0003, + "f1-count": 50.0, + "f1-mean": 0.5911, + "f1-std": 0.0003, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.633, + "roc_auc-std": 0.0008 + } + }, + "rf": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.6462, + "acc-std": 0.0032, + "f1-count": 50.0, + "f1-mean": 0.646, + "f1-std": 0.0032, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7008, + "roc_auc-std": 0.0033 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.6387, + "acc-std": 0.0041, + "f1-count": 50.0, + "f1-mean": 0.6386, + "f1-std": 0.0041, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6923, + "roc_auc-std": 0.0039 + } + }, + "mlp": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.6402, + "acc-std": 0.0059, + "f1-count": 50.0, + "f1-mean": 0.6396, + "f1-std": 0.0065, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6983, + "roc_auc-std": 0.0038 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.6331, + "acc-std": 0.0064, + "f1-count": 50.0, + "f1-mean": 0.6324, + "f1-std": 0.0071, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6884, + "roc_auc-std": 0.0036 + } + }, + "avg": { + "val": { + "f1-mean": 0.6034, + "acc-mean": 0.6058, + "f1-std": 0.0045 + }, + "test": { + "f1-mean": 0.5984, + "acc-mean": 0.6006, + "f1-std": 0.0045 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan-plus/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan-plus/info.json new file mode 100644 index 0000000000000000000000000000000000000000..d3db9ad284e44627ef2db1d07044fe8afbd450dd --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan-plus/info.json @@ -0,0 +1,10 @@ +{ + "name": "Higgs Small", + "id": "higgs-small--default", + "task_type": "binclass", + "n_num_features": 28, + "n_cat_features": 0, + "train_size": 62751, + "val_size": 15688, + "test_size": 19610 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..4d4e00d9f900df7caac29d33289bb5725cd9f3de --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan/config.toml @@ -0,0 +1,34 @@ +parent_dir = "exp/higgs-small/ctabgan" +real_data_path = "data/higgs-small/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.0005772589953788761 +epochs = 5000 +class_dim = [ + 256, + 128, + 128, + 128, +] +batch_size = 2048 +random_dim = 128 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 125400 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..fd3cbfdd90bc4c95e915d8ceece6ef404d7b55fc --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.602, + "acc-std": 0.0033, + "f1-count": 50.0, + "f1-mean": 0.5703, + "f1-std": 0.0028, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6427, + "roc_auc-std": 0.0075 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.6054, + "acc-std": 0.0028, + "f1-count": 50.0, + "f1-mean": 0.5748, + "f1-std": 0.0038, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6426, + "roc_auc-std": 0.0079 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..61eb7fdce4d1667241dbfb18ec2a5f1f0b47c7ae --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.557, + "acc-std": 0.004, + "f1-count": 50.0, + "f1-mean": 0.5436, + "f1-std": 0.0035, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.5277, + "roc_auc-std": 0.0049 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.558, + "acc-std": 0.0049, + "f1-count": 50.0, + "f1-mean": 0.5455, + "f1-std": 0.0047, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.5314, + "roc_auc-std": 0.0037 + } + }, + "lr": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.5647, + "acc-std": 0.0008, + "f1-count": 50.0, + "f1-mean": 0.4892, + "f1-std": 0.0018, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.5655, + "roc_auc-std": 0.0006 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.5651, + "acc-std": 0.001, + "f1-count": 50.0, + "f1-mean": 0.4904, + "f1-std": 0.0025, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.5648, + "roc_auc-std": 0.0005 + } + }, + "rf": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.5986, + "acc-std": 0.0023, + "f1-count": 50.0, + "f1-mean": 0.5567, + "f1-std": 0.0027, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.646, + "roc_auc-std": 0.0023 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.5999, + "acc-std": 0.002, + "f1-count": 50.0, + "f1-mean": 0.5592, + "f1-std": 0.0029, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6458, + "roc_auc-std": 0.0018 + } + }, + "mlp": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.5955, + "acc-std": 0.0055, + "f1-count": 50.0, + "f1-mean": 0.5585, + "f1-std": 0.0145, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6337, + "roc_auc-std": 0.0048 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.5964, + "acc-std": 0.0052, + "f1-count": 50.0, + "f1-mean": 0.5604, + "f1-std": 0.0137, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6345, + "roc_auc-std": 0.0044 + } + }, + "avg": { + "val": { + "f1-mean": 0.537, + "acc-mean": 0.579, + "f1-std": 0.0056 + }, + "test": { + "f1-mean": 0.5389, + "acc-mean": 0.5798, + "f1-std": 0.0058 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan/info.json new file mode 100644 index 0000000000000000000000000000000000000000..d3db9ad284e44627ef2db1d07044fe8afbd450dd --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan/info.json @@ -0,0 +1,10 @@ +{ + "name": "Higgs Small", + "id": "higgs-small--default", + "task_type": "binclass", + "n_num_features": 28, + "n_cat_features": 0, + "train_size": 62751, + "val_size": 15688, + "test_size": 19610 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..a2743716ad508a859c2d1c43ac4f4bb85970c0f1 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ctabgan/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.5748, + 0.5729, + 0.5769, + 0.5779, + 0.5772 + ], + "privacies": [ + 0.5709949110057996, + 0.5589, + 0.5626, + 0.5616, + 0.563 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..185fd57cf997409781fd94d26308a1ef4ef210b3 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/config.toml @@ -0,0 +1,60 @@ +seed = 0 +parent_dir = "exp/higgs-small/ddpm_cb_best" +real_data_path = "data/higgs-small/" +model_type = "mlp" +num_numerical_features = 28 +device = "cuda:1" + +[model_params] +d_in = 28 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 256, + 1024, + 1024, + 1024, + 1024, + 512, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.0010482394930684048 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 502000 +batch_size = 60000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" +model = "catboost" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..75c7f0c422435c1fc85a1326c5cdcc6acf4178e0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/eval_catboost.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7282, + "acc-std": 0.0011, + "f1-count": 50.0, + "f1-mean": 0.7266, + "f1-std": 0.0012, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8082, + "roc_auc-std": 0.0011 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7231, + "acc-std": 0.0013, + "f1-count": 50.0, + "f1-mean": 0.7218, + "f1-std": 0.0013, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8028, + "roc_auc-std": 0.001 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.732, + "acc-std": 0.0014, + "f1-count": 10.0, + "f1-mean": 0.7304, + "f1-std": 0.0014, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8101, + "roc_auc-std": 0.0008 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7251, + "acc-std": 0.0013, + "f1-count": 10.0, + "f1-mean": 0.7238, + "f1-std": 0.0012, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8049, + "roc_auc-std": 0.0005 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..5bc55674d63ad815b1fa340c74e5f368861bb4a1 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/eval_mlp.json @@ -0,0 +1,50 @@ +{ + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7212, + "acc-std": 0.0017, + "f1-count": 10.0, + "f1-mean": 0.7205, + "f1-std": 0.0018, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.802, + "roc_auc-std": 0.0009 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7173, + "acc-std": 0.0018, + "f1-count": 10.0, + "f1-mean": 0.7167, + "f1-std": 0.0017, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7949, + "roc_auc-std": 0.0012 + } + }, + "synthetic": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7212, + "acc-std": 0.0017, + "f1-count": 10.0, + "f1-mean": 0.7205, + "f1-std": 0.0018, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.802, + "roc_auc-std": 0.0009 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7173, + "acc-std": 0.0018, + "f1-count": 10.0, + "f1-mean": 0.7167, + "f1-std": 0.0017, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7949, + "roc_auc-std": 0.0012 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..341892dd7c4f36d863598e482c81d85abe5846ae --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/eval_simple.json @@ -0,0 +1,222 @@ +{ + "real": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6261, + "acc-std": 0.0012, + "f1-count": 10.0, + "f1-mean": 0.6246, + "f1-std": 0.0012, + "roc_auc-count": 10.0, + 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file mode 100644 index 0000000000000000000000000000000000000000..5cce9cc655336203c120668982616d1d669c2d43 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/importance.json @@ -0,0 +1,10 @@ +{ + "lr": 0.36398407749711836, + "num_samples": 0.22469517559565347, + "steps": 0.1961252304472083, + "batch_size": 0.07828220665129192, + "d_first": 0.05435045688922316, + "n_layers": 0.03933344236581922, + "d_last": 0.028120456982630444, + "num_timesteps": 0.015108953571055017 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..d3db9ad284e44627ef2db1d07044fe8afbd450dd --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/info.json @@ -0,0 +1,10 @@ +{ + "name": "Higgs Small", + "id": "higgs-small--default", + "task_type": "binclass", + "n_num_features": 28, + "n_cat_features": 0, + "train_size": 62751, + "val_size": 15688, + "test_size": 19610 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..b0d61176bcd97d8e9a4096f1e41da9183590e679 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.7218, + 0.7218, + 0.7221, + 0.7218, + 0.7226 + ], + "privacies": [ + 0.5015222467670555, + 0.4897, + 0.4894, + 0.4902, + 0.489 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..934e258f8798008733be6d8cb88c654600239072 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/ddpm_cb_best/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.44872078631668183 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/smote/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/smote/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..3ec38abe902cd6287ed31ef809ef20ece16f512e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/smote/config.toml @@ -0,0 +1,24 @@ +parent_dir = "exp/higgs-small/smote" +real_data_path = "data/higgs-small/" +seed = 0 + +[smote_params] +k_neighbours = 14 +frac_samples = 8 +frac_lam_del = 0.05 + +[sample] +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/smote/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/smote/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..b291eabcfe1d9f6b0126fd8e3f205232753bcb03 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/smote/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.7326, + "acc-std": 0.0008, + "f1-count": 50.0, + "f1-mean": 0.7318, + "f1-std": 0.0008, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8091, + "roc_auc-std": 0.0006 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.7225, + "acc-std": 0.0012, + "f1-count": 50.0, + "f1-mean": 0.7219, + "f1-std": 0.0012, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.8013, + "roc_auc-std": 0.0006 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/smote/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/smote/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..e168cb9bda025ac2a9ed41f10d4ceddefd5f8df1 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/smote/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6188, + "acc-std": 0.0014, + "f1-count": 10.0, + "f1-mean": 0.6181, + "f1-std": 0.0014, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.5967, + "roc_auc-std": 0.0017 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6161, + "acc-std": 0.0017, + "f1-count": 10.0, + "f1-mean": 0.6155, + "f1-std": 0.0017, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.5966, + "roc_auc-std": 0.002 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6439, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6389, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6875, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6415, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.6372, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6817, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7164, + "acc-std": 0.0018, + "f1-count": 10.0, + "f1-mean": 0.7159, + "f1-std": 0.0018, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7937, + "roc_auc-std": 0.001 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.7108, + "acc-std": 0.0015, + "f1-count": 10.0, + "f1-mean": 0.7104, + "f1-std": 0.0015, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.787, + "roc_auc-std": 0.0008 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.7034, + "acc-std": 0.0039, + "f1-count": 10.0, + "f1-mean": 0.7016, + "f1-std": 0.0064, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7746, + "roc_auc-std": 0.0024 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6962, + "acc-std": 0.0035, + "f1-count": 10.0, + "f1-mean": 0.6945, + "f1-std": 0.007, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7666, + "roc_auc-std": 0.0022 + } + }, + "avg": { + "val": { + "f1-mean": 0.6686, + "acc-mean": 0.6706, + "f1-std": 0.0024 + }, + "test": { + "f1-mean": 0.6644, + "acc-mean": 0.6662, + "f1-std": 0.0025 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/smote/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/smote/info.json new file mode 100644 index 0000000000000000000000000000000000000000..d3db9ad284e44627ef2db1d07044fe8afbd450dd --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/smote/info.json @@ -0,0 +1,10 @@ +{ + "name": "Higgs Small", + "id": "higgs-small--default", + "task_type": "binclass", + "n_num_features": 28, + "n_cat_features": 0, + "train_size": 62751, + "val_size": 15688, + "test_size": 19610 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/smote/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/smote/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..fb3cbb893fadb6f237b26adda2d597d75f2040ac --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/smote/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.31949807221466453 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/tvae/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/tvae/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..1e5a2806e7f838f3104c68d679e67d3cea2c6b37 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/tvae/config.toml @@ -0,0 +1,40 @@ +parent_dir = "exp/higgs-small/tvae" +real_data_path = "data/higgs-small/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.0003106059409975757 +epochs = 30000 +embedding_dim = 128 +batch_size = 4096 +loss_factor = 6.425117768649489 +compress_dims = [ + 256, + 512, + 512, + 512, +] +decompress_dims = [ + 256, + 512, + 512, + 512, +] + +[sample] +seed = 0 +num_samples = 31500 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/tvae/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/tvae/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..9c735427ac5e9f5265a9aff47396b96c2b24c455 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/tvae/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.6443, + "acc-std": 0.003, + "f1-count": 50.0, + "f1-mean": 0.6379, + "f1-std": 0.0033, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6939, + "roc_auc-std": 0.0038 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.6435, + "acc-std": 0.0029, + "f1-count": 50.0, + "f1-mean": 0.6378, + "f1-std": 0.0033, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.694, + "roc_auc-std": 0.0035 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/tvae/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/tvae/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..edb0debc2b53c5a7d51db01e776a2f6611eedddf --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/tvae/eval_simple.json @@ -0,0 +1,222 @@ +{ + "synthetic": { + "tree": { + "val": { + 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"roc_auc-count": 10.0, + "roc_auc-mean": 0.7158, + "roc_auc-std": 0.0013 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6516, + "acc-std": 0.0021, + "f1-count": 10.0, + "f1-mean": 0.6473, + "f1-std": 0.0022, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7063, + "roc_auc-std": 0.0012 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.6411, + "acc-std": 0.0016, + "f1-count": 10.0, + "f1-mean": 0.6343, + "f1-std": 0.0031, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6875, + "roc_auc-std": 0.0014 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.6359, + "acc-std": 0.0011, + "f1-count": 10.0, + "f1-mean": 0.6302, + "f1-std": 0.0022, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6782, + "roc_auc-std": 0.0017 + } + }, + "avg": { + "val": { + "f1-mean": 0.6256, + "acc-mean": 0.6315, + "f1-std": 0.0015 + }, + "test": { + "f1-mean": 0.6184, + "acc-mean": 0.6236, + "f1-std": 0.0015 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/tvae/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/tvae/info.json new file mode 100644 index 0000000000000000000000000000000000000000..d3db9ad284e44627ef2db1d07044fe8afbd450dd --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/higgs-small/tvae/info.json @@ -0,0 +1,10 @@ +{ + "name": "Higgs Small", + "id": "higgs-small--default", + "task_type": "binclass", + "n_num_features": 28, + "n_cat_features": 0, + "train_size": 62751, + "val_size": 15688, + "test_size": 19610 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..1eedff882e1afd546160773aa39adecc3b3b9fad --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/config.toml @@ -0,0 +1,57 @@ +seed = 0 +parent_dir = "exp/house/check" +real_data_path = "data/house/" +model_type = "mlp" +num_numerical_features = 16 +device = "cuda:0" + +[model_params] +d_in = 17 +num_classes = 0 +is_y_cond = false + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 116000 +batch_size = 30000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" +model = "catboost" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ctabgan-plus/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ctabgan-plus/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..24abf8f7b8a44f7d77a950967d24c4a0dd19d887 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ctabgan-plus/config.toml @@ -0,0 +1,33 @@ +parent_dir = "exp/house/ctabgan-plus" +real_data_path = "data/house/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.0002104949164041533 +epochs = 10000 +class_dim = [ + 256, + 64, + 256, +] +batch_size = 1024 +random_dim = 16 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 58000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ctabgan-plus/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ctabgan-plus/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..e2a80d2d2f2a4ba203afd812ed3697800c80e542 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ctabgan-plus/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.5431, + "r2-std": 0.0084, + "rmse-count": 50.0, + "rmse-mean": 36895.965, + "rmse-std": 338.6538 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.504, + "r2-std": 0.0053, + "rmse-count": 50.0, + "rmse-mean": 37841.4048, + "rmse-std": 203.6044 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ctabgan-plus/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ctabgan-plus/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..8911525f3591ccea239c6b7b0bb7ed1c8fc41132 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ctabgan-plus/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 50.0, + "r2-mean": -0.2342, + "r2-std": 0.1057, + "rmse-count": 50.0, + "rmse-mean": 60589.0464, + "rmse-std": 2595.0287 + }, + "test": { + "r2-count": 50.0, + "r2-mean": -0.2492, + "r2-std": 0.1764, + "rmse-count": 50.0, + "rmse-mean": 59910.3879, + "rmse-std": 4127.4452 + } + }, + "lr": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.2336, + "r2-std": 0.0024, + "rmse-count": 50.0, + "rmse-mean": 47789.6603, + "rmse-std": 73.4831 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.225, + "r2-std": 0.0014, + "rmse-count": 50.0, + "rmse-mean": 47300.232, + "rmse-std": 42.8727 + } + }, + "rf": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.4682, + "r2-std": 0.0106, + "rmse-count": 50.0, + "rmse-mean": 39805.5424, + "rmse-std": 393.1636 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.4443, + "r2-std": 0.0166, + "rmse-count": 50.0, + "rmse-mean": 40048.2031, + "rmse-std": 596.8223 + } + }, + "mlp": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.5013, + "r2-std": 0.0082, + "rmse-count": 50.0, + "rmse-mean": 38548.6853, + "rmse-std": 317.2295 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.4677, + "r2-std": 0.0109, + "rmse-count": 50.0, + "rmse-mean": 39199.8931, + "rmse-std": 399.9748 + } + }, + "avg": { + "val": { + "r2-mean": 0.2422, + "rmse-mean": 46683.2336, + "r2-std": 0.0317 + }, + "test": { + "r2-mean": 0.222, + "rmse-mean": 46614.679, + "r2-std": 0.0415 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ctabgan-plus/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ctabgan-plus/info.json new file mode 100644 index 0000000000000000000000000000000000000000..65079c2b085449ac564139ca1d0e90917400902c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ctabgan-plus/info.json @@ -0,0 +1,10 @@ +{ + "name": "House 16H", + "id": "house--default", + "task_type": "regression", + "n_num_features": 16, + "n_cat_features": 0, + "train_size": 14581, + "val_size": 3646, + "test_size": 4557 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..b18ea0fb99ec305761e2d5cb5588bc6ef210cd25 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/config.toml @@ -0,0 +1,61 @@ +seed = 0 +parent_dir = "exp/house/ddpm_cb_best" +real_data_path = "data/house/" +model_type = "mlp" +num_numerical_features = 16 +device = "cuda:0" + +[model_params] +d_in = 17 +num_classes = 0 +is_y_cond = false + +[model_params.rtdl_params] +d_layers = [ + 128, + 512, + 512, + 512, + 512, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 30000 +lr = 0.0013926185951764255 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 116000 +batch_size = 30000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" +model = "catboost" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..c2d18741bea54f7986ed413dd8f31b33a158b1ed --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/eval_catboost.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.718, + "r2-std": 0.0057, + "rmse-count": 50.0, + "rmse-mean": 28987.7573, + "rmse-std": 293.8959 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.6766, + "r2-std": 0.0096, + "rmse-count": 50.0, + "rmse-mean": 30550.4301, + "rmse-std": 455.2161 + } + }, + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.6953, + "r2-std": 0.003, + "rmse-count": 10.0, + "rmse-mean": 30133.4077, + "rmse-std": 149.5357 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.6616, + "r2-std": 0.0027, + "rmse-count": 10.0, + "rmse-mean": 31256.3238, + "rmse-std": 125.69 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..a139b6976f94d357e0861afb93b56e0e2b0e288f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/eval_mlp.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.7079, + "r2-std": 0.0057, + "rmse-count": 50.0, + "rmse-mean": 29502.4059, + "rmse-std": 285.3899 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.6433, + "r2-std": 0.0102, + "rmse-count": 50.0, + "rmse-mean": 32088.6273, + "rmse-std": 458.3604 + } + }, + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7017, + "r2-std": 0.0055, + "rmse-count": 10.0, + "rmse-mean": 29815.8792, + "rmse-std": 276.9507 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.6137, + "r2-std": 0.0061, + "rmse-count": 10.0, + "rmse-mean": 33392.8991, + "rmse-std": 265.109 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..a5ae817820ce34194b388adbe135162a3fb96864 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/eval_simple.json @@ -0,0 +1,174 @@ +{ + "real": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.2716, + "r2-std": 0.028, + "rmse-count": 10.0, + "rmse-mean": 46580.9171, + "rmse-std": 892.6794 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.2762, + "r2-std": 0.0204, + "rmse-count": 10.0, + "rmse-mean": 45707.4061, + "rmse-std": 646.0565 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.273, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 46545.8703, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.2662, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 46025.2727, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.6465, + "r2-std": 0.0026, + "rmse-count": 10.0, + "rmse-mean": 32457.8825, + "rmse-std": 117.3249 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.6102, + "r2-std": 0.0031, + "rmse-count": 10.0, + "rmse-mean": 33546.0579, + "rmse-std": 135.1099 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.5332, + "r2-std": 0.0061, + "rmse-count": 10.0, + "rmse-mean": 37294.164, + "rmse-std": 241.9236 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.5068, + "r2-std": 0.0052, + "rmse-count": 10.0, + "rmse-mean": 37732.1206, + "rmse-std": 199.919 + } + }, + "avg": { + "val": { + "r2-mean": 0.4311, + "rmse-mean": 40719.7085, + "r2-std": 0.0092 + }, + "test": { + "r2-mean": 0.4149, + "rmse-mean": 40752.7143, + "r2-std": 0.0074 + } + } + }, + "synthetic": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.4012, + "r2-std": 0.0149, + "rmse-count": 10.0, + "rmse-mean": 42240.2751, + "rmse-std": 522.6303 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.2766, + "r2-std": 0.0133, + "rmse-count": 10.0, + "rmse-mean": 45697.7718, + "rmse-std": 417.312 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.273, + "r2-std": 0.0, + 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"r2-std": 0.0069 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..a3e1bed849199f63bf4e0fb6e0f0e8f86f584f55 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/importance.json @@ -0,0 +1,10 @@ +{ + "num_samples": 0.41402482151392933, + "lr": 0.33044101495257544, + "steps": 0.1753047701550473, + "n_layers": 0.03794876107876129, + "d_first": 0.018403321541166622, + "batch_size": 0.010842569127784432, + "d_last": 0.00987227963711949, + "num_timesteps": 0.0031624619936161695 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..65079c2b085449ac564139ca1d0e90917400902c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/info.json @@ -0,0 +1,10 @@ +{ + "name": "House 16H", + "id": "house--default", + "task_type": "regression", + "n_num_features": 16, + "n_cat_features": 0, + "train_size": 14581, + "val_size": 3646, + "test_size": 4557 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..62ca7fcceb3cff982652e9e729007c43d5811cc2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.6766, + 0.6722, + 0.6731, + 0.6726, + 0.6718 + ], + "privacies": [ + 0.08573335948304457, + 0.0854, + 0.0856, + 0.0859, + 0.0861 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..33a0056a5da5cd60c325d906507012a7e8f7af26 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_cb_best/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.08585241886686137 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_mlp_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_mlp_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..bbb951f80f16864132778d699a404b80baeff941 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_mlp_best/config.toml @@ -0,0 +1,58 @@ +seed = 0 +parent_dir = "exp/house/ddpm_mlp_best" +real_data_path = "data/house/" +model_type = "mlp" +num_numerical_features = 16 +device = "cuda:1" + +[model_params] +d_in = 17 +num_classes = 0 +is_y_cond = false + +[model_params.rtdl_params] +d_layers = [ + 1024, + 256, + 256, + 128, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 20000 +lr = 0.002990485680629068 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 50400 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "mlp" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "one-hot" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_mlp_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_mlp_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..fef93a6a963388a7f80afc9fcf25e77c81c0241c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_mlp_best/eval_mlp.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.6959, + "r2-std": 0.0065, + "rmse-count": 50.0, + "rmse-mean": 30102.0498, + "rmse-std": 318.9789 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.6262, + "r2-std": 0.0093, + "rmse-count": 50.0, + "rmse-mean": 32846.5238, + "rmse-std": 405.4884 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_mlp_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_mlp_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..35b25829546996f34c2012ebe5fd76c19c7275bf --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_mlp_best/importance.json @@ -0,0 +1,10 @@ +{ + "steps": 0.32427809892747994, + "num_samples": 0.17240221709806136, + "lr": 0.1566025242195196, + "num_timesteps": 0.135054882578662, + "n_layers": 0.1135824502741633, + "d_last": 0.04883984148216505, + "d_first": 0.036051874060831456, + "batch_size": 0.01318811135911731 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_mlp_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_mlp_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..65079c2b085449ac564139ca1d0e90917400902c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/ddpm_mlp_best/info.json @@ -0,0 +1,10 @@ +{ + "name": "House 16H", + "id": "house--default", + "task_type": "regression", + "n_num_features": 16, + "n_cat_features": 0, + "train_size": 14581, + "val_size": 3646, + "test_size": 4557 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..8c8bcf34fa321e9e587f0cc796f587c945354e5a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/config.toml @@ -0,0 +1,24 @@ +parent_dir = "exp/house/smote" +real_data_path = "data/house/" +seed = 0 + +[smote_params] +k_neighbours = 14 +frac_samples = 8 +frac_lam_del = 0.75 + +[sample] +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..492995ed16a7fd8a0ead6451ee529447241a7540 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.6981, + "r2-std": 0.0027, + "rmse-count": 50.0, + "rmse-mean": 29992.0836, + "rmse-std": 133.1831 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.6625, + "r2-std": 0.0035, + "rmse-count": 50.0, + "rmse-mean": 31212.2509, + "rmse-std": 159.8617 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..2799c35c518f3c8f7ce96b7c4fd02f750c24aeef --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.3157, + "r2-std": 0.0143, + "rmse-count": 10.0, + "rmse-mean": 45155.366, + "rmse-std": 471.3779 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.2366, + "r2-std": 0.009, + "rmse-count": 10.0, + "rmse-mean": 46944.6743, + "rmse-std": 276.2307 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.2744, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 46499.5828, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.256, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 46346.477, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.6354, + "r2-std": 0.0034, + "rmse-count": 10.0, + "rmse-mean": 32963.5787, + "rmse-std": 152.908 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.597, + "r2-std": 0.0027, + "rmse-count": 10.0, + "rmse-mean": 34108.0118, + "rmse-std": 113.7006 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.5686, + "r2-std": 0.0107, + "rmse-count": 10.0, + "rmse-mean": 35853.9006, + "rmse-std": 445.5435 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.4876, + "r2-std": 0.0118, + "rmse-count": 10.0, + "rmse-mean": 38460.2795, + "rmse-std": 444.4825 + } + }, + "avg": { + "val": { + "r2-mean": 0.4485, + "rmse-mean": 40118.107, + "r2-std": 0.0071 + }, + "test": { + "r2-mean": 0.3943, + "rmse-mean": 41464.8606, + "r2-std": 0.0065 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/info.json new file mode 100644 index 0000000000000000000000000000000000000000..65079c2b085449ac564139ca1d0e90917400902c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/info.json @@ -0,0 +1,10 @@ +{ + "name": "House 16H", + "id": "house--default", + "task_type": "regression", + "n_num_features": 16, + "n_cat_features": 0, + "train_size": 14581, + "val_size": 3646, + "test_size": 4557 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..f1b12752f3000ee969d2e829390919caa399bb65 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.6625, + 0.6647, + 0.6673, + 0.6647, + 0.6663 + ], + "privacies": [ + 0.05600270934625494, + 0.0562, + 0.056, + 0.0557, + 0.0554 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..b2e12687a54464122f7b32e157de5341fcc41328 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/smote/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.05600270934625494 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/tvae/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/tvae/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..73a780153301e0e19e768c9e3824ace43075720a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/tvae/config.toml @@ -0,0 +1,36 @@ +parent_dir = "exp/house/tvae" +real_data_path = "data/house/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.0014569517316450873 +epochs = 30000 +embedding_dim = 64 +batch_size = 4096 +loss_factor = 0.9025964643509657 +compress_dims = [ + 256, + 512, +] +decompress_dims = [ + 256, + 512, +] + +[sample] +seed = 0 +num_samples = 29000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/tvae/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/tvae/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..07233418546f23c645c04c06a46cd7ad5d44d83d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/tvae/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.5306, + "r2-std": 0.008, + "rmse-count": 50.0, + "rmse-mean": 37398.0547, + "rmse-std": 319.6565 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.4926, + "r2-std": 0.0061, + "rmse-count": 50.0, + "rmse-mean": 38273.2758, + "rmse-std": 231.3771 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/tvae/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/tvae/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..323e9cd556aec96246f05a9fc82367dffba7a3b4 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/tvae/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": -0.2292, + "r2-std": 0.0475, + "rmse-count": 10.0, + "rmse-mean": 60511.1951, + "rmse-std": 1178.1578 + }, + "test": { + "r2-count": 10.0, + "r2-mean": -0.2784, + "r2-std": 0.0267, + "rmse-count": 10.0, + "rmse-mean": 60746.8374, + "rmse-std": 633.845 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.1663, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 49845.0226, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.1439, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 49714.4745, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.472, + "r2-std": 0.0047, + "rmse-count": 10.0, + "rmse-mean": 39664.4961, + "rmse-std": 178.0604 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.4366, + "r2-std": 0.0057, + "rmse-count": 10.0, + "rmse-mean": 40329.4948, + "rmse-std": 205.6391 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.4315, + "r2-std": 0.0071, + "rmse-count": 10.0, + "rmse-mean": 41157.4512, + "rmse-std": 255.9399 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.395, + "r2-std": 0.0078, + "rmse-count": 10.0, + "rmse-mean": 41790.5839, + "rmse-std": 268.5759 + } + }, + "avg": { + "val": { + "r2-mean": 0.2102, + "rmse-mean": 47794.5412, + "r2-std": 0.0148 + }, + "test": { + "r2-mean": 0.1743, + "rmse-mean": 48145.3477, + "r2-std": 0.0124 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/tvae/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/tvae/info.json new file mode 100644 index 0000000000000000000000000000000000000000..65079c2b085449ac564139ca1d0e90917400902c --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/house/tvae/info.json @@ -0,0 +1,10 @@ +{ + "name": "House 16H", + "id": "house--default", + "task_type": "regression", + "n_num_features": 16, + "n_cat_features": 0, + "train_size": 14581, + "val_size": 3646, + "test_size": 4557 +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..a080620eb33e4a6aaf11816e125aa011b40c649f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/config.toml @@ -0,0 +1,57 @@ +seed = 0 +parent_dir = "exp/insurance/check" +real_data_path = "data/insurance/" +num_numerical_features = 3 +model_type = "mlp" +device = "cuda:0" + +[model_params] +d_in = 12 +num_classes = 0 +is_y_cond = false + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 7200 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" +model = "catboost" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ctabgan-plus/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ctabgan-plus/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..99d1c1d00151e29d6d906a3efb31073e1aa008df --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ctabgan-plus/config.toml @@ -0,0 +1,33 @@ +parent_dir = "exp/insurance/ctabgan-plus" +real_data_path = "data/insurance/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.0016000672327730358 +epochs = 10000 +class_dim = [ + 64, + 64, + 64, +] +batch_size = 856 +random_dim = 16 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 3600 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ctabgan-plus/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ctabgan-plus/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..b8beb86b5ad00452aa1bdf6f6acba410fdc1537a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ctabgan-plus/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8089, + "r2-std": 0.006, + "rmse-count": 50.0, + "rmse-mean": 5066.6402, + "rmse-std": 79.3833 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.7966, + "r2-std": 0.0046, + "rmse-count": 50.0, + "rmse-mean": 4967.3568, + "rmse-std": 56.2682 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ctabgan-plus/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ctabgan-plus/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..e4a5c2ab64d90b24bf1cdc8ac7c21b3c914ca606 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ctabgan-plus/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.5245, + "r2-std": 0.0512, + "rmse-count": 50.0, + "rmse-mean": 7981.7832, + "rmse-std": 434.3798 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.439, + "r2-std": 0.0611, + "rmse-count": 50.0, + "rmse-mean": 8238.4672, + "rmse-std": 449.7378 + } + }, + "lr": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.7334, + "r2-std": 0.0013, + "rmse-count": 50.0, + "rmse-mean": 5984.741, + "rmse-std": 14.8143 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.6984, + "r2-std": 0.004, + "rmse-count": 50.0, + "rmse-mean": 6049.7362, + "rmse-std": 40.3656 + } + }, + "rf": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.7525, + "r2-std": 0.0145, + "rmse-count": 50.0, + "rmse-mean": 5763.8582, + "rmse-std": 169.8139 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.7473, + "r2-std": 0.0055, + "rmse-count": 50.0, + "rmse-mean": 5537.3107, + "rmse-std": 60.3685 + } + }, + "mlp": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8058, + "r2-std": 0.0036, + "rmse-count": 50.0, + "rmse-mean": 5108.0581, + "rmse-std": 46.951 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.7911, + "r2-std": 0.0036, + "rmse-count": 50.0, + "rmse-mean": 5034.1944, + "rmse-std": 43.1235 + } + }, + "avg": { + "val": { + "r2-mean": 0.704, + "rmse-mean": 6209.6101, + "r2-std": 0.0177 + }, + "test": { + "r2-mean": 0.669, + "rmse-mean": 6214.9271, + "r2-std": 0.0181 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ctabgan-plus/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ctabgan-plus/info.json new file mode 100644 index 0000000000000000000000000000000000000000..8995c1a2cacf220c556c47195008398c27969815 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ctabgan-plus/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "regression", + "name": "Insurance", + "id": "insurance--id", + "train_size": 856, + "val_size": 214, + "test_size": 268, + "n_num_features": 3, + "n_cat_features": 3 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..340c77f790a8f618527658d69a3526bc0f1c338d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/config.toml @@ -0,0 +1,61 @@ +seed = 0 +parent_dir = "exp/insurance/ddpm_cb_best" +real_data_path = "data/insurance/" +num_numerical_features = 3 +model_type = "mlp" +device = "cuda:0" + +[model_params] +d_in = 12 +num_classes = 0 +is_y_cond = false + +[model_params.rtdl_params] +d_layers = [ + 256, + 512, + 512, + 512, + 512, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 100 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 30000 +lr = 0.0011121628249569867 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 7200 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" +model = "catboost" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..bf937f743a865e4d9560d729f5514da9264f1e99 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/eval_catboost.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8337, + "r2-std": 0.003, + "rmse-count": 50.0, + "rmse-mean": 4727.1415, + "rmse-std": 42.5705 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.8092, + "r2-std": 0.0024, + "rmse-count": 50.0, + "rmse-mean": 4811.8622, + "rmse-std": 29.7722 + } + }, + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8392, + "r2-std": 0.0016, + "rmse-count": 10.0, + "rmse-mean": 4648.9362, + "rmse-std": 22.8215 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.8137, + "r2-std": 0.0012, + "rmse-count": 10.0, + "rmse-mean": 4754.9749, + "rmse-std": 15.7113 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..8e7e86a5945fb2fb4c992d5341180afbca8ebfcf --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/eval_mlp.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8052, + "r2-std": 0.0055, + "rmse-count": 50.0, + "rmse-mean": 5116.0502, + "rmse-std": 72.5528 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.7943, + "r2-std": 0.0077, + "rmse-count": 50.0, + "rmse-mean": 4995.7601, + "rmse-std": 92.6257 + } + }, + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8048, + "r2-std": 0.0037, + "rmse-count": 10.0, + "rmse-mean": 5120.7731, + "rmse-std": 48.8761 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.8002, + "r2-std": 0.003, + "rmse-count": 10.0, + "rmse-mean": 4924.267, + "rmse-std": 36.4981 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..f033da0d032b9fb12cf288a5e4c249e03ee7a5d4 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/eval_simple.json @@ -0,0 +1,174 @@ +{ + "real": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7062, + "r2-std": 0.0174, + "rmse-count": 10.0, + "rmse-mean": 6279.9472, + "rmse-std": 186.6296 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.5795, + "r2-std": 0.0137, + "rmse-count": 10.0, + "rmse-mean": 7142.4092, + "rmse-std": 116.548 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7312, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 6009.6148, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.6937, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 6096.2922, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8124, + "r2-std": 0.0025, + "rmse-count": 10.0, + "rmse-mean": 5020.5597, + "rmse-std": 33.3984 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.7876, + "r2-std": 0.0031, + "rmse-count": 10.0, + "rmse-mean": 5076.3494, + "rmse-std": 37.6496 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7891, + "r2-std": 0.0109, + "rmse-count": 10.0, + "rmse-mean": 5322.1432, + "rmse-std": 134.871 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.7709, + "r2-std": 0.0096, + "rmse-count": 10.0, + "rmse-mean": 5271.7258, + "rmse-std": 108.1873 + } + }, + "avg": { + "val": { + "r2-mean": 0.7597, + "rmse-mean": 5658.0662, + "r2-std": 0.0077 + }, + "test": { + "r2-mean": 0.7079, + "rmse-mean": 5896.6942, + "r2-std": 0.0066 + } + } + }, + "synthetic": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7161, + "r2-std": 0.0159, + "rmse-count": 10.0, + "rmse-mean": 6174.3326, + "rmse-std": 174.4496 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.6812, + "r2-std": 0.0152, + "rmse-count": 10.0, + "rmse-mean": 6218.3869, + "rmse-std": 148.6485 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7291, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 6033.8359, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.696, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 6073.4609, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.814, + "r2-std": 0.0021, + "rmse-count": 10.0, + "rmse-mean": 4999.314, + "rmse-std": 28.1002 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.7853, + "r2-std": 0.0021, + "rmse-count": 10.0, + "rmse-mean": 5104.2376, + "rmse-std": 24.4329 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8166, + "r2-std": 0.0038, + "rmse-count": 10.0, + "rmse-mean": 4964.0436, + "rmse-std": 51.647 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.7745, + "r2-std": 0.0085, + "rmse-count": 10.0, + "rmse-mean": 5230.2438, + "rmse-std": 98.8796 + } + }, + "avg": { + "val": { + "r2-mean": 0.769, + "rmse-mean": 5542.8815, + "r2-std": 0.0066 + }, + "test": { + "r2-mean": 0.7343, + "rmse-mean": 5656.5823, + "r2-std": 0.0066 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..e04f48e2c90aa19f84eb6e069df2c0e5dc321299 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/importance.json @@ -0,0 +1,9 @@ +{ + "lr": 0.7903552393024615, + "num_samples": 0.12521914677758006, + "d_last": 0.05216743488298744, + "steps": 0.0166909484138204, + "n_layers": 0.012632843256290155, + "d_first": 0.002774402282444718, + "num_timesteps": 0.00015998508441567881 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..8995c1a2cacf220c556c47195008398c27969815 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "regression", + "name": "Insurance", + "id": "insurance--id", + "train_size": 856, + "val_size": 214, + "test_size": 268, + "n_num_features": 3, + "n_cat_features": 3 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..bcb0af27baf1b7994f8a2fd82d692f5895f8c524 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.8092, + 0.8096, + 0.8092, + 0.8084, + 0.8078 + ], + "privacies": [ + 0.040526590490555875, + 0.0443, + 0.0486, + 0.0534, + 0.0587 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..1d8ab36be5d92eef3728e18d435bb821a92490df --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_cb_best/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.040526590490555875 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_mlp_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_mlp_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..c5363f675fc5af8e753784114c65dc72ef8aac06 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_mlp_best/config.toml @@ -0,0 +1,58 @@ +seed = 0 +parent_dir = "exp/insurance/ddpm_mlp_best" +real_data_path = "data/insurance/" +num_numerical_features = 3 +model_type = "mlp" +device = "cuda:0" + +[model_params] +d_in = 12 +num_classes = 0 +is_y_cond = false + +[model_params.rtdl_params] +d_layers = [ + 512, + 256, + 256, + 512, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 100 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 30000 +lr = 0.002680166862238138 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 3600 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "mlp" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "one-hot" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_mlp_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_mlp_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..47d0a50909bd11c67e59ea655fcf5401b26c9365 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_mlp_best/eval_catboost.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8293, + "r2-std": 0.0023, + "rmse-count": 50.0, + "rmse-mean": 4788.7314, + "rmse-std": 32.8514 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.8119, + "r2-std": 0.0021, + "rmse-count": 50.0, + "rmse-mean": 4777.5801, + "rmse-std": 26.8725 + } + }, + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8356, + "r2-std": 0.0022, + "rmse-count": 10.0, + "rmse-mean": 4699.3751, + "rmse-std": 31.6189 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.8137, + "r2-std": 0.0013, + "rmse-count": 10.0, + "rmse-mean": 4754.67, + "rmse-std": 16.2576 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_mlp_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_mlp_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..a31462afd11ece7d4d42e3dfcc41d32feeaeec21 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_mlp_best/eval_mlp.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8008, + "r2-std": 0.0045, + "rmse-count": 50.0, + "rmse-mean": 5173.3499, + "rmse-std": 58.2643 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.7997, + "r2-std": 0.0037, + "rmse-count": 50.0, + "rmse-mean": 4930.1636, + "rmse-std": 45.2811 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_mlp_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_mlp_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..78475ea660abcfcf338f0b4d3063e1398996d040 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_mlp_best/importance.json @@ -0,0 +1,10 @@ +{ + "lr": 0.8962319096883671, + "num_samples": 0.0721307827589758, + "steps": 0.01845252571077833, + "n_layers": 0.008381640016524328, + "d_last": 0.002832737096862196, + "num_timesteps": 0.0012824132640616456, + "d_first": 0.0004146518767875624, + "batch_size": 0.00027333958764289896 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_mlp_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_mlp_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..8995c1a2cacf220c556c47195008398c27969815 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/ddpm_mlp_best/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "regression", + "name": "Insurance", + "id": "insurance--id", + "train_size": 856, + "val_size": 214, + "test_size": 268, + "n_num_features": 3, + "n_cat_features": 3 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..f7b6693eaeb3552dd596e1d7132185544cec0e51 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/config.toml @@ -0,0 +1,24 @@ +parent_dir = "exp/insurance/smote" +real_data_path = "data/insurance/" +seed = 0 + +[smote_params] +k_neighbours = 6 +frac_samples = 1 +frac_lam_del = 0.1 + +[sample] +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..e8e68b438c8d0338888c781ac304c4fe293a3ac4 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8245, + "r2-std": 0.0054, + "rmse-count": 50.0, + "rmse-mean": 4855.2724, + "rmse-std": 75.4633 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.8119, + "r2-std": 0.0021, + "rmse-count": 50.0, + "rmse-mean": 4777.3215, + "rmse-std": 26.1486 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..c4f47a7957ef4fce7ab1cea59d43687c423801d4 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/eval_mlp.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.744, + "r2-std": 0.0066, + "rmse-count": 50.0, + "rmse-mean": 5864.5775, + "rmse-std": 75.5999 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.773, + "r2-std": 0.0039, + "rmse-count": 50.0, + "rmse-mean": 5248.0178, + "rmse-std": 44.9914 + } + }, + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8048, + "r2-std": 0.0037, + "rmse-count": 10.0, + "rmse-mean": 5120.7731, + "rmse-std": 48.8761 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.8002, + "r2-std": 0.003, + "rmse-count": 10.0, + "rmse-mean": 4924.267, + "rmse-std": 36.4981 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..fb43fb318853338e16c0f9749ecb3548e2497f43 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.718, + "r2-std": 0.0139, + "rmse-count": 10.0, + "rmse-mean": 6153.6154, + "rmse-std": 152.7375 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.6339, + "r2-std": 0.0208, + "rmse-count": 10.0, + "rmse-mean": 6662.2963, + "rmse-std": 189.7393 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7265, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 6062.1539, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.6849, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 6184.0401, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8083, + "r2-std": 0.0021, + "rmse-count": 10.0, + "rmse-mean": 5075.1279, + "rmse-std": 27.2785 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.7918, + "r2-std": 0.0017, + "rmse-count": 10.0, + "rmse-mean": 5025.9185, + "rmse-std": 19.9187 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7383, + "r2-std": 0.0124, + "rmse-count": 10.0, + "rmse-mean": 5928.5872, + "rmse-std": 138.9174 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.7241, + "r2-std": 0.0103, + "rmse-count": 10.0, + "rmse-mean": 5785.7733, + "rmse-std": 107.3312 + } + }, + "avg": { + "val": { + "r2-mean": 0.7478, + "rmse-mean": 5804.8711, + "r2-std": 0.0071 + }, + "test": { + "r2-mean": 0.7087, + "rmse-mean": 5914.507, + "r2-std": 0.0076 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/info.json new file mode 100644 index 0000000000000000000000000000000000000000..8995c1a2cacf220c556c47195008398c27969815 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "regression", + "name": "Insurance", + "id": "insurance--id", + "train_size": 856, + "val_size": 214, + "test_size": 268, + "n_num_features": 3, + "n_cat_features": 3 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..78cedb2dfe8d67389d49c98ec693b22255d1d189 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.8119, + 0.8113, + 0.8087, + 0.8099, + 0.8126 + ], + "privacies": [ + 0.030472555322331316, + 0.0345, + 0.0392, + 0.0437, + 0.0514 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..52baba4265c268d7e40f6f1400ec91783dafaaae --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/smote/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.030472555322331316 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/tvae/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/tvae/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..ec344396ee8065e05bc203ffb2901d17177c6c55 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/tvae/config.toml @@ -0,0 +1,40 @@ +parent_dir = "exp/insurance/tvae" +real_data_path = "data/insurance/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.0006796970164894311 +epochs = 30000 +embedding_dim = 256 +batch_size = 856 +loss_factor = 1.1222172608518253 +compress_dims = [ + 128, + 64, + 64, + 512, +] +decompress_dims = [ + 128, + 64, + 64, + 512, +] + +[sample] +seed = 0 +num_samples = 1800 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/tvae/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/tvae/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..609fed8a2a4ac1bc7d5eb65952a81d3fb97dd6d4 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/tvae/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.7926, + "r2-std": 0.0075, + "rmse-count": 50.0, + "rmse-mean": 5278.24, + "rmse-std": 94.8815 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.7842, + "r2-std": 0.0097, + "rmse-count": 50.0, + "rmse-mean": 5116.4478, + "rmse-std": 113.8681 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/tvae/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/tvae/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..7a5db4b366ea670ccc7bced9f6291e64b4b19e78 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/tvae/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": -0.1175, + "r2-std": 0.0707, + "rmse-count": 10.0, + "rmse-mean": 12247.9956, + "rmse-std": 386.8769 + }, + "test": { + "r2-count": 10.0, + "r2-mean": -0.1297, + "r2-std": 0.0921, + "rmse-count": 10.0, + "rmse-mean": 11698.543, + "rmse-std": 479.8878 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.6738, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 6620.2153, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.6729, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 6300.7947, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.5955, + "r2-std": 0.0107, + "rmse-count": 10.0, + "rmse-mean": 7371.4784, + "rmse-std": 97.7643 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.6099, + "r2-std": 0.0081, + "rmse-count": 10.0, + "rmse-mean": 6879.6039, + "rmse-std": 71.2656 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7242, + "r2-std": 0.0087, + "rmse-count": 10.0, + "rmse-mean": 6087.1223, + "rmse-std": 95.3456 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.7256, + "r2-std": 0.0048, + "rmse-count": 10.0, + "rmse-mean": 5769.9355, + "rmse-std": 50.0994 + } + }, + "avg": { + "val": { + "r2-mean": 0.469, + "rmse-mean": 8081.7029, + "r2-std": 0.0225 + }, + "test": { + "r2-mean": 0.4697, + "rmse-mean": 7662.2193, + "r2-std": 0.0244 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/tvae/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/tvae/info.json new file mode 100644 index 0000000000000000000000000000000000000000..8995c1a2cacf220c556c47195008398c27969815 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/insurance/tvae/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "regression", + "name": "Insurance", + "id": "insurance--id", + "train_size": 856, + "val_size": 214, + "test_size": 268, + "n_num_features": 3, + "n_cat_features": 3 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..a73daabf0cb53e1974a2ad615632896164d87a5f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/config.toml @@ -0,0 +1,56 @@ +seed = 0 +parent_dir = "exp/king/check" +real_data_path = "data/king/" +num_numerical_features = 17 +model_type = "mlp" +device = "cuda:1" + +[model_params] +d_in = 30 +is_y_cond = false +num_classes = 0 + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 27600 +batch_size = 20000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ctabgan-plus/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ctabgan-plus/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..fa07674604b293096b9e3046bd97b1ac764830d5 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ctabgan-plus/config.toml @@ -0,0 +1,34 @@ +parent_dir = "exp/king/ctabgan-plus" +real_data_path = "data/king/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.0006772380448845386 +epochs = 10000 +class_dim = [ + 128, + 256, + 256, + 256, +] +batch_size = 1024 +random_dim = 16 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 110400 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ctabgan-plus/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ctabgan-plus/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..1732c3afa5890431140dd4f454e7aa6f6934e4c5 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ctabgan-plus/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.4357, + "r2-std": 0.0149, + "rmse-count": 50.0, + "rmse-mean": 262097.1873, + "rmse-std": 3471.9543 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.4438, + "r2-std": 0.014, + "rmse-count": 50.0, + "rmse-mean": 262091.4844, + "rmse-std": 3300.2996 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ctabgan-plus/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ctabgan-plus/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..d4a55abfc78a4bc38a5b8357574a40e79fd52648 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ctabgan-plus/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 50.0, + "r2-mean": -0.4063, + "r2-std": 0.1649, + "rmse-count": 50.0, + "rmse-mean": 413129.7033, + "rmse-std": 23816.7972 + }, + "test": { + "r2-count": 50.0, + "r2-mean": -0.47, + "r2-std": 0.1472, + "rmse-count": 50.0, + "rmse-mean": 425631.2508, + "rmse-std": 20372.9836 + } + }, + "lr": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.4846, + "r2-std": 0.004, + "rmse-count": 50.0, + "rmse-mean": 250509.4668, + "rmse-std": 975.4408 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.5172, + "r2-std": 0.0039, + "rmse-count": 50.0, + "rmse-mean": 244196.518, + "rmse-std": 992.1781 + } + }, + "rf": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.3326, + "r2-std": 0.0111, + "rmse-count": 50.0, + "rmse-mean": 285068.8025, + "rmse-std": 2368.2988 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.3311, + "r2-std": 0.0181, + "rmse-count": 50.0, + "rmse-mean": 287411.8262, + "rmse-std": 3875.8156 + } + }, + "mlp": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.4002, + "r2-std": 0.0291, + "rmse-count": 50.0, + "rmse-mean": 270175.8275, + "rmse-std": 6499.7519 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.4084, + "r2-std": 0.0298, + "rmse-count": 50.0, + "rmse-mean": 270252.4246, + "rmse-std": 6746.6708 + } + }, + "avg": { + "val": { + "r2-mean": 0.2028, + "rmse-mean": 304720.95, + "r2-std": 0.0523 + }, + "test": { + "r2-mean": 0.1967, + "rmse-mean": 306873.0049, + "r2-std": 0.051 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ctabgan-plus/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ctabgan-plus/info.json new file mode 100644 index 0000000000000000000000000000000000000000..c682f10a4d152e1c5951b49c15b1e6ddf4641957 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ctabgan-plus/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "regression", + "name": "King", + "id": "king--id", + "train_size": 13832, + "val_size": 3458, + "test_size": 4323, + "n_num_features": 17, + "n_cat_features": 3 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..e872376c5e6aab0d1894ac23ab8b1638b1b53191 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/config.toml @@ -0,0 +1,59 @@ +seed = 0 +parent_dir = "exp/king/ddpm_cb_best" +real_data_path = "data/king/" +num_numerical_features = 17 +model_type = "mlp" +device = "cuda:1" + +[model_params] +d_in = 30 +is_y_cond = false +num_classes = 0 + +[model_params.rtdl_params] +d_layers = [ + 256, + 1024, + 1024, + 1024, + 1024, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 100 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.0007444465590958975 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 27600 +batch_size = 20000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..21d7c480188204dafd3656dd3f3a29f0d50e6369 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/eval_catboost.json @@ -0,0 +1,38 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8752, + "r2-std": 0.0059, + "rmse-count": 50.0, + "rmse-mean": 123238.9606, + "rmse-std": 2878.8241 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.8331, + "r2-std": 0.0143, + "rmse-count": 50.0, + "rmse-mean": 143472.3284, + "rmse-std": 6143.4332 + } + }, + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.9199, + "r2-std": 0.0011, + "rmse-count": 10.0, + "rmse-mean": 98777.0089, + "rmse-std": 696.7992 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.907, + "r2-std": 0.0022, + "rmse-count": 10.0, + "rmse-mean": 107162.1466, + "rmse-std": 1243.2756 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..8ff80dd5207d8323267bc1192f4e56a91ce866f7 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/eval_mlp.json @@ -0,0 +1,38 @@ +{ + "real": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8788, + "r2-std": 0.004, + "rmse-count": 10.0, + "rmse-mean": 121462.5573, + "rmse-std": 2008.091 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.8821, + "r2-std": 0.0038, + "rmse-count": 10.0, + "rmse-mean": 120642.0267, + "rmse-std": 1949.9816 + } + }, + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8083, + "r2-std": 0.0111, + "rmse-count": 50.0, + "rmse-mean": 152720.6946, + "rmse-std": 4389.719 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.8039, + "r2-std": 0.0152, + "rmse-count": 50.0, + "rmse-mean": 155530.7165, + "rmse-std": 6037.7979 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..fb5b6545b02fd685ba169f325ab01ad53cb80687 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/eval_simple.json @@ -0,0 +1,174 @@ +{ + "real": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7574, + "r2-std": 0.0075, + "rmse-count": 10.0, + "rmse-mean": 171868.1269, + "rmse-std": 2657.5968 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.6659, + "r2-std": 0.0069, + "rmse-count": 10.0, + "rmse-mean": 203151.2355, + "rmse-std": 2092.0521 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.6957, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 192501.2153, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.7132, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 188211.2966, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8896, + "r2-std": 0.001, + "rmse-count": 10.0, + "rmse-mean": 115940.8365, + "rmse-std": 537.438 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.8436, + "r2-std": 0.0032, + "rmse-count": 10.0, + "rmse-mean": 138991.1835, + "rmse-std": 1411.6026 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.839, + "r2-std": 0.0037, + "rmse-count": 10.0, + "rmse-mean": 140005.0808, + "rmse-std": 1627.0881 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.8512, + "r2-std": 0.0043, + "rmse-count": 10.0, + "rmse-mean": 135565.7738, + "rmse-std": 1966.7143 + } + }, + "avg": { + "val": { + "r2-mean": 0.7954, + "rmse-mean": 155078.8149, + "r2-std": 0.0031 + }, + "test": { + "r2-mean": 0.7685, + "rmse-mean": 166479.8724, + "r2-std": 0.0134 + } + } + }, + "synthetic": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.5038, + "r2-std": 0.1165, + "rmse-count": 10.0, + "rmse-mean": 243974.4723, + "rmse-std": 29866.0438 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.4913, + "r2-std": 0.0268, + "rmse-count": 10.0, + "rmse-mean": 250588.7299, + "rmse-std": 6607.582 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.3567, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 279876.5993, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.3954, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 273272.008, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8508, + "r2-std": 0.003, + "rmse-count": 10.0, + "rmse-mean": 134782.0782, + "rmse-std": 1349.8655 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.7851, + "r2-std": 0.0026, + "rmse-count": 10.0, + "rmse-mean": 162927.8919, + "rmse-std": 997.228 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7681, + "r2-std": 0.0152, + "rmse-count": 10.0, + "rmse-mean": 167959.2893, + "rmse-std": 5445.7083 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.771, + "r2-std": 0.0147, + "rmse-count": 10.0, + "rmse-mean": 168110.6892, + "rmse-std": 5357.1052 + } + }, + "avg": { + "val": { + "r2-mean": 0.6198, + "rmse-mean": 206648.1098, + "r2-std": 0.0184 + }, + "test": { + "r2-mean": 0.6107, + "rmse-mean": 213724.8298, + "r2-std": 0.0128 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..785a3429ef0ec3fdfa41bd18d15e9e5074a8f4f9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/importance.json @@ -0,0 +1,10 @@ +{ + "batch_size": 0.3397091304289337, + "lr": 0.2840554587047091, + "n_layers": 0.2002713181921346, + "num_samples": 0.09218357480852712, + "d_first": 0.047717561411212764, + "d_last": 0.01519399951552169, + "steps": 0.015084444972488068, + "num_timesteps": 0.005784511966472968 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..c682f10a4d152e1c5951b49c15b1e6ddf4641957 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "regression", + "name": "King", + "id": "king--id", + "train_size": 13832, + "val_size": 3458, + "test_size": 4323, + "n_num_features": 17, + "n_cat_features": 3 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..85c77ca0a8d4ba54d21960566a7c65d36f3ece73 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.8331, + 0.8256, + 0.8182, + 0.825, + 0.8266 + ], + "privacies": [ + 0.18834686468312467, + 0.1968, + 0.206, + 0.2167, + 0.2292 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..a90aff35c8c386451c061872e96d89fa3f146a6d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_cb_best/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.18873427136201132 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_mlp_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_mlp_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..2c027af2fb63286fb9572874c541dd49de3a1aee --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_mlp_best/config.toml @@ -0,0 +1,55 @@ +seed = 0 +parent_dir = "exp/king/ddpm_mlp_best" +real_data_path = "data/Tab-Cate-1/" +num_numerical_features = 17 +model_type = "mlp" +device = "cuda:1" + +[model_params] +d_in = 30 +is_y_cond = false +num_classes = 0 + +[model_params.rtdl_params] +d_layers = [ + 512, + 512, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.0006774069976719967 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 27600 +batch_size = 20000 +seed = 0 + +[eval.type] +eval_model = "mlp" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "one-hot" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_mlp_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_mlp_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..d83d503f345a7d10b8da59df3f6acc6f52e31445 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_mlp_best/eval_mlp.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8128, + "r2-std": 0.0145, + "rmse-count": 50.0, + "rmse-mean": 150864.9129, + "rmse-std": 5778.0119 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.7987, + "r2-std": 0.0177, + "rmse-count": 50.0, + "rmse-mean": 157516.4622, + "rmse-std": 7010.3703 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_mlp_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_mlp_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..e253a0023762a9a8d4aad16b7cc6e97d523f4195 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_mlp_best/importance.json @@ -0,0 +1,10 @@ +{ + "n_layers": 0.5502009799144776, + "lr": 0.35466957926238746, + "steps": 0.03968673994441703, + "batch_size": 0.02265113444297201, + "num_timesteps": 0.016933830843601668, + "d_last": 0.008395825039781427, + "d_first": 0.004116692110931949, + "num_samples": 0.003345218441430649 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_mlp_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_mlp_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..c682f10a4d152e1c5951b49c15b1e6ddf4641957 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/ddpm_mlp_best/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "regression", + "name": "King", + "id": "king--id", + "train_size": 13832, + "val_size": 3458, + "test_size": 4323, + "n_num_features": 17, + "n_cat_features": 3 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..38becfb7e5d152896f4c8f7bad4fd1e93dd64614 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/config.toml @@ -0,0 +1,24 @@ +parent_dir = "exp/king/smote" +real_data_path = "data/king/" +seed = 0 + +[smote_params] +k_neighbours = 6 +frac_samples = 2 +frac_lam_del = 0.1 + +[sample] +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..d75029160a1d082a2c0fba748e320487fb31be0a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8653, + "r2-std": 0.0021, + "rmse-count": 50.0, + "rmse-mean": 128053.622, + "rmse-std": 981.7928 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.8416, + "r2-std": 0.004, + "rmse-count": 50.0, + "rmse-mean": 139851.164, + "rmse-std": 1769.4697 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..2f63415d4f2ca52e3d1a15e432f0d81a987b0dfc --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.6775, + "r2-std": 0.0065, + "rmse-count": 10.0, + "rmse-mean": 198159.8667, + "rmse-std": 1994.2211 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.6713, + "r2-std": 0.0102, + "rmse-count": 10.0, + "rmse-mean": 201486.7215, + "rmse-std": 3109.0566 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.6791, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 197667.9978, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.7028, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 191611.4179, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8369, + "r2-std": 0.0022, + "rmse-count": 10.0, + "rmse-mean": 140935.6988, + "rmse-std": 951.1528 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.8159, + "r2-std": 0.0019, + "rmse-count": 10.0, + "rmse-mean": 150811.5133, + "rmse-std": 768.139 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.8161, + "r2-std": 0.0084, + "rmse-count": 10.0, + "rmse-mean": 149599.5948, + "rmse-std": 3399.2294 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.8128, + "r2-std": 0.0113, + "rmse-count": 10.0, + "rmse-mean": 151993.454, + "rmse-std": 4551.5233 + } + }, + "avg": { + "val": { + "r2-mean": 0.7524, + "rmse-mean": 171590.7895, + "r2-std": 0.0043 + }, + "test": { + "r2-mean": 0.7507, + "rmse-mean": 173975.7767, + "r2-std": 0.0051 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/info.json new file mode 100644 index 0000000000000000000000000000000000000000..c682f10a4d152e1c5951b49c15b1e6ddf4641957 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "regression", + "name": "King", + "id": "king--id", + "train_size": 13832, + "val_size": 3458, + "test_size": 4323, + "n_num_features": 17, + "n_cat_features": 3 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..7461a75ab5cec2f31c59525c4cd21d0f5ccff357 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.8416, + 0.8432, + 0.843, + 0.8386, + 0.8443 + ], + "privacies": [ + 0.06623694904095254, + 0.0742, + 0.0831, + 0.0932, + 0.1086 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..223e2bdc28200f3843564a19b4972da792ec6032 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/smote/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.06623694904095254 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/tvae/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/tvae/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..8e5d121480dfb86828165843199e73c2e4677e90 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/tvae/config.toml @@ -0,0 +1,36 @@ +parent_dir = "exp/king/tvae" +real_data_path = "data/king/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.002395008582967363 +epochs = 30000 +embedding_dim = 128 +batch_size = 4096 +loss_factor = 1.1076946053481818 +compress_dims = [ + 128, + 256, +] +decompress_dims = [ + 128, + 256, +] + +[sample] +seed = 0 +num_samples = 55200 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/tvae/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/tvae/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..ca7669156e24d3b0c8132c04effa53c0a94893f4 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/tvae/eval_catboost.json @@ -0,0 +1,20 @@ +{ + "synthetic": { + "val": { + "r2-count": 50.0, + "r2-mean": 0.8039, + "r2-std": 0.0046, + "rmse-count": 50.0, + "rmse-mean": 154519.7245, + "rmse-std": 1804.2818 + }, + "test": { + "r2-count": 50.0, + "r2-mean": 0.8238, + "r2-std": 0.0034, + "rmse-count": 50.0, + "rmse-mean": 147532.2939, + "rmse-std": 1416.2413 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/tvae/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/tvae/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..ee8f3c67175b7b8eca4cb7514ffb77259fe5c373 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/tvae/eval_simple.json @@ -0,0 +1,88 @@ +{ + "synthetic": { + "tree": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.4222, + "r2-std": 0.0208, + "rmse-count": 10.0, + "rmse-mean": 265209.4013, + "rmse-std": 4800.3425 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.3974, + "r2-std": 0.0154, + "rmse-count": 10.0, + "rmse-mean": 272810.1497, + "rmse-std": 3482.1796 + } + }, + "lr": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.6726, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 199654.4648, + "rmse-std": 0.0 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.6965, + "r2-std": 0.0, + "rmse-count": 10.0, + "rmse-mean": 193632.7148, + "rmse-std": 0.0 + } + }, + "rf": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7688, + "r2-std": 0.0015, + "rmse-count": 10.0, + "rmse-mean": 167783.9622, + "rmse-std": 535.8496 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.7767, + "r2-std": 0.0029, + "rmse-count": 10.0, + "rmse-mean": 166077.592, + "rmse-std": 1091.9256 + } + }, + "mlp": { + "val": { + "r2-count": 10.0, + "r2-mean": 0.7852, + "r2-std": 0.0048, + "rmse-count": 10.0, + "rmse-mean": 161728.8538, + "rmse-std": 1800.7654 + }, + "test": { + "r2-count": 10.0, + "r2-mean": 0.795, + "r2-std": 0.005, + "rmse-count": 10.0, + "rmse-mean": 159121.9942, + "rmse-std": 1935.7567 + } + }, + "avg": { + "val": { + "r2-mean": 0.6622, + "rmse-mean": 198594.1705, + "r2-std": 0.0068 + }, + "test": { + "r2-mean": 0.6664, + "rmse-mean": 197910.6127, + "r2-std": 0.0063 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/tvae/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/tvae/info.json new file mode 100644 index 0000000000000000000000000000000000000000..c682f10a4d152e1c5951b49c15b1e6ddf4641957 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/king/tvae/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "regression", + "name": "King", + "id": "king--id", + "train_size": 13832, + "val_size": 3458, + "test_size": 4323, + "n_num_features": 17, + "n_cat_features": 3 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..35cb3bc85017d0d2bcec745d70dd10ac28d2aa83 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/config.toml @@ -0,0 +1,56 @@ +seed = 0 +parent_dir = "exp/miniboone/check" +real_data_path = "data/miniboone/" +num_numerical_features = 50 +model_type = "mlp" +device = "cuda:1" + +[model_params] +d_in = 50 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 664000 +batch_size = 20000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan-plus/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan-plus/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..86d9144715ab6cfd087a43799a4667211e4a79d4 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan-plus/config.toml @@ -0,0 +1,34 @@ +parent_dir = "exp/miniboone/ctabgan-plus" +real_data_path = "data/miniboone/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.00013416837361450407 +epochs = 10000 +class_dim = [ + 128, + 128, + 128, + 128, +] +batch_size = 2048 +random_dim = 64 +num_channels = 32 + +[sample] +seed = 0 +num_samples = 332000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan-plus/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan-plus/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..1f868bf9f282e239d34428f5aad4014cd80c8894 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan-plus/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.9136, + "acc-std": 0.0008, + "f1-count": 50.0, + "f1-mean": 0.8905, + "f1-std": 0.0011, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9659, + "roc_auc-std": 0.0006 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.9142, + "acc-std": 0.001, + "f1-count": 50.0, + "f1-mean": 0.892, + "f1-std": 0.0015, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9668, + "roc_auc-std": 0.0006 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan-plus/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan-plus/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..1d2172881d4fd47b6070c47f27a94fc5beb304e1 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan-plus/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8682, + "acc-std": 0.0027, + "f1-count": 10.0, + "f1-mean": 0.8342, + "f1-std": 0.0034, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.781, + "roc_auc-std": 0.0018 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8666, + "acc-std": 0.002, + "f1-count": 10.0, + "f1-mean": 0.833, + "f1-std": 0.0022, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.777, + "roc_auc-std": 0.0042 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.895, + "acc-std": 0.0004, + "f1-count": 10.0, + "f1-mean": 0.8631, + "f1-std": 0.0007, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9485, + "roc_auc-std": 0.0001 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8947, + "acc-std": 0.0004, + "f1-count": 10.0, + "f1-mean": 0.8635, + "f1-std": 0.0007, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9473, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9048, + "acc-std": 0.0008, + "f1-count": 10.0, + "f1-mean": 0.8774, + "f1-std": 0.0009, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9599, + "roc_auc-std": 0.0005 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9055, + "acc-std": 0.0011, + "f1-count": 10.0, + "f1-mean": 0.8791, + "f1-std": 0.0013, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9616, + "roc_auc-std": 0.0004 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9114, + "acc-std": 0.0019, + "f1-count": 10.0, + "f1-mean": 0.8877, + "f1-std": 0.004, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.965, + "roc_auc-std": 0.0006 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9133, + "acc-std": 0.002, + "f1-count": 10.0, + "f1-mean": 0.8908, + "f1-std": 0.0039, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9659, + "roc_auc-std": 0.0007 + } + }, + "avg": { + "val": { + "f1-mean": 0.8656, + "acc-mean": 0.8948, + "f1-std": 0.0022 + }, + "test": { + "f1-mean": 0.8666, + "acc-mean": 0.895, + "f1-std": 0.0021 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan-plus/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan-plus/info.json new file mode 100644 index 0000000000000000000000000000000000000000..ec0c268473c6c05cbdd421ef527a6493730bcb63 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan-plus/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "MiniBooNE", + "id": "miniboone--id", + "train_size": 83240, + "val_size": 20811, + "test_size": 26013, + "n_num_features": 50, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..e74237e5b6c70eb0d9b59a8df755be93a8bf6cef --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan/config.toml @@ -0,0 +1,34 @@ +parent_dir = "exp/miniboone/ctabgan" +real_data_path = "data/miniboone/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.0010731076736125774 +epochs = 10000 +class_dim = [ + 512, + 512, + 512, + 256, +] +batch_size = 2048 +random_dim = 128 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 166000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..3c0d28895172ae7abf5ff9f79a8787a11590e746 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.9173, + "acc-std": 0.0008, + "f1-count": 50.0, + "f1-mean": 0.8944, + "f1-std": 0.0011, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9662, + "roc_auc-std": 0.0006 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.9127, + "acc-std": 0.001, + "f1-count": 50.0, + "f1-mean": 0.8892, + "f1-std": 0.0015, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9661, + "roc_auc-std": 0.0006 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..9ed00125418c5df3ab23e3bf55c8b3e12b2748a2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 25.0, + "acc-mean": 0.8595, + "acc-std": 0.0017, + "f1-count": 25.0, + "f1-mean": 0.8202, + "f1-std": 0.0022, + "roc_auc-count": 25.0, + "roc_auc-mean": 0.7722, + "roc_auc-std": 0.0178 + }, + "test": { + "acc-count": 25.0, + "acc-mean": 0.8573, + "acc-std": 0.0021, + "f1-count": 25.0, + "f1-mean": 0.8185, + "f1-std": 0.0026, + "roc_auc-count": 25.0, + "roc_auc-mean": 0.7689, + "roc_auc-std": 0.0218 + } + }, + "lr": { + "val": { + "acc-count": 25.0, + "acc-mean": 0.886, + "acc-std": 0.0006, + "f1-count": 25.0, + "f1-mean": 0.8472, + "f1-std": 0.0011, + "roc_auc-count": 25.0, + "roc_auc-mean": 0.9452, + "roc_auc-std": 0.0003 + }, + "test": { + "acc-count": 25.0, + "acc-mean": 0.886, + "acc-std": 0.0007, + "f1-count": 25.0, + "f1-mean": 0.8478, + "f1-std": 0.0011, + "roc_auc-count": 25.0, + "roc_auc-mean": 0.9447, + "roc_auc-std": 0.0003 + } + }, + "rf": { + "val": { + "acc-count": 25.0, + "acc-mean": 0.9033, + "acc-std": 0.0007, + "f1-count": 25.0, + "f1-mean": 0.8734, + "f1-std": 0.001, + "roc_auc-count": 25.0, + "roc_auc-mean": 0.9616, + "roc_auc-std": 0.0004 + }, + "test": { + "acc-count": 25.0, + "acc-mean": 0.9026, + "acc-std": 0.0008, + "f1-count": 25.0, + "f1-mean": 0.8736, + "f1-std": 0.0011, + "roc_auc-count": 25.0, + "roc_auc-mean": 0.9624, + "roc_auc-std": 0.0003 + } + }, + "mlp": { + "val": { + "acc-count": 25.0, + "acc-mean": 0.9119, + "acc-std": 0.0036, + "f1-count": 25.0, + "f1-mean": 0.8865, + "f1-std": 0.0064, + "roc_auc-count": 25.0, + "roc_auc-mean": 0.9623, + "roc_auc-std": 0.0013 + }, + "test": { + "acc-count": 25.0, + "acc-mean": 0.9092, + "acc-std": 0.0039, + "f1-count": 25.0, + "f1-mean": 0.8838, + "f1-std": 0.007, + "roc_auc-count": 25.0, + "roc_auc-mean": 0.9624, + "roc_auc-std": 0.0015 + } + }, + "avg": { + "val": { + "f1-mean": 0.8568, + "acc-mean": 0.8902, + "f1-std": 0.0027 + }, + "test": { + "f1-mean": 0.8559, + "acc-mean": 0.8888, + "f1-std": 0.0028 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan/info.json new file mode 100644 index 0000000000000000000000000000000000000000..ec0c268473c6c05cbdd421ef527a6493730bcb63 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ctabgan/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "MiniBooNE", + "id": "miniboone--id", + "train_size": 83240, + "val_size": 20811, + "test_size": 26013, + "n_num_features": 50, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..35ce40115bd94e77e8c11513fcaec719b3605fae --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/config.toml @@ -0,0 +1,57 @@ +seed = 0 +parent_dir = "exp/miniboone/ddpm_cb_best" +real_data_path = "data/miniboone/" +num_numerical_features = 50 +model_type = "mlp" +device = "cuda:1" + +[model_params] +d_in = 50 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 512, + 1024, + 1024, + 1024, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.0023518278056159554 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 664000 +batch_size = 20000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..127582e12342f9d208e752276ba035738c0ad63a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/eval_catboost.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.95, + "acc-std": 0.0005, + "f1-count": 50.0, + "f1-mean": 0.9381, + "f1-std": 0.0006, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9871, + "roc_auc-std": 0.0002 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.9481, + "acc-std": 0.0005, + "f1-count": 50.0, + "f1-mean": 0.9362, + "f1-std": 0.0006, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9864, + "roc_auc-std": 0.0001 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9481, + "acc-std": 0.0005, + "f1-count": 10.0, + "f1-mean": 0.9356, + "f1-std": 0.0007, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9865, + "roc_auc-std": 0.0001 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9466, + "acc-std": 0.0003, + "f1-count": 10.0, + "f1-mean": 0.9342, + "f1-std": 0.0004, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9859, + "roc_auc-std": 0.0001 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..8d1d0c5cfd4763a1ca713351d28a9dc3e2aedc5d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/eval_simple.json @@ -0,0 +1,222 @@ +{ + "real": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8933, + "acc-std": 0.0006, + "f1-count": 10.0, + "f1-mean": 0.8673, + "f1-std": 0.0007, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8325, + "roc_auc-std": 0.0012 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8889, + "acc-std": 0.0006, + "f1-count": 10.0, + "f1-mean": 0.8623, + "f1-std": 0.0007, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8237, + "roc_auc-std": 0.0013 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8472, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.789, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9015, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8468, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.7903, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9016, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9345, + "acc-std": 0.0003, + "f1-count": 10.0, + "f1-mean": 0.9179, + "f1-std": 0.0003, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9796, + "roc_auc-std": 0.0002 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9343, + "acc-std": 0.0006, + "f1-count": 10.0, + "f1-mean": 0.9182, + "f1-std": 0.0008, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9799, + "roc_auc-std": 0.0001 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8664, + "acc-std": 0.0087, + "f1-count": 10.0, + "f1-mean": 0.8282, + "f1-std": 0.0141, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9247, + "roc_auc-std": 0.0085 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8668, + "acc-std": 0.0094, + "f1-count": 10.0, + "f1-mean": 0.83, + "f1-std": 0.0141, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9252, + "roc_auc-std": 0.0086 + } + }, + "avg": { + "val": { + "f1-mean": 0.8506, + "acc-mean": 0.8854, + "f1-std": 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{ + "val": { + "acc-count": 10.0, + "acc-mean": 0.9345, + "acc-std": 0.0003, + "f1-count": 10.0, + "f1-mean": 0.9179, + "f1-std": 0.0003, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9796, + "roc_auc-std": 0.0002 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9343, + "acc-std": 0.0006, + "f1-count": 10.0, + "f1-mean": 0.9182, + "f1-std": 0.0008, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9799, + "roc_auc-std": 0.0001 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8664, + "acc-std": 0.0087, + "f1-count": 10.0, + "f1-mean": 0.8282, + "f1-std": 0.0141, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9247, + "roc_auc-std": 0.0085 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8668, + "acc-std": 0.0094, + "f1-count": 10.0, + "f1-mean": 0.83, + "f1-std": 0.0141, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9252, + "roc_auc-std": 0.0086 + } + }, + "avg": { + "val": { + "f1-mean": 0.8506, + "acc-mean": 0.8854, + "f1-std": 0.0038 + }, + "test": { + "f1-mean": 0.8502, + "acc-mean": 0.8842, + "f1-std": 0.0038 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..40eb1481e8dbc7780038f5722692c042d424feff --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/importance.json @@ -0,0 +1,10 @@ +{ + "n_layers": 0.5359902946725961, + "num_samples": 0.14558099276643524, + "steps": 0.10570951781155202, + "lr": 0.09486275463441243, + "d_first": 0.05133902368716852, + "batch_size": 0.02980026537632299, + "d_last": 0.021777947384698872, + "num_timesteps": 0.014939203666813674 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..ec0c268473c6c05cbdd421ef527a6493730bcb63 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "MiniBooNE", + "id": "miniboone--id", + "train_size": 83240, + "val_size": 20811, + "test_size": 26013, + "n_num_features": 50, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..33037355e4cb0e2161ee243ba6c9a5b329a3d5c4 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.9362, + 0.9359, + 0.9362, + 0.9357, + 0.9362 + ], + "privacies": [ + 0.0219182787986658, + 0.022, + 0.022, + 0.022, + 0.0219 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..144e92311d49c5dc87f4e7df81eba92c0b3a4a70 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/ddpm_cb_best/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.0219182787986658 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/smote/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/smote/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..5e472b503b490bd12237ffe213c98b81d19fc729 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/smote/config.toml @@ -0,0 +1,24 @@ +parent_dir = "exp/miniboone/smote" +real_data_path = "data/miniboone/" +seed = 0 + +[smote_params] +k_neighbours = 10 +frac_samples = 8 +frac_lam_del = 0.0 + +[sample] +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/smote/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/smote/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..384c6f98fcfa83bdf60d891c0d4bc32a7fc3f07d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/smote/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.9478, + "acc-std": 0.0005, + "f1-count": 50.0, + "f1-mean": 0.9349, + "f1-std": 0.0007, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9861, + "roc_auc-std": 0.0001 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.9453, + "acc-std": 0.0004, + "f1-count": 50.0, + "f1-mean": 0.9323, + "f1-std": 0.0005, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9851, + "roc_auc-std": 0.0001 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/smote/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/smote/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..693c96e36bd18ef337a5dbcea1a7cd42cc98b408 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/smote/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8874, + "acc-std": 0.0006, + "f1-count": 10.0, + "f1-mean": 0.8589, + "f1-std": 0.0005, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8107, + "roc_auc-std": 0.0009 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8856, + "acc-std": 0.001, + "f1-count": 10.0, + "f1-mean": 0.8579, + "f1-std": 0.0013, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8104, + "roc_auc-std": 0.0018 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8552, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.8063, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9082, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.854, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.8059, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9075, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9389, + "acc-std": 0.0003, + "f1-count": 10.0, + "f1-mean": 0.9231, + "f1-std": 0.0004, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9821, + "roc_auc-std": 0.0002 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9381, + "acc-std": 0.0006, + "f1-count": 10.0, + "f1-mean": 0.9226, + "f1-std": 0.0007, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9821, + "roc_auc-std": 0.0001 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8818, + "acc-std": 0.0054, + "f1-count": 10.0, + "f1-mean": 0.8517, + "f1-std": 0.0041, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9452, + "roc_auc-std": 0.0011 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8822, + "acc-std": 0.0052, + "f1-count": 10.0, + "f1-mean": 0.8532, + "f1-std": 0.0043, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9457, + "roc_auc-std": 0.0011 + } + }, + "avg": { + "val": { + "f1-mean": 0.86, + "acc-mean": 0.8908, + "f1-std": 0.0012 + }, + "test": { + "f1-mean": 0.8599, + "acc-mean": 0.89, + "f1-std": 0.0014 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/smote/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/smote/info.json new file mode 100644 index 0000000000000000000000000000000000000000..ec0c268473c6c05cbdd421ef527a6493730bcb63 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/smote/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "MiniBooNE", + "id": "miniboone--id", + "train_size": 83240, + "val_size": 20811, + "test_size": 26013, + "n_num_features": 50, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/smote/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/smote/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..09f2b79ec7e0227c4b4251bf29e6de8a917d4683 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/smote/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.01579189288008842 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/tvae/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/tvae/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..8117e2f3fce6de91f3ac9105116de09980d319dd --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/tvae/config.toml @@ -0,0 +1,34 @@ +parent_dir = "exp/miniboone/tvae" +real_data_path = "data/miniboone/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.000664515187310261 +epochs = 30000 +embedding_dim = 256 +batch_size = 4096 +loss_factor = 0.787216940718989 +compress_dims = [ + 512, +] +decompress_dims = [ + 512, +] + +[sample] +seed = 0 +num_samples = 664000 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/tvae/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/tvae/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..0b045f26aafe28c92abfad0bef29179557cd5d5f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/tvae/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 25.0, + "acc-mean": 0.9322, + "acc-std": 0.0005, + "f1-count": 25.0, + "f1-mean": 0.9148, + "f1-std": 0.0007, + "roc_auc-count": 25.0, + "roc_auc-mean": 0.9769, + "roc_auc-std": 0.0002 + }, + "test": { + "acc-count": 25.0, + "acc-mean": 0.93, + "acc-std": 0.0008, + "f1-count": 25.0, + "f1-mean": 0.9125, + "f1-std": 0.0011, + "roc_auc-count": 25.0, + "roc_auc-mean": 0.9769, + "roc_auc-std": 0.0002 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/tvae/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/tvae/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..dc1bf6b8f3b30be787a575583c21f7227dc23f75 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/tvae/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8862, + "acc-std": 0.0012, + "f1-count": 10.0, + "f1-mean": 0.8551, + "f1-std": 0.0012, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7957, + "roc_auc-std": 0.0016 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8863, + "acc-std": 0.0015, + "f1-count": 10.0, + "f1-mean": 0.8563, + "f1-std": 0.0019, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7938, + "roc_auc-std": 0.0043 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.8956, + "acc-std": 0.0001, + "f1-count": 10.0, + "f1-mean": 0.8634, + "f1-std": 0.0003, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9494, + "roc_auc-std": 0.0001 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.8978, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.8675, + "f1-std": 0.0002, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9492, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9139, + "acc-std": 0.0013, + "f1-count": 10.0, + "f1-mean": 0.8892, + "f1-std": 0.0015, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9678, + "roc_auc-std": 0.0002 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9128, + "acc-std": 0.0012, + "f1-count": 10.0, + "f1-mean": 0.8884, + "f1-std": 0.0014, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9684, + "roc_auc-std": 0.0002 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9279, + "acc-std": 0.0019, + "f1-count": 10.0, + "f1-mean": 0.9087, + "f1-std": 0.0031, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9742, + "roc_auc-std": 0.0003 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9269, + "acc-std": 0.0019, + "f1-count": 10.0, + "f1-mean": 0.908, + "f1-std": 0.0031, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9742, + "roc_auc-std": 0.0004 + } + }, + "avg": { + "val": { + "f1-mean": 0.8791, + "acc-mean": 0.9059, + "f1-std": 0.0015 + }, + "test": { + "f1-mean": 0.88, + "acc-mean": 0.906, + "f1-std": 0.0016 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/tvae/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/tvae/info.json new file mode 100644 index 0000000000000000000000000000000000000000..ec0c268473c6c05cbdd421ef527a6493730bcb63 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/miniboone/tvae/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "MiniBooNE", + "id": "miniboone--id", + "train_size": 83240, + "val_size": 20811, + "test_size": 26013, + "n_num_features": 50, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..6a496225ccc65b645d7a7d388b9ea5f63b6fc699 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/config.toml @@ -0,0 +1,55 @@ +seed = 0 +parent_dir = "exp/wilt/check" +real_data_path = "data/wilt/" +model_type = "mlp" +num_numerical_features = 5 +device = "cuda:0" + +[model_params] +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 256, + 256, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" +scheduler = "cosine" + +[train.main] +steps = 1000 +lr = 0.001 +weight_decay = 1e-05 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 24800 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan-plus/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan-plus/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..2ccffd17d8270e69e7f96abe08745158c3392401 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan-plus/config.toml @@ -0,0 +1,33 @@ +parent_dir = "exp/wilt/ctabgan-plus" +real_data_path = "data/wilt/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.0002993957976860954 +epochs = 10000 +class_dim = [ + 128, + 256, + 128, +] +batch_size = 512 +random_dim = 32 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 12400 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan-plus/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan-plus/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..e9af038315e099240a1f65c715bcf187ff6936e1 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan-plus/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.9787, + "acc-std": 0.0024, + "f1-count": 50.0, + "f1-mean": 0.8737, + "f1-std": 0.0169, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9942, + "roc_auc-std": 0.0021 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.9682, + "acc-std": 0.0024, + "f1-count": 50.0, + "f1-mean": 0.7983, + "f1-std": 0.0212, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9761, + "roc_auc-std": 0.008 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan-plus/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan-plus/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..1ca01d8f45712aa876a261dfe17fd8f4b0b28acf --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan-plus/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.9648, + "acc-std": 0.0048, + "f1-count": 50.0, + "f1-mean": 0.8106, + "f1-std": 0.0262, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7823, + "roc_auc-std": 0.0289 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.959, + "acc-std": 0.0039, + "f1-count": 50.0, + "f1-mean": 0.7726, + "f1-std": 0.0223, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7427, + "roc_auc-std": 0.0234 + } + }, + "lr": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.9458, + "acc-std": 0.0, + "f1-count": 50.0, + "f1-mean": 0.4861, + "f1-std": 0.0, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.719, + "roc_auc-std": 0.0086 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.9463, + "acc-std": 0.0, + "f1-count": 50.0, + "f1-mean": 0.4862, + "f1-std": 0.0, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.7684, + "roc_auc-std": 0.0069 + } + }, + "rf": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.973, + "acc-std": 0.0034, + "f1-count": 50.0, + "f1-mean": 0.8268, + "f1-std": 0.0283, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9837, + "roc_auc-std": 0.0094 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.9667, + "acc-std": 0.0019, + "f1-count": 50.0, + "f1-mean": 0.7805, + "f1-std": 0.0191, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9635, + "roc_auc-std": 0.0106 + } + }, + "mlp": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.9503, + "acc-std": 0.0033, + "f1-count": 50.0, + "f1-mean": 0.5605, + "f1-std": 0.0512, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.992, + "roc_auc-std": 0.0035 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.9514, + "acc-std": 0.0042, + "f1-count": 50.0, + "f1-mean": 0.5711, + "f1-std": 0.0656, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9838, + "roc_auc-std": 0.0031 + } + }, + "avg": { + "val": { + "f1-mean": 0.671, + "acc-mean": 0.9585, + "f1-std": 0.0264 + }, + "test": { + "f1-mean": 0.6526, + "acc-mean": 0.9558, + "f1-std": 0.0266 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan-plus/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan-plus/info.json new file mode 100644 index 0000000000000000000000000000000000000000..5ea3c98d1938d8e776b24c42c767522c23811794 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan-plus/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "wilt", + "id": "wilt--id", + "train_size": 3096, + "val_size": 775, + "test_size": 968, + "n_num_features": 5, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..d1fc46b86b50773a50436cedcfa588bc232c6a33 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan/config.toml @@ -0,0 +1,34 @@ +parent_dir = "exp/wilt/ctabgan" +real_data_path = "data/wilt/" +seed = 0 +device = "cuda:1" + +[train_params] +lr = 0.0012825908944717134 +epochs = 5000 +class_dim = [ + 512, + 128, + 128, + 512, +] +batch_size = 2048 +random_dim = 128 +num_channels = 64 + +[sample] +seed = 0 +num_samples = 24800 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..5257b82c30a973c9bf16bf211969806aac555c18 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.9863, + "acc-std": 0.0022, + "f1-count": 50.0, + "f1-mean": 0.9349, + "f1-std": 0.0109, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9942, + "roc_auc-std": 0.0038 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.9804, + "acc-std": 0.0035, + "f1-count": 50.0, + "f1-mean": 0.906, + "f1-std": 0.0186, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9876, + "roc_auc-std": 0.0035 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..ee96f07b91b820f22d8fa391ca5116ee16e92821 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9599, + "acc-std": 0.0053, + "f1-count": 10.0, + "f1-mean": 0.8269, + "f1-std": 0.0186, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8699, + "roc_auc-std": 0.0273 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9514, + "acc-std": 0.0067, + "f1-count": 10.0, + "f1-mean": 0.7946, + "f1-std": 0.0258, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8437, + "roc_auc-std": 0.0358 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9458, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.4861, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7025, + "roc_auc-std": 0.0042 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9463, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.4862, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7359, + "roc_auc-std": 0.0024 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9781, + "acc-std": 0.004, + "f1-count": 10.0, + "f1-mean": 0.8874, + "f1-std": 0.0211, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9804, + "roc_auc-std": 0.0126 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9728, + "acc-std": 0.0026, + "f1-count": 10.0, + "f1-mean": 0.8553, + "f1-std": 0.0186, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9726, + "roc_auc-std": 0.009 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9458, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.4861, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.718, + "roc_auc-std": 0.0152 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9463, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.4862, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7449, + "roc_auc-std": 0.0178 + } + }, + "avg": { + "val": { + "f1-mean": 0.6716, + "acc-mean": 0.9574, + "f1-std": 0.0099 + }, + "test": { + "f1-mean": 0.6556, + "acc-mean": 0.9542, + "f1-std": 0.0105 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan/info.json new file mode 100644 index 0000000000000000000000000000000000000000..5ea3c98d1938d8e776b24c42c767522c23811794 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "wilt", + "id": "wilt--id", + "train_size": 3096, + "val_size": 775, + "test_size": 968, + "n_num_features": 5, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..6d9210fcdcd8b0208ebe33fd37ac9ad7481f0666 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ctabgan/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.906, + 0.895, + 0.8875, + 0.8903, + 0.8802 + ], + "privacies": [ + 0.017503173994890757, + 0.0188, + 0.0202, + 0.0219, + 0.024 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..98a3125375462109cec478e17dce16138513c2c4 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/config.toml @@ -0,0 +1,60 @@ +seed = 0 +parent_dir = "exp/wilt/ddpm_cb_best" +real_data_path = "data/wilt/" +model_type = "mlp" +num_numerical_features = 5 +device = "cuda:0" + +[model_params] +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 1024, + 512, + 512, + 512, + 512, + 512, + 512, + 128, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 100 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.00010707356429215857 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 24800 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..6fe00f573b91548bc1e42c2c0d8ec66e2ef2c789 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/eval_catboost.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.9963, + "acc-std": 0.0012, + "f1-count": 50.0, + "f1-mean": 0.982, + "f1-std": 0.0056, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9994, + "roc_auc-std": 0.0004 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.9805, + "acc-std": 0.0019, + "f1-count": 50.0, + "f1-mean": 0.9045, + "f1-std": 0.009, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9923, + "roc_auc-std": 0.0006 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9907, + "acc-std": 0.001, + "f1-count": 10.0, + "f1-mean": 0.9532, + "f1-std": 0.0045, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9964, + "roc_auc-std": 0.002 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9801, + "acc-std": 0.0009, + "f1-count": 10.0, + "f1-mean": 0.8982, + "f1-std": 0.0063, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9883, + "roc_auc-std": 0.0019 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..2f652f2cf034673fbf6d29ff6cede5cc5cce1bb0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/eval_mlp.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.9933, + "acc-std": 0.0014, + "f1-count": 50.0, + "f1-mean": 0.9684, + "f1-std": 0.0064, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9995, + "roc_auc-std": 0.0002 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.9835, + "acc-std": 0.0011, + "f1-count": 50.0, + "f1-mean": 0.9208, + "f1-std": 0.0056, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9952, + "roc_auc-std": 0.0003 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9952, + "acc-std": 0.0015, + "f1-count": 10.0, + "f1-mean": 0.977, + "f1-std": 0.0073, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9995, + "roc_auc-std": 0.0001 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.981, + "acc-std": 0.0012, + "f1-count": 10.0, + "f1-mean": 0.9054, + "f1-std": 0.0063, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.995, + "roc_auc-std": 0.0003 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..28a4376c0674eed2ef9cb8250062b53e32dcfa11 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/eval_simple.json @@ -0,0 +1,222 @@ +{ + "real": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9828, + "acc-std": 0.0017, + "f1-count": 10.0, + 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"test": { + "acc-count": 10.0, + "acc-mean": 0.9786, + "acc-std": 0.0013, + "f1-count": 10.0, + "f1-mean": 0.8841, + "f1-std": 0.0078, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9866, + "roc_auc-std": 0.0023 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9458, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.4861, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7521, + "roc_auc-std": 0.0102 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9463, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.4862, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7827, + "roc_auc-std": 0.01 + } + }, + "avg": { + "val": { + "f1-mean": 0.7006, + "acc-mean": 0.9647, + "f1-std": 0.0041 + }, + "test": { + "f1-mean": 0.6836, + "acc-mean": 0.9616, + "f1-std": 0.0036 + } + } + }, + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9905, + "acc-std": 0.001, + "f1-count": 10.0, + "f1-mean": 0.9532, + "f1-std": 0.0047, + 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b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..29687d80c99cc084d27547678d38afb4683ae142 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/importance.json @@ -0,0 +1,10 @@ +{ + "num_samples": 0.9120117457092586, + "lr": 0.02787657256890614, + "d_first": 0.022254742380137537, + "batch_size": 0.010166672998388915, + "steps": 0.0101534273571216, + "num_timesteps": 0.008513376309233371, + "d_last": 0.006936316136391223, + "n_layers": 0.0020871465405626066 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..5ea3c98d1938d8e776b24c42c767522c23811794 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "wilt", + "id": "wilt--id", + "train_size": 3096, + "val_size": 775, + "test_size": 968, + "n_num_features": 5, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..9244892665173534f785af57403c231bf7046ed3 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.9045, + 0.8888, + 0.905, + 0.9021, + 0.9084 + ], + "privacies": [ + 0.016280221717633263, + 0.0174, + 0.0185, + 0.0197, + 0.0213 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..f5436f79367b02142d7389172ed84dcff6516a46 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_cb_best/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.016434497626387577 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_mlp_best/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_mlp_best/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..8757a35b51be0c077f7cf63ffd3ee053d7693de0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_mlp_best/config.toml @@ -0,0 +1,58 @@ +seed = 0 +parent_dir = "exp/wilt/ddpm_mlp_best" +real_data_path = "data/wilt/" +model_type = "mlp" +num_numerical_features = 5 +device = "cuda:0" + +[model_params] +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [ + 512, + 256, + 256, + 256, + 256, + 1024, +] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 100 +gaussian_loss_type = "mse" + +[train.main] +steps = 30000 +lr = 0.00013266424499210302 +weight_decay = 0.0 +batch_size = 4096 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 12400 +batch_size = 10000 +seed = 0 + +[eval.type] +eval_model = "mlp" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "one-hot" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_mlp_best/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_mlp_best/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..777893e19b96a19674a00b53f0b3b9e47e541da6 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_mlp_best/eval_catboost.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.9943, + "acc-std": 0.0014, + "f1-count": 50.0, + "f1-mean": 0.972, + "f1-std": 0.007, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9988, + "roc_auc-std": 0.0003 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.9795, + "acc-std": 0.0013, + "f1-count": 50.0, + "f1-mean": 0.8976, + "f1-std": 0.0059, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9889, + "roc_auc-std": 0.0021 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9907, + "acc-std": 0.001, + "f1-count": 10.0, + "f1-mean": 0.9532, + "f1-std": 0.0045, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9964, + "roc_auc-std": 0.002 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9801, + "acc-std": 0.0009, + "f1-count": 10.0, + "f1-mean": 0.8982, + "f1-std": 0.0063, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9883, + "roc_auc-std": 0.0019 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_mlp_best/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_mlp_best/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..e8d9ce4210070ce0ca3606dcbd61de2ac0882fc3 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_mlp_best/eval_mlp.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.9945, + "acc-std": 0.0013, + "f1-count": 50.0, + "f1-mean": 0.9735, + "f1-std": 0.006, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9995, + "roc_auc-std": 0.0001 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.9821, + "acc-std": 0.0012, + "f1-count": 50.0, + "f1-mean": 0.9136, + "f1-std": 0.0059, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9953, + "roc_auc-std": 0.0002 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_mlp_best/importance.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_mlp_best/importance.json new file mode 100644 index 0000000000000000000000000000000000000000..dbdab24b9fe0562a9c1ba369286372112abb67b3 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_mlp_best/importance.json @@ -0,0 +1,10 @@ +{ + "num_samples": 0.6542386670641036, + "batch_size": 0.1559992553592549, + "lr": 0.07651119270158713, + "d_first": 0.04463816635826496, + "n_layers": 0.018125893574676994, + "d_last": 0.01693462071780706, + "steps": 0.016909594857588653, + "num_timesteps": 0.016642609366716937 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_mlp_best/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_mlp_best/info.json new file mode 100644 index 0000000000000000000000000000000000000000..5ea3c98d1938d8e776b24c42c767522c23811794 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/ddpm_mlp_best/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "wilt", + "id": "wilt--id", + "train_size": 3096, + "val_size": 775, + "test_size": 968, + "n_num_features": 5, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..a2314fd44f034933fd4e9f67ea2c9606806d6f7f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/config.toml @@ -0,0 +1,24 @@ +parent_dir = "exp/wilt/smote" +real_data_path = "data/wilt/" +seed = 0 + +[smote_params] +k_neighbours = 14 +frac_samples = 8 +frac_lam_del = 0.05 + +[sample] +seed = 0 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..414d10c2661cd3ca001a362718971848c7f56584 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.9929, + "acc-std": 0.0009, + "f1-count": 50.0, + "f1-mean": 0.9644, + "f1-std": 0.004, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9968, + "roc_auc-std": 0.0014 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.9826, + "acc-std": 0.0013, + "f1-count": 50.0, + "f1-mean": 0.9127, + "f1-std": 0.0073, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9908, + "roc_auc-std": 0.0009 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/eval_mlp.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/eval_mlp.json new file mode 100644 index 0000000000000000000000000000000000000000..d61c2c99a8903481667fd0b65065b2b0f53643f0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/eval_mlp.json @@ -0,0 +1,50 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.9951, + "acc-std": 0.001, + "f1-count": 50.0, + "f1-mean": 0.9759, + "f1-std": 0.0046, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.9992, + "roc_auc-std": 0.0001 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.9815, + "acc-std": 0.0017, + "f1-count": 50.0, + "f1-mean": 0.9078, + "f1-std": 0.01, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.993, + "roc_auc-std": 0.0002 + } + }, + "real": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9952, + "acc-std": 0.0015, + "f1-count": 10.0, + "f1-mean": 0.977, + "f1-std": 0.0073, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9995, + "roc_auc-std": 0.0001 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.981, + "acc-std": 0.0012, + "f1-count": 10.0, + "f1-mean": 0.9054, + "f1-std": 0.0063, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.995, + "roc_auc-std": 0.0003 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..9dcf08a6879034163fa0acec2261171db5e6fba4 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9868, + "acc-std": 0.001, + "f1-count": 10.0, + "f1-mean": 0.9375, + "f1-std": 0.0043, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9493, + "roc_auc-std": 0.0037 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.976, + "acc-std": 0.0009, + "f1-count": 10.0, + "f1-mean": 0.8828, + "f1-std": 0.0039, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.8849, + "roc_auc-std": 0.0071 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9458, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.4861, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7553, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9463, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.4862, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7872, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9889, + "acc-std": 0.0006, + "f1-count": 10.0, + "f1-mean": 0.9441, + "f1-std": 0.0036, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.997, + "roc_auc-std": 0.0003 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9816, + "acc-std": 0.0009, + "f1-count": 10.0, + "f1-mean": 0.9063, + "f1-std": 0.0053, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9904, + "roc_auc-std": 0.0041 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9934, + "acc-std": 0.0011, + "f1-count": 10.0, + "f1-mean": 0.9674, + "f1-std": 0.0061, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9997, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9792, + "acc-std": 0.0019, + "f1-count": 10.0, + "f1-mean": 0.8972, + "f1-std": 0.0114, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.9942, + "roc_auc-std": 0.0002 + } + }, + "avg": { + "val": { + "f1-mean": 0.8338, + "acc-mean": 0.9787, + "f1-std": 0.0035 + }, + "test": { + "f1-mean": 0.7931, + "acc-mean": 0.9708, + "f1-std": 0.0043 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/info.json new file mode 100644 index 0000000000000000000000000000000000000000..5ea3c98d1938d8e776b24c42c767522c23811794 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "wilt", + "id": "wilt--id", + "train_size": 3096, + "val_size": 775, + "test_size": 968, + "n_num_features": 5, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/privacies.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/privacies.json new file mode 100644 index 0000000000000000000000000000000000000000..608380f37249fc68fb3ee528592d048839e56683 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/privacies.json @@ -0,0 +1,16 @@ +{ + "scores": [ + 0.9127, + 0.9096, + 0.9054, + 0.9045, + 0.9006 + ], + "privacies": [ + 0.007275902056495125, + 0.008, + 0.0087, + 0.0096, + 0.0104 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/privacy.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/privacy.json new file mode 100644 index 0000000000000000000000000000000000000000..d80ed4a5222d80ca58f895bdc7b7fcc36be18962 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/smote/privacy.json @@ -0,0 +1,3 @@ +{ + "privacy": 0.007275902056495125 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/tvae/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/tvae/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..737c35f911f1e1fd1197c0009bd2196c692e5197 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/tvae/config.toml @@ -0,0 +1,40 @@ +parent_dir = "exp/wilt/tvae" +real_data_path = "data/wilt/" +seed = 0 +device = "cuda:0" + +[train_params] +lr = 0.0013361191527546943 +epochs = 5000 +embedding_dim = 64 +batch_size = 256 +loss_factor = 6.543559371803048 +compress_dims = [ + 128, + 512, + 512, + 256, +] +decompress_dims = [ + 128, + 512, + 512, + 256, +] + +[sample] +seed = 0 +num_samples = 12400 + +[eval.type] +eval_model = "catboost" +eval_type = "synthetic" + +[eval.T] +seed = 0 +normalization = "__none__" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/tvae/eval_catboost.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/tvae/eval_catboost.json new file mode 100644 index 0000000000000000000000000000000000000000..e91c8a17187e61959bf630421a8cfd1ea79abbf1 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/tvae/eval_catboost.json @@ -0,0 +1,26 @@ +{ + "synthetic": { + "val": { + "acc-count": 50.0, + "acc-mean": 0.9465, + "acc-std": 0.0008, + "f1-count": 50.0, + "f1-mean": 0.5063, + "f1-std": 0.0141, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.5761, + "roc_auc-std": 0.0752 + }, + "test": { + "acc-count": 50.0, + "acc-mean": 0.9448, + "acc-std": 0.002, + "f1-count": 50.0, + "f1-mean": 0.5006, + "f1-std": 0.0123, + "roc_auc-count": 50.0, + "roc_auc-mean": 0.6021, + "roc_auc-std": 0.0812 + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/tvae/eval_simple.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/tvae/eval_simple.json new file mode 100644 index 0000000000000000000000000000000000000000..5bfe6b4dbeb7b68e635da022f297013ce631ff28 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/tvae/eval_simple.json @@ -0,0 +1,112 @@ +{ + "synthetic": { + "tree": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9452, + "acc-std": 0.0006, + "f1-count": 10.0, + "f1-mean": 0.5084, + "f1-std": 0.0004, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.5109, + "roc_auc-std": 0.0003 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9428, + "acc-std": 0.0005, + "f1-count": 10.0, + "f1-mean": 0.5125, + "f1-std": 0.0083, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.5127, + "roc_auc-std": 0.0047 + } + }, + "lr": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9458, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.4861, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7047, + "roc_auc-std": 0.0 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9463, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.4862, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7311, + "roc_auc-std": 0.0 + } + }, + "rf": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9458, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.4861, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.4878, + "roc_auc-std": 0.0092 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9459, + "acc-std": 0.0012, + "f1-count": 10.0, + "f1-mean": 0.5045, + "f1-std": 0.0007, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.5142, + "roc_auc-std": 0.0088 + } + }, + "mlp": { + "val": { + "acc-count": 10.0, + "acc-mean": 0.9458, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.4861, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.6714, + "roc_auc-std": 0.0136 + }, + "test": { + "acc-count": 10.0, + "acc-mean": 0.9463, + "acc-std": 0.0, + "f1-count": 10.0, + "f1-mean": 0.4862, + "f1-std": 0.0, + "roc_auc-count": 10.0, + "roc_auc-mean": 0.7047, + "roc_auc-std": 0.0135 + } + }, + "avg": { + "val": { + "f1-mean": 0.4917, + "acc-mean": 0.9457, + "f1-std": 0.0001 + }, + "test": { + "f1-mean": 0.4974, + "acc-mean": 0.9453, + "f1-std": 0.0012 + } + } + } +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/tvae/info.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/tvae/info.json new file mode 100644 index 0000000000000000000000000000000000000000..5ea3c98d1938d8e776b24c42c767522c23811794 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/exp/wilt/tvae/info.json @@ -0,0 +1,10 @@ +{ + "task_type": "binclass", + "name": "wilt", + "id": "wilt--id", + "train_size": 3096, + "val_size": 775, + "test_size": 968, + "n_num_features": 5, + "n_cat_features": 0 +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/__init__.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..54d6f6bbd104c00418af09162967e1fde1dea4fc --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/__init__.py @@ -0,0 +1,12 @@ +import torch +from icecream import install + +torch.set_num_threads(1) +install() + +from . import env # noqa +from .data import * # noqa +from .deep import * # noqa +from .env import * # noqa +from .metrics import * # noqa +from .util import * # noqa diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/data.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/data.py new file mode 100644 index 0000000000000000000000000000000000000000..de47f283d607c411a23df48a364f19f43d2727d1 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/data.py @@ -0,0 +1,719 @@ +import hashlib +from collections import Counter +from copy import deepcopy +from dataclasses import astuple, dataclass, replace +from importlib.resources import path +from pathlib import Path +from typing import Any, Literal, Optional, Union, cast, Tuple, Dict, List + +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.pipeline import make_pipeline +import sklearn.preprocessing +import torch +import os +from category_encoders import LeaveOneOutEncoder +from sklearn.impute import SimpleImputer +from sklearn.preprocessing import StandardScaler +from scipy.spatial.distance import cdist + +from . import env, util +from .metrics import calculate_metrics as calculate_metrics_ +from .util import TaskType, load_json + +ArrayDict = Dict[str, np.ndarray] +TensorDict = Dict[str, torch.Tensor] + + +CAT_MISSING_VALUE = '__nan__' +CAT_RARE_VALUE = '__rare__' +Normalization = Literal['standard', 'quantile', 'minmax'] +NumNanPolicy = Literal['drop-rows', 'mean'] +CatNanPolicy = Literal['most_frequent'] +CatEncoding = Literal['one-hot', 'counter'] +YPolicy = Literal['default'] + + +class StandardScaler1d(StandardScaler): + def partial_fit(self, X, *args, **kwargs): + assert X.ndim == 1 + return super().partial_fit(X[:, None], *args, **kwargs) + + def transform(self, X, *args, **kwargs): + assert X.ndim == 1 + return super().transform(X[:, None], *args, **kwargs).squeeze(1) + + def inverse_transform(self, X, *args, **kwargs): + assert X.ndim == 1 + return super().inverse_transform(X[:, None], *args, **kwargs).squeeze(1) + + +def get_category_sizes(X: Union[torch.Tensor, np.ndarray]) -> List[int]: + """Return K[i] s.t. F.one_hot(x[:,i], K[i]) is valid. Requires K[i] > max(x[:,i]).""" + XT = X.T.cpu().tolist() if isinstance(X, torch.Tensor) else X.T.tolist() + return [int(np.max(x)) + 1 if len(x) > 0 else 0 for x in XT] + + +@dataclass(frozen=False) +class Dataset: + X_num: Optional[ArrayDict] + X_cat: Optional[ArrayDict] + y: ArrayDict + y_info: Dict[str, Any] + task_type: TaskType + n_classes: Optional[int] + + @classmethod + def from_dir(cls, dir_: Union[Path, str]) -> 'Dataset': + dir_ = Path(dir_) + splits = [k for k in ['train', 'val', 'test'] if dir_.joinpath(f'y_{k}.npy').exists()] + + def load(item) -> ArrayDict: + return { + x: cast(np.ndarray, np.load(dir_ / f'{item}_{x}.npy', allow_pickle=True)) # type: ignore[code] + for x in splits + } + + if Path(dir_ / 'info.json').exists(): + info = util.load_json(dir_ / 'info.json') + else: + info = None + return Dataset( + load('X_num') if dir_.joinpath('X_num_train.npy').exists() else None, + load('X_cat') if dir_.joinpath('X_cat_train.npy').exists() else None, + load('y'), + {}, + TaskType(info['task_type']), + info.get('n_classes'), + ) + + @property + def is_binclass(self) -> bool: + return self.task_type == TaskType.BINCLASS + + @property + def is_multiclass(self) -> bool: + return self.task_type == TaskType.MULTICLASS + + @property + def is_regression(self) -> bool: + return self.task_type == TaskType.REGRESSION + + @property + def n_num_features(self) -> int: + return 0 if self.X_num is None else self.X_num['train'].shape[1] + + @property + def n_cat_features(self) -> int: + return 0 if self.X_cat is None else self.X_cat['train'].shape[1] + + @property + def n_features(self) -> int: + return self.n_num_features + self.n_cat_features + + def size(self, part: Optional[str]) -> int: + return sum(map(len, self.y.values())) if part is None else len(self.y[part]) + + @property + def nn_output_dim(self) -> int: + if self.is_multiclass: + assert self.n_classes is not None + return self.n_classes + else: + return 1 + + def get_category_sizes(self, part: str) -> List[int]: + return [] if self.X_cat is None else get_category_sizes(self.X_cat[part]) + + def calculate_metrics( + self, + predictions: Dict[str, np.ndarray], + prediction_type: Optional[str], + ) -> Dict[str, Any]: + metrics = { + x: calculate_metrics_( + self.y[x], predictions[x], self.task_type, prediction_type, self.y_info + ) + for x in predictions + } + if self.task_type == TaskType.REGRESSION: + score_key = 'rmse' + score_sign = -1 + else: + score_key = 'accuracy' + score_sign = 1 + for part_metrics in metrics.values(): + part_metrics['score'] = score_sign * part_metrics[score_key] + return metrics + +def change_val(dataset: Dataset, val_size: float = 0.2): + # should be done before transformations + + y = np.concatenate([dataset.y['train'], dataset.y['val']], axis=0) + + ixs = np.arange(y.shape[0]) + if dataset.is_regression: + train_ixs, val_ixs = train_test_split(ixs, test_size=val_size, random_state=777) + else: + train_ixs, val_ixs = train_test_split(ixs, test_size=val_size, random_state=777, stratify=y) + + dataset.y['train'] = y[train_ixs] + dataset.y['val'] = y[val_ixs] + + if dataset.X_num is not None: + X_num = np.concatenate([dataset.X_num['train'], dataset.X_num['val']], axis=0) + dataset.X_num['train'] = X_num[train_ixs] + dataset.X_num['val'] = X_num[val_ixs] + + if dataset.X_cat is not None: + X_cat = np.concatenate([dataset.X_cat['train'], dataset.X_cat['val']], axis=0) + dataset.X_cat['train'] = X_cat[train_ixs] + dataset.X_cat['val'] = X_cat[val_ixs] + + return dataset + +def num_process_nans(dataset: Dataset, policy: Optional[NumNanPolicy]) -> Dataset: + assert dataset.X_num is not None + nan_masks = {k: np.isnan(v) for k, v in dataset.X_num.items()} + if not any(x.any() for x in nan_masks.values()): # type: ignore[code] + assert policy is None + return dataset + + assert policy is not None + if policy == 'drop-rows': + valid_masks = {k: ~v.any(1) for k, v in nan_masks.items()} + assert valid_masks[ + 'test' + ].all(), 'Cannot drop test rows, since this will affect the final metrics.' + new_data = {} + for data_name in ['X_num', 'X_cat', 'y']: + data_dict = getattr(dataset, data_name) + if data_dict is not None: + new_data[data_name] = { + k: v[valid_masks[k]] for k, v in data_dict.items() + } + dataset = replace(dataset, **new_data) + elif policy == 'mean': + new_values = np.nanmean(dataset.X_num['train'], axis=0) + X_num = deepcopy(dataset.X_num) + for k, v in X_num.items(): + num_nan_indices = np.where(nan_masks[k]) + v[num_nan_indices] = np.take(new_values, num_nan_indices[1]) + dataset = replace(dataset, X_num=X_num) + else: + assert util.raise_unknown('policy', policy) + return dataset + + +# Inspired by: https://github.com/yandex-research/rtdl/blob/a4c93a32b334ef55d2a0559a4407c8306ffeeaee/lib/data.py#L20 +def normalize( + X: ArrayDict, normalization: Normalization, seed: Optional[int], return_normalizer : bool = False +) -> ArrayDict: + X_train = X['train'] + if normalization == 'standard': + normalizer = sklearn.preprocessing.StandardScaler() + elif normalization == 'minmax': + normalizer = sklearn.preprocessing.MinMaxScaler() + elif normalization == 'quantile': + normalizer = sklearn.preprocessing.QuantileTransformer( + output_distribution='normal', + n_quantiles=max(min(X['train'].shape[0] // 30, 1000), 10), + subsample=int(1e9), + random_state=seed, + ) + # noise = 1e-3 + # if noise > 0: + # assert seed is not None + # stds = np.std(X_train, axis=0, keepdims=True) + # noise_std = noise / np.maximum(stds, noise) # type: ignore[code] + # X_train = X_train + noise_std * np.random.default_rng(seed).standard_normal( + # X_train.shape + # ) + else: + util.raise_unknown('normalization', normalization) + normalizer.fit(X_train) + if return_normalizer: + return {k: normalizer.transform(v) for k, v in X.items()}, normalizer + return {k: normalizer.transform(v) for k, v in X.items()} + + +def cat_process_nans(X: ArrayDict, policy: Optional[CatNanPolicy]) -> ArrayDict: + assert X is not None + nan_masks = {k: np.asarray(v == CAT_MISSING_VALUE) for k, v in X.items()} + if any(np.asarray(x).any() for x in nan_masks.values()): # type: ignore[code] + if policy is None: + X_new = X + elif policy == 'most_frequent': + imputer = SimpleImputer(missing_values=CAT_MISSING_VALUE, strategy=policy) # type: ignore[code] + imputer.fit(X['train']) + X_new = {k: cast(np.ndarray, imputer.transform(v)) for k, v in X.items()} + else: + util.raise_unknown('categorical NaN policy', policy) + else: + assert policy is None + X_new = X + return X_new + + +def cat_drop_rare(X: ArrayDict, min_frequency: float) -> ArrayDict: + assert 0.0 < min_frequency < 1.0 + min_count = round(len(X['train']) * min_frequency) + X_new = {x: [] for x in X} + for column_idx in range(X['train'].shape[1]): + counter = Counter(X['train'][:, column_idx].tolist()) + popular_categories = {k for k, v in counter.items() if v >= min_count} + for part in X_new: + X_new[part].append( + [ + (x if x in popular_categories else CAT_RARE_VALUE) + for x in X[part][:, column_idx].tolist() + ] + ) + return {k: np.array(v).T for k, v in X_new.items()} + + +def cat_encode( + X: ArrayDict, + encoding: Optional[CatEncoding], + y_train: Optional[np.ndarray], + seed: Optional[int], + return_encoder : bool = False +) -> Tuple[ArrayDict, bool, Optional[Any]]: # (X, is_converted_to_numerical) + if encoding != 'counter': + y_train = None + + # Step 1. Map strings to 0-based ranges + + if encoding is None: + unknown_value = np.iinfo('int64').max - 3 + oe = sklearn.preprocessing.OrdinalEncoder( + handle_unknown='use_encoded_value', # type: ignore[code] + unknown_value=unknown_value, # type: ignore[code] + dtype='int64', # type: ignore[code] + ).fit(X['train']) + encoder = make_pipeline(oe) + encoder.fit(X['train']) + X = {k: encoder.transform(v) for k, v in X.items()} + max_values = X['train'].max(axis=0) + for part in X.keys(): + if part == 'train': continue + for column_idx in range(X[part].shape[1]): + X[part][X[part][:, column_idx] == unknown_value, column_idx] = ( + max_values[column_idx] + 1 + ) + if return_encoder: + return (X, False, encoder) + return (X, False) + + # Step 2. Encode. + + elif encoding == 'one-hot': + ohe = sklearn.preprocessing.OneHotEncoder( + handle_unknown='ignore', sparse=False, dtype=np.float32 # type: ignore[code] + ) + encoder = make_pipeline(ohe) + + # encoder.steps.append(('ohe', ohe)) + encoder.fit(X['train']) + X = {k: encoder.transform(v) for k, v in X.items()} + elif encoding == 'counter': + assert y_train is not None + assert seed is not None + loe = LeaveOneOutEncoder(sigma=0.1, random_state=seed, return_df=False) + encoder.steps.append(('loe', loe)) + encoder.fit(X['train'], y_train) + X = {k: encoder.transform(v).astype('float32') for k, v in X.items()} # type: ignore[code] + if not isinstance(X['train'], pd.DataFrame): + X = {k: v.values for k, v in X.items()} # type: ignore[code] + else: + util.raise_unknown('encoding', encoding) + + if return_encoder: + return X, True, encoder # type: ignore[code] + return (X, True) + + +def build_target( + y: ArrayDict, policy: Optional[YPolicy], task_type: TaskType +) -> Tuple[ArrayDict, Dict[str, Any]]: + info: Dict[str, Any] = {'policy': policy} + if policy is None: + pass + elif policy == 'default': + if task_type == TaskType.REGRESSION: + mean, std = float(y['train'].mean()), float(y['train'].std()) + y = {k: (v - mean) / std for k, v in y.items()} + info['mean'] = mean + info['std'] = std + else: + util.raise_unknown('policy', policy) + return y, info + + +@dataclass(frozen=True) +class Transformations: + seed: int = 0 + normalization: Optional[Normalization] = None + num_nan_policy: Optional[NumNanPolicy] = None + cat_nan_policy: Optional[CatNanPolicy] = None + cat_min_frequency: Optional[float] = None + cat_encoding: Optional[CatEncoding] = None + y_policy: Optional[YPolicy] = 'default' + + +def transform_dataset( + dataset: Dataset, + transformations: Transformations, + cache_dir: Optional[Path], + return_transforms: bool = False +) -> Dataset: + # WARNING: the order of transformations matters. Moreover, the current + # implementation is not ideal in that sense. + if cache_dir is not None: + transformations_md5 = hashlib.md5( + str(transformations).encode('utf-8') + ).hexdigest() + transformations_str = '__'.join(map(str, astuple(transformations))) + cache_path = ( + cache_dir / f'cache__{transformations_str}__{transformations_md5}.pickle' + ) + if cache_path.exists(): + cache_transformations, value = util.load_pickle(cache_path) + if transformations == cache_transformations: + print( + f"Using cached features: {cache_dir.name + '/' + cache_path.name}" + ) + return value + else: + raise RuntimeError(f'Hash collision for {cache_path}') + else: + cache_path = None + + if dataset.X_num is not None: + dataset = num_process_nans(dataset, transformations.num_nan_policy) + + num_transform = None + cat_transform = None + X_num = dataset.X_num + + if X_num is not None and transformations.normalization is not None: + X_num, num_transform = normalize( + X_num, + transformations.normalization, + transformations.seed, + return_normalizer=True + ) + num_transform = num_transform + + if dataset.X_cat is None: + assert transformations.cat_nan_policy is None + assert transformations.cat_min_frequency is None + # assert transformations.cat_encoding is None + X_cat = None + else: + X_cat = cat_process_nans(dataset.X_cat, transformations.cat_nan_policy) + if transformations.cat_min_frequency is not None: + X_cat = cat_drop_rare(X_cat, transformations.cat_min_frequency) + X_cat, is_num, cat_transform = cat_encode( + X_cat, + transformations.cat_encoding, + dataset.y['train'], + transformations.seed, + return_encoder=True + ) + if is_num: + X_num = ( + X_cat + if X_num is None + else {x: np.hstack([X_num[x], X_cat[x]]) for x in X_num} + ) + X_cat = None + + y, y_info = build_target(dataset.y, transformations.y_policy, dataset.task_type) + + dataset = replace(dataset, X_num=X_num, X_cat=X_cat, y=y, y_info=y_info) + dataset.num_transform = num_transform + dataset.cat_transform = cat_transform + + if cache_path is not None: + util.dump_pickle((transformations, dataset), cache_path) + # if return_transforms: + # return dataset, num_transform, cat_transform + return dataset + + +def build_dataset( + path: Union[str, Path], + transformations: Transformations, + cache: bool +) -> Dataset: + path = Path(path) + dataset = Dataset.from_dir(path) + return transform_dataset(dataset, transformations, path if cache else None) + + +def prepare_tensors( + dataset: Dataset, device: Union[str, torch.device] +) -> Tuple[Optional[TensorDict], Optional[TensorDict], TensorDict]: + X_num, X_cat, Y = ( + None if x is None else {k: torch.as_tensor(v) for k, v in x.items()} + for x in [dataset.X_num, dataset.X_cat, dataset.y] + ) + if device.type != 'cpu': + X_num, X_cat, Y = ( + None if x is None else {k: v.to(device) for k, v in x.items()} + for x in [X_num, X_cat, Y] + ) + assert X_num is not None + assert Y is not None + if not dataset.is_multiclass: + Y = {k: v.float() for k, v in Y.items()} + return X_num, X_cat, Y + +############### +## DataLoader## +############### + +class TabDataset(torch.utils.data.Dataset): + def __init__( + self, dataset : Dataset, split : Literal['train', 'val', 'test'] + ): + super().__init__() + + self.X_num = torch.from_numpy(dataset.X_num[split]) if dataset.X_num is not None else None + self.X_cat = torch.from_numpy(dataset.X_cat[split]) if dataset.X_cat is not None else None + self.y = torch.from_numpy(dataset.y[split]) + + assert self.y is not None + assert self.X_num is not None or self.X_cat is not None + + def __len__(self): + return len(self.y) + + def __getitem__(self, idx): + out_dict = { + 'y': self.y[idx].long() if self.y is not None else None, + } + + x = np.empty((0,)) + if self.X_num is not None: + x = self.X_num[idx] + if self.X_cat is not None: + x = torch.cat([x, self.X_cat[idx]], dim=0) + return x.float(), out_dict + +def prepare_dataloader( + dataset : Dataset, + split : str, + batch_size: int, +): + + torch_dataset = TabDataset(dataset, split) + loader = torch.utils.data.DataLoader( + torch_dataset, + batch_size=batch_size, + shuffle=(split == 'train'), + num_workers=1, + ) + while True: + yield from loader + +def prepare_torch_dataloader( + dataset : Dataset, + split : str, + shuffle : bool, + batch_size: int, +) -> torch.utils.data.DataLoader: + + torch_dataset = TabDataset(dataset, split) + loader = torch.utils.data.DataLoader(torch_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=1) + + return loader + +def dataset_from_csv(paths : Dict[str, str], cat_features, target, T): + assert 'train' in paths + y = {} + X_num = {} + X_cat = {} if len(cat_features) else None + for split in paths.keys(): + df = pd.read_csv(paths[split]) + y[split] = df[target].to_numpy().astype(float) + if X_cat is not None: + X_cat[split] = df[cat_features].to_numpy().astype(str) + X_num[split] = df.drop(cat_features + [target], axis=1).to_numpy().astype(float) + + dataset = Dataset(X_num, X_cat, y, {}, None, len(np.unique(y['train']))) + return transform_dataset(dataset, T, None) + +class FastTensorDataLoader: + """ + A DataLoader-like object for a set of tensors that can be much faster than + TensorDataset + DataLoader because dataloader grabs individual indices of + the dataset and calls cat (slow). + Source: https://discuss.pytorch.org/t/dataloader-much-slower-than-manual-batching/27014/6 + """ + def __init__(self, *tensors, batch_size=32, shuffle=False): + """ + Initialize a FastTensorDataLoader. + :param *tensors: tensors to store. Must have the same length @ dim 0. + :param batch_size: batch size to load. + :param shuffle: if True, shuffle the data *in-place* whenever an + iterator is created out of this object. + :returns: A FastTensorDataLoader. + """ + assert all(t.shape[0] == tensors[0].shape[0] for t in tensors) + self.tensors = tensors + + self.dataset_len = self.tensors[0].shape[0] + self.batch_size = batch_size + self.shuffle = shuffle + + # Calculate # batches + n_batches, remainder = divmod(self.dataset_len, self.batch_size) + if remainder > 0: + n_batches += 1 + self.n_batches = n_batches + def __iter__(self): + if self.shuffle: + r = torch.randperm(self.dataset_len) + self.tensors = [t[r] for t in self.tensors] + self.i = 0 + return self + + def __next__(self): + if self.i >= self.dataset_len: + raise StopIteration + batch = tuple(t[self.i:self.i+self.batch_size] for t in self.tensors) + self.i += self.batch_size + return batch + + def __len__(self): + return self.n_batches + +def prepare_fast_dataloader( + D : Dataset, + split : str, + batch_size: int +): + if D.X_cat is not None: + if D.X_num is not None: + X = torch.from_numpy(np.concatenate([D.X_num[split], D.X_cat[split]], axis=1)).float() + else: + X = torch.from_numpy(D.X_cat[split]).float() + else: + X = torch.from_numpy(D.X_num[split]).float() + y = torch.from_numpy(D.y[split]) + dataloader = FastTensorDataLoader(X, y, batch_size=batch_size, shuffle=(split=='train')) + while True: + yield from dataloader + +def prepare_fast_torch_dataloader( + D : Dataset, + split : str, + batch_size: int +): + if D.X_cat is not None: + X = torch.from_numpy(np.concatenate([D.X_num[split], D.X_cat[split]], axis=1)).float() + else: + X = torch.from_numpy(D.X_num[split]).float() + y = torch.from_numpy(D.y[split]) + dataloader = FastTensorDataLoader(X, y, batch_size=batch_size, shuffle=(split=='train')) + return dataloader + +def round_columns(X_real, X_synth, columns): + for col in columns: + uniq = np.unique(X_real[:,col]) + dist = cdist(X_synth[:, col][:, np.newaxis].astype(float), uniq[:, np.newaxis].astype(float)) + X_synth[:, col] = uniq[dist.argmin(axis=1)] + return X_synth + +def concat_features(D : Dataset): + if D.X_num is None: + assert D.X_cat is not None + X = {k: pd.DataFrame(v, columns=range(D.n_features)) for k, v in D.X_cat.items()} + elif D.X_cat is None: + assert D.X_num is not None + X = {k: pd.DataFrame(v, columns=range(D.n_features)) for k, v in D.X_num.items()} + else: + X = { + part: pd.concat( + [ + pd.DataFrame(D.X_num[part], columns=range(D.n_num_features)), + pd.DataFrame( + D.X_cat[part], + columns=range(D.n_num_features, D.n_features), + ), + ], + axis=1, + ) + for part in D.y.keys() + } + + return X + +def concat_to_pd(X_num, X_cat, y): + if X_num is None: + return pd.concat([ + pd.DataFrame(X_cat, columns=list(range(X_cat.shape[1]))), + pd.DataFrame(y, columns=['y']) + ], axis=1) + if X_cat is not None: + return pd.concat([ + pd.DataFrame(X_num, columns=list(range(X_num.shape[1]))), + pd.DataFrame(X_cat, columns=list(range(X_num.shape[1], X_num.shape[1] + X_cat.shape[1]))), + pd.DataFrame(y, columns=['y']) + ], axis=1) + return pd.concat([ + pd.DataFrame(X_num, columns=list(range(X_num.shape[1]))), + pd.DataFrame(y, columns=['y']) + ], axis=1) + +def read_pure_data(path, split='train'): + y = np.load(os.path.join(path, f'y_{split}.npy'), allow_pickle=True) + X_num = None + X_cat = None + if os.path.exists(os.path.join(path, f'X_num_{split}.npy')): + X_num = np.load(os.path.join(path, f'X_num_{split}.npy'), allow_pickle=True) + if os.path.exists(os.path.join(path, f'X_cat_{split}.npy')): + X_cat = np.load(os.path.join(path, f'X_cat_{split}.npy'), allow_pickle=True) + + return X_num, X_cat, y + +def read_changed_val(path, val_size=0.2): + path = Path(path) + X_num_train, X_cat_train, y_train = read_pure_data(path, 'train') + X_num_val, X_cat_val, y_val = read_pure_data(path, 'val') + is_regression = load_json(path / 'info.json')['task_type'] == 'regression' + + y = np.concatenate([y_train, y_val], axis=0) + + ixs = np.arange(y.shape[0]) + if is_regression: + train_ixs, val_ixs = train_test_split(ixs, test_size=val_size, random_state=777) + else: + train_ixs, val_ixs = train_test_split(ixs, test_size=val_size, random_state=777, stratify=y) + y_train = y[train_ixs] + y_val = y[val_ixs] + + if X_num_train is not None: + X_num = np.concatenate([X_num_train, X_num_val], axis=0) + X_num_train = X_num[train_ixs] + X_num_val = X_num[val_ixs] + + if X_cat_train is not None: + X_cat = np.concatenate([X_cat_train, X_cat_val], axis=0) + X_cat_train = X_cat[train_ixs] + X_cat_val = X_cat[val_ixs] + + return X_num_train, X_cat_train, y_train, X_num_val, X_cat_val, y_val + +############# + +def load_dataset_info(dataset_dir_name: str) -> Dict[str, Any]: + path = Path("data/" + dataset_dir_name) + info = util.load_json(path / 'info.json') + info['size'] = info['train_size'] + info['val_size'] + info['test_size'] + info['n_features'] = info['n_num_features'] + info['n_cat_features'] + info['path'] = path + return info diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/deep.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/deep.py new file mode 100644 index 0000000000000000000000000000000000000000..aeed3e2ada4f9d0ee1a6f86b7e5d1a35d486149f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/deep.py @@ -0,0 +1,168 @@ +import statistics +from dataclasses import dataclass +from typing import Any, Callable, Literal, cast + +import rtdl +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import zero +from torch import Tensor + +from .util import TaskType + + +def cos_sin(x: Tensor) -> Tensor: + return torch.cat([torch.cos(x), torch.sin(x)], -1) + + +@dataclass +class PeriodicOptions: + n: int # the output size is 2 * n + sigma: float + trainable: bool + initialization: Literal['log-linear', 'normal'] + + +class Periodic(nn.Module): + def __init__(self, n_features: int, options: PeriodicOptions) -> None: + super().__init__() + if options.initialization == 'log-linear': + coefficients = options.sigma ** (torch.arange(options.n) / options.n) + coefficients = coefficients[None].repeat(n_features, 1) + else: + assert options.initialization == 'normal' + coefficients = torch.normal(0.0, options.sigma, (n_features, options.n)) + if options.trainable: + self.coefficients = nn.Parameter(coefficients) # type: ignore[code] + else: + self.register_buffer('coefficients', coefficients) + + def forward(self, x: Tensor) -> Tensor: + assert x.ndim == 2 + return cos_sin(2 * torch.pi * self.coefficients[None] * x[..., None]) + + +def get_n_parameters(m: nn.Module): + return sum(x.numel() for x in m.parameters() if x.requires_grad) + + +def get_loss_fn(task_type: TaskType) -> Callable[..., Tensor]: + return ( + F.binary_cross_entropy_with_logits + if task_type == TaskType.BINCLASS + else F.cross_entropy + if task_type == TaskType.MULTICLASS + else F.mse_loss + ) + + +def default_zero_weight_decay_condition(module_name, module, parameter_name, parameter): + del module_name, parameter + return parameter_name.endswith('bias') or isinstance( + module, + ( + nn.BatchNorm1d, + nn.LayerNorm, + nn.InstanceNorm1d, + rtdl.CLSToken, + rtdl.NumericalFeatureTokenizer, + rtdl.CategoricalFeatureTokenizer, + Periodic, + ), + ) + + +def split_parameters_by_weight_decay( + model: nn.Module, zero_weight_decay_condition=default_zero_weight_decay_condition +) -> list[dict[str, Any]]: + parameters_info = {} + for module_name, module in model.named_modules(): + for parameter_name, parameter in module.named_parameters(): + full_parameter_name = ( + f'{module_name}.{parameter_name}' if module_name else parameter_name + ) + parameters_info.setdefault(full_parameter_name, ([], parameter))[0].append( + zero_weight_decay_condition( + module_name, module, parameter_name, parameter + ) + ) + params_with_wd = {'params': []} + params_without_wd = {'params': [], 'weight_decay': 0.0} + for full_parameter_name, (results, parameter) in parameters_info.items(): + (params_without_wd if any(results) else params_with_wd)['params'].append( + parameter + ) + return [params_with_wd, params_without_wd] + + +def make_optimizer( + config: dict[str, Any], + parameter_groups, +) -> optim.Optimizer: + if config['optimizer'] == 'FT-Transformer-default': + return optim.AdamW(parameter_groups, lr=1e-4, weight_decay=1e-5) + return getattr(optim, config['optimizer'])( + parameter_groups, + **{x: config[x] for x in ['lr', 'weight_decay', 'momentum'] if x in config}, + ) + + +def get_lr(optimizer: optim.Optimizer) -> float: + return next(iter(optimizer.param_groups))['lr'] + + +def is_oom_exception(err: RuntimeError) -> bool: + return any( + x in str(err) + for x in [ + 'CUDA out of memory', + 'CUBLAS_STATUS_ALLOC_FAILED', + 'CUDA error: out of memory', + ] + ) + + +def train_with_auto_virtual_batch( + optimizer, + loss_fn, + step, + batch, + chunk_size: int, +) -> tuple[Tensor, int]: + batch_size = len(batch) + random_state = zero.random.get_state() + loss = None + while chunk_size != 0: + try: + zero.random.set_state(random_state) + optimizer.zero_grad() + if batch_size <= chunk_size: + loss = loss_fn(*step(batch)) + loss.backward() + else: + loss = None + for chunk in zero.iter_batches(batch, chunk_size): + chunk_loss = loss_fn(*step(chunk)) + chunk_loss = chunk_loss * (len(chunk) / batch_size) + chunk_loss.backward() + if loss is None: + loss = chunk_loss.detach() + else: + loss += chunk_loss.detach() + except RuntimeError as err: + if not is_oom_exception(err): + raise + chunk_size //= 2 + else: + break + if not chunk_size: + raise RuntimeError('Not enough memory even for batch_size=1') + optimizer.step() + return cast(Tensor, loss), chunk_size + + +def process_epoch_losses(losses: list[Tensor]) -> tuple[list[float], float]: + losses_ = torch.stack(losses).tolist() + return losses_, statistics.mean(losses_) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/env.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/env.py new file mode 100644 index 0000000000000000000000000000000000000000..64be89d7d72c70e2ed9c7e0ecbbe682c14da3517 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/env.py @@ -0,0 +1,39 @@ +""" +Have not used in TabDDPM project. +""" + +import datetime +import os +import shutil +import typing as ty +from pathlib import Path + +PROJ = Path('tab-ddpm/').absolute().resolve() +EXP = PROJ / 'exp' +DATA = PROJ / 'data' + + +def get_path(path: ty.Union[str, Path]) -> Path: + if isinstance(path, str): + path = Path(path) + if not path.is_absolute(): + path = PROJ / path + return path.resolve() + + +def get_relative_path(path: ty.Union[str, Path]) -> Path: + return get_path(path).relative_to(PROJ) + + +def duplicate_path( + src: ty.Union[str, Path], alternative_project_dir: ty.Union[str, Path] +) -> None: + src = get_path(src) + alternative_project_dir = get_path(alternative_project_dir) + dst = alternative_project_dir / src.relative_to(PROJ) + dst.parent.mkdir(parents=True, exist_ok=True) + if dst.exists(): + dst = dst.with_name( + dst.name + '_' + datetime.datetime.now().strftime('%Y%m%dT%H%M%S') + ) + (shutil.copytree if src.is_dir() else shutil.copyfile)(src, dst) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/metrics.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..bdcac8171208065da730e974024aae0dc32b4665 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/metrics.py @@ -0,0 +1,158 @@ +import enum +from typing import Any, Optional, Tuple, Dict, Union, cast +from functools import partial + +import numpy as np +import scipy.special +import sklearn.metrics as skm + +from . import util +from .util import TaskType + + +class PredictionType(enum.Enum): + LOGITS = 'logits' + PROBS = 'probs' + +class MetricsReport: + def __init__(self, report: dict, task_type: TaskType): + self._res = {k: {} for k in report.keys()} + if task_type in (TaskType.BINCLASS, TaskType.MULTICLASS): + self._metrics_names = ["acc", "f1"] + for k in report.keys(): + self._res[k]["acc"] = report[k]["accuracy"] + self._res[k]["f1"] = report[k]["macro avg"]["f1-score"] + if task_type == TaskType.BINCLASS: + self._res[k]["roc_auc"] = report[k]["roc_auc"] + self._metrics_names.append("roc_auc") + + elif task_type == TaskType.REGRESSION: + self._metrics_names = ["r2", "rmse"] + for k in report.keys(): + self._res[k]["r2"] = report[k]["r2"] + self._res[k]["rmse"] = report[k]["rmse"] + else: + raise "Unknown TaskType!" + + def get_splits_names(self) -> list[str]: + return self._res.keys() + + def get_metrics_names(self) -> list[str]: + return self._metrics_names + + def get_metric(self, split: str, metric: str) -> float: + return self._res[split][metric] + + def get_val_score(self) -> float: + return self._res["val"]["r2"] if "r2" in self._res["val"] else self._res["val"]["f1"] + + def get_test_score(self) -> float: + return self._res["test"]["r2"] if "r2" in self._res["test"] else self._res["test"]["f1"] + + def print_metrics(self) -> None: + res = { + "val": {k: np.around(self._res["val"][k], 4) for k in self._res["val"]}, + "test": {k: np.around(self._res["test"][k], 4) for k in self._res["test"]} + } + + print("*"*100) + print("[val]") + print(res["val"]) + print("[test]") + print(res["test"]) + + return res + +class SeedsMetricsReport: + def __init__(self): + self._reports = [] + + def add_report(self, report: MetricsReport) -> None: + self._reports.append(report) + + def get_mean_std(self) -> dict: + res = {k: {} for k in ["train", "val", "test"]} + for split in self._reports[0].get_splits_names(): + for metric in self._reports[0].get_metrics_names(): + res[split][metric] = [x.get_metric(split, metric) for x in self._reports] + + agg_res = {k: {} for k in ["train", "val", "test"]} + for split in self._reports[0].get_splits_names(): + for metric in self._reports[0].get_metrics_names(): + for k, f in [("count", len), ("mean", np.mean), ("std", np.std)]: + agg_res[split][f"{metric}-{k}"] = f(res[split][metric]) + self._res = res + self._agg_res = agg_res + + return agg_res + + def print_result(self) -> dict: + res = {split: {k: float(np.around(self._agg_res[split][k], 4)) for k in self._agg_res[split]} for split in ["val", "test"]} + print("="*100) + print("EVAL RESULTS:") + print("[val]") + print(res["val"]) + print("[test]") + print(res["test"]) + print("="*100) + return res + +def calculate_rmse( + y_true: np.ndarray, y_pred: np.ndarray, std: Optional[float] +) -> float: + rmse = skm.mean_squared_error(y_true, y_pred) ** 0.5 + if std is not None: + rmse *= std + return rmse + + +def _get_labels_and_probs( + y_pred: np.ndarray, task_type: TaskType, prediction_type: Optional[PredictionType] +) -> Tuple[np.ndarray, Optional[np.ndarray]]: + assert task_type in (TaskType.BINCLASS, TaskType.MULTICLASS) + + if prediction_type is None: + return y_pred, None + + if prediction_type == PredictionType.LOGITS: + probs = ( + scipy.special.expit(y_pred) + if task_type == TaskType.BINCLASS + else scipy.special.softmax(y_pred, axis=1) + ) + elif prediction_type == PredictionType.PROBS: + probs = y_pred + else: + util.raise_unknown('prediction_type', prediction_type) + + assert probs is not None + labels = np.round(probs) if task_type == TaskType.BINCLASS else probs.argmax(axis=1) + return labels.astype('int64'), probs + + +def calculate_metrics( + y_true: np.ndarray, + y_pred: np.ndarray, + task_type: Union[str, TaskType], + prediction_type: Optional[Union[str, PredictionType]], + y_info: Dict[str, Any], +) -> Dict[str, Any]: + # Example: calculate_metrics(y_true, y_pred, 'binclass', 'logits', {}) + task_type = TaskType(task_type) + if prediction_type is not None: + prediction_type = PredictionType(prediction_type) + + if task_type == TaskType.REGRESSION: + assert prediction_type is None + assert 'std' in y_info + rmse = calculate_rmse(y_true, y_pred, y_info['std']) + r2 = skm.r2_score(y_true, y_pred) + result = {'rmse': rmse, 'r2': r2} + else: + labels, probs = _get_labels_and_probs(y_pred, task_type, prediction_type) + result = cast( + Dict[str, Any], skm.classification_report(y_true, labels, output_dict=True) + ) + if task_type == TaskType.BINCLASS: + result['roc_auc'] = skm.roc_auc_score(y_true, probs) + return result diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/util.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/util.py new file mode 100644 index 0000000000000000000000000000000000000000..75e05c9c9d1f0f9d6687f6d5fe1e91cad1b4ea20 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/lib/util.py @@ -0,0 +1,433 @@ +import argparse +import atexit +import enum +import json +import os +import pickle +import shutil +import sys +import time +import uuid +from copy import deepcopy +from dataclasses import asdict, fields, is_dataclass +from pathlib import Path +from pprint import pprint +from typing import Any, Callable, List, Dict, Type, Optional, Tuple, TypeVar, Union, cast, get_args, get_origin + +import __main__ +import numpy as np +import tomli +import tomli_w +import torch +import zero + +from . import env + +RawConfig = Dict[str, Any] +Report = Dict[str, Any] +T = TypeVar('T') + + +class Part(enum.Enum): + TRAIN = 'train' + VAL = 'val' + TEST = 'test' + + def __str__(self) -> str: + return self.value + + +class TaskType(enum.Enum): + BINCLASS = 'binclass' + MULTICLASS = 'multiclass' + REGRESSION = 'regression' + + def __str__(self) -> str: + return self.value + + +class Timer(zero.Timer): + @classmethod + def launch(cls) -> 'Timer': + timer = cls() + timer.run() + return timer + + +def update_training_log(training_log, data, metrics): + def _update(log_part, data_part): + for k, v in data_part.items(): + if isinstance(v, dict): + _update(log_part.setdefault(k, {}), v) + elif isinstance(v, list): + log_part.setdefault(k, []).extend(v) + else: + log_part.setdefault(k, []).append(v) + + _update(training_log, data) + transposed_metrics = {} + for part, part_metrics in metrics.items(): + for metric_name, value in part_metrics.items(): + transposed_metrics.setdefault(metric_name, {})[part] = value + _update(training_log, transposed_metrics) + + +def raise_unknown(unknown_what: str, unknown_value: Any): + raise ValueError(f'Unknown {unknown_what}: {unknown_value}') + + +def _replace(data, condition, value): + def do(x): + if isinstance(x, dict): + return {k: do(v) for k, v in x.items()} + elif isinstance(x, list): + return [do(y) for y in x] + else: + return value if condition(x) else x + + return do(data) + + +_CONFIG_NONE = '__none__' + + +def unpack_config(config: RawConfig) -> RawConfig: + config = cast(RawConfig, _replace(config, lambda x: x == _CONFIG_NONE, None)) + return config + + +def pack_config(config: RawConfig) -> RawConfig: + config = cast(RawConfig, _replace(config, lambda x: x is None, _CONFIG_NONE)) + return config + + +def load_config(path: Union[Path, str]) -> Any: + with open(path, 'rb') as f: + return unpack_config(tomli.load(f)) + + +def dump_config(config: Any, path: Union[Path, str]) -> None: + with open(path, 'wb') as f: + tomli_w.dump(pack_config(config), f) + # check that there are no bugs in all these "pack/unpack" things + assert config == load_config(path) + + +def load_json(path: Union[Path, str], **kwargs) -> Any: + return json.loads(Path(path).read_text(), **kwargs) + + +def dump_json(x: Any, path: Union[Path, str], **kwargs) -> None: + kwargs.setdefault('indent', 4) + Path(path).write_text(json.dumps(x, **kwargs) + '\n') + + +def load_pickle(path: Union[Path, str], **kwargs) -> Any: + return pickle.loads(Path(path).read_bytes(), **kwargs) + + +def dump_pickle(x: Any, path: Union[Path, str], **kwargs) -> None: + Path(path).write_bytes(pickle.dumps(x, **kwargs)) + + +def load(path: Union[Path, str], **kwargs) -> Any: + return globals()[f'load_{Path(path).suffix[1:]}'](Path(path), **kwargs) + + +def dump(x: Any, path: Union[Path, str], **kwargs) -> Any: + return globals()[f'dump_{Path(path).suffix[1:]}'](x, Path(path), **kwargs) + + +def _get_output_item_path( + path: Union[str, Path], filename: str, must_exist: bool +) -> Path: + path = env.get_path(path) + if path.suffix == '.toml': + path = path.with_suffix('') + if path.is_dir(): + path = path / filename + else: + assert path.name == filename + assert path.parent.exists() + if must_exist: + assert path.exists() + return path + + +def load_report(path: Path) -> Report: + return load_json(_get_output_item_path(path, 'report.json', True)) + + +def dump_report(report: dict, path: Path) -> None: + dump_json(report, _get_output_item_path(path, 'report.json', False)) + + +def load_predictions(path: Path) -> Dict[str, np.ndarray]: + with np.load(_get_output_item_path(path, 'predictions.npz', True)) as predictions: + return {x: predictions[x] for x in predictions} + + +def dump_predictions(predictions: Dict[str, np.ndarray], path: Path) -> None: + np.savez(_get_output_item_path(path, 'predictions.npz', False), **predictions) + + +def dump_metrics(metrics: Dict[str, Any], path: Path) -> None: + dump_json(metrics, _get_output_item_path(path, 'metrics.json', False)) + + +def load_checkpoint(path: Path, *args, **kwargs) -> Dict[str, np.ndarray]: + return torch.load( + _get_output_item_path(path, 'checkpoint.pt', True), *args, **kwargs + ) + + +def get_device() -> torch.device: + if torch.cuda.is_available(): + assert os.environ.get('CUDA_VISIBLE_DEVICES') is not None + return torch.device('cuda:0') + else: + return torch.device('cpu') + + +def _print_sep(c, size=100): + print(c * size) + + +def start( + config_cls: Type[T] = RawConfig, + argv: Optional[List[str]] = None, + patch_raw_config: Optional[Callable[[RawConfig], None]] = None, +) -> Tuple[T, Path, Report]: # config # output dir # report + parser = argparse.ArgumentParser() + parser.add_argument('config', metavar='FILE') + parser.add_argument('--force', action='store_true') + parser.add_argument('--continue', action='store_true', dest='continue_') + if argv is None: + program = __main__.__file__ + args = parser.parse_args() + else: + program = argv[0] + try: + args = parser.parse_args(argv[1:]) + except Exception: + print( + 'Failed to parse `argv`.' + ' Remember that the first item of `argv` must be the path (relative to' + ' the project root) to the script/notebook.' + ) + raise + args = parser.parse_args(argv) + + snapshot_dir = os.environ.get('SNAPSHOT_PATH') + if snapshot_dir and Path(snapshot_dir).joinpath('CHECKPOINTS_RESTORED').exists(): + assert args.continue_ + + config_path = env.get_path(args.config) + output_dir = config_path.with_suffix('') + _print_sep('=') + print(f'[output] {output_dir}') + _print_sep('=') + + assert config_path.exists() + raw_config = load_config(config_path) + if patch_raw_config is not None: + patch_raw_config(raw_config) + if is_dataclass(config_cls): + config = from_dict(config_cls, raw_config) + full_raw_config = asdict(config) + else: + assert config_cls is dict + full_raw_config = config = raw_config + full_raw_config = asdict(config) + + if output_dir.exists(): + if args.force: + print('Removing the existing output and creating a new one...') + shutil.rmtree(output_dir) + output_dir.mkdir() + elif not args.continue_: + backup_output(output_dir) + print('The output directory already exists. Done!\n') + sys.exit() + elif output_dir.joinpath('DONE').exists(): + backup_output(output_dir) + print('The "DONE" file already exists. Done!') + sys.exit() + else: + print('Continuing with the existing output...') + else: + print('Creating the output...') + output_dir.mkdir() + + report = { + 'program': str(env.get_relative_path(program)), + 'environment': {}, + 'config': full_raw_config, + } + if torch.cuda.is_available(): # type: ignore[code] + report['environment'].update( + { + 'CUDA_VISIBLE_DEVICES': os.environ.get('CUDA_VISIBLE_DEVICES'), + 'gpus': zero.hardware.get_gpus_info(), + 'torch.version.cuda': torch.version.cuda, + 'torch.backends.cudnn.version()': torch.backends.cudnn.version(), # type: ignore[code] + 'torch.cuda.nccl.version()': torch.cuda.nccl.version(), # type: ignore[code] + } + ) + dump_report(report, output_dir) + dump_json(raw_config, output_dir / 'raw_config.json') + _print_sep('-') + pprint(full_raw_config, width=100) + _print_sep('-') + return cast(config_cls, config), output_dir, report + + +_LAST_SNAPSHOT_TIME = None + + +def backup_output(output_dir: Path) -> None: + backup_dir = os.environ.get('TMP_OUTPUT_PATH') + snapshot_dir = os.environ.get('SNAPSHOT_PATH') + if backup_dir is None: + assert snapshot_dir is None + return + assert snapshot_dir is not None + + try: + relative_output_dir = output_dir.relative_to(env.PROJ) + except ValueError: + return + + for dir_ in [backup_dir, snapshot_dir]: + new_output_dir = dir_ / relative_output_dir + prev_backup_output_dir = new_output_dir.with_name(new_output_dir.name + '_prev') + new_output_dir.parent.mkdir(exist_ok=True, parents=True) + if new_output_dir.exists(): + new_output_dir.rename(prev_backup_output_dir) + shutil.copytree(output_dir, new_output_dir) + # the case for evaluate.py which automatically creates configs + if output_dir.with_suffix('.toml').exists(): + shutil.copyfile( + output_dir.with_suffix('.toml'), new_output_dir.with_suffix('.toml') + ) + if prev_backup_output_dir.exists(): + shutil.rmtree(prev_backup_output_dir) + + global _LAST_SNAPSHOT_TIME + if _LAST_SNAPSHOT_TIME is None or time.time() - _LAST_SNAPSHOT_TIME > 10 * 60: + import nirvana_dl.snapshot # type: ignore[code] + + nirvana_dl.snapshot.dump_snapshot() + _LAST_SNAPSHOT_TIME = time.time() + print('The snapshot was saved!') + + +def _get_scores(metrics: Dict[str, Dict[str, Any]]) -> Optional[Dict[str, float]]: + return ( + {k: v['score'] for k, v in metrics.items()} + if 'score' in next(iter(metrics.values())) + else None + ) + + +def format_scores(metrics: Dict[str, Dict[str, Any]]) -> str: + return ' '.join( + f"[{x}] {metrics[x]['score']:.3f}" + for x in ['test', 'val', 'train'] + if x in metrics + ) + + +def finish(output_dir: Path, report: dict) -> None: + print() + _print_sep('=') + + metrics = report.get('metrics') + if metrics is not None: + scores = _get_scores(metrics) + if scores is not None: + dump_json(scores, output_dir / 'scores.json') + print(format_scores(metrics)) + _print_sep('-') + + dump_report(report, output_dir) + json_output_path = os.environ.get('JSON_OUTPUT_FILE') + if json_output_path: + try: + key = str(output_dir.relative_to(env.PROJ)) + except ValueError: + pass + else: + json_output_path = Path(json_output_path) + try: + json_data = json.loads(json_output_path.read_text()) + except (FileNotFoundError, json.decoder.JSONDecodeError): + json_data = {} + json_data[key] = load_json(output_dir / 'report.json') + json_output_path.write_text(json.dumps(json_data, indent=4)) + shutil.copyfile( + json_output_path, + os.path.join(os.environ['SNAPSHOT_PATH'], 'json_output.json'), + ) + + output_dir.joinpath('DONE').touch() + backup_output(output_dir) + print(f'Done! | {report.get("time")} | {output_dir}') + _print_sep('=') + print() + + +def from_dict(datacls: Type[T], data: dict) -> T: + assert is_dataclass(datacls) + data = deepcopy(data) + for field in fields(datacls): + if field.name not in data: + continue + if is_dataclass(field.type): + data[field.name] = from_dict(field.type, data[field.name]) + elif ( + get_origin(field.type) is Union + and len(get_args(field.type)) == 2 + and get_args(field.type)[1] is type(None) + and is_dataclass(get_args(field.type)[0]) + ): + if data[field.name] is not None: + data[field.name] = from_dict(get_args(field.type)[0], data[field.name]) + return datacls(**data) + + +def replace_factor_with_value( + config: RawConfig, + key: str, + reference_value: int, + bounds: Tuple[float, float], +) -> None: + factor_key = key + '_factor' + if factor_key not in config: + assert key in config + else: + assert key not in config + factor = config.pop(factor_key) + assert bounds[0] <= factor <= bounds[1] + config[key] = int(factor * reference_value) + + +def get_temporary_copy(path: Union[str, Path]) -> Path: + path = env.get_path(path) + assert not path.is_dir() and not path.is_symlink() + tmp_path = path.with_name( + path.stem + '___' + str(uuid.uuid4()).replace('-', '') + path.suffix + ) + shutil.copyfile(path, tmp_path) + atexit.register(lambda: tmp_path.unlink()) + return tmp_path + + +def get_python(): + python = Path('python3.9') + return str(python) if python.exists() else 'python' + +def get_catboost_config(real_data_path, is_cv=False): + ds_name = Path(real_data_path).name + C = load_json(f'tuned_models/catboost/{ds_name}_cv.json') + return C \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/requirements.txt b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..0ce34ec62c2c3c306ad6b26213187ebdaedf8c60 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/requirements.txt @@ -0,0 +1,22 @@ +catboost==1.0.3 +category-encoders==2.3.0 +dython==0.5.1 +icecream==2.1.2 +libzero==0.0.8 +numpy==1.21.4 +optuna==2.10.1 +pandas==1.3.4 +pyarrow==6.0.0 +rtdl==0.0.9 +scikit-learn==1.0.2 +scipy==1.7.2 +skorch==0.11.0 +tomli-w==0.4.0 +tomli==1.2.2 +tqdm==4.62.3 + +# smote +imbalanced-learn==0.7.0 + +# tvae +rdt==0.6.4 \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/__init__.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/eval_catboost.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/eval_catboost.py new file mode 100644 index 0000000000000000000000000000000000000000..55066c2176c33c8fec14416d34736899e039a64f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/eval_catboost.py @@ -0,0 +1,145 @@ +from catboost import CatBoostClassifier, CatBoostRegressor +from sklearn.metrics import classification_report, r2_score +import numpy as np +import os +from sklearn.utils import shuffle +import zero +from pathlib import Path +import lib +from pprint import pprint +from lib import concat_features, read_pure_data, get_catboost_config, read_changed_val + +def train_catboost( + parent_dir, + real_data_path, + eval_type, + T_dict, + seed = 0, + params = None, + change_val = True, + device = None # dummy +): + zero.improve_reproducibility(seed) + if eval_type != "real": + synthetic_data_path = os.path.join(parent_dir) + info = lib.load_json(os.path.join(real_data_path, 'info.json')) + T = lib.Transformations(**T_dict) + + if change_val: + X_num_real, X_cat_real, y_real, X_num_val, X_cat_val, y_val = read_changed_val(real_data_path, val_size=0.2) + + X = None + print('-'*100) + if eval_type == 'merged': + print('loading merged data...') + if not change_val: + X_num_real, X_cat_real, y_real = read_pure_data(real_data_path) + X_num_fake, X_cat_fake, y_fake = read_pure_data(synthetic_data_path) + + ### + # dists = privacy_metrics(real_data_path, synthetic_data_path) + # bad_fakes = dists.argsort()[:int(0.25 * len(y_fake))] + # X_num_fake = np.delete(X_num_fake, bad_fakes, axis=0) + # X_cat_fake = np.delete(X_cat_fake, bad_fakes, axis=0) if X_cat_fake is not None else None + # y_fake = np.delete(y_fake, bad_fakes, axis=0) + ### + + y = np.concatenate([y_real, y_fake], axis=0) + + X_num = None + if X_num_real is not None: + X_num = np.concatenate([X_num_real, X_num_fake], axis=0) + + X_cat = None + if X_cat_real is not None: + X_cat = np.concatenate([X_cat_real, X_cat_fake], axis=0) + + elif eval_type == 'synthetic': + print(f'loading synthetic data: {parent_dir}') + X_num, X_cat, y = read_pure_data(synthetic_data_path) + + elif eval_type == 'real': + print('loading real data...') + if not change_val: + X_num, X_cat, y = read_pure_data(real_data_path) + else: + raise "Choose eval method" + + if not change_val: + X_num_val, X_cat_val, y_val = read_pure_data(real_data_path, 'val') + X_num_test, X_cat_test, y_test = read_pure_data(real_data_path, 'test') + + D = lib.Dataset( + {'train': X_num, 'val': X_num_val, 'test': X_num_test} if X_num is not None else None, + {'train': X_cat, 'val': X_cat_val, 'test': X_cat_test} if X_cat is not None else None, + {'train': y, 'val': y_val, 'test': y_test}, + {}, + lib.TaskType(info['task_type']), + info.get('n_classes') + ) + + D = lib.transform_dataset(D, T, None) + X = concat_features(D) + print(f'Train size: {X["train"].shape}, Val size {X["val"].shape}') + + if params is None: + catboost_config = get_catboost_config(real_data_path, is_cv=True) + else: + catboost_config = params + + if 'cat_features' not in catboost_config: + catboost_config['cat_features'] = list(range(D.n_num_features, D.n_features)) + + for col in range(D.n_features): + for split in X.keys(): + if col in catboost_config['cat_features']: + X[split][col] = X[split][col].astype(str) + else: + X[split][col] = X[split][col].astype(float) + print(T_dict) + pprint(catboost_config, width=100) + print('-'*100) + + if D.is_regression: + model = CatBoostRegressor( + **catboost_config, + eval_metric='RMSE', + random_seed=seed + ) + predict = model.predict + else: + model = CatBoostClassifier( + loss_function="MultiClass" if D.is_multiclass else "Logloss", + **catboost_config, + eval_metric='TotalF1', + random_seed=seed, + class_names=[str(i) for i in range(D.n_classes)] if D.is_multiclass else ["0", "1"] + ) + predict = ( + model.predict_proba + if D.is_multiclass + else lambda x: model.predict_proba(x)[:, 1] + ) + + model.fit( + X['train'], D.y['train'], + eval_set=(X['val'], D.y['val']), + verbose=100 + ) + predictions = {k: predict(v) for k, v in X.items()} + print(predictions['train'].shape) + + report = {} + report['eval_type'] = eval_type + report['dataset'] = real_data_path + report['metrics'] = D.calculate_metrics(predictions, None if D.is_regression else 'probs') + + metrics_report = lib.MetricsReport(report['metrics'], D.task_type) + metrics_report.print_metrics() + + if parent_dir is not None: + lib.dump_json(report, os.path.join(parent_dir, "results_catboost.json")) + + return metrics_report + + \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/eval_mlp.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/eval_mlp.py new file mode 100644 index 0000000000000000000000000000000000000000..396de4ef30589ff3c115b3bff7daa0bddeb9a57b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/eval_mlp.py @@ -0,0 +1,176 @@ +from sklearn.metrics import classification_report, r2_score, f1_score +import numpy as np +import os +from sklearn.utils import shuffle +import zero +from pathlib import Path +import lib +from tab_ddpm.modules import MLP +from skorch.regressor import NeuralNetRegressor +from skorch.classifier import NeuralNetClassifier +from skorch.dataset import Dataset as SkDataset +from skorch.callbacks import EarlyStopping, EpochScoring +from skorch.helper import predefined_split +from torch.optim import AdamW +from torch.nn import MSELoss, BCEWithLogitsLoss, CrossEntropyLoss + +def train_mlp( + parent_dir, + real_data_path, + eval_type, + T_dict, + params = None, + change_val = False, + seed = 0, + device = "cuda:0" +): + zero.improve_reproducibility(seed) + synthetic_data_path = os.path.join(parent_dir) if parent_dir is not None else None + info = lib.load_json(os.path.join(real_data_path, 'info.json')) + T = lib.Transformations(**T_dict) + + if change_val: + X_num_real, X_cat_real, y_real, X_num_val, X_cat_val, y_val = lib.read_changed_val(real_data_path, val_size=0.2) + + X = None + print('-'*100) + if eval_type == 'merged': + print('loading merged data...') + if not change_val: + X_num_real, X_cat_real, y_real = lib.read_pure_data(real_data_path) + X_num_fake, X_cat_fake, y_fake = lib.read_pure_data(synthetic_data_path) + y = np.concatenate([y_real, y_fake], axis=0) + + X_num = None + if X_num_real is not None: + X_num = np.concatenate([X_num_real, X_num_fake], axis=0) + + X_cat = None + if X_cat_real is not None: + X_cat = np.concatenate([X_cat_real, X_cat_fake], axis=0) + + elif eval_type == 'synthetic': + print('loading synthetic data...') + X_num, X_cat, y = lib.read_pure_data(synthetic_data_path) + + elif eval_type == 'real': + print('loading real data...') + if not change_val: + X_num, X_cat, y = lib.read_pure_data(real_data_path) + else: + raise "Choose eval method" + + if not change_val: + X_num_val, X_cat_val, y_val = lib.read_pure_data(real_data_path, 'val') + X_num_test, X_cat_test, y_test = lib.read_pure_data(real_data_path, 'test') + + D = lib.Dataset( + {'train': X_num, 'val': X_num_val, 'test': X_num_test} if X_num is not None else None, + {'train': X_cat, 'val': X_cat_val, 'test': X_cat_test} if X_cat is not None else None, + {'train': y, 'val': y_val, 'test': y_test}, + {}, + lib.TaskType(info['task_type']), + info.get('n_classes') + ) + + D = lib.transform_dataset(D, T, None) + X = lib.concat_features(D) + + X["train"], D.y["train"] = shuffle(X["train"], D.y["train"], random_state=seed) + print(f'Train size: {X["train"].shape}, Val size {X["val"].shape}') + + if params is None: + params = lib.load_json(f"tuned_models/mlp/{Path(real_data_path).name}_cv.json") + + mlp_params = {} + if params is not None: + mlp_params["d_layers"] = params["d_layers"] + mlp_params["dropout"] = params["dropout"] + # mlp_params["n_blocks"] = params["n_blocks"] + # mlp_params["d_main"] = params["d_main"] + # mlp_params["d_hidden"] = params["d_hidden"] + # mlp_params["dropout_first"] = params["dropout_first"] + # mlp_params["dropout_second"] = params["dropout_second"] + mlp_params["d_in"] = X["train"].shape[1] + mlp_params["d_out"] = D.nn_output_dim + + model = MLP.make_baseline(**mlp_params) + + if D.is_regression: + y = {k: D.y[k].reshape(-1, 1).astype(np.float32) for k in D.y} + elif D.is_binclass: + y = {k: D.y[k].reshape(-1, 1).astype(np.float32) for k in D.y} + else: + y = {k: D.y[k].astype(np.int64) for k in D.y} + + train_ds = SkDataset(X = X["train"].to_numpy(), y = y["train"]) + val_ds = SkDataset(X = X["val"].to_numpy(), y = y["val"]) + es = EarlyStopping(monitor="valid_loss", patience=16) + + print('-'*100) + + def f1(net, X, y): + y_pred = net.predict(X) + return f1_score(y, y_pred, average="macro") + + def r2(net, X, y): + y_pred = net.predict(X) + return r2_score(y, y_pred) + + if D.is_regression: + net = NeuralNetRegressor( + model, + criterion=MSELoss, + optimizer=AdamW, + lr=params["lr"], + optimizer__weight_decay=params["weight_decay"], + batch_size=128 if len(D.y["train"]) < 10_000 else 256, + max_epochs=1000, + train_split=predefined_split(val_ds), + iterator_train__shuffle=True, + device=device, + callbacks=[es, EpochScoring(r2, lower_is_better=False)], + ) + + else: + net = NeuralNetClassifier( + model, + criterion=BCEWithLogitsLoss if D.is_binclass else CrossEntropyLoss, + optimizer=AdamW, + lr=params["lr"], + optimizer__weight_decay=params["weight_decay"], + batch_size=128 if len(D.y["train"]) < 10_000 else 256, + max_epochs=1000, + train_split=predefined_split(val_ds), + iterator_train__shuffle=True, + device=device, + callbacks=[es, EpochScoring(f1, lower_is_better=False)], + ) + + net.fit( + X=train_ds.X, + y=train_ds.y + ) + + print("LAST:", len(net.history)) + + predictions = {k: net.predict_proba(v.to_numpy())[:, 1] if D.is_binclass else + net.predict_proba(v.to_numpy()) if D.is_multiclass else + net.predict(v.to_numpy()) + for k, v in X.items() + } + + report = {} + report['eval_type'] = eval_type + report['dataset'] = real_data_path + report['metrics'] = D.calculate_metrics(predictions, None if D.is_regression else 'probs') + + metrics_report = lib.MetricsReport(report['metrics'], D.task_type) + metrics_report.print_metrics() + + if parent_dir is not None: + lib.dump_json(report, os.path.join(parent_dir, "results_mlp.json")) + + return metrics_report + + \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/eval_seeds.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/eval_seeds.py new file mode 100644 index 0000000000000000000000000000000000000000..cc4970e2a7293234d9d08afba2798719499bfc75 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/eval_seeds.py @@ -0,0 +1,121 @@ +import argparse +import subprocess +import tempfile +import lib +import os +import shutil +from pathlib import Path +from copy import deepcopy +from scripts.eval_catboost import train_catboost +from scripts.eval_mlp import train_mlp +from scripts.eval_simple import train_simple + +pipeline = { + 'ddpm': 'scripts/pipeline.py', + 'smote': 'smote/pipeline_smote.py', + 'ctabgan': 'CTAB-GAN/pipeline_ctabgan.py', + 'ctabgan-plus': 'CTAB-GAN-Plus/pipeline_ctabgan.py', + 'tvae': 'CTGAN/pipeline_tvae.py' +} + +def eval_seeds( + raw_config, + n_seeds, + eval_type, + sampling_method="ddpm", + model_type="catboost", + n_datasets=1, + dump=True, + change_val=False +): + + metrics_seeds_report = lib.SeedsMetricsReport() + parent_dir = Path(raw_config["parent_dir"]) + + if eval_type == 'real': + n_datasets = 1 + + temp_config = deepcopy(raw_config) + with tempfile.TemporaryDirectory() as dir_: + dir_ = Path(dir_) + temp_config["parent_dir"] = str(dir_) + if sampling_method == "ddpm": + shutil.copy2(parent_dir / "model.pt", temp_config["parent_dir"]) + elif sampling_method in ["ctabgan", "ctabgan-plus"]: + shutil.copy2(parent_dir / "ctabgan.obj", temp_config["parent_dir"]) + elif sampling_method == "tvae": + shutil.copy2(parent_dir / "tvae.obj", temp_config["parent_dir"]) + + for sample_seed in range(n_datasets): + temp_config['sample']['seed'] = sample_seed + lib.dump_config(temp_config, dir_ / "config.toml") + if eval_type != 'real' and n_datasets > 1: + subprocess.run(['python3.9', f'{pipeline[sampling_method]}', '--config', f'{str(dir_ / "config.toml")}', '--sample'], check=True) + + T_dict = deepcopy(raw_config['eval']['T']) + for seed in range(n_seeds): + print(f'**Eval Iter: {sample_seed*n_seeds + (seed + 1)}/{n_seeds * n_datasets}**') + if model_type == "catboost": + T_dict["normalization"] = None + T_dict["cat_encoding"] = None + metric_report = train_catboost( + parent_dir=temp_config['parent_dir'], + real_data_path=temp_config['real_data_path'], + eval_type=eval_type, + T_dict=T_dict, + seed=seed, + change_val=change_val + ) + elif model_type == "mlp": + T_dict["normalization"] = "quantile" + T_dict["cat_encoding"] = "one-hot" + metric_report = train_mlp( + parent_dir=temp_config['parent_dir'], + real_data_path=temp_config['real_data_path'], + eval_type=eval_type, + T_dict=T_dict, + seed=seed, + change_val=change_val + ) + + metrics_seeds_report.add_report(metric_report) + + metrics_seeds_report.get_mean_std() + res = metrics_seeds_report.print_result() + if os.path.exists(parent_dir/ f"eval_{model_type}.json"): + eval_dict = lib.load_json(parent_dir / f"eval_{model_type}.json") + eval_dict = eval_dict | {eval_type: res} + else: + eval_dict = {eval_type: res} + + if dump: + lib.dump_json(eval_dict, parent_dir / f"eval_{model_type}.json") + + raw_config['sample']['seed'] = 0 + lib.dump_config(raw_config, parent_dir / 'config.toml') + return res + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--config', metavar='FILE') + parser.add_argument('n_seeds', type=int, default=10) + parser.add_argument('sampling_method', type=str, default="ddpm") + parser.add_argument('eval_type', type=str, default='synthetic') + parser.add_argument('model_type', type=str, default='catboost') + parser.add_argument('n_datasets', type=int, default=1) + parser.add_argument('--no_dump', action='store_false', default=True) + + args = parser.parse_args() + raw_config = lib.load_config(args.config) + eval_seeds( + raw_config, + n_seeds=args.n_seeds, + sampling_method=args.sampling_method, + eval_type=args.eval_type, + model_type=args.model_type, + n_datasets=args.n_datasets, + dump=args.no_dump + ) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/eval_seeds_simple.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/eval_seeds_simple.py new file mode 100644 index 0000000000000000000000000000000000000000..187c88d31abc73efd4744bc1aa37bcc21d8c117f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/eval_seeds_simple.py @@ -0,0 +1,130 @@ +import argparse +import subprocess +import tempfile +import lib +import os +import pandas as pd +import numpy as np +from pathlib import Path +from eval_simple import train_simple +from copy import deepcopy +import shutil + +pipeline = { + 'ddpm': 'scripts/pipeline.py', + 'smote': 'smote/pipeline_smote.py', + 'ctabgan': 'CTAB-GAN/pipeline_ctabgan.py', + 'ctabgan-plus': 'CTAB-GAN-Plus/pipeline_ctabganp.py', + 'tvae': 'CTGAN/pipeline_tvae.py' +} + + +def eval_seeds( + raw_config, + n_seeds, + eval_type, + sampling_method="ddpm", + model_type="simple", + n_datasets=1, + dump=True, + change_val=False +): + parent_dir = Path(raw_config["parent_dir"]) + models = ["tree", "lr", "rf", "mlp"] + metrics_seeds_report = { + k: lib.SeedsMetricsReport() for k in models + } + + if eval_type == 'real': + n_datasets = 1 + + T_dict = deepcopy(raw_config['eval']['T']) + T_dict["normalization"] = "minmax" + T_dict["cat_encoding"] = None + + temp_config = deepcopy(raw_config) + with tempfile.TemporaryDirectory() as dir_: + dir_ = Path(dir_) + temp_config["parent_dir"] = str(dir_) + if sampling_method == "ddpm": + shutil.copy2(parent_dir / "model.pt", temp_config["parent_dir"]) + elif sampling_method in ["ctabgan", "ctabgan-plus"]: + shutil.copy2(parent_dir / "ctabgan.obj", temp_config["parent_dir"]) + elif sampling_method == "tvae": + shutil.copy2(parent_dir / "tvae.obj", temp_config["parent_dir"]) + + for sample_seed in range(n_datasets): + temp_config['sample']['seed'] = sample_seed + lib.dump_config(temp_config, dir_ / "config.toml") + if eval_type != 'real': + subprocess.run(['python3.9', f'{pipeline[sampling_method]}', '--config', f'{str(dir_ / "config.toml")}', '--sample'], check=True) + + for seed in range(n_seeds): + print(f'**Eval Iter: {sample_seed*n_seeds + (seed + 1)}/{n_seeds * n_datasets}**') + for model in models: + metric_report = train_simple( + parent_dir=temp_config['parent_dir'], + real_data_path=temp_config['real_data_path'], + model_name=model, + eval_type=eval_type, + T_dict=T_dict, + seed=seed, + change_val=change_val + ) + + metrics_seeds_report[model].add_report(metric_report) + for k in models: + metrics_seeds_report[k].get_mean_std() + res = { + k: metrics_seeds_report[k].print_result() for k in models + } + + m1, m2 = ("r2-mean", "rmse-mean") if "r2-mean" in res["tree"]["val"] else ("f1-mean", "acc-mean") + res["avg"] = { + "val": { + m1: np.around(np.mean([res[k]["val"][m1] for k in models]), 4), + m2: np.around(np.mean([res[k]["val"][m2] for k in models]), 4) + }, + "test": { + m1: np.around(np.mean([res[k]["test"][m1] for k in models]), 4), + m2: np.around(np.mean([res[k]["test"][m2] for k in models]), 4) + }, + } + + if os.path.exists(parent_dir / f"eval_{model_type}.json"): + eval_dict = lib.load_json(parent_dir / f"eval_{model_type}.json") + eval_dict = eval_dict | {eval_type: res} + else: + eval_dict = {eval_type: res} + + if dump: + lib.dump_json(eval_dict, parent_dir / f"eval_{model_type}.json") + + raw_config['sample']['seed'] = 0 + lib.dump_config(raw_config, parent_dir / 'config.toml') + return res + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--config', metavar='FILE') + parser.add_argument('n_seeds', type=int, default=10) + parser.add_argument('sampling_method', type=str, default="ddpm") + parser.add_argument('eval_type', type=str, default='synthetic') + parser.add_argument('model_type', type=str, default='catboost') + parser.add_argument('n_datasets', type=int, default=1) + parser.add_argument('--no_dump', action='store_false', default=True) + + args = parser.parse_args() + raw_config = lib.load_config(args.config) + eval_seeds( + raw_config, + n_seeds=args.n_seeds, + sampling_method=args.sampling_method, + eval_type=args.eval_type, + model_type=args.model_type, + n_datasets=args.n_datasets, + dump=args.no_dump + ) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/eval_simple.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/eval_simple.py new file mode 100644 index 0000000000000000000000000000000000000000..5e58199394b29839052de5abde1194530150abf8 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/eval_simple.py @@ -0,0 +1,141 @@ +import numpy as np +import os +from sklearn.utils import shuffle +import zero +from pathlib import Path +import lib +from lib import concat_features, read_pure_data, read_changed_val +from sklearn.utils import shuffle +import lib +from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor +from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor +from sklearn.linear_model import LogisticRegression, Ridge +from sklearn.neural_network import MLPClassifier, MLPRegressor + +def train_simple( + parent_dir, + real_data_path, + eval_type, + T_dict, + model_name = "tree", + seed = 0, + change_val = True, + params = None, # dummy + device = None # dummy +): + zero.improve_reproducibility(seed) + if eval_type != "real": + synthetic_data_path = os.path.join(parent_dir) + + T_dict["normalization"] = "minmax" + T_dict["cat_encoding"] = None + T = lib.Transformations(**T_dict) + info = lib.load_json(os.path.join(real_data_path, 'info.json')) + + if change_val: + X_num_real, X_cat_real, y_real, X_num_val, X_cat_val, y_val = read_changed_val(real_data_path, val_size=0.2) + + X = None + print('-'*100) + if eval_type == 'merged': + print('loading merged data...') + if not change_val: + X_num_real, X_cat_real, y_real = read_pure_data(real_data_path) + X_num_fake, X_cat_fake, y_fake = read_pure_data(synthetic_data_path) + + ### + # dists = privacy_metrics(real_data_path, synthetic_data_path) + # bad_fakes = dists.argsort()[:int(0.25 * len(y_fake))] + # X_num_fake = np.delete(X_num_fake, bad_fakes, axis=0) + # X_cat_fake = np.delete(X_cat_fake, bad_fakes, axis=0) if X_cat_fake is not None else None + # y_fake = np.delete(y_fake, bad_fakes, axis=0) + ### + + y = np.concatenate([y_real, y_fake], axis=0) + + X_num = None + if X_num_real is not None: + X_num = np.concatenate([X_num_real, X_num_fake], axis=0) + + X_cat = None + if X_cat_real is not None: + X_cat = np.concatenate([X_cat_real, X_cat_fake], axis=0) + + elif eval_type == 'synthetic': + print(f'loading synthetic data: {parent_dir}') + X_num, X_cat, y = read_pure_data(synthetic_data_path) + + elif eval_type == 'real': + print('loading real data...') + if not change_val: + X_num, X_cat, y = read_pure_data(real_data_path) + else: + raise "Choose eval method" + + if not change_val: + X_num_val, X_cat_val, y_val = read_pure_data(real_data_path, 'val') + X_num_test, X_cat_test, y_test = read_pure_data(real_data_path, 'test') + + D = lib.Dataset( + {'train': X_num, 'val': X_num_val, 'test': X_num_test} if X_num is not None else None, + {'train': X_cat, 'val': X_cat_val, 'test': X_cat_test} if X_cat is not None else None, + {'train': y, 'val': y_val, 'test': y_test}, + {}, + lib.TaskType(info['task_type']), + info.get('n_classes') + ) + + D = lib.transform_dataset(D, T, None) + X = concat_features(D) + # ixs = np.random.choice(len(D.y["train"]), min(info["train_size"], len(D.y["train"])), replace=False) + # X["train"] = X["train"].iloc[ixs] + # D.y["train"] = D.y["train"][ixs] + + print(f'Train size: {X["train"].shape}, Val size {X["val"].shape}') + print(T_dict) + print('-'*100) + + if D.is_regression: + models = { + "tree": DecisionTreeRegressor(max_depth=28, random_state=seed), + "rf": RandomForestRegressor(max_depth=28, random_state=seed), + "lr": Ridge(max_iter=500, random_state=seed), + "mlp": MLPRegressor(max_iter=100, random_state=seed) + } + else: + models = { + "tree": DecisionTreeClassifier(max_depth=28, random_state=seed), + "rf": RandomForestClassifier(max_depth=28, random_state=seed), + "lr": LogisticRegression(max_iter=500, n_jobs=2, random_state=seed), + "mlp": MLPClassifier(max_iter=100, random_state=seed) + } + + model = models[model_name] + + predict = ( + model.predict + if D.is_regression + else model.predict_proba + if D.is_multiclass + else lambda x: model.predict_proba(x)[:, 1] + ) + + model.fit(X['train'], D.y['train']) + + predictions = {k: predict(v) for k, v in X.items()} + + report = {} + report['eval_type'] = eval_type + report['dataset'] = real_data_path + report['metrics'] = D.calculate_metrics(predictions, None if D.is_regression else 'probs') + + metrics_report = lib.MetricsReport(report['metrics'], D.task_type) + print(model.__class__.__name__) + metrics_report.print_metrics() + + # if parent_dir is not None: + # lib.dump_json(report, os.path.join(parent_dir, "results_catboost.json")) + + return metrics_report + + \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/pipeline.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..64a297813fd582cb86438f00981939571b53c162 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/pipeline.py @@ -0,0 +1,112 @@ +import tomli +import shutil +import os +import argparse +from scripts.train import train +from scripts.sample import sample +from scripts.eval_catboost import train_catboost +from scripts.eval_mlp import train_mlp +from scripts.eval_simple import train_simple +import pandas as pd +import matplotlib.pyplot as plt +import zero +import lib +import torch + +def load_config(path) : + with open(path, 'rb') as f: + return tomli.load(f) + +def save_file(parent_dir, config_path): + try: + dst = os.path.join(parent_dir) + os.makedirs(os.path.dirname(dst), exist_ok=True) + shutil.copyfile(os.path.abspath(config_path), dst) + except shutil.SameFileError: + pass + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--config', metavar='FILE') + parser.add_argument('--train', action='store_true', default=False) + parser.add_argument('--sample', action='store_true', default=False) + parser.add_argument('--eval', action='store_true', default=False) + parser.add_argument('--change_val', action='store_true', default=False) + + args = parser.parse_args() + raw_config = lib.load_config(args.config) + if 'device' in raw_config: + device = torch.device(raw_config['device']) + else: + device = torch.device('cuda:1') + + timer = zero.Timer() + timer.run() + save_file(os.path.join(raw_config['parent_dir'], 'config.toml'), args.config) + + if args.train: + train( + **raw_config['train']['main'], + **raw_config['diffusion_params'], + parent_dir=raw_config['parent_dir'], + real_data_path=raw_config['real_data_path'], + model_type=raw_config['model_type'], + model_params=raw_config['model_params'], + T_dict=raw_config['train']['T'], + num_numerical_features=raw_config['num_numerical_features'], + device=device, + change_val=args.change_val + ) + if args.sample: + sample( + num_samples=raw_config['sample']['num_samples'], + batch_size=raw_config['sample']['batch_size'], + disbalance=raw_config['sample'].get('disbalance', None), + **raw_config['diffusion_params'], + parent_dir=raw_config['parent_dir'], + real_data_path=raw_config['real_data_path'], + model_path=os.path.join(raw_config['parent_dir'], 'model.pt'), + model_type=raw_config['model_type'], + model_params=raw_config['model_params'], + T_dict=raw_config['train']['T'], + num_numerical_features=raw_config['num_numerical_features'], + device=device, + seed=raw_config['sample'].get('seed', 0), + change_val=args.change_val + ) + + save_file(os.path.join(raw_config['parent_dir'], 'info.json'), os.path.join(raw_config['real_data_path'], 'info.json')) + if args.eval: + if raw_config['eval']['type']['eval_model'] == 'catboost': + train_catboost( + parent_dir=raw_config['parent_dir'], + real_data_path=raw_config['real_data_path'], + eval_type=raw_config['eval']['type']['eval_type'], + T_dict=raw_config['eval']['T'], + seed=raw_config['seed'], + change_val=args.change_val + ) + elif raw_config['eval']['type']['eval_model'] == 'mlp': + train_mlp( + parent_dir=raw_config['parent_dir'], + real_data_path=raw_config['real_data_path'], + eval_type=raw_config['eval']['type']['eval_type'], + T_dict=raw_config['eval']['T'], + seed=raw_config['seed'], + change_val=args.change_val, + device=device + ) + elif raw_config['eval']['type']['eval_model'] == 'simple': + train_simple( + parent_dir=raw_config['parent_dir'], + real_data_path=raw_config['real_data_path'], + eval_type=raw_config['eval']['type']['eval_type'], + T_dict=raw_config['eval']['T'], + seed=raw_config['seed'], + change_val=args.change_val + ) + + print(f'Elapsed time: {str(timer)}') + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/resample_privacy.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/resample_privacy.py new file mode 100644 index 0000000000000000000000000000000000000000..54d320c3bb19ce5275d25704ad82f086601ee65d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/resample_privacy.py @@ -0,0 +1,257 @@ +""" +Adapted from https://github.com/Team-TUD/CTAB-GAN/tree/main/model/eval +""" + +import argparse +import lib +import os +import shutil +import zero +from sample import sample +from smote.sample_smote import sample_smote +from sklearn.preprocessing import MinMaxScaler, OneHotEncoder +from sklearn.metrics import pairwise_distances +from pathlib import Path +import tempfile +from eval_seeds import eval_seeds +import numpy as np +import subprocess +import warnings +import torch + +zero.improve_reproducibility(0) + +warnings.filterwarnings("ignore", category=FutureWarning) + + +def privacy_metrics(real_path,fake_path, data_percent=15): + + """ + Returns privacy metrics + + Inputs: + 1) real_path -> path to real data + 2) fake_path -> path to corresponding synthetic data + 3) data_percent -> percentage of data to be sampled from real and synthetic datasets for computing privacy metrics + Outputs: + 1) List containing the 5th percentile distance to closest record (DCR) between real and synthetic as well as within real and synthetic datasets + along with 5th percentile of nearest neighbour distance ratio (NNDR) between real and synthetic as well as within real and synthetic datasets + + """ + task_type = lib.load_json(real_path + "/info.json")["task_type"] + X_num_real, X_cat_real, y_real = lib.read_pure_data(real_path, 'train') + X_num_fake, X_cat_fake, y_fake = lib.read_pure_data(fake_path, 'train') + + if task_type == 'regression': + X_num_real = np.concatenate([X_num_real, y_real[:, np.newaxis]], axis=1) + X_num_fake = np.concatenate([X_num_fake, y_fake[:, np.newaxis]], axis=1) + else: + if X_cat_fake is None: + X_cat_real = y_real[:, np.newaxis].astype(int).astype(str) + X_cat_fake = y_fake[:, np.newaxis].astype(int).astype(str) + else: + X_cat_real = np.concatenate([X_cat_real, y_real[:, np.newaxis].astype(int).astype(str)], axis=1) + X_cat_fake = np.concatenate([X_cat_fake, y_fake[:, np.newaxis].astype(int).astype(str)], axis=1) + + if len(y_real) > 50000: + ixs = np.random.choice(len(y_real), 50000, replace=False) + X_num_real = X_num_real[ixs] + X_cat_real = X_cat_real[ixs] if X_cat_real is not None else None + + if len(y_fake) > 50000: + ixs = np.random.choice(len(y_fake), 50000, replace=False) + X_num_fake = X_num_fake[ixs] + X_cat_fake = X_cat_fake[ixs] if X_cat_fake is not None else None + + + mm = MinMaxScaler().fit(X_num_real) + X_real = mm.transform(X_num_real) + X_fake = mm.transform(X_num_fake) + if X_cat_real is not None: + ohe = OneHotEncoder().fit(X_cat_real) + X_cat_real = ohe.transform(X_cat_real) / np.sqrt(2) + X_cat_fake = ohe.transform(X_cat_fake) / np.sqrt(2) + + X_real = np.concatenate([X_real, X_cat_real.todense()], axis=1) + X_fake = np.concatenate([X_fake, X_cat_fake.todense()], axis=1) + + # X_real = np.unique(X_real, axis=0) + # X_fake = np.unique(X_fake, axis=0) + + # Computing pair-wise distances between real and synthetic + dist_rf = pairwise_distances(X_fake, Y=X_real, metric='l2', n_jobs=-1) + # Computing pair-wise distances within real + # dist_rr = pairwise_distances(X_real, Y=None, metric='l2', n_jobs=-1) + # Computing pair-wise distances within synthetic + # dist_ff = pairwise_distances(X_fake, Y=None, metric='l2', n_jobs=-1) + + + # Removes distances of data points to themselves to avoid 0s within real and synthetic + # rd_dist_rr = dist_rr[~np.eye(dist_rr.shape[0],dtype=bool)].reshape(dist_rr.shape[0],-1) + # rd_dist_ff = dist_ff[~np.eye(dist_ff.shape[0],dtype=bool)].reshape(dist_ff.shape[0],-1) + + # Computing first and second smallest nearest neighbour distances between real and synthetic + smallest_two_indexes_rf = [dist_rf[i].argsort()[:2] for i in range(len(dist_rf))] + smallest_two_rf = [dist_rf[i][smallest_two_indexes_rf[i]] for i in range(len(dist_rf))] + # Computing first and second smallest nearest neighbour distances within real + # smallest_two_indexes_rr = [rd_dist_rr[i].argsort()[:2] for i in range(len(rd_dist_rr))] + # smallest_two_rr = [rd_dist_rr[i][smallest_two_indexes_rr[i]] for i in range(len(rd_dist_rr))] + # Computing first and second smallest nearest neighbour distances within synthetic + # smallest_two_indexes_ff = [rd_dist_ff[i].argsort()[:2] for i in range(len(rd_dist_ff))] + # smallest_two_ff = [rd_dist_ff[i][smallest_two_indexes_ff[i]] for i in range(len(rd_dist_ff))] + + + # Computing 5th percentiles for DCR and NNDR between and within real and synthetic datasets + min_dist_rf = np.array([i[0] for i in smallest_two_rf]) + fifth_perc_rf = np.percentile(min_dist_rf,5) + # min_dist_rr = np.array([i[0] for i in smallest_two_rr]) + # fifth_perc_rr = np.percentile(min_dist_rr,5) + # min_dist_ff = np.array([i[0] for i in smallest_two_ff]) + # fifth_perc_ff = np.percentile(min_dist_ff,5) + # nn_ratio_rf = np.array([i[0]/i[1] for i in smallest_two_rf]) + # nn_fifth_perc_rf = np.percentile(nn_ratio_rf,5) + # nn_ratio_rr = np.array([i[0]/i[1] for i in smallest_two_rr]) + # nn_fifth_perc_rr = np.percentile(nn_ratio_rr,5) + # nn_ratio_ff = np.array([i[0]/i[1] for i in smallest_two_ff]) + # nn_fifth_perc_ff = np.percentile(nn_ratio_ff,5) + + # return np.array([fifth_perc_rf,fifth_perc_rr,fifth_perc_ff,nn_fifth_perc_rf,nn_fifth_perc_rr,nn_fifth_perc_ff]).reshape(1,6) + return min_dist_rf # , min_dist_rr + +def sample_wrapper(method, config, num_samples=None, seed=0): + if method == "ddpm": + sample( + num_samples=num_samples, + batch_size=config['sample']['batch_size'], + disbalance=config['sample'].get('disbalance', None), + **config['diffusion_params'], + parent_dir=config['parent_dir'], + real_data_path=config['real_data_path'], + model_path=os.path.join(config['parent_dir'], 'model.pt'), + model_type=config['model_type'], + model_params=config['model_params'], + T_dict=config['train']['T'], + num_numerical_features=config['num_numerical_features'], + seed=seed, + change_val=False, + device=torch.device(config["device"]) + ) + elif method == "smote": + sample_smote( + parent_dir=config['parent_dir'], + real_data_path=config['real_data_path'], + **config['smote_params'], + seed=seed, + change_val=False + ) + +def resample_privacy(config_path, method, q): + with tempfile.TemporaryDirectory() as dir_: + config = lib.load_config(config_path) + if method == "ddpm": + shutil.copy2(os.path.join(config['parent_dir'], 'model.pt'), os.path.join(dir_, 'model.pt')) + config["parent_dir"] = str(dir_) + parent_dir = config["parent_dir"] + + sample_wrapper(method, config, num_samples=config["sample"].get("num_samples", 0)) + + dists = privacy_metrics(config["real_data_path"], parent_dir) + old_privacy = np.median(dists) + + q10 = np.quantile(dists, q=q) + print(f"Q: {q10}") + to_drop = np.where(dists < q10) + + X_num, X_cat, y = lib.read_pure_data(parent_dir) + num_samples = len(y) + X_num = np.delete(X_num, to_drop, axis=0) + X_cat = np.delete(X_cat, to_drop, axis=0) if X_cat is not None else None + y = np.delete(y, to_drop, axis=0) + i = 1 + + while len(y) < num_samples and i <= 10: + print(f"{len(y)}/{num_samples}") + + sample_wrapper(method, config, num_samples=config["sample"].get("batch_size", 0), seed=i) + + i += 1 + + X_num_t, X_cat_t, y_t = lib.read_pure_data(parent_dir) + dists = privacy_metrics(config["real_data_path"], parent_dir) + to_drop = np.where(dists < q10) + X_num_t = np.delete(X_num_t, to_drop, axis=0) + X_cat_t = np.delete(X_cat_t, to_drop, axis=0) if X_cat is not None else None + y_t = np.delete(y_t, to_drop, axis=0) + + X_num = np.concatenate([X_num, X_num_t], axis=0)[:num_samples] + X_cat = np.concatenate([X_cat, X_cat_t], axis=0)[:num_samples] if X_cat is not None else None + y = np.concatenate([y, y_t], axis=0)[:num_samples] + + # np.save(os.path.join(parent_dir, 'X_num_train'), X_num) + # if X_cat is not None: + # np.save(os.path.join(parent_dir, 'X_cat_train'), X_cat) + # np.save(os.path.join(parent_dir, 'y_train'), y) + + np.save(os.path.join(parent_dir, 'X_num_train'), X_num) + if X_cat is not None: + np.save(os.path.join(parent_dir, 'X_cat_train'), X_cat) + np.save(os.path.join(parent_dir, 'y_train'), y) + + new_dists = privacy_metrics(config["real_data_path"], parent_dir) + + res = eval_seeds( + config, + n_seeds=10, + eval_type="synthetic", + model_type="catboost", + n_datasets=1, + dump=False + ) + print(f"Old: {old_privacy:.4f}, New: {np.median(new_dists):.4f}") + + metric = "r2-mean" if "r2-mean" in res["test"] else "f1-mean" + return res["test"][metric], np.around(np.median(new_dists), 4) + +def resample_privacy_qs(config_path, method): + config = lib.load_config(config_path) + scores = [] + privacies = [] + + eval_res = lib.load_json(Path(config["parent_dir"]) / "eval_catboost.json")["synthetic"]["test"] + metric = "r2-mean" if "r2-mean" in eval_res else "f1-mean" + scores.append(eval_res[metric]) + privacies.append(np.median(privacy_metrics(config["real_data_path"], config["parent_dir"]))) + + for q in [0.1, 0.2, 0.3, 0.4]: + score, privacy = resample_privacy(config_path, method, q) + scores.append(score) + privacies.append(privacy) + + lib.dump_json( + {"scores": scores, "privacies": privacies}, + Path(config["parent_dir"]) / "privacies.json" + ) + +def calc_privacy(config_path, method, seed=0): + config = lib.load_config(config_path) + sample_wrapper(method, config, num_samples=config["sample"]["num_samples"], seed=seed) + timer = zero.Timer() + timer.run() + dists = privacy_metrics(config["real_data_path"], config["parent_dir"]) + privacy_val = np.median(dists) + lib.dump_json({"privacy": privacy_val}, os.path.join(config["parent_dir"], "privacy.json")) + print(f"Elapsed tine:{str(timer)}") + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--config', metavar='FILE') + parser.add_argument('method', type=str) + args = parser.parse_args() + + calc_privacy( + args.config, + args.method + ) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/sample.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/sample.py new file mode 100644 index 0000000000000000000000000000000000000000..35d7f2158ba8c345bb57a840de1a7936ba59a0f6 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/sample.py @@ -0,0 +1,160 @@ +import torch +import numpy as np +import zero +import os +from tab_ddpm.gaussian_multinomial_diffsuion import GaussianMultinomialDiffusion +from tab_ddpm.utils import FoundNANsError +from scripts.utils_train import get_model, make_dataset +from lib import round_columns +import lib + +def to_good_ohe(ohe, X): + indices = np.cumsum([0] + ohe._n_features_outs) + Xres = [] + for i in range(1, len(indices)): + x_ = np.max(X[:, indices[i - 1]:indices[i]], axis=1) + t = X[:, indices[i - 1]:indices[i]] - x_.reshape(-1, 1) + Xres.append(np.where(t >= 0, 1, 0)) + return np.hstack(Xres) + +def sample( + parent_dir, + real_data_path = 'data/higgs-small', + batch_size = 2000, + num_samples = 0, + model_type = 'mlp', + model_params = None, + model_path = None, + num_timesteps = 1000, + gaussian_loss_type = 'mse', + scheduler = 'cosine', + T_dict = None, + num_numerical_features = 0, + disbalance = None, + device = torch.device('cuda:1'), + seed = 0, + change_val = False +): + zero.improve_reproducibility(seed) + + T = lib.Transformations(**T_dict) + D = make_dataset( + real_data_path, + T, + num_classes=model_params['num_classes'], + is_y_cond=model_params['is_y_cond'], + change_val=change_val + ) + + K = np.array(D.get_category_sizes('train')) + if len(K) == 0 or T_dict['cat_encoding'] == 'one-hot': + K = np.array([0]) + + num_numerical_features_ = D.X_num['train'].shape[1] if D.X_num is not None else 0 + d_in = np.sum(K) + num_numerical_features_ + model_params['d_in'] = int(d_in) + model = get_model( + model_type, + model_params, + num_numerical_features_, + category_sizes=D.get_category_sizes('train') + ) + + model.load_state_dict( + torch.load(model_path, map_location="cpu") + ) + + diffusion = GaussianMultinomialDiffusion( + K, + num_numerical_features=num_numerical_features_, + denoise_fn=model, num_timesteps=num_timesteps, + gaussian_loss_type=gaussian_loss_type, scheduler=scheduler, device=device + ) + + diffusion.to(device) + diffusion.eval() + + _, empirical_class_dist = torch.unique(torch.from_numpy(D.y['train']), return_counts=True) + # empirical_class_dist = empirical_class_dist.float() + torch.tensor([-5000., 10000.]).float() + if disbalance == 'fix': + empirical_class_dist[0], empirical_class_dist[1] = empirical_class_dist[1], empirical_class_dist[0] + x_gen, y_gen = diffusion.sample_all(num_samples, batch_size, empirical_class_dist.float(), ddim=False) + + elif disbalance == 'fill': + ix_major = empirical_class_dist.argmax().item() + val_major = empirical_class_dist[ix_major].item() + x_gen, y_gen = [], [] + for i in range(empirical_class_dist.shape[0]): + if i == ix_major: + continue + distrib = torch.zeros_like(empirical_class_dist) + distrib[i] = 1 + num_samples = val_major - empirical_class_dist[i].item() + x_temp, y_temp = diffusion.sample_all(num_samples, batch_size, distrib.float(), ddim=False) + x_gen.append(x_temp) + y_gen.append(y_temp) + + x_gen = torch.cat(x_gen, dim=0) + y_gen = torch.cat(y_gen, dim=0) + + else: + x_gen, y_gen = diffusion.sample_all(num_samples, batch_size, empirical_class_dist.float(), ddim=False) + + + # try: + # except FoundNANsError as ex: + # print("Found NaNs during sampling!") + # loader = lib.prepare_fast_dataloader(D, 'train', 8) + # x_gen = next(loader)[0] + # y_gen = torch.multinomial( + # empirical_class_dist.float(), + # num_samples=8, + # replacement=True + # ) + X_gen, y_gen = x_gen.numpy(), y_gen.numpy() + + ### + # X_num_unnorm = X_gen[:, :num_numerical_features] + # lo = np.percentile(X_num_unnorm, 2.5, axis=0) + # hi = np.percentile(X_num_unnorm, 97.5, axis=0) + # idx = (lo < X_num_unnorm) & (hi > X_num_unnorm) + # X_gen = X_gen[np.all(idx, axis=1)] + # y_gen = y_gen[np.all(idx, axis=1)] + ### + + num_numerical_features = num_numerical_features + int(D.is_regression and not model_params["is_y_cond"]) + + X_num_ = X_gen + if num_numerical_features < X_gen.shape[1]: + np.save(os.path.join(parent_dir, 'X_cat_unnorm'), X_gen[:, num_numerical_features:]) + # _, _, cat_encoder = lib.cat_encode({'train': X_cat_real}, T_dict['cat_encoding'], y_real, T_dict['seed'], True) + if T_dict['cat_encoding'] == 'one-hot': + X_gen[:, num_numerical_features:] = to_good_ohe(D.cat_transform.steps[0][1], X_num_[:, num_numerical_features:]) + X_cat = D.cat_transform.inverse_transform(X_gen[:, num_numerical_features:]) + + if num_numerical_features_ != 0: + # _, normalize = lib.normalize({'train' : X_num_real}, T_dict['normalization'], T_dict['seed'], True) + np.save(os.path.join(parent_dir, 'X_num_unnorm'), X_gen[:, :num_numerical_features]) + X_num_ = D.num_transform.inverse_transform(X_gen[:, :num_numerical_features]) + X_num = X_num_[:, :num_numerical_features] + + X_num_real = np.load(os.path.join(real_data_path, "X_num_train.npy"), allow_pickle=True) + disc_cols = [] + for col in range(X_num_real.shape[1]): + uniq_vals = np.unique(X_num_real[:, col]) + if len(uniq_vals) <= 32 and ((uniq_vals - np.round(uniq_vals)) == 0).all(): + disc_cols.append(col) + print("Discrete cols:", disc_cols) + # 仅当 regression 且 y 在 X_num 中（非 is_y_cond）时才提取 y；否则 y_gen 已由 sample_all 返回 + if model_params['num_classes'] == 0 and not model_params.get('is_y_cond', True): + y_gen = X_num[:, 0] + X_num = X_num[:, 1:] + if len(disc_cols): + X_num = round_columns(X_num_real, X_num, disc_cols) + + if num_numerical_features != 0: + print("Num shape: ", X_num.shape) + np.save(os.path.join(parent_dir, 'X_num_train'), X_num) + if num_numerical_features < X_gen.shape[1]: + np.save(os.path.join(parent_dir, 'X_cat_train'), X_cat) + np.save(os.path.join(parent_dir, 'y_train'), y_gen) \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/train.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/train.py new file mode 100644 index 0000000000000000000000000000000000000000..84a4eef3c97b080d1cf1b122c7d4117b74a1cefd --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/train.py @@ -0,0 +1,158 @@ +from copy import deepcopy +import torch +import os +import numpy as np +import zero +from tab_ddpm import GaussianMultinomialDiffusion +from scripts.utils_train import get_model, make_dataset, update_ema +import lib +import pandas as pd + +class Trainer: + def __init__(self, diffusion, train_iter, lr, weight_decay, steps, device=torch.device('cuda:1')): + self.diffusion = diffusion + self.ema_model = deepcopy(self.diffusion._denoise_fn) + for param in self.ema_model.parameters(): + param.detach_() + + self.train_iter = train_iter + self.steps = steps + self.init_lr = lr + self.optimizer = torch.optim.AdamW(self.diffusion.parameters(), lr=lr, weight_decay=weight_decay) + self.device = device + self.loss_history = pd.DataFrame(columns=['step', 'mloss', 'gloss', 'loss']) + self.log_every = 100 + self.print_every = 500 + self.ema_every = 1000 + + def _anneal_lr(self, step): + frac_done = step / self.steps + lr = self.init_lr * (1 - frac_done) + for param_group in self.optimizer.param_groups: + param_group["lr"] = lr + + def _run_step(self, x, out_dict): + x = x.to(self.device) + for k in out_dict: + out_dict[k] = out_dict[k].long().to(self.device) + self.optimizer.zero_grad() + loss_multi, loss_gauss = self.diffusion.mixed_loss(x, out_dict) + loss = loss_multi + loss_gauss + loss.backward() + self.optimizer.step() + + return loss_multi, loss_gauss + + def run_loop(self): + step = 0 + curr_loss_multi = 0.0 + curr_loss_gauss = 0.0 + + curr_count = 0 + while step < self.steps: + x, out_dict = next(self.train_iter) + out_dict = {'y': out_dict} + batch_loss_multi, batch_loss_gauss = self._run_step(x, out_dict) + + self._anneal_lr(step) + + curr_count += len(x) + curr_loss_multi += batch_loss_multi.item() * len(x) + curr_loss_gauss += batch_loss_gauss.item() * len(x) + + if (step + 1) % self.log_every == 0: + mloss = np.around(curr_loss_multi / curr_count, 4) + gloss = np.around(curr_loss_gauss / curr_count, 4) + if (step + 1) % self.print_every == 0: + print(f'Step {(step + 1)}/{self.steps} MLoss: {mloss} GLoss: {gloss} Sum: {mloss + gloss}') + self.loss_history.loc[len(self.loss_history)] =[step + 1, mloss, gloss, mloss + gloss] + curr_count = 0 + curr_loss_gauss = 0.0 + curr_loss_multi = 0.0 + + update_ema(self.ema_model.parameters(), self.diffusion._denoise_fn.parameters()) + + step += 1 + +def train( + parent_dir, + real_data_path = 'data/higgs-small', + steps = 1000, + lr = 0.002, + weight_decay = 1e-4, + batch_size = 1024, + model_type = 'mlp', + model_params = None, + num_timesteps = 1000, + gaussian_loss_type = 'mse', + scheduler = 'cosine', + T_dict = None, + num_numerical_features = 0, + device = torch.device('cuda:1'), + seed = 0, + change_val = False +): + real_data_path = os.path.normpath(real_data_path) + parent_dir = os.path.normpath(parent_dir) + + zero.improve_reproducibility(seed) + + T = lib.Transformations(**T_dict) + + dataset = make_dataset( + real_data_path, + T, + num_classes=model_params['num_classes'], + is_y_cond=model_params['is_y_cond'], + change_val=change_val + ) + + K = np.array(dataset.get_category_sizes('train')) + if len(K) == 0 or T_dict['cat_encoding'] == 'one-hot': + K = np.array([0]) + print(K) + + num_numerical_features = dataset.X_num['train'].shape[1] if dataset.X_num is not None else 0 + d_in = np.sum(K) + num_numerical_features + model_params['d_in'] = d_in + print(d_in) + + print(model_params) + model = get_model( + model_type, + model_params, + num_numerical_features, + category_sizes=dataset.get_category_sizes('train') + ) + model.to(device) + + # train_loader = lib.prepare_beton_loader(dataset, split='train', batch_size=batch_size) + train_loader = lib.prepare_fast_dataloader(dataset, split='train', batch_size=batch_size) + + + + diffusion = GaussianMultinomialDiffusion( + num_classes=K, + num_numerical_features=num_numerical_features, + denoise_fn=model, + gaussian_loss_type=gaussian_loss_type, + num_timesteps=num_timesteps, + scheduler=scheduler, + device=device + ) + diffusion.to(device) + diffusion.train() + + trainer = Trainer( + diffusion, + train_loader, + lr=lr, + weight_decay=weight_decay, + steps=steps, + device=device + ) + trainer.run_loop() + + trainer.loss_history.to_csv(os.path.join(parent_dir, 'loss.csv'), index=False) + torch.save(diffusion._denoise_fn.state_dict(), os.path.join(parent_dir, 'model.pt')) + torch.save(trainer.ema_model.state_dict(), os.path.join(parent_dir, 'model_ema.pt')) \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/tune_ddpm.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/tune_ddpm.py new file mode 100644 index 0000000000000000000000000000000000000000..5a95dc23cab775a9ca7b7eb496bcefef58691dce --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/tune_ddpm.py @@ -0,0 +1,127 @@ +import subprocess +import lib +import os +import optuna +from copy import deepcopy +import shutil +import argparse +from pathlib import Path + +parser = argparse.ArgumentParser() +parser.add_argument('ds_name', type=str) +parser.add_argument('train_size', type=int) +parser.add_argument('eval_type', type=str) +parser.add_argument('eval_model', type=str) +parser.add_argument('prefix', type=str) +parser.add_argument('--eval_seeds', action='store_true', default=False) + +args = parser.parse_args() +train_size = args.train_size +ds_name = args.ds_name +eval_type = args.eval_type +assert eval_type in ('merged', 'synthetic') +prefix = str(args.prefix) + +pipeline = f'scripts/pipeline.py' +base_config_path = f'exp/{ds_name}/config.toml' +parent_path = Path(f'exp/{ds_name}/') +exps_path = Path(f'exp/{ds_name}/many-exps/') # temporary dir. maybe will be replaced with tempdiвdr +eval_seeds = f'scripts/eval_seeds.py' + +os.makedirs(exps_path, exist_ok=True) + +def _suggest_mlp_layers(trial): + def suggest_dim(name): + t = trial.suggest_int(name, d_min, d_max) + return 2 ** t + min_n_layers, max_n_layers, d_min, d_max = 1, 4, 7, 10 + n_layers = 2 * trial.suggest_int('n_layers', min_n_layers, max_n_layers) + d_first = [suggest_dim('d_first')] if n_layers else [] + d_middle = ( + [suggest_dim('d_middle')] * (n_layers - 2) + if n_layers > 2 + else [] + ) + d_last = [suggest_dim('d_last')] if n_layers > 1 else [] + d_layers = d_first + d_middle + d_last + return d_layers + +def objective(trial): + + lr = trial.suggest_loguniform('lr', 0.00001, 0.003) + d_layers = _suggest_mlp_layers(trial) + weight_decay = 0.0 + batch_size = trial.suggest_categorical('batch_size', [256, 4096]) + steps = trial.suggest_categorical('steps', [5000, 20000, 30000]) + # steps = trial.suggest_categorical('steps', [500]) # for debug + gaussian_loss_type = 'mse' + # scheduler = trial.suggest_categorical('scheduler', ['cosine', 'linear']) + num_timesteps = trial.suggest_categorical('num_timesteps', [100, 1000]) + num_samples = int(train_size * (2 ** trial.suggest_int('num_samples', -2, 1))) + + base_config = lib.load_config(base_config_path) + + base_config['train']['main']['lr'] = lr + base_config['train']['main']['steps'] = steps + base_config['train']['main']['batch_size'] = batch_size + base_config['train']['main']['weight_decay'] = weight_decay + base_config['model_params']['rtdl_params']['d_layers'] = d_layers + base_config['eval']['type']['eval_type'] = eval_type + base_config['sample']['num_samples'] = num_samples + base_config['diffusion_params']['gaussian_loss_type'] = gaussian_loss_type + base_config['diffusion_params']['num_timesteps'] = num_timesteps + # base_config['diffusion_params']['scheduler'] = scheduler + + base_config['parent_dir'] = str(exps_path / f"{trial.number}") + base_config['eval']['type']['eval_model'] = args.eval_model + if args.eval_model == "mlp": + base_config['eval']['T']['normalization'] = "quantile" + base_config['eval']['T']['cat_encoding'] = "one-hot" + + trial.set_user_attr("config", base_config) + + lib.dump_config(base_config, exps_path / 'config.toml') + + subprocess.run(['python3.9', f'{pipeline}', '--config', f'{exps_path / "config.toml"}', '--train', '--change_val'], check=True) + + n_datasets = 5 + score = 0.0 + + for sample_seed in range(n_datasets): + base_config['sample']['seed'] = sample_seed + lib.dump_config(base_config, exps_path / 'config.toml') + + subprocess.run(['python3.9', f'{pipeline}', '--config', f'{exps_path / "config.toml"}', '--sample', '--eval', '--change_val'], check=True) + + report_path = str(Path(base_config['parent_dir']) / f'results_{args.eval_model}.json') + report = lib.load_json(report_path) + + if 'r2' in report['metrics']['val']: + score += report['metrics']['val']['r2'] + else: + score += report['metrics']['val']['macro avg']['f1-score'] + + shutil.rmtree(exps_path / f"{trial.number}") + + return score / n_datasets + +study = optuna.create_study( + direction='maximize', + sampler=optuna.samplers.TPESampler(seed=0), +) + +study.optimize(objective, n_trials=50, show_progress_bar=True) + +best_config_path = parent_path / f'{prefix}_best/config.toml' +best_config = study.best_trial.user_attrs['config'] +best_config["parent_dir"] = str(parent_path / f'{prefix}_best/') + +os.makedirs(parent_path / f'{prefix}_best', exist_ok=True) +lib.dump_config(best_config, best_config_path) +lib.dump_json(optuna.importance.get_param_importances(study), parent_path / f'{prefix}_best/importance.json') + +subprocess.run(['python3.9', f'{pipeline}', '--config', f'{best_config_path}', '--train', '--sample'], check=True) + +if args.eval_seeds: + best_exp = str(parent_path / f'{prefix}_best/config.toml') + subprocess.run(['python3.9', f'{eval_seeds}', '--config', f'{best_exp}', '10', "ddpm", eval_type, args.eval_model, '5'], check=True) \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/tune_evaluation_model.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/tune_evaluation_model.py new file mode 100644 index 0000000000000000000000000000000000000000..8def5fd6cd1f609893e4de5f5c9708edb0a2efb7 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/tune_evaluation_model.py @@ -0,0 +1,145 @@ +import optuna +import lib +import argparse +from eval_catboost import train_catboost +from eval_mlp import train_mlp +from pathlib import Path + +parser = argparse.ArgumentParser() +parser.add_argument('ds_name', type=str) +parser.add_argument('model', type=str) +parser.add_argument('tune_type', type=str) +parser.add_argument('device', type=str) + +args = parser.parse_args() +data_path = Path(f"data/{args.ds_name}") +best_params = None + +assert args.tune_type in ("cv", "val") + +def _suggest(trial: optuna.trial.Trial, distribution: str, label: str, *args): + return getattr(trial, f'suggest_{distribution}')(label, *args) + +def _suggest_optional(trial: optuna.trial.Trial, distribution: str, label: str, *args): + if trial.suggest_categorical(f"optional_{label}", [True, False]): + return _suggest(trial, distribution, label, *args) + else: + return 0.0 + +def _suggest_mlp_layers(trial: optuna.trial.Trial, mlp_d_layers: list[int]): + + min_n_layers, max_n_layers = mlp_d_layers[0], mlp_d_layers[1] + d_min, d_max = mlp_d_layers[2], mlp_d_layers[3] + + def suggest_dim(name): + t = trial.suggest_int(name, d_min, d_max) + return 2 ** t + + + n_layers = trial.suggest_int('n_layers', min_n_layers, max_n_layers) + d_first = [suggest_dim('d_first')] if n_layers else [] + d_middle = ( + [suggest_dim('d_middle')] * (n_layers - 2) + if n_layers > 2 + else [] + ) + d_last = [suggest_dim('d_last')] if n_layers > 1 else [] + d_layers = d_first + d_middle + d_last + + return d_layers + +def suggest_mlp_params(trial): + params = {} + params["lr"] = trial.suggest_loguniform("lr", 5e-5, 0.005) + params["dropout"] = _suggest_optional(trial, "uniform", "dropout", 0.0, 0.5) + params["weight_decay"] = _suggest_optional(trial, "loguniform", "weight_decay", 1e-6, 1e-2) + params["d_layers"] = _suggest_mlp_layers(trial, [1, 8, 6, 10]) + + return params + +def suggest_catboost_params(trial): + params = {} + params["learning_rate"] = trial.suggest_loguniform("learning_rate", 0.001, 1.0) + params["depth"] = trial.suggest_int("depth", 3, 10) + params["l2_leaf_reg"] = trial.suggest_uniform("l2_leaf_reg", 0.1, 10.0) + params["bagging_temperature"] = trial.suggest_uniform("bagging_temperature", 0.0, 1.0) + params["leaf_estimation_iterations"] = trial.suggest_int("leaf_estimation_iterations", 1, 10) + + params = params | { + "iterations": 2000, + "early_stopping_rounds": 50, + "od_pval": 0.001, + "task_type": "CPU", # "GPU", may affect performance + "thread_count": 4, + # "devices": "0", # for GPU + } + + return params + +def objective(trial): + if args.model == "mlp": + params = suggest_mlp_params(trial) + train_func = train_mlp + T_dict = { + "seed": 0, + "normalization": "quantile", + "num_nan_policy": None, + "cat_nan_policy": None, + "cat_min_frequency": None, + "cat_encoding": "one-hot", + "y_policy": "default" + } + else: + params = suggest_catboost_params(trial) + train_func = train_catboost + T_dict = { + "seed": 0, + "normalization": None, + "num_nan_policy": None, + "cat_nan_policy": None, + "cat_min_frequency": None, + "cat_encoding": None, + "y_policy": "default" + } + trial.set_user_attr("params", params) + if args.tune_type == "cv": + score = 0.0 + for fold in range(5): + metrics_report = train_func( + parent_dir=None, + real_data_path=data_path / f"kfolds/{fold}", + eval_type="real", + T_dict=T_dict, + params=params, + change_val=False, + device=args.device + ) + score += metrics_report.get_val_score() + score /= 5 + + elif args.tune_type == "val": + metrics_report = train_func( + parent_dir=None, + real_data_path=data_path, + eval_type="real", + T_dict=T_dict, + params=params, + change_val=False, + device=args.device + ) + score = metrics_report.get_val_score() + + return score + +study = optuna.create_study( + direction='maximize', + sampler=optuna.samplers.TPESampler(seed=0), +) + +study.optimize(objective, n_trials=100, show_progress_bar=True) + +bets_params = study.best_trial.user_attrs['params'] + +best_params_path = f"tuned_models/{args.model}/{args.ds_name}_{args.tune_type}.json" + +lib.dump_json(bets_params, best_params_path) \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/utils_train.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/utils_train.py new file mode 100644 index 0000000000000000000000000000000000000000..4132ca56fb8e063111f916f903ef4e99206486e3 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/scripts/utils_train.py @@ -0,0 +1,89 @@ +import numpy as np +import os +import lib +from tab_ddpm.modules import MLPDiffusion, ResNetDiffusion + +def get_model( + model_name, + model_params, + n_num_features, + category_sizes +): + print(model_name) + if model_name == 'mlp': + model = MLPDiffusion(**model_params) + elif model_name == 'resnet': + model = ResNetDiffusion(**model_params) + else: + raise "Unknown model!" + return model + +def update_ema(target_params, source_params, rate=0.999): + """ + Update target parameters to be closer to those of source parameters using + an exponential moving average. + :param target_params: the target parameter sequence. + :param source_params: the source parameter sequence. + :param rate: the EMA rate (closer to 1 means slower). + """ + for targ, src in zip(target_params, source_params): + targ.detach().mul_(rate).add_(src.detach(), alpha=1 - rate) + +def concat_y_to_X(X, y): + if X is None: + return y.reshape(-1, 1) + return np.concatenate([y.reshape(-1, 1), X], axis=1) + +def make_dataset( + data_path: str, + T: lib.Transformations, + num_classes: int, + is_y_cond: bool, + change_val: bool +): + # classification + if num_classes > 0: + X_cat = {} if os.path.exists(os.path.join(data_path, 'X_cat_train.npy')) or not is_y_cond else None + X_num = {} if os.path.exists(os.path.join(data_path, 'X_num_train.npy')) else None + y = {} + + for split in ['train']: + X_num_t, X_cat_t, y_t = lib.read_pure_data(data_path, split) + if X_num is not None: + X_num[split] = X_num_t + if not is_y_cond: + X_cat_t = concat_y_to_X(X_cat_t, y_t) + if X_cat is not None: + X_cat[split] = X_cat_t + y[split] = y_t + else: + # regression + X_cat = {} if os.path.exists(os.path.join(data_path, 'X_cat_train.npy')) else None + X_num = {} if os.path.exists(os.path.join(data_path, 'X_num_train.npy')) or not is_y_cond else None + y = {} + + for split in ['train']: + X_num_t, X_cat_t, y_t = lib.read_pure_data(data_path, split) + if not is_y_cond: + X_num_t = concat_y_to_X(X_num_t, y_t) + if X_num is not None: + X_num[split] = X_num_t + if X_cat is not None: + X_cat[split] = X_cat_t + y[split] = y_t + + info = lib.load_json(os.path.join(data_path, 'info.json')) + + D = lib.Dataset( + X_num, + X_cat, + y, + y_info={}, + task_type=lib.TaskType(info['task_type']), + n_classes=info.get('n_classes') + ) + + if change_val: + D = lib.change_val(D) + + return lib.transform_dataset(D, T, None) \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/smote/pipeline_smote.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/smote/pipeline_smote.py new file mode 100644 index 0000000000000000000000000000000000000000..4a6775493e975104c268a2cd177f953ea9589d0a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/smote/pipeline_smote.py @@ -0,0 +1,68 @@ +import tomli +import shutil +import os +import argparse +from sample_smote import sample_smote +from scripts.eval_catboost import train_catboost +# from scripts.eval_mlp import train_mlp +import zero +import lib + +def load_config(path) : + with open(path, 'rb') as f: + return tomli.load(f) + +def save_file(parent_dir, config_path): + try: + dst = os.path.join(parent_dir) + os.makedirs(os.path.dirname(dst), exist_ok=True) + shutil.copyfile(os.path.abspath(config_path), dst) + except shutil.SameFileError: + pass + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--config', metavar='FILE') + parser.add_argument('--sample', action='store_true', default=False) + parser.add_argument('--eval', action='store_true', default=False) + parser.add_argument('--change_val', action='store_true', default=False) + + args = parser.parse_args() + raw_config = lib.load_config(args.config) + timer = zero.Timer() + timer.run() + save_file(os.path.join(raw_config['parent_dir'], 'config.toml'), args.config) + if args.sample: + sample_smote( + parent_dir=raw_config['parent_dir'], + real_data_path=raw_config['real_data_path'], + **raw_config['smote_params'], + seed=raw_config['seed'], + change_val=args.change_val + ) + + save_file(os.path.join(raw_config['parent_dir'], 'info.json'), os.path.join(raw_config['real_data_path'], 'info.json')) + if args.eval: + if raw_config['eval']['type']['eval_model'] == 'catboost': + train_catboost( + parent_dir=raw_config['parent_dir'], + real_data_path=raw_config['real_data_path'], + eval_type=raw_config['eval']['type']['eval_type'], + T_dict=raw_config['eval']['T'], + seed=raw_config['seed'], + change_val=args.change_val + ) + # elif raw_config['eval']['type']['eval_model'] == 'mlp': + # train_mlp( + # parent_dir=raw_config['parent_dir'], + # real_data_path=raw_config['real_data_path'], + # eval_type=raw_config['eval']['type']['eval_type'], + # T_dict=raw_config['eval']['T'], + # seed=raw_config['seed'], + # change_val=args.change_val + # ) + + print(f'Elapsed time: {str(timer)}') + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/smote/sample_smote.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/smote/sample_smote.py new file mode 100644 index 0000000000000000000000000000000000000000..63674186c3f1524d57bb4882f0c3dd432147230f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/smote/sample_smote.py @@ -0,0 +1,210 @@ +import os +import lib +import argparse +import numpy as np +from pathlib import Path +from typing import Union, Any +from imblearn.over_sampling import SMOTE, SMOTENC +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import MinMaxScaler +from sklearn.utils import check_random_state + +class MySMOTE(SMOTE): + def __init__( + self, + lam1=0.0, + lam2=1.0, + *, + sampling_strategy="auto", + random_state=None, + k_neighbors=5, + n_jobs=None, + ): + super().__init__( + sampling_strategy=sampling_strategy, + random_state=random_state, + k_neighbors=k_neighbors, + n_jobs=n_jobs, + ) + + self.lam1=lam1 + self.lam2=lam2 + + def _make_samples( + self, X, y_dtype, y_type, nn_data, nn_num, n_samples, step_size=1.0 + ): + random_state = check_random_state(self.random_state) + samples_indices = random_state.randint(low=0, high=nn_num.size, size=n_samples) + + # np.newaxis for backwards compatability with random_state + steps = step_size * random_state.uniform(low=self.lam1, high=self.lam2, size=n_samples)[:, np.newaxis] + rows = np.floor_divide(samples_indices, nn_num.shape[1]) + cols = np.mod(samples_indices, nn_num.shape[1]) + + X_new = self._generate_samples(X, nn_data, nn_num, rows, cols, steps) + y_new = np.full(n_samples, fill_value=y_type, dtype=y_dtype) + return X_new, y_new + +class MySMOTENC(SMOTENC): + def __init__( + self, + lam1=0.0, + lam2=1.0, + *, + categorical_features, + sampling_strategy="auto", + random_state=None, + k_neighbors=5, + n_jobs=None + ): + super().__init__( + categorical_features=categorical_features, + sampling_strategy=sampling_strategy, + random_state=random_state, + k_neighbors=k_neighbors, + n_jobs=n_jobs, + ) + + self.lam1=0.0 + self.lam2=1.0 + + def _make_samples( + self, X, y_dtype, y_type, nn_data, nn_num, n_samples, step_size=1.0, lam1=0.0, lam2=1.0 + ): + random_state = check_random_state(self.random_state) + samples_indices = random_state.randint(low=0, high=nn_num.size, size=n_samples) + + # np.newaxis for backwards compatability with random_state + steps = step_size * random_state.uniform(low=self.lam1, high=self.lam2, size=n_samples)[:, np.newaxis] + rows = np.floor_divide(samples_indices, nn_num.shape[1]) + cols = np.mod(samples_indices, nn_num.shape[1]) + + X_new = self._generate_samples(X, nn_data, nn_num, rows, cols, steps) + y_new = np.full(n_samples, fill_value=y_type, dtype=y_dtype) + return X_new, y_new + +def save_data(X, y, path, n_cat_features=0): + if n_cat_features > 0: + X_num = X[:, :-n_cat_features] + X_cat = X[:, -n_cat_features:] + else: + X_num = X + X_cat = None + + + np.save(path / "X_num_train", X_num.astype(float), allow_pickle=True) + np.save(path / "y_train", y, allow_pickle=True) + if X_cat is not None: + np.save(path / "X_cat_train", X_cat, allow_pickle=True) + +def sample_smote( + parent_dir, + real_data_path, + eval_type = "synthetic", + k_neighbours = 5, + frac_samples = 1.0, + frac_lam_del = 0.0, + change_val = False, + save = True, + seed = 0 +): + lam1 = 0.0 + frac_lam_del / 2 + lam2 = 1.0 - frac_lam_del / 2 + + real_data_path = Path(real_data_path) + info = lib.load_json(real_data_path / 'info.json') + is_regression = info['task_type'] == 'regression' + + X_num = {} + X_cat = {} + y = {} + + if change_val: + X_num['train'], X_cat['train'], y['train'], X_num['val'], X_cat['val'], y['val'] = lib.read_changed_val(real_data_path) + else: + X_num['train'], X_cat['train'], y['train'] = lib.read_pure_data(real_data_path, 'train') + X_num['val'], X_cat['val'], y['val'] = lib.read_pure_data(real_data_path, 'val') + X_num['test'], X_cat['test'], y['test'] = lib.read_pure_data(real_data_path, 'test') + + + X = {k: X_num[k] for k in X_num.keys()} + + if is_regression: + X['train'] = np.concatenate([X["train"], y["train"].reshape(-1, 1)], axis=1, dtype=object) + y['train'] = np.where(y["train"] > np.median(y["train"]), 1, 0) + + n_num_features = X['train'].shape[1] + n_cat_features = X_cat['train'].shape[1] if X_cat['train'] is not None else 0 + cat_features = list(range(n_num_features, n_num_features+n_cat_features)) + print(cat_features) + + scaler = MinMaxScaler().fit(X["train"]) + X["train"] = scaler.transform(X["train"]).astype(object) + + if X_cat['train'] is not None: + for k in X_num.keys(): + X[k] = np.concatenate([X[k], X_cat[k]], axis=1, dtype=object) + + print("Before:", X['train'].shape) + + if eval_type != 'real': + strat = {k: int((1 + frac_samples) * np.sum(y['train'] == k)) for k in np.unique(y['train'])} + print(strat) + if n_cat_features > 0: + sm = MySMOTENC( + lam1=lam1, + lam2=lam2, + random_state=seed, + k_neighbors=k_neighbours, + categorical_features=cat_features, + sampling_strategy=strat + ) + else: + sm = MySMOTE( + lam1=lam1, + lam2=lam2, + random_state=seed, + k_neighbors=k_neighbours, + sampling_strategy=strat + ) + + X_res, y_res = sm.fit_resample(X['train'], y['train']) + if is_regression: + X_res[:, :X_num["train"].shape[1]+1] = scaler.inverse_transform(X_res[:, :X_num["train"].shape[1]+1]) + y_res = X_res[:, X_num["train"].shape[1]] + X_res = np.delete(X_res, [X_num["train"].shape[1]], axis=1) + else: + X_res[:, :X_num["train"].shape[1]] = scaler.inverse_transform(X_res[:, :X_num["train"].shape[1]]) + y_res = y_res.astype(int) + + if eval_type == "synthetic": + X_res = X_res[X['train'].shape[0]:] + y_res = y_res[X['train'].shape[0]:] + + disc_cols = [] + for col in range(X_num["train"].shape[1]): + uniq_vals = np.unique(X_num["train"][:, col]) + if len(uniq_vals) <= 32 and ((uniq_vals - np.round(uniq_vals)) == 0).all(): + disc_cols.append(col) + if len(disc_cols): + X_res[:, :X_num["train"].shape[1]] = lib.round_columns(X_num["train"], X_res[:, :X_num["train"].shape[1]], disc_cols) + + if save: + save_data(X_res, y_res, Path(parent_dir), n_cat_features) + + X['train'] = X_res + y['train'] = y_res + + return X, y + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('data_path', type=str) + parser.add_argument('method', type=str) + + args = parser.parse_args() + + sample_smote(args.data_path, args.method, save=False) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/smote/tune_smote.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/smote/tune_smote.py new file mode 100644 index 0000000000000000000000000000000000000000..9c98e205150bd02fee9ce399280714101bd427c3 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/smote/tune_smote.py @@ -0,0 +1,98 @@ +import optuna +import lib +from copy import deepcopy +import argparse +import tempfile +from pathlib import Path +import os +from scripts.eval_catboost import train_catboost +from sample_smote import sample_smote +import subprocess + +parser = argparse.ArgumentParser() +parser.add_argument('data_path', type=str) +parser.add_argument('eval_type', type=str) + +args = parser.parse_args() +real_data_path = args.data_path +eval_type = args.eval_type + +def objective(trial): + + k_neighbours = trial.suggest_int("k_neighbours", 5, 20) + frac_samples = 2 ** trial.suggest_int('frac_samples', -2, 3) + + # z = \lam*x + (1 - \lam)*y, \lam ~ U[frac_lam_del/2, 1-frac_lam_del/2] + frac_lam_del = trial.suggest_float("frac_lam_del", 0.0, 0.95, step=0.05) + + score = 0.0 + with tempfile.TemporaryDirectory() as dir_: + dir_ = Path(dir_) + for seed in range(5): + sample_smote( + parent_dir=dir_, + real_data_path=real_data_path, + eval_type=eval_type, + frac_samples=frac_samples, + frac_lam_del=frac_lam_del, + k_neighbours=k_neighbours, + change_val=True, + seed=seed + ) + T_dict = { + "seed": 0, + "normalization": None, + "num_nan_policy": None, + "cat_nan_policy": None, + "cat_min_frequency": None, + "cat_encoding": None, + "y_policy": "default" + } + metrics = train_catboost( + parent_dir=dir_, + real_data_path=real_data_path, + eval_type=eval_type, + T_dict=T_dict, + change_val=True, + seed = 0 + ) + + score += metrics.get_val_score() + + return score / 5 + +study = optuna.create_study( + direction='maximize', + sampler=optuna.samplers.TPESampler(seed=0), +) + +study.optimize(objective, n_trials=5, show_progress_bar=True) + +os.makedirs(f"exp/{Path(real_data_path).name}/smote/", exist_ok=True) +config = { + "parent_dir": f"exp/{Path(real_data_path).name}/smote/", + "real_data_path": real_data_path, + "seed": 0, + "smote_params": {}, + "sample": {"seed": 0}, + "eval": { + "type": {"eval_model": "catboost", "eval_type": eval_type}, + "T": { + "seed": 0, + "normalization": None, + "num_nan_policy": None, + "cat_nan_policy": None, + "cat_min_frequency": None, + "cat_encoding": None, + "y_policy": "default" + }, + } +} + +config["smote_params"] = study.best_params +config["smote_params"]["frac_samples"] = 2 ** config["smote_params"]["frac_samples"] + +lib.dump_config(config, config["parent_dir"]+"config.toml") + +subprocess.run(['python3.9', "scripts/eval_seeds.py", '--config', f'{config["parent_dir"]+"config.toml"}', + '10', "smote", eval_type, "catboost", "5"], check=True) \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tab_ddpm/__init__.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tab_ddpm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8ad340c806b61da5a372186d04f2e7ab88a74daa --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tab_ddpm/__init__.py @@ -0,0 +1,2 @@ +from .gaussian_multinomial_diffsuion import * # noqa +from .modules import * # noqa \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tab_ddpm/gaussian_multinomial_diffsuion.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tab_ddpm/gaussian_multinomial_diffsuion.py new file mode 100644 index 0000000000000000000000000000000000000000..5e3cb1c3086b2fa5862552e91e648677bb673dd9 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tab_ddpm/gaussian_multinomial_diffsuion.py @@ -0,0 +1,993 @@ +""" +Based on https://github.com/openai/guided-diffusion/blob/main/guided_diffusion +and https://github.com/ehoogeboom/multinomial_diffusion +""" + +import torch.nn.functional as F +import torch +import math + +import numpy as np +from .utils import * + +""" +Based in part on: https://github.com/lucidrains/denoising-diffusion-pytorch/blob/5989f4c77eafcdc6be0fb4739f0f277a6dd7f7d8/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py#L281 +""" +eps = 1e-8 + +def get_named_beta_schedule(schedule_name, num_diffusion_timesteps): + """ + Get a pre-defined beta schedule for the given name. + The beta schedule library consists of beta schedules which remain similar + in the limit of num_diffusion_timesteps. + Beta schedules may be added, but should not be removed or changed once + they are committed to maintain backwards compatibility. + """ + if schedule_name == "linear": + # Linear schedule from Ho et al, extended to work for any number of + # diffusion steps. + scale = 1000 / num_diffusion_timesteps + beta_start = scale * 0.0001 + beta_end = scale * 0.02 + return np.linspace( + beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64 + ) + elif schedule_name == "cosine": + return betas_for_alpha_bar( + num_diffusion_timesteps, + lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2, + ) + else: + raise NotImplementedError(f"unknown beta schedule: {schedule_name}") + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, + which defines the cumulative product of (1-beta) over time from t = [0,1]. + :param num_diffusion_timesteps: the number of betas to produce. + :param alpha_bar: a lambda that takes an argument t from 0 to 1 and + produces the cumulative product of (1-beta) up to that + part of the diffusion process. + :param max_beta: the maximum beta to use; use values lower than 1 to + prevent singularities. + """ + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) + +class GaussianMultinomialDiffusion(torch.nn.Module): + def __init__( + self, + num_classes: np.array, + num_numerical_features: int, + denoise_fn, + num_timesteps=1000, + gaussian_loss_type='mse', + gaussian_parametrization='eps', + multinomial_loss_type='vb_stochastic', + parametrization='x0', + scheduler='cosine', + device=torch.device('cpu') + ): + + super(GaussianMultinomialDiffusion, self).__init__() + assert multinomial_loss_type in ('vb_stochastic', 'vb_all') + assert parametrization in ('x0', 'direct') + + if multinomial_loss_type == 'vb_all': + print('Computing the loss using the bound on _all_ timesteps.' + ' This is expensive both in terms of memory and computation.') + + self.num_numerical_features = num_numerical_features + self.num_classes = num_classes # it as a vector [K1, K2, ..., Km] + self.num_classes_expanded = torch.from_numpy( + np.concatenate([num_classes[i].repeat(num_classes[i]) for i in range(len(num_classes))]) + ).to(device) + + self.slices_for_classes = [np.arange(self.num_classes[0])] + offsets = np.cumsum(self.num_classes) + for i in range(1, len(offsets)): + self.slices_for_classes.append(np.arange(offsets[i - 1], offsets[i])) + self.offsets = torch.from_numpy(np.append([0], offsets)).to(device) + + self._denoise_fn = denoise_fn + self.gaussian_loss_type = gaussian_loss_type + self.gaussian_parametrization = gaussian_parametrization + self.multinomial_loss_type = multinomial_loss_type + self.num_timesteps = num_timesteps + self.parametrization = parametrization + self.scheduler = scheduler + + alphas = 1. - get_named_beta_schedule(scheduler, num_timesteps) + alphas = torch.tensor(alphas.astype('float64')) + betas = 1. - alphas + + log_alpha = np.log(alphas) + log_cumprod_alpha = np.cumsum(log_alpha) + + log_1_min_alpha = log_1_min_a(log_alpha) + log_1_min_cumprod_alpha = log_1_min_a(log_cumprod_alpha) + + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = torch.tensor(np.append(1.0, alphas_cumprod[:-1])) + alphas_cumprod_next = torch.tensor(np.append(alphas_cumprod[1:], 0.0)) + sqrt_alphas_cumprod = np.sqrt(alphas_cumprod) + sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - alphas_cumprod) + sqrt_recip_alphas_cumprod = np.sqrt(1.0 / alphas_cumprod) + sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / alphas_cumprod - 1) + + # Gaussian diffusion + + self.posterior_variance = ( + betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod) + ) + self.posterior_log_variance_clipped = torch.from_numpy( + np.log(np.append(self.posterior_variance[1], self.posterior_variance[1:])) + ).float().to(device) + self.posterior_mean_coef1 = ( + betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod) + ).float().to(device) + self.posterior_mean_coef2 = ( + (1.0 - alphas_cumprod_prev) + * np.sqrt(alphas.numpy()) + / (1.0 - alphas_cumprod) + ).float().to(device) + + assert log_add_exp(log_alpha, log_1_min_alpha).abs().sum().item() < 1.e-5 + assert log_add_exp(log_cumprod_alpha, log_1_min_cumprod_alpha).abs().sum().item() < 1e-5 + assert (np.cumsum(log_alpha) - log_cumprod_alpha).abs().sum().item() < 1.e-5 + + # Convert to float32 and register buffers. + self.register_buffer('alphas', alphas.float().to(device)) + self.register_buffer('log_alpha', log_alpha.float().to(device)) + self.register_buffer('log_1_min_alpha', log_1_min_alpha.float().to(device)) + self.register_buffer('log_1_min_cumprod_alpha', log_1_min_cumprod_alpha.float().to(device)) + self.register_buffer('log_cumprod_alpha', log_cumprod_alpha.float().to(device)) + self.register_buffer('alphas_cumprod', alphas_cumprod.float().to(device)) + self.register_buffer('alphas_cumprod_prev', alphas_cumprod_prev.float().to(device)) + self.register_buffer('alphas_cumprod_next', alphas_cumprod_next.float().to(device)) + self.register_buffer('sqrt_alphas_cumprod', sqrt_alphas_cumprod.float().to(device)) + self.register_buffer('sqrt_one_minus_alphas_cumprod', sqrt_one_minus_alphas_cumprod.float().to(device)) + self.register_buffer('sqrt_recip_alphas_cumprod', sqrt_recip_alphas_cumprod.float().to(device)) + self.register_buffer('sqrt_recipm1_alphas_cumprod', sqrt_recipm1_alphas_cumprod.float().to(device)) + + self.register_buffer('Lt_history', torch.zeros(num_timesteps)) + self.register_buffer('Lt_count', torch.zeros(num_timesteps)) + + # Gaussian part + def gaussian_q_mean_variance(self, x_start, t): + mean = ( + extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + ) + variance = extract(1.0 - self.alphas_cumprod, t, x_start.shape) + log_variance = extract( + self.log_1_min_cumprod_alpha, t, x_start.shape + ) + return mean, variance, log_variance + + def gaussian_q_sample(self, x_start, t, noise=None): + if noise is None: + noise = torch.randn_like(x_start) + assert noise.shape == x_start.shape + return ( + extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) + * noise + ) + + def gaussian_q_posterior_mean_variance(self, x_start, x_t, t): + assert x_start.shape == x_t.shape + posterior_mean = ( + extract(self.posterior_mean_coef1, t, x_t.shape) * x_start + + extract(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = extract(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = extract( + self.posterior_log_variance_clipped, t, x_t.shape + ) + assert ( + posterior_mean.shape[0] + == posterior_variance.shape[0] + == posterior_log_variance_clipped.shape[0] + == x_start.shape[0] + ) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def gaussian_p_mean_variance( + self, model_output, x, t, clip_denoised=False, denoised_fn=None, model_kwargs=None + ): + if model_kwargs is None: + model_kwargs = {} + + B, C = x.shape[:2] + assert t.shape == (B,) + + model_variance = torch.cat([self.posterior_variance[1].unsqueeze(0).to(x.device), (1. - self.alphas)[1:]], dim=0) + # model_variance = self.posterior_variance.to(x.device) + model_log_variance = torch.log(model_variance) + + model_variance = extract(model_variance, t, x.shape) + model_log_variance = extract(model_log_variance, t, x.shape) + + + if self.gaussian_parametrization == 'eps': + pred_xstart = self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output) + elif self.gaussian_parametrization == 'x0': + pred_xstart = model_output + else: + raise NotImplementedError + + model_mean, _, _ = self.gaussian_q_posterior_mean_variance( + x_start=pred_xstart, x_t=x, t=t + ) + + assert ( + model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape + ), f'{model_mean.shape}, {model_log_variance.shape}, {pred_xstart.shape}, {x.shape}' + + return { + "mean": model_mean, + "variance": model_variance, + "log_variance": model_log_variance, + "pred_xstart": pred_xstart, + } + + def _vb_terms_bpd( + self, model_output, x_start, x_t, t, clip_denoised=False, model_kwargs=None + ): + true_mean, _, true_log_variance_clipped = self.gaussian_q_posterior_mean_variance( + x_start=x_start, x_t=x_t, t=t + ) + out = self.gaussian_p_mean_variance( + model_output, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs + ) + kl = normal_kl( + true_mean, true_log_variance_clipped, out["mean"], out["log_variance"] + ) + kl = mean_flat(kl) / np.log(2.0) + + decoder_nll = -discretized_gaussian_log_likelihood( + x_start, means=out["mean"], log_scales=0.5 * out["log_variance"] + ) + assert decoder_nll.shape == x_start.shape + decoder_nll = mean_flat(decoder_nll) / np.log(2.0) + + # At the first timestep return the decoder NLL, + # otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t)) + output = torch.where((t == 0), decoder_nll, kl) + return {"output": output, "pred_xstart": out["pred_xstart"], "out_mean": out["mean"], "true_mean": true_mean} + + def _prior_gaussian(self, x_start): + """ + Get the prior KL term for the variational lower-bound, measured in + bits-per-dim. + + This term can't be optimized, as it only depends on the encoder. + + :param x_start: the [N x C x ...] tensor of inputs. + :return: a batch of [N] KL values (in bits), one per batch element. + """ + batch_size = x_start.shape[0] + t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) + qt_mean, _, qt_log_variance = self.gaussian_q_mean_variance(x_start, t) + kl_prior = normal_kl( + mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0 + ) + return mean_flat(kl_prior) / np.log(2.0) + + def _gaussian_loss(self, model_out, x_start, x_t, t, noise, model_kwargs=None): + if model_kwargs is None: + model_kwargs = {} + + terms = {} + if self.gaussian_loss_type == 'mse': + terms["loss"] = mean_flat((noise - model_out) ** 2) + elif self.gaussian_loss_type == 'kl': + terms["loss"] = self._vb_terms_bpd( + model_output=model_out, + x_start=x_start, + x_t=x_t, + t=t, + clip_denoised=False, + model_kwargs=model_kwargs, + )["output"] + + + return terms['loss'] + + def _predict_xstart_from_eps(self, x_t, t, eps): + assert x_t.shape == eps.shape + return ( + extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t + - extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps + ) + + def _predict_eps_from_xstart(self, x_t, t, pred_xstart): + return ( + extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t + - pred_xstart + ) / extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) + + def gaussian_p_sample( + self, + model_out, + x, + t, + clip_denoised=False, + denoised_fn=None, + model_kwargs=None, + ): + out = self.gaussian_p_mean_variance( + model_out, + x, + t, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + model_kwargs=model_kwargs, + ) + noise = torch.randn_like(x) + nonzero_mask = ( + (t != 0).float().view(-1, *([1] * (len(x.shape) - 1))) + ) # no noise when t == 0 + + sample = out["mean"] + nonzero_mask * torch.exp(0.5 * out["log_variance"]) * noise + return {"sample": sample, "pred_xstart": out["pred_xstart"]} + + # Multinomial part + + def multinomial_kl(self, log_prob1, log_prob2): + kl = (log_prob1.exp() * (log_prob1 - log_prob2)).sum(dim=1) + return kl + + def q_pred_one_timestep(self, log_x_t, t): + log_alpha_t = extract(self.log_alpha, t, log_x_t.shape) + log_1_min_alpha_t = extract(self.log_1_min_alpha, t, log_x_t.shape) + + # alpha_t * E[xt] + (1 - alpha_t) 1 / K + log_probs = log_add_exp( + log_x_t + log_alpha_t, + log_1_min_alpha_t - torch.log(self.num_classes_expanded) + ) + + return log_probs + + def q_pred(self, log_x_start, t): + log_cumprod_alpha_t = extract(self.log_cumprod_alpha, t, log_x_start.shape) + log_1_min_cumprod_alpha = extract(self.log_1_min_cumprod_alpha, t, log_x_start.shape) + + log_probs = log_add_exp( + log_x_start + log_cumprod_alpha_t, + log_1_min_cumprod_alpha - torch.log(self.num_classes_expanded) + ) + + return log_probs + + def predict_start(self, model_out, log_x_t, t, out_dict): + + # model_out = self._denoise_fn(x_t, t.to(x_t.device), **out_dict) + + assert model_out.size(0) == log_x_t.size(0) + assert model_out.size(1) == self.num_classes.sum(), f'{model_out.size()}' + + log_pred = torch.empty_like(model_out) + for ix in self.slices_for_classes: + log_pred[:, ix] = F.log_softmax(model_out[:, ix], dim=1) + return log_pred + + def q_posterior(self, log_x_start, log_x_t, t): + # q(xt-1 | xt, x0) = q(xt | xt-1, x0) * q(xt-1 | x0) / q(xt | x0) + # where q(xt | xt-1, x0) = q(xt | xt-1). + + # EV_log_qxt_x0 = self.q_pred(log_x_start, t) + + # print('sum exp', EV_log_qxt_x0.exp().sum(1).mean()) + # assert False + + # log_qxt_x0 = (log_x_t.exp() * EV_log_qxt_x0).sum(dim=1) + t_minus_1 = t - 1 + # Remove negative values, will not be used anyway for final decoder + t_minus_1 = torch.where(t_minus_1 < 0, torch.zeros_like(t_minus_1), t_minus_1) + log_EV_qxtmin_x0 = self.q_pred(log_x_start, t_minus_1) + + num_axes = (1,) * (len(log_x_start.size()) - 1) + t_broadcast = t.to(log_x_start.device).view(-1, *num_axes) * torch.ones_like(log_x_start) + log_EV_qxtmin_x0 = torch.where(t_broadcast == 0, log_x_start, log_EV_qxtmin_x0.to(torch.float32)) + + # unnormed_logprobs = log_EV_qxtmin_x0 + + # log q_pred_one_timestep(x_t, t) + # Note: _NOT_ x_tmin1, which is how the formula is typically used!!! + # Not very easy to see why this is true. But it is :) + unnormed_logprobs = log_EV_qxtmin_x0 + self.q_pred_one_timestep(log_x_t, t) + + log_EV_xtmin_given_xt_given_xstart = \ + unnormed_logprobs \ + - sliced_logsumexp(unnormed_logprobs, self.offsets) + + return log_EV_xtmin_given_xt_given_xstart + + def p_pred(self, model_out, log_x, t, out_dict): + if self.parametrization == 'x0': + log_x_recon = self.predict_start(model_out, log_x, t=t, out_dict=out_dict) + log_model_pred = self.q_posterior( + log_x_start=log_x_recon, log_x_t=log_x, t=t) + elif self.parametrization == 'direct': + log_model_pred = self.predict_start(model_out, log_x, t=t, out_dict=out_dict) + else: + raise ValueError + return log_model_pred + + @torch.no_grad() + def p_sample(self, model_out, log_x, t, out_dict): + model_log_prob = self.p_pred(model_out, log_x=log_x, t=t, out_dict=out_dict) + out = self.log_sample_categorical(model_log_prob) + return out + + @torch.no_grad() + def p_sample_loop(self, shape, out_dict): + device = self.log_alpha.device + + b = shape[0] + # start with random normal image. + img = torch.randn(shape, device=device) + + for i in reversed(range(1, self.num_timesteps)): + img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long), out_dict) + return img + + @torch.no_grad() + def _sample(self, image_size, out_dict, batch_size = 16): + return self.p_sample_loop((batch_size, 3, image_size, image_size), out_dict) + + @torch.no_grad() + def interpolate(self, x1, x2, t = None, lam = 0.5): + b, *_, device = *x1.shape, x1.device + t = default(t, self.num_timesteps - 1) + + assert x1.shape == x2.shape + + t_batched = torch.stack([torch.tensor(t, device=device)] * b) + xt1, xt2 = map(lambda x: self.q_sample(x, t=t_batched), (x1, x2)) + + img = (1 - lam) * xt1 + lam * xt2 + for i in reversed(range(0, t)): + img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long)) + + return img + + def log_sample_categorical(self, logits): + full_sample = [] + for i in range(len(self.num_classes)): + one_class_logits = logits[:, self.slices_for_classes[i]] + uniform = torch.rand_like(one_class_logits) + gumbel_noise = -torch.log(-torch.log(uniform + 1e-30) + 1e-30) + sample = (gumbel_noise + one_class_logits).argmax(dim=1) + full_sample.append(sample.unsqueeze(1)) + full_sample = torch.cat(full_sample, dim=1) + log_sample = index_to_log_onehot(full_sample, self.num_classes) + return log_sample + + def q_sample(self, log_x_start, t): + log_EV_qxt_x0 = self.q_pred(log_x_start, t) + + log_sample = self.log_sample_categorical(log_EV_qxt_x0) + + return log_sample + + def nll(self, log_x_start, out_dict): + b = log_x_start.size(0) + device = log_x_start.device + loss = 0 + for t in range(0, self.num_timesteps): + t_array = (torch.ones(b, device=device) * t).long() + + kl = self.compute_Lt( + log_x_start=log_x_start, + log_x_t=self.q_sample(log_x_start=log_x_start, t=t_array), + t=t_array, + out_dict=out_dict) + + loss += kl + + loss += self.kl_prior(log_x_start) + + return loss + + def kl_prior(self, log_x_start): + b = log_x_start.size(0) + device = log_x_start.device + ones = torch.ones(b, device=device).long() + + log_qxT_prob = self.q_pred(log_x_start, t=(self.num_timesteps - 1) * ones) + log_half_prob = -torch.log(self.num_classes_expanded * torch.ones_like(log_qxT_prob)) + + kl_prior = self.multinomial_kl(log_qxT_prob, log_half_prob) + return sum_except_batch(kl_prior) + + def compute_Lt(self, model_out, log_x_start, log_x_t, t, out_dict, detach_mean=False): + log_true_prob = self.q_posterior( + log_x_start=log_x_start, log_x_t=log_x_t, t=t) + log_model_prob = self.p_pred(model_out, log_x=log_x_t, t=t, out_dict=out_dict) + + if detach_mean: + log_model_prob = log_model_prob.detach() + + kl = self.multinomial_kl(log_true_prob, log_model_prob) + kl = sum_except_batch(kl) + + decoder_nll = -log_categorical(log_x_start, log_model_prob) + decoder_nll = sum_except_batch(decoder_nll) + + mask = (t == torch.zeros_like(t)).float() + loss = mask * decoder_nll + (1. - mask) * kl + + return loss + + def sample_time(self, b, device, method='uniform'): + if method == 'importance': + if not (self.Lt_count > 10).all(): + return self.sample_time(b, device, method='uniform') + + Lt_sqrt = torch.sqrt(self.Lt_history + 1e-10) + 0.0001 + Lt_sqrt[0] = Lt_sqrt[1] # Overwrite decoder term with L1. + pt_all = (Lt_sqrt / Lt_sqrt.sum()).to(device) + + t = torch.multinomial(pt_all, num_samples=b, replacement=True).to(device) + + pt = pt_all.gather(dim=0, index=t) + + return t, pt + + elif method == 'uniform': + t = torch.randint(0, self.num_timesteps, (b,), device=device).long() + + pt = torch.ones_like(t).float() / self.num_timesteps + return t, pt + else: + raise ValueError + + def _multinomial_loss(self, model_out, log_x_start, log_x_t, t, pt, out_dict): + + if self.multinomial_loss_type == 'vb_stochastic': + kl = self.compute_Lt( + model_out, log_x_start, log_x_t, t, out_dict + ) + kl_prior = self.kl_prior(log_x_start) + # Upweigh loss term of the kl + vb_loss = kl / pt + kl_prior + + return vb_loss + + elif self.multinomial_loss_type == 'vb_all': + # Expensive, dont do it ;). + # DEPRECATED + return -self.nll(log_x_start) + else: + raise ValueError() + + def log_prob(self, x, out_dict): + b, device = x.size(0), x.device + if self.training: + return self._multinomial_loss(x, out_dict) + + else: + log_x_start = index_to_log_onehot(x, self.num_classes) + + t, pt = self.sample_time(b, device, 'importance') + + kl = self.compute_Lt( + log_x_start, self.q_sample(log_x_start=log_x_start, t=t), t, out_dict) + + kl_prior = self.kl_prior(log_x_start) + + # Upweigh loss term of the kl + loss = kl / pt + kl_prior + + return -loss + + def mixed_loss(self, x, out_dict): + b = x.shape[0] + device = x.device + t, pt = self.sample_time(b, device, 'uniform') + + x_num = x[:, :self.num_numerical_features] + x_cat = x[:, self.num_numerical_features:] + + x_num_t = x_num + log_x_cat_t = x_cat + if x_num.shape[1] > 0: + noise = torch.randn_like(x_num) + x_num_t = self.gaussian_q_sample(x_num, t, noise=noise) + if x_cat.shape[1] > 0: + log_x_cat = index_to_log_onehot(x_cat.long(), self.num_classes) + log_x_cat_t = self.q_sample(log_x_start=log_x_cat, t=t) + + x_in = torch.cat([x_num_t, log_x_cat_t], dim=1) + + model_out = self._denoise_fn( + x_in, + t, + **out_dict + ) + + model_out_num = model_out[:, :self.num_numerical_features] + model_out_cat = model_out[:, self.num_numerical_features:] + + loss_multi = torch.zeros((1,)).float() + loss_gauss = torch.zeros((1,)).float() + if x_cat.shape[1] > 0: + loss_multi = self._multinomial_loss(model_out_cat, log_x_cat, log_x_cat_t, t, pt, out_dict) / len(self.num_classes) + + if x_num.shape[1] > 0: + loss_gauss = self._gaussian_loss(model_out_num, x_num, x_num_t, t, noise) + + # loss_multi = torch.where(out_dict['y'] == 1, loss_multi, 2 * loss_multi) + # loss_gauss = torch.where(out_dict['y'] == 1, loss_gauss, 2 * loss_gauss) + + return loss_multi.mean(), loss_gauss.mean() + + @torch.no_grad() + def mixed_elbo(self, x0, out_dict): + b = x0.size(0) + device = x0.device + + x_num = x0[:, :self.num_numerical_features] + x_cat = x0[:, self.num_numerical_features:] + has_cat = x_cat.shape[1] > 0 + if has_cat: + log_x_cat = index_to_log_onehot(x_cat.long(), self.num_classes).to(device) + + gaussian_loss = [] + xstart_mse = [] + mse = [] + mu_mse = [] + out_mean = [] + true_mean = [] + multinomial_loss = [] + for t in range(self.num_timesteps): + t_array = (torch.ones(b, device=device) * t).long() + noise = torch.randn_like(x_num) + + x_num_t = self.gaussian_q_sample(x_start=x_num, t=t_array, noise=noise) + if has_cat: + log_x_cat_t = self.q_sample(log_x_start=log_x_cat, t=t_array) + else: + log_x_cat_t = x_cat + + model_out = self._denoise_fn( + torch.cat([x_num_t, log_x_cat_t], dim=1), + t_array, + **out_dict + ) + + model_out_num = model_out[:, :self.num_numerical_features] + model_out_cat = model_out[:, self.num_numerical_features:] + + kl = torch.tensor([0.0]) + if has_cat: + kl = self.compute_Lt( + model_out=model_out_cat, + log_x_start=log_x_cat, + log_x_t=log_x_cat_t, + t=t_array, + out_dict=out_dict + ) + + out = self._vb_terms_bpd( + model_out_num, + x_start=x_num, + x_t=x_num_t, + t=t_array, + clip_denoised=False + ) + + multinomial_loss.append(kl) + gaussian_loss.append(out["output"]) + xstart_mse.append(mean_flat((out["pred_xstart"] - x_num) ** 2)) + # mu_mse.append(mean_flat(out["mean_mse"])) + out_mean.append(mean_flat(out["out_mean"])) + true_mean.append(mean_flat(out["true_mean"])) + + eps = self._predict_eps_from_xstart(x_num_t, t_array, out["pred_xstart"]) + mse.append(mean_flat((eps - noise) ** 2)) + + gaussian_loss = torch.stack(gaussian_loss, dim=1) + multinomial_loss = torch.stack(multinomial_loss, dim=1) + xstart_mse = torch.stack(xstart_mse, dim=1) + mse = torch.stack(mse, dim=1) + # mu_mse = torch.stack(mu_mse, dim=1) + out_mean = torch.stack(out_mean, dim=1) + true_mean = torch.stack(true_mean, dim=1) + + + + prior_gauss = self._prior_gaussian(x_num) + + prior_multin = torch.tensor([0.0]) + if has_cat: + prior_multin = self.kl_prior(log_x_cat) + + total_gauss = gaussian_loss.sum(dim=1) + prior_gauss + total_multin = multinomial_loss.sum(dim=1) + prior_multin + return { + "total_gaussian": total_gauss, + "total_multinomial": total_multin, + "losses_gaussian": gaussian_loss, + "losses_multinimial": multinomial_loss, + "xstart_mse": xstart_mse, + "mse": mse, + # "mu_mse": mu_mse + "out_mean": out_mean, + "true_mean": true_mean + } + + @torch.no_grad() + def gaussian_ddim_step( + self, + model_out_num, + x, + t, + clip_denoised=False, + denoised_fn=None, + eta=0.0 + ): + out = self.gaussian_p_mean_variance( + model_out_num, + x, + t, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + model_kwargs=None, + ) + + eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"]) + + alpha_bar = extract(self.alphas_cumprod, t, x.shape) + alpha_bar_prev = extract(self.alphas_cumprod_prev, t, x.shape) + sigma = ( + eta + * torch.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar)) + * torch.sqrt(1 - alpha_bar / alpha_bar_prev) + ) + + noise = torch.randn_like(x) + mean_pred = ( + out["pred_xstart"] * torch.sqrt(alpha_bar_prev) + + torch.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps + ) + nonzero_mask = ( + (t != 0).float().view(-1, *([1] * (len(x.shape) - 1))) + ) # no noise when t == 0 + sample = mean_pred + nonzero_mask * sigma * noise + + return sample + + @torch.no_grad() + def gaussian_ddim_sample( + self, + noise, + T, + out_dict, + eta=0.0 + ): + x = noise + b = x.shape[0] + device = x.device + for t in reversed(range(T)): + print(f'Sample timestep {t:4d}', end='\r') + t_array = (torch.ones(b, device=device) * t).long() + out_num = self._denoise_fn(x, t_array, **out_dict) + x = self.gaussian_ddim_step( + out_num, + x, + t_array + ) + print() + return x + + + @torch.no_grad() + def gaussian_ddim_reverse_step( + self, + model_out_num, + x, + t, + clip_denoised=False, + eta=0.0 + ): + assert eta == 0.0, "Eta must be zero." + out = self.gaussian_p_mean_variance( + model_out_num, + x, + t, + clip_denoised=clip_denoised, + denoised_fn=None, + model_kwargs=None, + ) + + eps = ( + extract(self.sqrt_recip_alphas_cumprod, t, x.shape) * x + - out["pred_xstart"] + ) / extract(self.sqrt_recipm1_alphas_cumprod, t, x.shape) + alpha_bar_next = extract(self.alphas_cumprod_next, t, x.shape) + + mean_pred = ( + out["pred_xstart"] * torch.sqrt(alpha_bar_next) + + torch.sqrt(1 - alpha_bar_next) * eps + ) + + return mean_pred + + @torch.no_grad() + def gaussian_ddim_reverse_sample( + self, + x, + T, + out_dict, + ): + b = x.shape[0] + device = x.device + for t in range(T): + print(f'Reverse timestep {t:4d}', end='\r') + t_array = (torch.ones(b, device=device) * t).long() + out_num = self._denoise_fn(x, t_array, **out_dict) + x = self.gaussian_ddim_reverse_step( + out_num, + x, + t_array, + eta=0.0 + ) + print() + + return x + + + @torch.no_grad() + def multinomial_ddim_step( + self, + model_out_cat, + log_x_t, + t, + out_dict, + eta=0.0 + ): + # not ddim, essentially + log_x0 = self.predict_start(model_out_cat, log_x_t=log_x_t, t=t, out_dict=out_dict) + + alpha_bar = extract(self.alphas_cumprod, t, log_x_t.shape) + alpha_bar_prev = extract(self.alphas_cumprod_prev, t, log_x_t.shape) + sigma = ( + eta + * torch.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar)) + * torch.sqrt(1 - alpha_bar / alpha_bar_prev) + ) + + coef1 = sigma + coef2 = alpha_bar_prev - sigma * alpha_bar + coef3 = 1 - coef1 - coef2 + + + log_ps = torch.stack([ + torch.log(coef1) + log_x_t, + torch.log(coef2) + log_x0, + torch.log(coef3) - torch.log(self.num_classes_expanded) + ], dim=2) + + log_prob = torch.logsumexp(log_ps, dim=2) + + out = self.log_sample_categorical(log_prob) + + return out + + @torch.no_grad() + def sample_ddim(self, num_samples, y_dist): + b = num_samples + device = self.log_alpha.device + z_norm = torch.randn((b, self.num_numerical_features), device=device) + + has_cat = self.num_classes[0] != 0 + log_z = torch.zeros((b, 0), device=device).float() + if has_cat: + uniform_logits = torch.zeros((b, len(self.num_classes_expanded)), device=device) + log_z = self.log_sample_categorical(uniform_logits) + + y = torch.multinomial( + y_dist, + num_samples=b, + replacement=True + ) + out_dict = {'y': y.long().to(device)} + for i in reversed(range(0, self.num_timesteps)): + print(f'Sample timestep {i:4d}', end='\r') + t = torch.full((b,), i, device=device, dtype=torch.long) + model_out = self._denoise_fn( + torch.cat([z_norm, log_z], dim=1).float(), + t, + **out_dict + ) + model_out_num = model_out[:, :self.num_numerical_features] + model_out_cat = model_out[:, self.num_numerical_features:] + z_norm = self.gaussian_ddim_step(model_out_num, z_norm, t, clip_denoised=False) + if has_cat: + log_z = self.multinomial_ddim_step(model_out_cat, log_z, t, out_dict) + + print() + z_ohe = torch.exp(log_z).round() + z_cat = log_z + if has_cat: + z_cat = ohe_to_categories(z_ohe, self.num_classes) + sample = torch.cat([z_norm, z_cat], dim=1).cpu() + return sample, out_dict + + + @torch.no_grad() + def sample(self, num_samples, y_dist): + b = num_samples + device = self.log_alpha.device + z_norm = torch.randn((b, self.num_numerical_features), device=device) + + has_cat = self.num_classes[0] != 0 + log_z = torch.zeros((b, 0), device=device).float() + if has_cat: + uniform_logits = torch.zeros((b, len(self.num_classes_expanded)), device=device) + log_z = self.log_sample_categorical(uniform_logits) + + y = torch.multinomial( + y_dist, + num_samples=b, + replacement=True + ) + out_dict = {'y': y.long().to(device)} + for i in reversed(range(0, self.num_timesteps)): + print(f'Sample timestep {i:4d}', end='\r') + t = torch.full((b,), i, device=device, dtype=torch.long) + model_out = self._denoise_fn( + torch.cat([z_norm, log_z], dim=1).float(), + t, + **out_dict + ) + model_out_num = model_out[:, :self.num_numerical_features] + model_out_cat = model_out[:, self.num_numerical_features:] + z_norm = self.gaussian_p_sample(model_out_num, z_norm, t, clip_denoised=False)['sample'] + if has_cat: + log_z = self.p_sample(model_out_cat, log_z, t, out_dict) + + print() + z_ohe = torch.exp(log_z).round() + z_cat = log_z + if has_cat: + z_cat = ohe_to_categories(z_ohe, self.num_classes) + sample = torch.cat([z_norm, z_cat], dim=1).cpu() + return sample, out_dict + + def sample_all(self, num_samples, batch_size, y_dist, ddim=False): + if ddim: + print('Sample using DDIM.') + sample_fn = self.sample_ddim + else: + sample_fn = self.sample + + b = batch_size + + all_y = [] + all_samples = [] + num_generated = 0 + while num_generated < num_samples: + sample, out_dict = sample_fn(b, y_dist) + mask_nan = torch.any(sample.isnan(), dim=1) + sample = sample[~mask_nan] + out_dict['y'] = out_dict['y'][~mask_nan] + + all_samples.append(sample) + all_y.append(out_dict['y'].cpu()) + if sample.shape[0] != b: + raise FoundNANsError + num_generated += sample.shape[0] + + x_gen = torch.cat(all_samples, dim=0)[:num_samples] + y_gen = torch.cat(all_y, dim=0)[:num_samples] + + return x_gen, y_gen \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tab_ddpm/modules.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tab_ddpm/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..472ba5b5f44b646e83d429f0d6272a8fe6d7130d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tab_ddpm/modules.py @@ -0,0 +1,486 @@ +""" +Code was adapted from https://github.com/Yura52/rtdl +""" + +import math +from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union, cast + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim +from torch import Tensor + +ModuleType = Union[str, Callable[..., nn.Module]] + +class SiLU(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + +def timestep_embedding(timesteps, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + +def _is_glu_activation(activation: ModuleType): + return ( + isinstance(activation, str) + and activation.endswith('GLU') + or activation in [ReGLU, GEGLU] + ) + + +def _all_or_none(values): + assert all(x is None for x in values) or all(x is not None for x in values) + +def reglu(x: Tensor) -> Tensor: + """The ReGLU activation function from [1]. + References: + [1] Noam Shazeer, "GLU Variants Improve Transformer", 2020 + """ + assert x.shape[-1] % 2 == 0 + a, b = x.chunk(2, dim=-1) + return a * F.relu(b) + + +def geglu(x: Tensor) -> Tensor: + """The GEGLU activation function from [1]. + References: + [1] Noam Shazeer, "GLU Variants Improve Transformer", 2020 + """ + assert x.shape[-1] % 2 == 0 + a, b = x.chunk(2, dim=-1) + return a * F.gelu(b) + +class ReGLU(nn.Module): + """The ReGLU activation function from [shazeer2020glu]. + + Examples: + .. testcode:: + + module = ReGLU() + x = torch.randn(3, 4) + assert module(x).shape == (3, 2) + + References: + * [shazeer2020glu] Noam Shazeer, "GLU Variants Improve Transformer", 2020 + """ + + def forward(self, x: Tensor) -> Tensor: + return reglu(x) + + +class GEGLU(nn.Module): + """The GEGLU activation function from [shazeer2020glu]. + + Examples: + .. testcode:: + + module = GEGLU() + x = torch.randn(3, 4) + assert module(x).shape == (3, 2) + + References: + * [shazeer2020glu] Noam Shazeer, "GLU Variants Improve Transformer", 2020 + """ + + def forward(self, x: Tensor) -> Tensor: + return geglu(x) + +def _make_nn_module(module_type: ModuleType, *args) -> nn.Module: + return ( + ( + ReGLU() + if module_type == 'ReGLU' + else GEGLU() + if module_type == 'GEGLU' + else getattr(nn, module_type)(*args) + ) + if isinstance(module_type, str) + else module_type(*args) + ) + + +class MLP(nn.Module): + """The MLP model used in [gorishniy2021revisiting]. + + The following scheme describes the architecture: + + .. code-block:: text + + MLP: (in) -> Block -> ... -> Block -> Linear -> (out) + Block: (in) -> Linear -> Activation -> Dropout -> (out) + + Examples: + .. testcode:: + + x = torch.randn(4, 2) + module = MLP.make_baseline(x.shape[1], [3, 5], 0.1, 1) + assert module(x).shape == (len(x), 1) + + References: + * [gorishniy2021revisiting] Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, Artem Babenko, "Revisiting Deep Learning Models for Tabular Data", 2021 + """ + + class Block(nn.Module): + """The main building block of `MLP`.""" + + def __init__( + self, + *, + d_in: int, + d_out: int, + bias: bool, + activation: ModuleType, + dropout: float, + ) -> None: + super().__init__() + self.linear = nn.Linear(d_in, d_out, bias) + self.activation = _make_nn_module(activation) + self.dropout = nn.Dropout(dropout) + + def forward(self, x: Tensor) -> Tensor: + return self.dropout(self.activation(self.linear(x))) + + def __init__( + self, + *, + d_in: int, + d_layers: List[int], + dropouts: Union[float, List[float]], + activation: Union[str, Callable[[], nn.Module]], + d_out: int, + ) -> None: + """ + Note: + `make_baseline` is the recommended constructor. + """ + super().__init__() + if isinstance(dropouts, float): + dropouts = [dropouts] * len(d_layers) + assert len(d_layers) == len(dropouts) + assert activation not in ['ReGLU', 'GEGLU'] + + self.blocks = nn.ModuleList( + [ + MLP.Block( + d_in=d_layers[i - 1] if i else d_in, + d_out=d, + bias=True, + activation=activation, + dropout=dropout, + ) + for i, (d, dropout) in enumerate(zip(d_layers, dropouts)) + ] + ) + self.head = nn.Linear(d_layers[-1] if d_layers else d_in, d_out) + + @classmethod + def make_baseline( + cls: Type['MLP'], + d_in: int, + d_layers: List[int], + dropout: float, + d_out: int, + ) -> 'MLP': + """Create a "baseline" `MLP`. + + This variation of MLP was used in [gorishniy2021revisiting]. Features: + + * :code:`Activation` = :code:`ReLU` + * all linear layers except for the first one and the last one are of the same dimension + * the dropout rate is the same for all dropout layers + + Args: + d_in: the input size + d_layers: the dimensions of the linear layers. If there are more than two + layers, then all of them except for the first and the last ones must + have the same dimension. Valid examples: :code:`[]`, :code:`[8]`, + :code:`[8, 16]`, :code:`[2, 2, 2, 2]`, :code:`[1, 2, 2, 4]`. Invalid + example: :code:`[1, 2, 3, 4]`. + dropout: the dropout rate for all hidden layers + d_out: the output size + Returns: + MLP + + References: + * [gorishniy2021revisiting] Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, Artem Babenko, "Revisiting Deep Learning Models for Tabular Data", 2021 + """ + assert isinstance(dropout, float) + if len(d_layers) > 2: + assert len(set(d_layers[1:-1])) == 1, ( + 'if d_layers contains more than two elements, then' + ' all elements except for the first and the last ones must be equal.' + ) + return MLP( + d_in=d_in, + d_layers=d_layers, # type: ignore + dropouts=dropout, + activation='ReLU', + d_out=d_out, + ) + + def forward(self, x: Tensor) -> Tensor: + x = x.float() + for block in self.blocks: + x = block(x) + x = self.head(x) + return x + + +class ResNet(nn.Module): + """The ResNet model used in [gorishniy2021revisiting]. + The following scheme describes the architecture: + .. code-block:: text + ResNet: (in) -> Linear -> Block -> ... -> Block -> Head -> (out) + |-> Norm -> Linear -> Activation -> Dropout -> Linear -> Dropout ->| + | | + Block: (in) ------------------------------------------------------------> Add -> (out) + Head: (in) -> Norm -> Activation -> Linear -> (out) + Examples: + .. testcode:: + x = torch.randn(4, 2) + module = ResNet.make_baseline( + d_in=x.shape[1], + n_blocks=2, + d_main=3, + d_hidden=4, + dropout_first=0.25, + dropout_second=0.0, + d_out=1 + ) + assert module(x).shape == (len(x), 1) + References: + * [gorishniy2021revisiting] Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, Artem Babenko, "Revisiting Deep Learning Models for Tabular Data", 2021 + """ + + class Block(nn.Module): + """The main building block of `ResNet`.""" + + def __init__( + self, + *, + d_main: int, + d_hidden: int, + bias_first: bool, + bias_second: bool, + dropout_first: float, + dropout_second: float, + normalization: ModuleType, + activation: ModuleType, + skip_connection: bool, + ) -> None: + super().__init__() + self.normalization = _make_nn_module(normalization, d_main) + self.linear_first = nn.Linear(d_main, d_hidden, bias_first) + self.activation = _make_nn_module(activation) + self.dropout_first = nn.Dropout(dropout_first) + self.linear_second = nn.Linear(d_hidden, d_main, bias_second) + self.dropout_second = nn.Dropout(dropout_second) + self.skip_connection = skip_connection + + def forward(self, x: Tensor) -> Tensor: + x_input = x + x = self.normalization(x) + x = self.linear_first(x) + x = self.activation(x) + x = self.dropout_first(x) + x = self.linear_second(x) + x = self.dropout_second(x) + if self.skip_connection: + x = x_input + x + return x + + class Head(nn.Module): + """The final module of `ResNet`.""" + + def __init__( + self, + *, + d_in: int, + d_out: int, + bias: bool, + normalization: ModuleType, + activation: ModuleType, + ) -> None: + super().__init__() + self.normalization = _make_nn_module(normalization, d_in) + self.activation = _make_nn_module(activation) + self.linear = nn.Linear(d_in, d_out, bias) + + def forward(self, x: Tensor) -> Tensor: + if self.normalization is not None: + x = self.normalization(x) + x = self.activation(x) + x = self.linear(x) + return x + + def __init__( + self, + *, + d_in: int, + n_blocks: int, + d_main: int, + d_hidden: int, + dropout_first: float, + dropout_second: float, + normalization: ModuleType, + activation: ModuleType, + d_out: int, + ) -> None: + """ + Note: + `make_baseline` is the recommended constructor. + """ + super().__init__() + + self.first_layer = nn.Linear(d_in, d_main) + if d_main is None: + d_main = d_in + self.blocks = nn.Sequential( + *[ + ResNet.Block( + d_main=d_main, + d_hidden=d_hidden, + bias_first=True, + bias_second=True, + dropout_first=dropout_first, + dropout_second=dropout_second, + normalization=normalization, + activation=activation, + skip_connection=True, + ) + for _ in range(n_blocks) + ] + ) + self.head = ResNet.Head( + d_in=d_main, + d_out=d_out, + bias=True, + normalization=normalization, + activation=activation, + ) + + @classmethod + def make_baseline( + cls: Type['ResNet'], + *, + d_in: int, + n_blocks: int, + d_main: int, + d_hidden: int, + dropout_first: float, + dropout_second: float, + d_out: int, + ) -> 'ResNet': + """Create a "baseline" `ResNet`. + This variation of ResNet was used in [gorishniy2021revisiting]. Features: + * :code:`Activation` = :code:`ReLU` + * :code:`Norm` = :code:`BatchNorm1d` + Args: + d_in: the input size + n_blocks: the number of Blocks + d_main: the input size (or, equivalently, the output size) of each Block + d_hidden: the output size of the first linear layer in each Block + dropout_first: the dropout rate of the first dropout layer in each Block. + dropout_second: the dropout rate of the second dropout layer in each Block. + References: + * [gorishniy2021revisiting] Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, Artem Babenko, "Revisiting Deep Learning Models for Tabular Data", 2021 + """ + return cls( + d_in=d_in, + n_blocks=n_blocks, + d_main=d_main, + d_hidden=d_hidden, + dropout_first=dropout_first, + dropout_second=dropout_second, + normalization='BatchNorm1d', + activation='ReLU', + d_out=d_out, + ) + + def forward(self, x: Tensor) -> Tensor: + x = x.float() + x = self.first_layer(x) + x = self.blocks(x) + x = self.head(x) + return x +#### For diffusion + +class MLPDiffusion(nn.Module): + def __init__(self, d_in, num_classes, is_y_cond, rtdl_params, dim_t = 128): + super().__init__() + self.dim_t = dim_t + self.num_classes = num_classes + self.is_y_cond = is_y_cond + + # d0 = rtdl_params['d_layers'][0] + + rtdl_params['d_in'] = dim_t + rtdl_params['d_out'] = d_in + + self.mlp = MLP.make_baseline(**rtdl_params) + + if self.num_classes > 0 and is_y_cond: + self.label_emb = nn.Embedding(self.num_classes, dim_t) + elif self.num_classes == 0 and is_y_cond: + self.label_emb = nn.Linear(1, dim_t) + + self.proj = nn.Linear(d_in, dim_t) + self.time_embed = nn.Sequential( + nn.Linear(dim_t, dim_t), + nn.SiLU(), + nn.Linear(dim_t, dim_t) + ) + + def forward(self, x, timesteps, y=None): + emb = self.time_embed(timestep_embedding(timesteps, self.dim_t)) + if self.is_y_cond and y is not None: + if self.num_classes > 0: + y = y.squeeze() + else: + y = y.resize(y.size(0), 1).float() + emb += F.silu(self.label_emb(y)) + x = self.proj(x) + emb + return self.mlp(x) + +class ResNetDiffusion(nn.Module): + def __init__(self, d_in, num_classes, rtdl_params, dim_t = 256): + super().__init__() + self.dim_t = dim_t + self.num_classes = num_classes + + rtdl_params['d_in'] = d_in + rtdl_params['d_out'] = d_in + rtdl_params['emb_d'] = dim_t + self.resnet = ResNet.make_baseline(**rtdl_params) + + if self.num_classes > 0: + self.label_emb = nn.Embedding(self.num_classes, dim_t) + + self.time_embed = nn.Sequential( + nn.Linear(dim_t, dim_t), + nn.SiLU(), + nn.Linear(dim_t, dim_t) + ) + + def forward(self, x, timesteps, y=None): + emb = self.time_embed(timestep_embedding(timesteps, self.dim_t)) + if y is not None and self.num_classes > 0: + emb += self.label_emb(y.squeeze()) + return self.resnet(x, emb) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tab_ddpm/utils.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tab_ddpm/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6376bfbfb6971c3e465fafe9d56320d92a5f508a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tab_ddpm/utils.py @@ -0,0 +1,174 @@ +import torch +import numpy as np +import torch.nn.functional as F +from torch.profiler import record_function +from inspect import isfunction + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + Compute the KL divergence between two gaussians. + + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, torch.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for torch.exp(). + logvar1, logvar2 = [ + x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + torch.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) + ) + +def approx_standard_normal_cdf(x): + """ + A fast approximation of the cumulative distribution function of the + standard normal. + """ + return 0.5 * (1.0 + torch.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * torch.pow(x, 3)))) + + +def discretized_gaussian_log_likelihood(x, *, means, log_scales): + """ + Compute the log-likelihood of a Gaussian distribution discretizing to a + given image. + + :param x: the target images. It is assumed that this was uint8 values, + rescaled to the range [-1, 1]. + :param means: the Gaussian mean Tensor. + :param log_scales: the Gaussian log stddev Tensor. + :return: a tensor like x of log probabilities (in nats). + """ + assert x.shape == means.shape == log_scales.shape + centered_x = x - means + inv_stdv = torch.exp(-log_scales) + plus_in = inv_stdv * (centered_x + 1.0 / 255.0) + cdf_plus = approx_standard_normal_cdf(plus_in) + min_in = inv_stdv * (centered_x - 1.0 / 255.0) + cdf_min = approx_standard_normal_cdf(min_in) + log_cdf_plus = torch.log(cdf_plus.clamp(min=1e-12)) + log_one_minus_cdf_min = torch.log((1.0 - cdf_min).clamp(min=1e-12)) + cdf_delta = cdf_plus - cdf_min + log_probs = torch.where( + x < -0.999, + log_cdf_plus, + torch.where(x > 0.999, log_one_minus_cdf_min, torch.log(cdf_delta.clamp(min=1e-12))), + ) + assert log_probs.shape == x.shape + return log_probs + +def sum_except_batch(x, num_dims=1): + ''' + Sums all dimensions except the first. + + Args: + x: Tensor, shape (batch_size, ...) + num_dims: int, number of batch dims (default=1) + + Returns: + x_sum: Tensor, shape (batch_size,) + ''' + return x.reshape(*x.shape[:num_dims], -1).sum(-1) + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + +def ohe_to_categories(ohe, K): + K = torch.from_numpy(K) + indices = torch.cat([torch.zeros((1,)), K.cumsum(dim=0)], dim=0).int().tolist() + res = [] + for i in range(len(indices) - 1): + res.append(ohe[:, indices[i]:indices[i+1]].argmax(dim=1)) + return torch.stack(res, dim=1) + +def log_1_min_a(a): + return torch.log(1 - a.exp() + 1e-40) + + +def log_add_exp(a, b): + maximum = torch.max(a, b) + return maximum + torch.log(torch.exp(a - maximum) + torch.exp(b - maximum)) + +def exists(x): + return x is not None + +def extract(a, t, x_shape): + b, *_ = t.shape + t = t.to(a.device) + out = a.gather(-1, t) + while len(out.shape) < len(x_shape): + out = out[..., None] + return out.expand(x_shape) + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + +def log_categorical(log_x_start, log_prob): + return (log_x_start.exp() * log_prob).sum(dim=1) + +def index_to_log_onehot(x, num_classes): + onehots = [] + for i in range(len(num_classes)): + onehots.append(F.one_hot(x[:, i], num_classes[i])) + + x_onehot = torch.cat(onehots, dim=1) + log_onehot = torch.log(x_onehot.float().clamp(min=1e-30)) + return log_onehot + +def log_sum_exp_by_classes(x, slices): + device = x.device + res = torch.zeros_like(x) + for ixs in slices: + res[:, ixs] = torch.logsumexp(x[:, ixs], dim=1, keepdim=True) + + assert x.size() == res.size() + + return res + +@torch.jit.script +def log_sub_exp(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: + m = torch.maximum(a, b) + return torch.log(torch.exp(a - m) - torch.exp(b - m)) + m + +@torch.jit.script +def sliced_logsumexp(x, slices): + lse = torch.logcumsumexp( + torch.nn.functional.pad(x, [1, 0, 0, 0], value=-float('inf')), + dim=-1) + + slice_starts = slices[:-1] + slice_ends = slices[1:] + + slice_lse = log_sub_exp(lse[:, slice_ends], lse[:, slice_starts]) + slice_lse_repeated = torch.repeat_interleave( + slice_lse, + slice_ends - slice_starts, + dim=-1 + ) + return slice_lse_repeated + +def log_onehot_to_index(log_x): + return log_x.argmax(1) + +class FoundNANsError(BaseException): + """Found NANs during sampling""" + def __init__(self, message='Found NANs during sampling.'): + super(FoundNANsError, self).__init__(message) \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tools/convert_synth_to_csv.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tools/convert_synth_to_csv.py new file mode 100644 index 0000000000000000000000000000000000000000..be1bb6180758714a938e9c5c73a51029523e20f0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tools/convert_synth_to_csv.py @@ -0,0 +1,89 @@ +#!/usr/bin/env python3 +""" +Convert generated synthetic data from npy files to CSV format. +""" +import os +import sys +import numpy as np +import pandas as pd +import argparse + +def convert_to_csv(parent_dir, output_path=None): + """ + Convert generated synthetic data to CSV. + + Args: + parent_dir: Directory containing X_num_train.npy, X_cat_train.npy, y_train.npy + output_path: Output CSV file path (default: parent_dir/synth_train.csv) + """ + parent_dir = os.path.abspath(parent_dir) + + # Load npy files + x_num_path = os.path.join(parent_dir, 'X_num_train.npy') + x_cat_path = os.path.join(parent_dir, 'X_cat_train.npy') + y_path = os.path.join(parent_dir, 'y_train.npy') + + data_parts = [] + column_names = [] + + # Load numerical features + if os.path.exists(x_num_path): + X_num = np.load(x_num_path, allow_pickle=True) + print(f"Loaded X_num: shape {X_num.shape}") + data_parts.append(X_num) + # Create column names for numerical features + for i in range(X_num.shape[1]): + column_names.append(f'num_{i}') + + # Load categorical features + if os.path.exists(x_cat_path): + X_cat = np.load(x_cat_path, allow_pickle=True) + print(f"Loaded X_cat: shape {X_cat.shape}") + data_parts.append(X_cat) + # Create column names for categorical features + for i in range(X_cat.shape[1]): + column_names.append(f'cat_{i}') + + # Load target + if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + print(f"Loaded y: shape {y.shape}") + # Reshape if needed + if y.ndim == 1: + y = y.reshape(-1, 1) + data_parts.append(y) + column_names.append('y') + + if not data_parts: + raise ValueError(f"No data files found in {parent_dir}") + + # Concatenate all parts + data = np.hstack(data_parts) + print(f"Combined data shape: {data.shape}") + print(f"Number of columns: {len(column_names)}") + + # Create DataFrame + df = pd.DataFrame(data, columns=column_names) + + # Determine output path + if output_path is None: + output_path = os.path.join(parent_dir, 'synth_train.csv') + + # Save to CSV + df.to_csv(output_path, index=False) + print(f"[OK] Saved synthetic data to: {output_path}") + print(f"[OK] Total samples: {len(df)}, Total columns: {len(df.columns)}") + + # Print summary statistics + print("\n=== Data Summary ===") + print(df.describe()) + + return output_path + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Convert synthetic npy files to CSV') + parser.add_argument('parent_dir', type=str, help='Directory containing generated npy files') + parser.add_argument('--output', '-o', type=str, default=None, help='Output CSV file path (default: parent_dir/synth_train.csv)') + + args = parser.parse_args() + convert_to_csv(args.parent_dir, args.output) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tools/make_tabddpm_info.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tools/make_tabddpm_info.py new file mode 100644 index 0000000000000000000000000000000000000000..30acf64d04521bfa8fc1810c893395eed5bd57a4 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tools/make_tabddpm_info.py @@ -0,0 +1,97 @@ +import os, json +import numpy as np + +def load(path): + return np.load(path, allow_pickle=True) + +def main(data_dir: str): + # required files + req = [ + "X_num_train.npy","X_num_val.npy","X_num_test.npy", + "X_cat_train.npy","X_cat_val.npy","X_cat_test.npy", + "y_train.npy","y_val.npy","y_test.npy" + ] + for f in req: + p = os.path.join(data_dir, f) + if not os.path.exists(p): + raise FileNotFoundError(p) + + Xn_tr = load(os.path.join(data_dir,"X_num_train.npy")) + Xc_tr = load(os.path.join(data_dir,"X_cat_train.npy")) + y_tr = load(os.path.join(data_dir,"y_train.npy")) + + # basic dims + n_num = 0 if Xn_tr.ndim < 2 else int(Xn_tr.shape[1]) + n_cat = 0 if Xc_tr.ndim < 2 else int(Xc_tr.shape[1]) + + # infer task / y info + y_flat = y_tr.reshape(-1) + uniq = np.unique(y_flat) + # if y is integer and has few unique values, could be classification + is_int = np.issubdtype(y_flat.dtype, np.integer) + num_classes = int(len(uniq)) if is_int else 0 + + # determine task_type + if is_int and num_classes == 2: + task_type = "binclass" + elif is_int and num_classes > 2 and num_classes <= 100: + task_type = "multiclass" + else: + task_type = "regression" + + # cat sizes (per categorical column) + cat_sizes = [] + if n_cat > 0: + # compute max+1 per column (assume categories encoded 0..K-1) + for j in range(n_cat): + col = Xc_tr[:, j].reshape(-1) + if col.size == 0: + cat_sizes.append(0) + else: + mx = int(np.max(col)) + cat_sizes.append(mx + 1) + + # numeric stats (optional but useful) + num_stats = {} + if n_num > 0: + # mean/std/min/max over train numeric + num_stats = { + "mean": np.mean(Xn_tr, axis=0).tolist(), + "std": (np.std(Xn_tr, axis=0) + 1e-12).tolist(), + "min": np.min(Xn_tr, axis=0).tolist(), + "max": np.max(Xn_tr, axis=0).tolist(), + } + + # This repo expects info.json. Keep fields simple & robust. + info = { + "task_type": task_type, + "n_num_features": n_num, + "n_cat_features": n_cat, + "cat_sizes": cat_sizes, + "y_dtype": str(y_flat.dtype), + "y_unique_count": int(len(uniq)), + "y_unique_head": uniq[:20].tolist(), + # heuristics: user can override in config.toml + "is_classification_like": bool(is_int and len(uniq) <= 100), + "num_classes_like": num_classes, + } + + # write files + with open(os.path.join(data_dir, "info.json"), "w", encoding="utf-8") as f: + json.dump(info, f, ensure_ascii=False, indent=2) + + # some codepaths may look for these (harmless if unused) + with open(os.path.join(data_dir, "cat_sizes.json"), "w", encoding="utf-8") as f: + json.dump({"cat_sizes": cat_sizes}, f, ensure_ascii=False, indent=2) + + with open(os.path.join(data_dir, "num_stats.json"), "w", encoding="utf-8") as f: + json.dump(num_stats, f, ensure_ascii=False, indent=2) + + print("[OK] wrote:", os.path.join(data_dir,"info.json")) + print("[OK] n_num =", n_num, "n_cat =", n_cat, "cat_sizes =", cat_sizes) + print("[OK] y unique count =", len(uniq), "head =", uniq[:20]) + +if __name__ == "__main__": + data_dir = "data/Tab-Cate-1" + main(data_dir) + diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/abalone_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/abalone_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..07333ed0f7f4064d6f373bc86ce12455d3a9e76a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/abalone_cv.json @@ -0,0 +1,15 @@ +{ + "learning_rate": 0.028050502468157906, + "depth": 5, + "l2_leaf_reg": 7.780211394737271, + "bagging_temperature": 0.026696235942186064, + "leaf_estimation_iterations": 9, + "iterations": 2000, + "early_stopping_rounds": 50, + "od_pval": 0.001, + "task_type": "CPU", + "thread_count": 4, + "cat_features": [ + 7 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/adult_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/adult_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..f6a35af010c9e1391651f85381dec9d6e9cb434a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/adult_cv.json @@ -0,0 +1,22 @@ +{ + "learning_rate": 0.16886992997713726, + "depth": 3, + "l2_leaf_reg": 0.19334681185025449, + "bagging_temperature": 0.11959130879575816, + "leaf_estimation_iterations": 8, + "iterations": 2000, + "early_stopping_rounds": 50, + "od_pval": 0.001, + "task_type": "CPU", + "thread_count": 4, + "cat_features": [ + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/buddy_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/buddy_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..6df95ddcbdc0a4bee7495335b382bb0d9a04ae59 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/buddy_cv.json @@ -0,0 +1,19 @@ +{ + "learning_rate": 0.16147381808339628, + "depth": 4, + "l2_leaf_reg": 7.395339489195834, + "bagging_temperature": 0.6340360060041843, + "leaf_estimation_iterations": 10, + "iterations": 2000, + "early_stopping_rounds": 50, + "od_pval": 0.001, + "task_type": "CPU", + "thread_count": 4, + "cat_features": [ + 4, + 5, + 6, + 7, + 8 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/california_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/california_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..a89139fbab7c31f6712c8e11a4e5a04b602dc26f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/california_cv.json @@ -0,0 +1,13 @@ +{ + "learning_rate": 0.04790555955853721, + "depth": 9, + "l2_leaf_reg": 3.5239162123685057, + "bagging_temperature": 0.25057429631750866, + "leaf_estimation_iterations": 5, + "iterations": 2000, + "early_stopping_rounds": 50, + "od_pval": 0.001, + "task_type": "CPU", + "thread_count": 4, + "cat_features": [] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/cardio_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/cardio_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..383482f780eed9af778562b06069ccde7cf1a880 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/cardio_cv.json @@ -0,0 +1,20 @@ +{ + "learning_rate": 0.1257261760434331, + "depth": 4, + "l2_leaf_reg": 2.78475695259274, + "bagging_temperature": 0.03659915180663771, + "leaf_estimation_iterations": 2, + "iterations": 2000, + "early_stopping_rounds": 50, + "od_pval": 0.001, + "task_type": "CPU", + "thread_count": 4, + "cat_features": [ + 5, + 6, + 7, + 8, + 9, + 10 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/churn2_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/churn2_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..abf501bedd561502d2eec9c5082630593704ed9b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/churn2_cv.json @@ -0,0 +1,18 @@ +{ + "learning_rate": 0.4667069360390258, + "depth": 3, + "l2_leaf_reg": 8.856733942123162, + "bagging_temperature": 0.2955334069354449, + "leaf_estimation_iterations": 1, + "iterations": 2000, + "early_stopping_rounds": 50, + "od_pval": 0.001, + "task_type": "CPU", + "thread_count": 4, + "cat_features": [ + 7, + 8, + 9, + 10 + ] +} \ No newline at end of file diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/default_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/default_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..e4e5013a8fe34edd043c8edb558519cf04a1ff9e --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/default_cv.json @@ -0,0 +1,17 @@ +{ + "learning_rate": 0.2902756591396544, + "depth": 4, + "l2_leaf_reg": 4.408066401330523, + "bagging_temperature": 0.8052091766313958, + "leaf_estimation_iterations": 1, + "iterations": 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b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/higgs-small_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..295c64c0550d99d66a6b32eb6968d2081940f884 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/higgs-small_cv.json @@ -0,0 +1,13 @@ +{ + "learning_rate": 0.059421510534574236, + "depth": 8, + "l2_leaf_reg": 4.164640566364768, + "bagging_temperature": 0.7435082899382577, + "leaf_estimation_iterations": 4, + "iterations": 2000, + "early_stopping_rounds": 50, + "od_pval": 0.001, + "task_type": "CPU", + "thread_count": 4, + "cat_features": [] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/house_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/catboost/house_cv.json new file mode 100644 index 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b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/abalone_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..4aea9a9b85c1f0d51c105319836d11acb384dd5a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/abalone_cv.json @@ -0,0 +1,11 @@ +{ + "lr": 0.00011446054410937482, + "dropout": 0.0, + "weight_decay": 0.0, + "d_layers": [ + 1024, + 64, + 64, + 128 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/adult_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/adult_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..9f779fd6407bb9c97f34cdc38d750cd4b956b54f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/adult_cv.json @@ -0,0 +1,15 @@ +{ + "lr": 9.242295119548746e-05, + "dropout": 0.3309793397581198, + "weight_decay": 0.0, + "d_layers": [ + 128, + 1024, + 1024, + 1024, + 1024, + 1024, + 1024, + 64 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/buddy_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/buddy_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..94a7dd64f7d573af462c7cf4c831e0e05f66cd25 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/buddy_cv.json @@ -0,0 +1,9 @@ +{ + "lr": 0.0014494067174058204, + "dropout": 0.49739282118634903, + "weight_decay": 0.0022747364312818074, + "d_layers": [ + 1024, + 64 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/california_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/california_cv.json new file mode 100644 index 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a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/churn2_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/churn2_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..1ad54c6ebd7978d3a0a28d9b2a4e866f58d4a2ca --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/churn2_cv.json @@ -0,0 +1,8 @@ +{ + "lr": 0.0010705717937159068, + "dropout": 0.1011188507559055, + "weight_decay": 0.00010324752560189497, + "d_layers": [ + 256 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/default_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/default_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..b3dd4abcf463aa7cae791690c68d4fe4a175cc25 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/default_cv.json @@ -0,0 +1,12 @@ +{ + "lr": 0.0017806846926671652, + "dropout": 0.22881769257195214, + "weight_decay": 0.0003642138241603649, + "d_layers": [ + 256, + 64, + 64, + 64, + 512 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/diabetes_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/diabetes_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..d63ee708d422f74cfde27880d05ad95e133ee447 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/diabetes_cv.json @@ -0,0 +1,9 @@ +{ + "lr": 0.0001316285155465466, + "dropout": 0.30802808938889303, + "weight_decay": 0.0, + "d_layers": [ + 128, + 512 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/gesture_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/gesture_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..ae3a609d65d182f241a5dc8e83529de3a4226e87 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/gesture_cv.json @@ -0,0 +1,12 @@ +{ + "lr": 0.00035197525015908895, + "dropout": 0.47977604847882277, + "weight_decay": 0.0, + "d_layers": [ + 512, + 1024, + 1024, + 1024, + 256 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/higgs-small_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/higgs-small_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..1e505f6e2aa538a6f628b46de69e95e9818b48eb --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/higgs-small_cv.json @@ -0,0 +1,14 @@ +{ + "lr": 0.0003525741129259625, + "dropout": 0.40440530331802926, + "weight_decay": 4.629006319039983e-05, + "d_layers": [ + 256, + 256, + 256, + 256, + 256, + 256, + 256 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/house_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/house_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..846664792cef60b7fe02e340ea3f841afa3dce15 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/house_cv.json @@ -0,0 +1,9 @@ +{ + "lr": 0.00023746086306142492, + "dropout": 0.13652052941485932, + "weight_decay": 0.002508251489121031, + "d_layers": [ + 512, + 64 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/insurance_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/insurance_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..8e434217f7902a6cf27e98380a2b35ba04b28271 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/insurance_cv.json @@ -0,0 +1,11 @@ +{ + "lr": 7.427031901553865e-05, + "dropout": 0.0343495433930248, + "weight_decay": 0.0, + "d_layers": [ + 1024, + 64, + 64, + 128 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/king_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/king_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..363ebd50cb8fb8f0e9cf9443c31659d4076806a0 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/king_cv.json @@ -0,0 +1,9 @@ +{ + "lr": 0.00017320137120229096, + "dropout": 0.21013114283637715, + "weight_decay": 0.0092612705080437, + "d_layers": [ + 256, + 128 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/miniboone_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/miniboone_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..75c189b8a69672279044add5f59b114f1a4d5840 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/miniboone_cv.json @@ -0,0 +1,11 @@ +{ + "lr": 0.00013393788795448594, + "dropout": 0.38728766340853726, + "weight_decay": 0.0, + "d_layers": [ + 1024, + 256, + 256, + 128 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/wilt_cv.json b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/wilt_cv.json new file mode 100644 index 0000000000000000000000000000000000000000..0b94c027370b2c456f32379af256592ff4c77c4f --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime/tuned_models/mlp/wilt_cv.json @@ -0,0 +1,9 @@ +{ + "lr": 0.0007113363951987974, + "dropout": 0.2620400940479479, + "weight_decay": 0.0, + "d_layers": [ + 64, + 64 + ] +} diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r0.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..c3a1a7afcac21abbb5a4515da11a1adcd2a28b43 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r0.py @@ -0,0 +1,75 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +runtime_root = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" + +if not os.path.exists(runtime_root): + def _ignore(_, names): + skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"} + return [n for n in names if n in skip or n.endswith(".pyc")] + import shutil + shutil.copytree(tabddpm_root, runtime_root, ignore=_ignore) + +env = os.environ.copy() +env["PYTHONPATH"] = runtime_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(runtime_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 60800 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r0.toml", + "--sample"], + cwd=runtime_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r1.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r1.py new file mode 100644 index 0000000000000000000000000000000000000000..cc40ba307965c57e55d2bdc2c32e7e879e2daff5 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r1.py @@ -0,0 +1,75 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +runtime_root = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" + +if not os.path.exists(runtime_root): + def _ignore(_, names): + skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"} + return [n for n in names if n in skip or n.endswith(".pyc")] + import shutil + shutil.copytree(tabddpm_root, runtime_root, ignore=_ignore) + +env = os.environ.copy() +env["PYTHONPATH"] = runtime_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(runtime_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 60800 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r1.toml", + "--sample"], + cwd=runtime_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r2.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r2.py new file mode 100644 index 0000000000000000000000000000000000000000..16d09c224d695f8a57ee483a827e7914a43d586d --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r2.py @@ -0,0 +1,75 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +runtime_root = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" + +if not os.path.exists(runtime_root): + def _ignore(_, names): + skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"} + return [n for n in names if n in skip or n.endswith(".pyc")] + import shutil + shutil.copytree(tabddpm_root, runtime_root, ignore=_ignore) + +env = os.environ.copy() +env["PYTHONPATH"] = runtime_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(runtime_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 60800 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r2.toml", + "--sample"], + cwd=runtime_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r3.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r3.py new file mode 100644 index 0000000000000000000000000000000000000000..4fbaf923c41bc9aec048fb7099b29e1b41f48c2b --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r3.py @@ -0,0 +1,75 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +runtime_root = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" + +if not os.path.exists(runtime_root): + def _ignore(_, names): + skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"} + return [n for n in names if n in skip or n.endswith(".pyc")] + import shutil + shutil.copytree(tabddpm_root, runtime_root, ignore=_ignore) + +env = os.environ.copy() +env["PYTHONPATH"] = runtime_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(runtime_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 60800 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r3.toml", + "--sample"], + cwd=runtime_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r4.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r4.py new file mode 100644 index 0000000000000000000000000000000000000000..90276b4c03a1a4a6c8b17dd6da15eeb99b949790 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r4.py @@ -0,0 +1,75 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +runtime_root = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" + +if not os.path.exists(runtime_root): + def _ignore(_, names): + skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"} + return [n for n in names if n in skip or n.endswith(".pyc")] + import shutil + shutil.copytree(tabddpm_root, runtime_root, ignore=_ignore) + +env = os.environ.copy() +env["PYTHONPATH"] = runtime_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(runtime_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 60800 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r4.toml", + "--sample"], + cwd=runtime_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r5.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r5.py new file mode 100644 index 0000000000000000000000000000000000000000..2407ecdab7687ebafa1394c65141af8ffdcf8b27 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r5.py @@ -0,0 +1,75 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +runtime_root = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" + +if not os.path.exists(runtime_root): + def _ignore(_, names): + skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"} + return [n for n in names if n in skip or n.endswith(".pyc")] + import shutil + shutil.copytree(tabddpm_root, runtime_root, ignore=_ignore) + +env = os.environ.copy() +env["PYTHONPATH"] = runtime_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(runtime_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 60800 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r5.toml", + "--sample"], + cwd=runtime_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r6.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r6.py new file mode 100644 index 0000000000000000000000000000000000000000..9782e64e111c529121c7d4474afb0eca029558db --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r6.py @@ -0,0 +1,75 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +runtime_root = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" + +if not os.path.exists(runtime_root): + def _ignore(_, names): + skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"} + return [n for n in names if n in skip or n.endswith(".pyc")] + import shutil + shutil.copytree(tabddpm_root, runtime_root, ignore=_ignore) + +env = os.environ.copy() +env["PYTHONPATH"] = runtime_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(runtime_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 60800 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r6.toml", + "--sample"], + cwd=runtime_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r7.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r7.py new file mode 100644 index 0000000000000000000000000000000000000000..3d3c6b083504bb745c9377ebaeff025986e31bc3 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r7.py @@ -0,0 +1,75 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +runtime_root = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" + +if not os.path.exists(runtime_root): + def _ignore(_, names): + skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"} + return [n for n in names if n in skip or n.endswith(".pyc")] + import shutil + shutil.copytree(tabddpm_root, runtime_root, ignore=_ignore) + +env = os.environ.copy() +env["PYTHONPATH"] = runtime_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(runtime_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 60800 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r7.toml", + "--sample"], + cwd=runtime_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r8.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r8.py new file mode 100644 index 0000000000000000000000000000000000000000..da5bc8df06120579eab01756662806224fb21c9a --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_sample_r8.py @@ -0,0 +1,75 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +runtime_root = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" + +if not os.path.exists(runtime_root): + def _ignore(_, names): + skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"} + return [n for n in names if n in skip or n.endswith(".pyc")] + import shutil + shutil.copytree(tabddpm_root, runtime_root, ignore=_ignore) + +env = os.environ.copy() +env["PYTHONPATH"] = runtime_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(runtime_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 60800 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r8.toml", + "--sample"], + cwd=runtime_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/tabddpm-n18-60800-20260511_002941.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_train.py b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..238febcea39ce444423e1fcf857ce6204a422944 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_train.py @@ -0,0 +1,42 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +runtime_root = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/_tabddpm_runtime" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" + +def _ignore(_, names): + skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"} + return [n for n in names if n in skip or n.endswith(".pyc")] + +import shutil +shutil.rmtree(runtime_root, ignore_errors=True) +shutil.copytree(tabddpm_root, runtime_root, ignore=_ignore) + +env = os.environ.copy() +env["PYTHONPATH"] = runtime_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(runtime_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/config.toml", + "--train"], + cwd=runtime_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..cfa8a013daec106a875ed2256b91b8d59966bef3 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/data" +model_type = "mlp" +num_numerical_features = 149 +device = "cuda:0" + +[model_params] +d_in = 170 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 256 +seed = 0 diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r0.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..e4765ade1c20c62a1f3491c1b6bbe26fc6bd1214 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/data" +model_type = "mlp" +num_numerical_features = 149 +device = "cuda:0" + +[model_params] +d_in = 170 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 60800 +batch_size = 256 +seed = 0 diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r1.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r1.toml new file mode 100644 index 0000000000000000000000000000000000000000..dbad4cf7e7b074cd1f067fa809bafddaa0139875 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r1.toml @@ -0,0 +1,39 @@ +seed = 0 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b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r2.toml new file mode 100644 index 0000000000000000000000000000000000000000..356d56ebe1e13d7ce40e4c44f8d596f23d4ee2c2 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r2.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/data" +model_type = "mlp" +num_numerical_features = 149 +device = "cuda:0" + +[model_params] +d_in = 170 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 60800 +batch_size = 64 +seed = 2 diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r3.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r3.toml new file mode 100644 index 0000000000000000000000000000000000000000..9e766726b913d2fd101d98c54cd760c3c5721231 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r3.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/data" +model_type = "mlp" +num_numerical_features = 149 +device = "cuda:0" + +[model_params] +d_in = 170 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 60800 +batch_size = 32 +seed = 3 diff --git a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r4.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r4.toml new file mode 100644 index 0000000000000000000000000000000000000000..057f87707b2b2d1d45957a02144be19d969a80b1 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r4.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/output" +real_data_path = 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a/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r6.toml b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r6.toml new file mode 100644 index 0000000000000000000000000000000000000000..697932b24ff182d974945d53b95ba8a1eb16a006 --- /dev/null +++ b/syntheticFail/n18/tabddpm/tabddpm-n18-20260511_002808/config_sample_20260511_002941_r6.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n18/tabddpm/tabddpm-n18-20260511_002808/data" +model_type = "mlp" +num_numerical_features = 149 +device = "cuda:0" + +[model_params] +d_in = 170 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + 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