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SynthData0523/hyper_parameter_tuning/n11/tabdiff/runs/tabdiff-n11-20260521_035804/_tabdiff_runtime/tabdiff/modules/transformer.py

@@ -0,0 +1,258 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.init as nn_init
+import torch.nn.functional as F
+from torch import Tensor
+
+import math
+
+class Tokenizer(nn.Module):
+
+    def __init__(self, d_numerical, categories, d_token, bias):
+        super().__init__()
+        if categories is None:
+            d_bias = d_numerical
+            self.category_offsets = None
+            self.category_embeddings = None
+        else:
+            d_bias = d_numerical + len(categories)
+            category_offsets = torch.tensor([0] + list(categories[:-1])).cumsum(0)
+            self.register_buffer('category_offsets', category_offsets)
+            self.cat_weight = nn.Parameter(Tensor(sum(categories), d_token))
+            nn.init.kaiming_uniform_(self.cat_weight, a=math.sqrt(5))
+
+        # take [CLS] token into account
+        self.weight = nn.Parameter(Tensor(d_numerical + 1, d_token))
+        self.bias = nn.Parameter(Tensor(d_bias, d_token)) if bias else None
+        # The initialization is inspired by nn.Linear
+        nn_init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+        if self.bias is not None:
+            nn_init.kaiming_uniform_(self.bias, a=math.sqrt(5))
+
+    @property
+    def n_tokens(self):
+        return len(self.weight) + (
+            0 if self.category_offsets is None else len(self.category_offsets)
+        )
+
+    def forward(self, x_num, x_cat):
+        x_some = x_num if x_cat is None else x_cat
+        assert x_some is not None
+        x_num = torch.cat(
+            [torch.ones(len(x_some), 1, device=x_some.device)]  # [CLS]
+            + ([] if x_num is None else [x_num]),
+            dim=1,
+        )
+    
+        x = self.weight[None] * x_num[:, :, None]
+
+        if x_cat is not None:
+            for start, end in zip(self.category_offsets, torch.cat([self.category_offsets[1:], torch.tensor([x_cat.shape[1]], device=x_cat.device)])):
+                if start < end:
+                    x = torch.cat(
+                        [x, x_cat[:, start:end].unsqueeze(1) @ self.cat_weight[start:end][None]],
+                        dim=1,
+                    )
+        if self.bias is not None:
+            bias = torch.cat(
+                [
+                    torch.zeros(1, self.bias.shape[1], device=x.device),
+                    self.bias,
+                ]
+            )
+            x = x + bias[None]
+
+        return x
+
+
+class MultiheadAttention(nn.Module):
+    def __init__(self, d, n_heads, dropout, initialization = 'kaiming'):
+
+        if n_heads > 1:
+            assert d % n_heads == 0
+        assert initialization in ['xavier', 'kaiming']
+
+        super().__init__()
+        self.W_q = nn.Linear(d, d)
+        self.W_k = nn.Linear(d, d)
+        self.W_v = nn.Linear(d, d)
+        self.W_out = nn.Linear(d, d) if n_heads > 1 else None
+        self.n_heads = n_heads
+        self.dropout = nn.Dropout(dropout) if dropout else None
+
+        for m in [self.W_q, self.W_k, self.W_v]:
+            if initialization == 'xavier' and (n_heads > 1 or m is not self.W_v):
+                # gain is needed since W_qkv is represented with 3 separate layers
+                nn_init.xavier_uniform_(m.weight, gain=1 / math.sqrt(2))
+            nn_init.zeros_(m.bias)
+        if self.W_out is not None:
+            nn_init.zeros_(self.W_out.bias)
+
+    def _reshape(self, x):
+        batch_size, n_tokens, d = x.shape
+        d_head = d // self.n_heads
+        return (
+            x.reshape(batch_size, n_tokens, self.n_heads, d_head)
+            .transpose(1, 2)
+            .reshape(batch_size * self.n_heads, n_tokens, d_head)
+        )
+
+    def forward(self, x_q, x_kv, key_compression = None, value_compression = None):
+  
+        q, k, v = self.W_q(x_q), self.W_k(x_kv), self.W_v(x_kv)
+        for tensor in [q, k, v]:
+            assert tensor.shape[-1] % self.n_heads == 0
+        if key_compression is not None:
+            assert value_compression is not None
+            k = key_compression(k.transpose(1, 2)).transpose(1, 2)
+            v = value_compression(v.transpose(1, 2)).transpose(1, 2)
+        else:
+            assert value_compression is None
+
+        batch_size = len(q)
+        d_head_key = k.shape[-1] // self.n_heads
+        d_head_value = v.shape[-1] // self.n_heads
+        n_q_tokens = q.shape[1]
+
+        q = self._reshape(q)
+        k = self._reshape(k)
+
+        a = q @ k.transpose(1, 2)
+        b = math.sqrt(d_head_key)
+        attention = F.softmax(a/b , dim=-1)
+
+        
+        if self.dropout is not None:
+            attention = self.dropout(attention)
+        x = attention @ self._reshape(v)
+        x = (
+            x.reshape(batch_size, self.n_heads, n_q_tokens, d_head_value)
+            .transpose(1, 2)
+            .reshape(batch_size, n_q_tokens, self.n_heads * d_head_value)
+        )
+        if self.W_out is not None:
+            x = self.W_out(x)
+
+        return x
+        
+class Transformer(nn.Module):
+
+    def __init__(
+        self,
+        n_layers: int,
+        d_token: int,
+        n_heads: int,
+        d_out: int,
+        d_ffn_factor: int,
+        attention_dropout = 0.0,
+        ffn_dropout = 0.0,
+        residual_dropout = 0.0,
+        activation = 'relu',
+        prenormalization = True,
+        initialization = 'kaiming',      
+    ):
+        super().__init__()
+
+        def make_normalization():
+            return nn.LayerNorm(d_token)
+
+        d_hidden = int(d_token * d_ffn_factor)
+        self.layers = nn.ModuleList([])
+        for layer_idx in range(n_layers):
+            layer = nn.ModuleDict(
+                {
+                    'attention': MultiheadAttention(
+                        d_token, n_heads, attention_dropout, initialization
+                    ),
+                    'linear0': nn.Linear(
+                        d_token, d_hidden
+                    ),
+                    'linear1': nn.Linear(d_hidden, d_token),
+                    'norm1': make_normalization(),
+                }
+            )
+            if not prenormalization or layer_idx:
+                layer['norm0'] = make_normalization()
+   
+            self.layers.append(layer)
+
+        self.activation = nn.ReLU()
+        self.last_activation = nn.ReLU()
+        # self.activation = lib.get_activation_fn(activation)
+        # self.last_activation = lib.get_nonglu_activation_fn(activation)
+        self.prenormalization = prenormalization
+        self.last_normalization = make_normalization() if prenormalization else None
+        self.ffn_dropout = ffn_dropout
+        self.residual_dropout = residual_dropout
+        self.head = nn.Linear(d_token, d_out)
+
+
+    def _start_residual(self, x, layer, norm_idx):
+        x_residual = x
+        if self.prenormalization:
+            norm_key = f'norm{norm_idx}'
+            if norm_key in layer:
+                x_residual = layer[norm_key](x_residual)
+        return x_residual
+
+    def _end_residual(self, x, x_residual, layer, norm_idx):
+        if self.residual_dropout:
+            x_residual = F.dropout(x_residual, self.residual_dropout, self.training)
+        x = x + x_residual
+        if not self.prenormalization:
+            x = layer[f'norm{norm_idx}'](x)
+        return x
+
+    def forward(self, x):
+        for layer_idx, layer in enumerate(self.layers):
+            is_last_layer = layer_idx + 1 == len(self.layers)
+
+            x_residual = self._start_residual(x, layer, 0)
+            x_residual = layer['attention'](
+                # for the last attention, it is enough to process only [CLS]
+                x_residual,
+                x_residual,
+            )
+
+            x = self._end_residual(x, x_residual, layer, 0)
+
+            x_residual = self._start_residual(x, layer, 1)
+            x_residual = layer['linear0'](x_residual)
+            x_residual = self.activation(x_residual)
+            if self.ffn_dropout:
+                x_residual = F.dropout(x_residual, self.ffn_dropout, self.training)
+            x_residual = layer['linear1'](x_residual)
+            x = self._end_residual(x, x_residual, layer, 1)
+        return x
+
+
+class Reconstructor(nn.Module):
+    def __init__(self, d_numerical, categories, d_token):
+        super(Reconstructor, self).__init__()
+
+        self.d_numerical = d_numerical
+        self.categories = categories
+        self.d_token = d_token
+        
+        self.weight = nn.Parameter(Tensor(d_numerical, d_token))  
+        nn.init.xavier_uniform_(self.weight, gain=1 / math.sqrt(2))
+        self.cat_recons = nn.ModuleList()
+
+        for d in categories:
+            recon = nn.Linear(d_token, d)
+            nn.init.xavier_uniform_(recon.weight, gain=1 / math.sqrt(2))
+            self.cat_recons.append(recon)
+
+    def forward(self, h):
+        h_num  = h[:, :self.d_numerical]
+        h_cat  = h[:, self.d_numerical:]
+
+        recon_x_num = torch.mul(h_num, self.weight.unsqueeze(0)).sum(-1)
+        recon_x_cat = []
+
+        for i, recon in enumerate(self.cat_recons):
+      
+            recon_x_cat.append(recon(h_cat[:, i]))
+
+        return recon_x_num, recon_x_cat

SynthData0523/hyper_parameter_tuning/n11/tabdiff/runs/tabdiff-n11-20260521_035804/_tabdiff_runtime/tabdiff/trainer.py

@@ -0,0 +1,657 @@
+import os
+import glob
+import time
+import torch
+from torch.optim.lr_scheduler import ReduceLROnPlateau
+import numpy as np
+import pandas as pd
+import json
+
+from copy import deepcopy
+
+from utils_train import update_ema
+
+from tqdm import tqdm
+
+BAR = "=============="
+def print_with_bar(log_msg):
+    log_msg = BAR + log_msg + BAR
+    if "End" in log_msg:
+         log_msg += "\n"
+    print(log_msg)
+
+class Trainer:
+    def __init__(
+            self, diffusion, train_iter, dataset, test_dataset,  metrics, logger, 
+            lr, weight_decay,
+            steps, batch_size, check_val_every,
+            sample_batch_size, model_save_path, result_save_path,
+            num_samples_to_generate=None,
+            lr_scheduler='reduce_lr_on_plateau',
+            reduce_lr_patience=100, factor=0.9, 
+            ema_decay=0.997,
+            closs_weight_schedule = "fixed",
+            c_lambda = 1.0,
+            d_lambda = 1.0,
+            device=torch.device('cuda:1'),
+            ckpt_path = None,
+            y_only=False,
+            **kwargs
+    ):
+        self.y_only = y_only
+        self.diffusion = diffusion
+        self.ema_model = deepcopy(self.diffusion._denoise_fn)
+        for param in self.ema_model.parameters():
+            param.detach_()
+        self.ema_num_schedule = deepcopy(self.diffusion.num_schedule)
+        for param in self.ema_num_schedule.parameters():
+            param.detach_()
+        self.ema_cat_schedule = deepcopy(self.diffusion.cat_schedule)
+        for param in self.ema_cat_schedule.parameters():
+            param.detach_()
+
+        self.train_iter = train_iter
+        self.dataset = dataset
+        self.test_dataset = test_dataset
+        self.steps = steps
+        self.init_lr = lr
+        self.optimizer = torch.optim.AdamW(self.diffusion.parameters(), lr=lr, weight_decay=weight_decay)
+        self.ema_decay = ema_decay
+        self.lr_scheduler = lr_scheduler
+        # PyTorch >= 2.8: ReduceLROnPlateau no longer accepts `verbose=`
+        self.scheduler = ReduceLROnPlateau(
+            self.optimizer, mode="min", factor=factor, patience=reduce_lr_patience
+        )
+        self.closs_weight_schedule = closs_weight_schedule
+        self.c_lambda = c_lambda
+        self.d_lambda = d_lambda
+
+        self.batch_size = batch_size
+        self.sample_batch_size = sample_batch_size
+        self.num_samples_to_generate = num_samples_to_generate
+        self.metrics = metrics
+        self.logger = logger
+        self.check_val_every = check_val_every
+        
+        self.device = device
+        self.model_save_path = model_save_path
+        self.result_save_path = result_save_path
+        self.ckpt_path = ckpt_path
+        if self.ckpt_path is not None:
+            state_dicts = torch.load(self.ckpt_path, map_location=self.device)
+            self.diffusion._denoise_fn.load_state_dict(state_dicts['denoise_fn'])
+            self.diffusion.num_schedule.load_state_dict(state_dicts['num_schedule'])
+            self.diffusion.cat_schedule.load_state_dict(state_dicts['cat_schedule'])   
+            print(f"Weights are loaded from {self.ckpt_path}")     
+        
+        self.curr_epoch = int(os.path.basename(self.ckpt_path).split('_')[-1].split('.')[0]) if self.ckpt_path is not None else 0
+
+    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, closs_weight, dloss_weight):
+        x = x.to(self.device)
+        
+        self.diffusion.train()
+
+        self.optimizer.zero_grad()
+
+        dloss, closs = self.diffusion.mixed_loss(x)
+
+        loss = dloss_weight * dloss + closs_weight * closs
+        loss.backward()
+        self.optimizer.step()
+
+        return dloss, closs
+    
+    def compute_loss(self):      # eval loss is not weighted
+        curr_dloss = 0.0
+        curr_closs = 0.0
+        curr_count = 0
+        data_iter = self.train_iter
+        for batch in data_iter:
+            x = batch.float().to(self.device)
+            self.diffusion.eval()
+            with torch.no_grad():
+                batch_dloss, batch_closs = self.diffusion.mixed_loss(x)
+            curr_dloss += batch_dloss.item() * len(x)
+            curr_closs += batch_closs.item() * len(x)
+            curr_count += len(x)
+        mloss = np.around(curr_dloss / curr_count, 4)
+        gloss = np.around(curr_closs / curr_count, 4)
+        return mloss, gloss
+    
+    def run_loop(self):
+        patience = 0
+        closs_weight, dloss_weight = self.c_lambda, self.d_lambda
+        best_loss = np.inf
+        best_ema_loss = np.inf
+        best_val_loss = np.inf
+        start_time = time.time()
+        print_with_bar(f"Starting Trainin Loop, total number of epoch = {self.steps}")
+        # Set up wandb's step metric
+        self.logger.define_metric("epoch")
+        self.logger.define_metric("*", step_metric="epoch")
+        
+        start_epoch = self.curr_epoch
+        if start_epoch > 0:
+            print_with_bar(f"Resuming training from epoch {start_epoch}, with validation check every {self.check_val_every} epoches")
+        for epoch in range (start_epoch, self.steps):
+            self.curr_epoch = epoch+1
+            # Set up pbar
+            pbar = tqdm(self.train_iter, total=len(self.train_iter))
+            pbar.set_description(f"Epoch {epoch+1}/{self.steps}")
+            
+            # Compute the loss weights
+            if self.closs_weight_schedule == "fixed":
+                pass
+            elif self.closs_weight_schedule == "anneal":
+                frac_done = epoch / self.steps
+                closs_weight = self.c_lambda * (1 - frac_done)
+            else:
+                raise NotImplementedError(f"The continuous loss weight schedule {self.closs_weight_schedule} is not implemneted")
+
+            # Training Step
+            curr_dloss = 0.0
+            curr_closs = 0.0
+            curr_count = 0
+            curr_lr = self.optimizer.param_groups[0]['lr']
+            for batch in pbar:
+                x = batch.float().to(self.device)
+                batch_dloss, batch_closs = self._run_step(x, closs_weight, dloss_weight)
+                curr_dloss += batch_dloss.item() * len(x)
+                curr_closs += batch_closs.item() * len(x)
+                curr_count += len(x)
+                pbar.set_postfix({
+                    "lr": curr_lr,
+                    "DLoss": np.around(curr_dloss/curr_count, 4),
+                    "CLoss": np.around(curr_closs/curr_count, 4),
+                    "TotalLoss": np.around((curr_dloss + curr_closs)/curr_count, 4),
+                    "closs_weight": closs_weight,
+                    "dloss_weight": dloss_weight,
+                })
+                
+            # Log training Loss
+            log_dict = {}
+            mloss = np.around(curr_dloss / curr_count, 4)
+            gloss = np.around(curr_closs / curr_count, 4)
+            total_loss = mloss + gloss
+            if np.isnan(gloss):
+                    print('Finding Nan in gaussian loss')
+                    break
+            loss_dict = {
+                "epoch": epoch + 1,
+                "lr": curr_lr,
+                "closs_weight": closs_weight,
+                "dloss_weight": dloss_weight,
+                "loss/c_loss": gloss,
+                "loss/d_loss": mloss,
+                "loss/total_loss": total_loss
+            }
+            log_dict.update(loss_dict)
+            
+            # Log the learned noise schedules for numerical dimensions
+            if self.dataset.d_numerical > 0:    # numerical data is not empty
+                num_noise_dict = {}
+                if self.diffusion.num_schedule.rho().dim() == 0:   # non-learnable num schedule
+                    num_noise_dict = {"num_noise/rho": self.diffusion.num_schedule.rho().item()}
+                else:
+                    num_noise_dict = {f"num_noise/rho_col_{i}": value.item() for i, value in enumerate(self.diffusion.num_schedule.rho())}
+                log_dict.update(num_noise_dict)            
+
+            # Log the learned noise schedules for categlrical dimensions
+            if len(self.dataset.categories) > 0:    # categorical data is not empty
+                cat_noise_dict = {}
+                if self.diffusion.cat_schedule.k().dim() == 0:   # non-learnable cat schedule
+                    cat_noise_dict = {"cat_noise/k": self.diffusion.cat_schedule.k().item()}
+                else:
+                    cat_noise_dict = {f"cat_noise/k_col_{i}": value.item() for i, value in enumerate(self.diffusion.cat_schedule.k())}
+                log_dict.update(cat_noise_dict)
+            
+            # Adjust learning rate
+            if self.lr_scheduler == 'reduce_lr_on_plateau':
+                self.scheduler.step(total_loss)
+            elif  self.lr_scheduler == 'anneal':
+                self._anneal_lr(epoch)
+            elif self.lr_scheduler == 'fixed':
+                pass
+            else:
+                raise NotImplementedError(f"LR scheduler with name '{self.lr_scheduler}' is not implemented")
+            
+            # Update EMA models
+            update_ema(self.ema_model.parameters(), self.diffusion._denoise_fn.parameters(), rate=self.ema_decay)
+            update_ema(self.ema_num_schedule.parameters(), self.diffusion.num_schedule.parameters(), rate=self.ema_decay)
+            update_ema(self.ema_cat_schedule.parameters(), self.diffusion.cat_schedule.parameters(), rate=self.ema_decay)
+
+            # Save ckpt base on the best training loss
+            if total_loss < best_loss and self.curr_epoch > 4000:
+                best_loss = total_loss
+                to_remove = glob.glob(os.path.join(self.model_save_path, f"best_model_*"))
+                if to_remove:
+                    os.remove(to_remove[0])
+                state_dicts = {
+                    'denoise_fn': self.diffusion._denoise_fn.state_dict(), 
+                    'num_schedule':self.diffusion.num_schedule.state_dict(), 
+                    'cat_schedule': self.diffusion.cat_schedule.state_dict(),
+                }
+                torch.save(state_dicts, os.path.join(self.model_save_path, f'best_model_{np.round(total_loss,4)}_{epoch+1}.pt'))
+                patience = 0
+            else:
+                patience += 1   # increment patience if best loss is not surpassed
+            
+            # Compute and log EMA model loss
+            curr_model, curr_num_schedule, curr_cat_schedule = self.to_ema_model()
+            ema_mloss, ema_gloss = self.compute_loss()
+            self.to_model(curr_model, curr_num_schedule, curr_cat_schedule)
+            ema_total_loss = ema_mloss + ema_gloss
+            ema_loss_dict = {
+                "ema_loss/c_loss": ema_gloss,
+                "ema_loss/d_loss": ema_mloss,
+                "ema_loss/total_loss": ema_total_loss
+            }
+            
+            # Save the best ema ckpt
+            if ema_total_loss < best_ema_loss and self.curr_epoch > 4000:
+                best_ema_loss = ema_total_loss
+                to_remove = glob.glob(os.path.join(self.model_save_path, f"best_ema_model_*"))
+                if to_remove:
+                    os.remove(to_remove[0])
+                state_dicts = {
+                    'denoise_fn': self.ema_model.state_dict(), 
+                    'num_schedule':self.ema_num_schedule.state_dict(), 
+                    'cat_schedule': self.ema_cat_schedule.state_dict(),
+                }
+                torch.save(state_dicts, os.path.join(self.model_save_path, f'best_ema_model_{np.round(ema_total_loss,4)}_{epoch+1}.pt'))
+            
+            # Evaluate Sample Quality
+            if (epoch+1) % self.check_val_every == 0:
+                state_dicts = {
+                    'denoise_fn': self.diffusion._denoise_fn.state_dict(), 
+                    'num_schedule':self.diffusion.num_schedule.state_dict(), 
+                    'cat_schedule': self.diffusion.cat_schedule.state_dict(),
+                }
+                torch.save(state_dicts, os.path.join(self.model_save_path, f'model_{epoch+1}.pt'))
+                
+                print_with_bar(f"Routine Generation Evaluation every {self.check_val_every}, currently at epoch #{epoch+1}, wiht total_loss={total_loss}.")
+                # 适配器训练：容器内无 Chrome，跳过 Kaleido 密度图
+                _plot_density = os.environ.get("TABDIFF_ADAPTER_TRAIN", "").strip().lower() not in ("1", "true", "yes")
+                out_metrics, _, _ = self.evaluate_generation(save_metric_details=True, plot_density=_plot_density)
+                log_dict.update(out_metrics)
+                print(f"Eval Resutls of the Non-EMA model:\n {out_metrics}")
+
+                # Evaluate the EMA model
+                torch.save(self.ema_model.state_dict(), os.path.join(self.model_save_path, f'ema_model_{epoch+1}.pt'))
+                ema_out_metrics, _, _ = self.evaluate_generation(ema=True, save_metric_details=True, plot_density=_plot_density)
+                log_dict.update({
+                    "ema": ema_out_metrics,
+                })
+                print(f"Eval Resutls of the EMA model:\n {ema_out_metrics}")
+            
+            # Submit logs
+            self.logger.log(log_dict)
+
+        end_time = time.time()
+        print_with_bar(f"Ending Trainnig Loop, totoal training time = {end_time - start_time}")
+        self.logger.log({
+            'training_time': end_time - start_time
+        })
+        
+    def report_test(self, num_runs):
+        save_dir = self.result_save_path
+        
+        shape_ = []
+        trend_ = []
+        mle_ = []
+        c2st_ = []
+        for i in range(num_runs):
+            print_with_bar(f"GENERAL Evaluation Run {i}")
+            out_metrics, extras, syn_df = self.evaluate_generation()
+            print(f"Results of Run {i} are: \n{out_metrics}")
+            shape_.append(out_metrics["density/Shape"])
+            trend_.append(out_metrics["density/Trend"])
+            mle_.append(out_metrics["mle"])
+            c2st_.append(out_metrics["c2st"])
+            # Save samples for quality evaluation
+            save_path = os.path.join(save_dir, "all_samples")
+            if not os.path.exists(save_path):
+                os.makedirs(save_path)
+            syn_df.to_csv(os.path.join(save_path, f"samples_{i}.csv"), index=False)
+
+        shape_ = np.array(shape_)
+        trend_ = np.array(trend_)
+        mle_ = np.array(mle_)
+        c2st_ = np.array(c2st_)
+        
+        shape_error = (1 - shape_)*100
+        trend_error = (1 - trend_)*100
+        c2st_percent = c2st_ * 100
+        
+        all_results = pd.DataFrame({
+            "shape": shape_error,
+            "trend": trend_error,
+            "mle": mle_,
+            "c2st": c2st_percent,
+        })
+        avg = all_results.mean(axis=0).round(3)
+        std = all_results.std(axis=0).round(3)
+        avg_std = pd.concat([avg, std], axis=1, ignore_index=True)
+        avg_std.columns = ["avg", "std"]
+        avg_std.index = [
+            "shape", 
+            "trend", 
+            "mle", 
+            "c2st", 
+        ]
+        
+        # Savings
+        all_results.to_csv(f"{save_dir}/all_results.csv", index=True)
+        avg_std.to_csv(f"{save_dir}/avg_std.csv", index=True)
+        print_with_bar(f"The AVG over {num_runs} runs are: \n{avg_std}")
+        
+    def report_test_dcr(self, num_runs):
+        save_dir = self.result_save_path
+        
+        dcr_ = []
+        dcr_real_ = []
+        dcr_test_ = []
+        for i in range(num_runs):
+            print_with_bar(f"DCR Evaluation Run {i}")
+            out_metrics, extras, syn_df = self.evaluate_generation()
+            print(f"Results of Run {i} are: \n{out_metrics}")
+            dcr_.append(out_metrics["dcr"])
+            dcr_real_.append(extras["dcr_real"])
+            dcr_test_.append(extras["dcr_test"])
+            save_path = os.path.join(save_dir, "all_samples")
+            if not os.path.exists(save_path):
+                os.makedirs(save_path)
+            syn_df.to_csv(os.path.join(save_path, f"samples_{i}.csv"), index=False)
+
+        dcr_ = np.array(dcr_)
+        
+        dcr_percent = dcr_ * 100
+        
+        all_results = pd.DataFrame({
+            "dcr": dcr_percent,
+        })
+        avg = all_results.mean(axis=0).round(3)
+        std = all_results.std(axis=0).round(3)
+        avg_std = pd.concat([avg, std], axis=1, ignore_index=True)
+        avg_std.columns = ["avg", "std"]
+        avg_std.index = [
+            "dcr", 
+        ]
+        
+        # Savings
+        all_results.to_csv(f"{save_dir}/all_results.csv", index=True)
+        avg_std.to_csv(f"{save_dir}/avg_std.csv", index=True)
+        dcr_real = np.concatenate(dcr_real_, axis=0)
+        dcr_test = np.concatenate(dcr_test_, axis=0)
+        dcr_df = pd.DataFrame({
+            "dcr_real": dcr_real,
+            "dcr_test": dcr_test
+        })
+        dcr_df.to_csv(f"{save_dir}/dcr.csv", index=False)
+        
+        print_with_bar(f"The AVG over {num_runs} runs are: \n{avg_std}")
+        
+    def test(self):
+        _plot_density = os.environ.get("TABDIFF_ADAPTER_TRAIN", "").strip().lower() not in ("1", "true", "yes")
+        out_metrics, _, _ = self.evaluate_generation(save_metric_details=True, plot_density=_plot_density)
+        print_with_bar(f"Results of the test are: \n{out_metrics}")
+        self.logger.log(out_metrics)
+        print(out_metrics)
+
+    def evaluate_generation(self, save_metric_details=False, plot_density=False, ema=False):
+        self.diffusion.eval()
+        
+        # Sample a synthetic table
+        num_samples = self.num_samples_to_generate if self.num_samples_to_generate else self.metrics.real_data_size # By default, num_samples_to_generate is not specified. In this case, we generate the same number of samples as the real dataset. This approach is consistently used across all experiments in the paper.
+        syn_df = self.sample_synthetic(num_samples, ema=ema)
+        
+        # Save the sample
+        save_path = os.path.join(self.result_save_path, str(self.curr_epoch), "ema" if ema else "")
+        if not os.path.exists(save_path):
+            os.makedirs(save_path)
+        path = os.path.join(save_path, "samples.csv")
+        syn_df.to_csv(path, index=False)
+        print(
+            f"Samples are saved at {path}"
+        )
+
+        # 流水线仅需要 CSV：跳过 MLE/C2ST 等（合成表为字符串类别时 MLE 会报错）
+        if os.environ.get("TABDIFF_ADAPTER_SAMPLE_ONLY", "").strip().lower() in ("1", "true", "yes"):
+            return {}, {}, syn_df
+        
+        # Compute evaluation metrics on the sample
+        syn_df_loaded = pd.read_csv(os.path.join(save_path, "samples.csv")) # In the original tabsyn code, syn_data is implicitly casted into float.64 when it gets loaded with pd.read_csv in the evaluation script. If we don't cast, the density evluation for some columns (especially those with tailed and peaked distribution) will collapse.
+        out_metrics, extras = self.metrics.evaluate(syn_df_loaded)
+        
+        # Save metrics and metric details
+        path = os.path.join(save_path, "all_results.json")
+        with open(path, "w") as json_file:
+            json.dump(out_metrics, json_file, indent=4, separators=(", ", ": "))        # always locally save the output metrics
+        if save_metric_details:
+            for name, extra in extras.items():
+                if isinstance(extra, pd.DataFrame):
+                    extra.to_csv(os.path.join(save_path, f"{name}.csv"))
+                elif isinstance(extra, dict):
+                    with open(os.path.join(save_path, f"{name}.json"), "w") as json_file:
+                        json.dump(extra, json_file, indent=4, separators=(", ", ": "))
+                else:
+                    raise NotImplementedError(f"Extra file generated during evaluations has type {type(extra)}, and code to save this type of file is not implemented")
+        
+        # Plot density figures
+        if plot_density:
+            img = self.metrics.plot_density(syn_df_loaded)
+            path = os.path.join(save_path, "density_plots.png")
+            img.save(path)
+            print(
+                f"The density plots are saved at {path}"
+            )
+        return out_metrics, extras, syn_df
+        
+
+    def sample_synthetic(self, num_samples, keep_nan_samples=True, ema=False):
+        if ema:
+            curr_model, curr_num_schedule, curr_cat_schedule = self.to_ema_model()
+        info = self.metrics.info
+        
+        print_with_bar(f"Starting Sampling, total samples to generate = {num_samples}")
+        start_time = time.time()
+        
+        syn_data = self.diffusion.sample_all(num_samples, self.sample_batch_size, keep_nan_samples=keep_nan_samples)
+        print(f"Shape of the generated sample = {syn_data.shape}")
+        
+        if keep_nan_samples:
+            num_all_zero_row = (syn_data.sum(dim=1) == 0).sum()
+            if num_all_zero_row:
+                print(f"The generated samples contain {num_all_zero_row} Nan instances!!!")
+                self.logger.log({
+                    'num_Nan_sample': num_all_zero_row
+                })
+
+        # Recover tables
+        num_inverse = self.dataset.num_inverse
+        int_inverse = self.dataset.int_inverse
+        cat_inverse = self.dataset.cat_inverse
+        
+        if self.y_only:
+            if info['task_type'] == 'binclass':
+                syn_data = cat_inverse(syn_data)
+            else:
+                syn_data = num_inverse(syn_data)
+            syn_df = pd.DataFrame()
+            syn_df[info['column_names'][info['target_col_idx'][0]]] = syn_data[:, 0]
+        else:
+            syn_num, syn_cat, syn_target = split_num_cat_target(syn_data, info, num_inverse, int_inverse, cat_inverse) 
+            syn_df = recover_data(syn_num, syn_cat, syn_target, info)
+            
+
+            idx_name_mapping = info['idx_name_mapping']
+            idx_name_mapping = {int(key): value for key, value in idx_name_mapping.items()}
+
+            syn_df.rename(columns = idx_name_mapping, inplace=True)
+        
+        end_time = time.time()
+        print_with_bar(f"Ending Sampling, totoal sampling time = {end_time - start_time}")
+        
+        if ema:
+            self.to_model(curr_model, curr_num_schedule, curr_cat_schedule)
+
+        return syn_df
+    
+    def to_ema_model(self):
+        curr_model = self.diffusion._denoise_fn
+        curr_num_schedule = self.diffusion.num_schedule
+        curr_cat_schedule = self.diffusion.cat_schedule
+        self.diffusion._denoise_fn = self.ema_model  # temporarily install the ema parameters into the model
+        self.diffusion.num_schedule = self.ema_num_schedule
+        self.diffusion.cat_schedule = self.ema_cat_schedule
+        
+        return curr_model, curr_num_schedule, curr_cat_schedule
+
+    def to_model(self, curr_model, curr_num_schedule, curr_cat_schedule):
+        self.diffusion._denoise_fn = curr_model      # give back the parameters
+        self.diffusion.num_schedule = curr_num_schedule
+        self.diffusion.cat_schedule = curr_cat_schedule
+        
+    def test_impute(self, trail_start, trial_size, resample_rounds, impute_condition, imputed_sample_save_dir, w_num, w_cat):
+        self.diffusion.eval()
+        
+        info = self.metrics.info
+        task_type = info['task_type']
+        d_numerical, categories = self.dataset.d_numerical, self.dataset.categories
+        num_mask_idx, cat_mask_idx = [], []
+        X_train = self.dataset.X
+        X_train = X_train
+        x_num_train, x_cat_train = X_train[:,:d_numerical], X_train[:,d_numerical:]
+        
+        if task_type == 'binclass':    # for cat cols, push the masked col to [MASK]
+            cat_mask_idx += [0]
+        else:      # for num cols, set the masked col to the col mean
+            num_mask_idx += [0]
+            avg = x_num_train[:, num_mask_idx].mean(0).to(self.device)
+        
+        with torch.no_grad():
+            
+            for trial in range(trail_start, trail_start+trial_size):
+                print_with_bar(f"Imputing trial {trial}")
+                X_test = self.test_dataset.X
+                X_test = deepcopy(X_test).to(self.device)
+                x_num_test, x_cat_test = X_test[:, :d_numerical], X_test[:, d_numerical:].long()
+                
+                # Apply mask to x_0
+                if num_mask_idx:
+                    x_num_test[:, num_mask_idx] = avg
+                if cat_mask_idx:
+                    x_cat_test[:, cat_mask_idx] = torch.tensor(categories, dtype=x_cat_test.dtype, device=x_cat_test.device)[cat_mask_idx]
+                
+                # Sample imputed tables
+                syn_data = self.diffusion.sample_impute(x_num_test, x_cat_test, num_mask_idx, cat_mask_idx, resample_rounds, impute_condition, w_num, w_cat)
+                print(f"Shape of the imputed sample = {syn_data.shape}")
+
+                # Recover tables
+                num_inverse = self.dataset.num_inverse
+                int_inverse = self.dataset.int_inverse
+                cat_inverse = self.dataset.cat_inverse
+                
+                if torch.any((syn_data[:, d_numerical+1:]).max(dim=0).values > (x_cat_train[:,1:]).max(dim=0).values):     # if the test set contains categories not presented in the train set, we can not do cat_inverse. So we implement a patch that set those columns to the same as the train set
+                    print("Test set contains extra categories, and so does imputed syn data. We cannot do cat_inverse. So we set the cat columns as the same as the train set")
+                    syn_data[:, d_numerical+1:] = x_cat_train[:syn_data.shape[0],1:]
+                    
+                
+                syn_num, syn_cat, syn_target = split_num_cat_target(syn_data, info, num_inverse, int_inverse, cat_inverse) 
+                syn_df = recover_data(syn_num, syn_cat, syn_target, info)
+
+                idx_name_mapping = info['idx_name_mapping']
+                idx_name_mapping = {int(key): value for key, value in idx_name_mapping.items()}
+
+                syn_df.rename(columns = idx_name_mapping, inplace=True)
+                
+                # Save imputed samples
+                os.makedirs(imputed_sample_save_dir) if not os.path.exists(imputed_sample_save_dir) else None
+                print(f"Imputed samples are saved to {imputed_sample_save_dir}/{trial}.csv")
+                syn_df.to_csv(f'{imputed_sample_save_dir}/{trial}.csv', index = False)
+        
+def _as_numpy_float32(x):
+    """Inverse 变换可能返回 Tensor；统一为 numpy float32（含 0 列）。"""
+    if x is None:
+        return np.array([], dtype=np.float32)
+    if isinstance(x, torch.Tensor):
+        x = x.detach().cpu().numpy()
+    return np.asarray(x, dtype=np.float32)
+
+
+@torch.no_grad()
+def split_num_cat_target(syn_data, info, num_inverse, int_inverse, cat_inverse):
+    task_type = info['task_type']
+
+    num_col_idx = info['num_col_idx']
+    cat_col_idx = info['cat_col_idx']
+    target_col_idx = info['target_col_idx']
+
+    n_num_feat = len(num_col_idx)
+    n_cat_feat = len(cat_col_idx)
+
+    if task_type == 'regression':
+        n_num_feat += len(target_col_idx)
+    else:
+        n_cat_feat += len(target_col_idx)
+
+    syn_num = syn_data[:, :n_num_feat]
+    syn_cat = syn_data[:, n_num_feat:]
+
+    if n_num_feat > 0:
+        syn_num = _as_numpy_float32(num_inverse(syn_num))
+        syn_num = _as_numpy_float32(int_inverse(syn_num))
+    else:
+        syn_num = np.zeros((syn_data.shape[0], 0), dtype=np.float32)
+    syn_cat = cat_inverse(syn_cat)
+
+
+    if info['task_type'] == 'regression':
+        syn_target = syn_num[:, :len(target_col_idx)]
+        syn_num = syn_num[:, len(target_col_idx):]
+    
+    else:
+        print(syn_cat.shape)
+        syn_target = syn_cat[:, :len(target_col_idx)]
+        syn_cat = syn_cat[:, len(target_col_idx):]
+
+    return syn_num, syn_cat, syn_target
+
+def recover_data(syn_num, syn_cat, syn_target, info):
+
+    num_col_idx = info['num_col_idx']
+    cat_col_idx = info['cat_col_idx']
+    target_col_idx = info['target_col_idx']
+
+
+    idx_mapping = info['idx_mapping']
+    idx_mapping = {int(key): value for key, value in idx_mapping.items()}
+
+    syn_df = pd.DataFrame()
+
+    if info['task_type'] == 'regression':
+        for i in range(len(num_col_idx) + len(cat_col_idx) + len(target_col_idx)):
+            if i in set(num_col_idx):
+                syn_df[i] = syn_num[:, idx_mapping[i]] 
+            elif i in set(cat_col_idx):
+                syn_df[i] = syn_cat[:, idx_mapping[i] - len(num_col_idx)]
+            else:
+                syn_df[i] = syn_target[:, idx_mapping[i] - len(num_col_idx) - len(cat_col_idx)]
+
+
+    else:
+        for i in range(len(num_col_idx) + len(cat_col_idx) + len(target_col_idx)):
+            if i in set(num_col_idx):
+                syn_df[i] = syn_num[:, idx_mapping[i]]
+            elif i in set(cat_col_idx):
+                syn_df[i] = syn_cat[:, idx_mapping[i] - len(num_col_idx)]
+            else:
+                syn_df[i] = syn_target[:, idx_mapping[i] - len(num_col_idx) - len(cat_col_idx)]
+
+    return syn_df

SynthData0523/hyper_parameter_tuning/n11/tabdiff/runs/tabdiff-n11-20260521_035804/_tabdiff_runtime/utils_train.py

@@ -0,0 +1,198 @@
+import numpy as np
+import os
+
+import src
+from torch.utils.data import Dataset
+
+import torch
+
+
+class TabularDataset(Dataset):
+    def __init__(self, X_num, X_cat):
+        self.X_num = X_num
+        self.X_cat = X_cat
+
+    def __getitem__(self, index):
+        this_num = self.X_num[index]
+        this_cat = self.X_cat[index]
+
+        sample = (this_num, this_cat)
+
+        return sample
+
+    def __len__(self):
+        return self.X_num.shape[0]
+    
+class TabDiffDataset(Dataset):
+    def __init__(self, dataname, data_dir, info, isTrain=True, y_only=False, dequant_dist='none', int_dequant_factor=0.0):
+        self.dataname = dataname
+        self.data_dir = data_dir
+        self.info = info
+        self.isTrain = isTrain
+
+        X_num, X_cat, categories, d_numerical, num_inverse, int_inverse, cat_inverse = preprocess(data_dir, y_only, dequant_dist, int_dequant_factor, task_type = info['task_type'], inverse=True)
+        categories = np.array(categories)
+
+        X_train_num, _ = X_num
+        X_train_cat, _ = X_cat
+
+        X_train_num, X_test_num = X_num
+        X_train_cat, X_test_cat = X_cat
+
+        X_train_num, X_test_num = torch.tensor(X_train_num).float(), torch.tensor(X_test_num).float()
+        X_train_cat, X_test_cat =  torch.tensor(X_train_cat), torch.tensor(X_test_cat)
+
+        self.X = torch.cat((X_train_num, X_train_cat), dim=1) if isTrain else torch.cat((X_test_num, X_test_cat), dim=1)
+        self.num_inverse = num_inverse
+        self.int_inverse = int_inverse
+        self.cat_inverse = cat_inverse
+        self.d_numerical = d_numerical
+        self.categories = categories
+
+    def __getitem__(self, index):
+        return self.X[index]
+
+    def __len__(self):
+        return self.X.shape[0]
+
+def preprocess(dataset_path, y_only=False, dequant_dist='none', int_dequant_factor=0.0, task_type = 'binclass', inverse = False, cat_encoding = None, concat = True):
+    
+    T_dict = {}
+
+    T_dict['normalization'] = "quantile"
+    T_dict['num_nan_policy'] = 'mean'
+    T_dict['cat_nan_policy'] =  None
+    T_dict['cat_min_frequency'] = None
+    T_dict['cat_encoding'] = cat_encoding
+    T_dict['y_policy'] = "default"
+    T_dict['dequant_dist'] = dequant_dist
+    T_dict['int_dequant_factor'] = int_dequant_factor
+
+    T = src.Transformations(**T_dict)
+
+    dataset = make_dataset(
+        data_path = dataset_path,
+        T = T,
+        task_type = task_type,
+        change_val = False,
+        concat = concat,
+        y_only = y_only,
+    )
+
+    if cat_encoding is None:
+        X_num = dataset.X_num
+        X_cat = dataset.X_cat
+
+        X_train_num, X_test_num = X_num['train'], X_num['test']
+        if X_cat is None:
+            X_train_cat = np.empty((X_train_num.shape[0], 0), dtype=np.int64)
+            X_test_cat = np.empty((X_test_num.shape[0], 0), dtype=np.int64)
+            categories = []
+        else:
+            X_train_cat, X_test_cat = X_cat['train'], X_cat['test']
+            categories = src.get_categories(X_train_cat)
+        d_numerical = X_train_num.shape[1]
+
+        X_num = (X_train_num, X_test_num)
+        X_cat = (X_train_cat, X_test_cat)
+
+
+        if inverse:
+            num_inverse = dataset.num_transform.inverse_transform if dataset.num_transform is not None else lambda x: x
+            int_inverse = dataset.int_transform.inverse_transform if dataset.int_transform is not None else lambda x: x
+            cat_inverse = dataset.cat_transform.inverse_transform if dataset.cat_transform is not None else lambda x: x
+
+            return X_num, X_cat, categories, d_numerical, num_inverse, int_inverse, cat_inverse
+        else:
+            return X_num, X_cat, categories, d_numerical
+    else:
+        return dataset
+
+
+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 target, source in zip(target_params, source_params):
+        target.detach().mul_(rate).add_(source.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: src.Transformations,
+    task_type,
+    change_val: bool,
+    concat = True,
+    y_only = False,
+):
+
+    # classification
+    if task_type == 'binclass' or task_type == 'multiclass':
+        has_cat = os.path.exists(os.path.join(data_path, 'X_cat_train.npy'))
+        X_cat = {} if (has_cat or concat) else None
+        X_num = {} if os.path.exists(os.path.join(data_path, 'X_num_train.npy')) else None
+        y = {} if os.path.exists(os.path.join(data_path, 'y_train.npy')) else None
+
+        for split in ['train', 'test']:
+            X_num_t, X_cat_t, y_t = src.read_pure_data(data_path, split)
+            if y_only:
+                X_num_t = X_num_t[:, :0]
+                X_cat_t = X_cat_t[:, :0]
+            if X_num is not None:
+                X_num[split] = X_num_t
+            if X_cat is not None:
+                if concat:
+                    X_cat_t = concat_y_to_X(X_cat_t, y_t)
+                X_cat[split] = X_cat_t  
+            if y is not None:
+                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')) else None
+        y = {} if os.path.exists(os.path.join(data_path, 'y_train.npy')) else None
+
+        for split in ['train', 'test']:
+            X_num_t, X_cat_t, y_t = src.read_pure_data(data_path, split)
+            if y_only:
+                X_num_t = X_num_t[:, :0]
+                X_cat_t = X_cat_t[:, :0]
+            if X_num is not None:
+                if concat:
+                    X_num_t = concat_y_to_X(X_num_t, y_t)
+                X_num[split] = X_num_t
+            if X_cat is not None:
+                X_cat[split] = X_cat_t
+            if y is not None:
+                y[split] = y_t
+
+    info = src.load_json(os.path.join(data_path, 'info.json'))
+    int_col_idx_wrt_num = info['int_col_idx_wrt_num']
+
+    if y_only:
+        int_col_idx_wrt_num = []
+    D = src.Dataset(
+        X_num,
+        X_cat,
+        y,
+        int_col_idx_wrt_num,
+        y_info={},
+        task_type=src.TaskType(info['task_type']),
+        n_classes=info.get('n_classes')
+    )
+
+    if change_val:
+        D = src.change_val(D)
+
+    return src.transform_dataset(D, T, None)

SynthData0523/hyper_parameter_tuning/n11/tabdiff/runs/tabdiff-n11-20260521_035804/_tabdiff_train.py

@@ -0,0 +1,78 @@
+
+import os, shutil, subprocess, sys
+td = r"/workspace/TabDiff"
+name = r"pipeline_n11"
+src = r"/work/output-Benchmark-trainonly-v1/n11/tabdiff/tabdiff-n11-20260521_035804/tabular_bundle/pipeline_n11"
+rt = r"/work/output-Benchmark-trainonly-v1/n11/tabdiff/tabdiff-n11-20260521_035804/_tabdiff_runtime"
+shutil.rmtree(rt, ignore_errors=True)
+
+def _ignore(_, names):
+    skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"}
+    return [n for n in names if n in skip or n.endswith(".pyc")]
+
+shutil.copytree(td, rt, ignore=_ignore)
+
+def _replace_once(path, old, new):
+    text = open(path, "r", encoding="utf-8").read()
+    if old not in text:
+        raise RuntimeError(f"patch anchor not found in {path}")
+    text = text.replace(old, new, 1)
+    with open(path, "w", encoding="utf-8") as f:
+        f.write(text)
+
+_replace_once(
+    os.path.join(rt, "utils_train.py"),
+    "        X_train_num, X_test_num = X_num['train'], X_num['test']\n        X_train_cat, X_test_cat = X_cat['train'], X_cat['test']\n        \n        categories = src.get_categories(X_train_cat)\n",
+    "        X_train_num, X_test_num = X_num['train'], X_num['test']\n        if X_cat is None:\n            X_train_cat = np.empty((X_train_num.shape[0], 0), dtype=np.int64)\n            X_test_cat = np.empty((X_test_num.shape[0], 0), dtype=np.int64)\n            categories = []\n        else:\n            X_train_cat, X_test_cat = X_cat['train'], X_cat['test']\n            categories = src.get_categories(X_train_cat)\n",
+)
+_replace_once(
+    os.path.join(rt, "utils_train.py"),
+    "        X_cat = {} if os.path.exists(os.path.join(data_path, 'X_cat_train.npy'))  else None\n        X_num = {} if os.path.exists(os.path.join(data_path, 'X_num_train.npy')) else None\n        y = {} if os.path.exists(os.path.join(data_path, 'y_train.npy')) else None\n",
+    "        has_cat = os.path.exists(os.path.join(data_path, 'X_cat_train.npy'))\n        X_cat = {} if (has_cat or concat) else None\n        X_num = {} if os.path.exists(os.path.join(data_path, 'X_num_train.npy')) else None\n        y = {} if os.path.exists(os.path.join(data_path, 'y_train.npy')) else None\n",
+)
+_replace_once(
+    os.path.join(rt, "src", "data.py"),
+    "        num_workers=1,\n",
+    "        num_workers=int(os.environ.get('TABDIFF_NUM_WORKERS', '0')),\n",
+)
+_replace_once(
+    os.path.join(rt, "src", "data.py"),
+    "    loader = torch.utils.data.DataLoader(torch_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=1)\n",
+    "    loader = torch.utils.data.DataLoader(torch_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=int(os.environ.get('TABDIFF_NUM_WORKERS', '0')))\n",
+)
+_replace_once(
+    os.path.join(rt, "tabdiff", "main.py"),
+    "    if os.environ.get(\"TABDIFF_ADAPTER_TRAIN\", \"\").strip() and args.mode == \"train\":\n        raw_config[\"train\"][\"main\"][\"check_val_every\"] = int(raw_config[\"train\"][\"main\"][\"steps\"])\n\n    ## Load training data\n",
+    "    if os.environ.get(\"TABDIFF_ADAPTER_TRAIN\", \"\").strip() and args.mode == \"train\":\n        raw_config[\"train\"][\"main\"][\"check_val_every\"] = int(raw_config[\"train\"][\"main\"][\"steps\"])\n\n    _train_batch = os.environ.get(\"TABDIFF_BATCH_SIZE\", \"\").strip() or os.environ.get(\"TABDIFF_TRAIN_BATCH_SIZE\", \"\").strip()\n    if _train_batch:\n        raw_config[\"train\"][\"main\"][\"batch_size\"] = max(1, int(_train_batch))\n    _sample_batch = os.environ.get(\"TABDIFF_SAMPLE_BATCH_SIZE\", \"\").strip()\n    if _sample_batch:\n        raw_config[\"sample\"][\"batch_size\"] = max(1, int(_sample_batch))\n    _train_lr = os.environ.get(\"TABDIFF_LR\", \"\").strip() or os.environ.get(\"TABDIFF_LEARNING_RATE\", \"\").strip()\n    if _train_lr:\n        raw_config[\"train\"][\"main\"][\"lr\"] = float(_train_lr)\n    _num_timesteps = os.environ.get(\"TABDIFF_NUM_TIMESTEPS\", \"\").strip() or os.environ.get(\"TABDIFF_TIMESTEPS\", \"\").strip()\n    if _num_timesteps:\n        raw_config[\"diffusion_params\"][\"num_timesteps\"] = max(1, int(_num_timesteps))\n\n    ## Load training data\n",
+)
+_replace_once(
+    os.path.join(rt, "tabdiff", "main.py"),
+    "        num_workers = 4,\n",
+    "        num_workers = int(os.environ.get('TABDIFF_NUM_WORKERS', '0')),\n",
+)
+dst_data = os.path.join(rt, "data", name)
+dst_syn = os.path.join(rt, "synthetic", name)
+shutil.rmtree(dst_data, ignore_errors=True)
+os.makedirs(os.path.dirname(dst_data), exist_ok=True)
+shutil.copytree(src, dst_data)
+os.makedirs(dst_syn, exist_ok=True)
+for fn in ("real.csv", "test.csv", "val.csv"):
+    shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn))
+os.chdir(rt)
+os.environ["PYTHONPATH"] = rt + os.pathsep + os.environ.get("PYTHONPATH", "")
+os.environ["TABDIFF_SMOKE_STEPS"] = "500"
+os.environ["TABDIFF_STEPS"] = "500"
+os.environ["TABDIFF_BATCH_SIZE"] = "512"
+os.environ["TABDIFF_TRAIN_BATCH_SIZE"] = "512"
+os.environ["TABDIFF_SAMPLE_BATCH_SIZE"] = "512"
+os.environ["TABDIFF_LR"] = "0.0001"
+os.environ["TABDIFF_LEARNING_RATE"] = "0.0001"
+os.environ["TABDIFF_NUM_TIMESTEPS"] = "25"
+os.environ["TABDIFF_TIMESTEPS"] = "25"
+os.environ["TABDIFF_NUM_WORKERS"] = "0"
+os.environ["TABDIFF_ADAPTER_TRAIN"] = "1"
+subprocess.check_call([
+    sys.executable, "-m", "tabdiff.main",
+    "--dataname", name, "--mode", "train", "--gpu", "0",
+    "--no_wandb", "--exp_name", r"adapter_learnable",
+])

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SynthData0523/hyper_parameter_tuning/n11/tabdiff/runs/tabdiff-n11-20260521_040854/_tabdiff_gen.py

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+
+import os, shutil, subprocess, sys
+td = r"/workspace/TabDiff"
+name = r"pipeline_n11"
+src = r"/work/output-Benchmark-trainonly-v1/n11/tabdiff/tabdiff-n11-20260521_040854/tabular_bundle/pipeline_n11"
+rt = r"/work/output-Benchmark-trainonly-v1/n11/tabdiff/tabdiff-n11-20260521_040854/_tabdiff_runtime"
+if not os.path.exists(rt):
+    def _ignore(_, names):
+        skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"}
+        return [n for n in names if n in skip or n.endswith(".pyc")]
+    shutil.copytree(td, rt, ignore=_ignore)
+dst_data = os.path.join(rt, "data", name)
+dst_syn = os.path.join(rt, "synthetic", name)
+shutil.rmtree(dst_data, ignore_errors=True)
+os.makedirs(os.path.dirname(dst_data), exist_ok=True)
+shutil.copytree(src, dst_data)
+os.makedirs(dst_syn, exist_ok=True)
+for fn in ("real.csv", "test.csv", "val.csv"):
+    shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn))
+os.chdir(rt)
+os.environ["PYTHONPATH"] = rt + os.pathsep + os.environ.get("PYTHONPATH", "")
+subprocess.check_call([
+    sys.executable, "-m", "tabdiff.main",
+    "--dataname", name, "--mode", "test", "--gpu", "0",
+    "--no_wandb", "--exp_name", r"adapter_learnable",
+    "--ckpt_path", r"/work/output-Benchmark-trainonly-v1/n11/tabdiff/tabdiff-n11-20260521_040854/_tabdiff_runtime/tabdiff/ckpt/pipeline_n11/adapter_learnable/model_500.pt",
+    "--num_samples_to_generate", str(int(15215)),
+])
+# test() 写入 tabdiff/result/<dataname>/<exp>/<epoch>/samples.csv
+base = os.path.join(rt, "tabdiff", "result", name, r"adapter_learnable")
+best = None
+best_t = -1.0
+for root, _, files in os.walk(base):
+    if "samples.csv" in files:
+        p = os.path.join(root, "samples.csv")
+        t = os.path.getmtime(p)
+        if t > best_t:
+            best_t = t
+            best = p
+if not best:
+    raise SystemExit("tabdiff: no samples.csv under " + base)
+shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n11/tabdiff/tabdiff-n11-20260521_040854/tabdiff-n11-15215-20260521_041828.csv")

SynthData0523/hyper_parameter_tuning/n11/tabdiff/runs/tabdiff-n11-20260521_040854/_tabdiff_runtime/.gitignore

@@ -0,0 +1,15 @@
+**/__pycache__/**
+*.pyc
+
+data/*
+!/data/Info/
+
+wandb/
+eval/
+synthetic/
+impute/
+workspace/
+debug/
+tabdiff/result/
+
+**/ckpt/*

SynthData0523/hyper_parameter_tuning/n11/tabdiff/runs/tabdiff-n11-20260521_040854/_tabdiff_runtime/LICENSE

@@ -0,0 +1,7 @@
+Copyright 2024 Minkai Xu
+
+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.

SynthData0523/hyper_parameter_tuning/n11/tabdiff/runs/tabdiff-n11-20260521_040854/_tabdiff_runtime/README.md

@@ -0,0 +1,201 @@
+# TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation
+
+<p align="center">
+  <a href="https://github.com/MinkaiXu/TabDiff/blob/main/LICENSE">
+    <img alt="MIT License" src="https://img.shields.io/badge/License-MIT-yellow.svg">
+  </a>
+  <a href="https://openreview.net/forum?id=swvURjrt8z">
+    <img alt="Openreview" src="https://img.shields.io/badge/review-OpenReview-blue">
+  </a>
+  <a href="https://arxiv.org/abs/2410.20626">
+    <img alt="Paper URL" src="https://img.shields.io/badge/cs.LG-2410.20626-B31B1B.svg">
+  </a>
+</p>
+
+<div align="center">
+  <img src="images/tabdiff_demo.gif" alt="Model Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+  <p><em>Figure 1: Visualing the generative process of TabDiff. A high-quality version of this video can be found at <a href="images/tabdiff_demo.mp4" download>tabdiff_demo.mp4</a></em></p>
+</div>
+
+This repository provides the official implementation of TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation (ICLR 2025).
+
+## Latest Update
+- [2025.04]：The categorical-heavy dataset **[Diabetes](https://archive.ics.uci.edu/dataset/296/diabetes+130-us+hospitals+for+years+1999-2008)** evaluated in the paper has now been released!
+- [2025.02]：Our code is finally released! We have released part of the tested datasets. The rest will be released soon!
+
+## Introduction
+
+<div align="center">
+  <img src="images/tabdiff_flowchart.jpg" alt="Model Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+  <p><em>Figure 2: The high-level schema of TabDiff</a></em></p>
+</div>
+TabDiff is a unified diffusion framework designed to model all muti-modal distributions of tabular data in a single model. Its key innovations include:  
+
+1) Framing the joint diffusion process in continuous time,
+2) A feature-wised learnable diffusion process that offsets the heterogeneity across different feature distributions,
+3) Classifier-free guidance conditional generation for missing column value imputation. 
+
+The schema of TabDiff is presented in the figure above. For more details, please refer to [our paper](https://arxiv.org/abs/2410.20626).
+
+
+## Environment Setup
+
+Create the main environment with [tabdiff.yaml](tabdiff.yaml). This environment will be used for all tasks except for the evaluation of additional data fidelity metrics (i.e., $\alpha$-precision and $\beta$-recall scores)
+
+```
+conda env create -f tabdiff.yaml
+```
+
+Create another environment with [synthcity.yaml](synthcity.yaml) to evaluate additional data fidelity metrics
+
+```
+conda env create -f synthcity.yaml
+```
+
+## Datasets Preparation
+
+### Using the datasets experimented in the paper
+
+Download raw datasets:
+
+```
+python download_dataset.py
+```
+
+Process datasets:
+
+```
+python process_dataset.py
+```
+
+### Using your own dataset
+
+First, create a directory for your dataset in [./data](./data):
+```
+cd data
+mkdir <NAME_OF_YOUR_DATASET>
+```
+
+Compile your raw tabular data in .csv format. **The first row should be the header** indicating the name of each column, and the remaining rows are records. After finishing these steps, place you data's csv file in the directory you just created and name it as <NAME_OF_YOUR_DATASET>.csv. 
+
+Then, create <NAME_OF_YOUR_DATASET>.json in [./data/Info](./data/Info). Write this file with the metadata of your dataset, covering the following information:
+```
+{
+    "name": "<NAME_OF_YOUR_DATASET>",
+    "task_type": "[NAME_OF_TASK]", # binclass or regression
+    "header": "infer",
+    "column_names": null,
+    "num_col_idx": [LIST],  # list of indices of numerical columns
+    "cat_col_idx": [LIST],  # list of indices of categorical columns
+    "target_col_idx": [list], # list of indices of the target columns (for MLE)
+    "file_type": "csv",
+    "data_path": "data/<NAME_OF_YOUR_DATASET>/<NAME_OF_YOUR_DATASET>.csv"
+    "test_path": null,
+}
+```
+
+### Important Notes When Creating the Info File
+- The MLE evaluation and the imputation task (see later sections for details) assume that one column of your data is the regression or classification target. To enable these tasks, you will need to specify `target_col_idx`. If you don't need to evalute MLE, you can comment out the following line: https://github.com/MinkaiXu/TabDiff/blob/0c4fc3bbfa19046d36c5dce64628df52d5c73d15/tabdiff/main.py#L152
+- The fields `target_col_idx`, `num_col_idx` and `cat_col_idx` must be multually exclusive—no column should appear in more than one of these lists. 
+- Set the task_type to "regression" if the target column is numerical, or "binclass" if it is categorical.
+
+Finally, run the following command to process your dataset:
+```
+python process_dataset.py --dataname <NAME_OF_YOUR_DATASET>
+```
+
+## Training TabDiff
+
+To train an unconditional TabDiff model across the entire table, run
+
+```
+python main.py --dataname <NAME_OF_DATASET> --mode train
+```
+
+Current Options of ```<NAME_OF_DATASET>``` are: adult, default, shoppers, magic, beijing, news
+
+Wanb logging is enabled by default. To disable it and log locally, add the ```--no_wandb``` flag.
+
+To disable the learnable noise schedules, add the ```--non_learnable_schedule```. Please note that in order for the code to test/sample from such model properly, you need to add this flag for all commands below.
+
+To specify your own experiment name, which will be used for logging and saving files, add ```--exp_name <your experiment name>```. This flag overwrites the default experiment name (learnable_schedule/non_learnable_schedule), so, similar to ```--non_learnable_schedule```, once added to training, you need to add it to all following commands as well.
+
+## Sampling and Evaluating TabDiff (Density, MLE, C2ST)
+
+To sample synthetic tables from trained TabDiff models and evaluate them, run
+```
+python main.py --dataname <NAME_OF_DATASET> --mode test --report --no_wandb
+```
+
+This will sample 20 synthetic tables randomly. Meanwhile, it will evaluate the density, mle, and c2st scores for each sample and report their average and standard deviation. The results will be printed out in the terminal, and the samples and detailed evaluation results will be placed in ./eval/report_runs/<EXP_NAME>/<NAME_OF_DATASET>/.
+
+## Evaluating on Additional Fidelity Metrics ($\alpha$-precision and $\beta$-recall scores)
+To evaluate TabDiff on the additional fidelity metrics ($\alpha$-precision and $\beta$-recall scores), you need to first make sure that you have already generated some samples by the previous commands. Then, you need to switch to the `synthcity` environment (as the synthcity packet used to compute those metrics conflicts with the main environment), by running
+```
+conda activate synthcity
+```
+Then, evaluate the metrics by running
+```
+python eval/eval_quality.py --dataname <NAME_OF_DATASET>
+```
+
+Similarly, the results will be printed out in the terminal and added to ./eval/report_runs/<EXP_NAME>/<NAME_OF_DATASET>/
+
+## Evaluating Data Privacy (DCR score)
+To evalute the privacy metric DCR score, you first need to retrain all the models, as the metric requires an equal split between the training and testing data (our initial splits employ a 90/10 ratio). To retrain with an equal split, run the training command but append `_dcr` to ```<NAME_OF_DATASET>```
+```
+python main.py --dataname <NAME_OF_DATASET>_dcr --mode train
+```
+
+Then, test the models on DCR with the same `_dcr` suffix
+```
+python main.py --dataname <NAME_OF_DATASET>_dcr --mode test --report --no_wandb
+```
+
+
+
+## Missing Value Imputation with Classifier-free Guidance (CFG)
+Our current experiments only include imputing the target column. However, our implementation, located at ```sample_impute()``` in [unified_ctime_diffusion.py](./tabdiff/models/unified_ctime_diffusion.py), should support imputing multiple columns with different data types.
+
+### Training Guidance Model
+In order to enable classifier-free guidance (CFG), you need to first train an unconditional guidance model on the target column by running the training command with the `--y_only` flag
+```
+python main.py --dataname <NAME_OF_DATASET> --mode train --y_only
+```
+
+### Sampling Imputed Tables
+With the trained guidance model, you can then impute the missing target column by running the testing command with the `--impute` flag
+```
+python main.py --dataname <NAME_OF_DATASET> --mode test --impute --no_wandb
+```
+This will, by default, randomly produce 50 imputed tables and save them to ./impute/<NAME_OF_DATASET>/<EXP_NAME>.
+
+### Evaluating Imputation
+You can then evaluate the imputation quality by running
+```
+python eval_impute.py --dataname <NAME_OF_DATASET>
+```
+
+## License
+
+This work is licensed undeer the MIT License.
+
+## Acknowledgement
+This repo is built upon the previous work TabSyn's [[codebase]](https://github.com/amazon-science/tabsyn). Many thanks to Hengrui!
+
+## Citation
+Please consider citing our work if you find it helpful in your research!
+```
+@inproceedings{
+shi2025tabdiff,
+title={TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation},
+author={Juntong Shi and Minkai Xu and Harper Hua and Hengrui Zhang and Stefano Ermon and Jure Leskovec},
+booktitle={The Thirteenth International Conference on Learning Representations},
+year={2025},
+url={https://openreview.net/forum?id=swvURjrt8z}
+}
+```
+## Contact
+If you encounter any problem, please file an issue on this GitHub repo.
+
+If you have any question regarding the paper, please contact Minkai at [minkai@stanford.edu](minkai@stanford.edu) or Juntong at [shisteve@usc.edu](shisteve@usc.edu).

SynthData0523/hyper_parameter_tuning/n11/tabdiff/runs/tabdiff-n11-20260521_040854/_tabdiff_runtime/download_dataset.py

@@ -0,0 +1,51 @@
+import os
+from urllib import request
+import zipfile
+
+DATA_DIR = 'data'
+
+
+NAME_URL_DICT_UCI = {
+    'adult': 'https://archive.ics.uci.edu/static/public/2/adult.zip',
+    'default': 'https://archive.ics.uci.edu/static/public/350/default+of+credit+card+clients.zip',
+    'magic': 'https://archive.ics.uci.edu/static/public/159/magic+gamma+telescope.zip',
+    'shoppers': 'https://archive.ics.uci.edu/static/public/468/online+shoppers+purchasing+intention+dataset.zip',
+    'beijing': 'https://archive.ics.uci.edu/static/public/381/beijing+pm2+5+data.zip',
+    'news': 'https://archive.ics.uci.edu/static/public/332/online+news+popularity.zip',
+    'news_nocat': 'https://archive.ics.uci.edu/static/public/332/online+news+popularity.zip',
+    'diabetes': 'https://archive.ics.uci.edu/static/public/296/diabetes+130-us+hospitals+for+years+1999-2008.zip',
+    'adult_dcr': 'https://archive.ics.uci.edu/static/public/2/adult.zip',
+    'default_dcr': 'https://archive.ics.uci.edu/static/public/350/default+of+credit+card+clients.zip',
+    'magic_dcr': 'https://archive.ics.uci.edu/static/public/159/magic+gamma+telescope.zip',
+    'shoppers_dcr': 'https://archive.ics.uci.edu/static/public/468/online+shoppers+purchasing+intention+dataset.zip',
+    'beijing_dcr': 'https://archive.ics.uci.edu/static/public/381/beijing+pm2+5+data.zip',
+    'news_dcr': 'https://archive.ics.uci.edu/static/public/332/online+news+popularity.zip',
+    'diabetes_dcr': 'https://archive.ics.uci.edu/static/public/296/diabetes+130-us+hospitals+for+years+1999-2008.zip',
+}
+
+def unzip_file(zip_filepath, dest_path):
+    with zipfile.ZipFile(zip_filepath, 'r') as zip_ref:
+        zip_ref.extractall(dest_path)
+
+
+def download_from_uci(name):
+
+    print(f'Start processing dataset {name} from UCI.')
+    save_dir = f'{DATA_DIR}/{name}'
+    if not os.path.exists(save_dir):
+        os.makedirs(save_dir)
+
+        url = NAME_URL_DICT_UCI[name]
+        request.urlretrieve(url, f'{save_dir}/{name}.zip')
+        print(f'Finish downloading dataset from {url}, data has been saved to {save_dir}.')
+        
+        unzip_file(f'{save_dir}/{name}.zip', save_dir)
+        print(f'Finish unzipping {name}.')
+    
+    else:
+        print('Aready downloaded.')
+
+if __name__ == '__main__':
+    for name in NAME_URL_DICT_UCI.keys():
+        download_from_uci(name)
+    

SynthData0523/hyper_parameter_tuning/n11/tabdiff/runs/tabdiff-n11-20260521_040854/_tabdiff_runtime/eval_impute.py

@@ -0,0 +1,82 @@
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.metrics import f1_score, roc_auc_score
+from sklearn.metrics import root_mean_squared_error
+import argparse
+import json
+
+
+parser = argparse.ArgumentParser(description='Missing Value Imputation')
+
+parser.add_argument('--dataname', type=str, default='adult', help='Name of dataset.')
+parser.add_argument('--exp_name', type=str, default=None)
+parser.add_argument('--col', type=int, default=0, help='Numerical Column to Impute')
+parser.add_argument('--non_learnable_schedule', action='store_true')
+
+args = parser.parse_args()
+
+dataname = args.dataname
+exp_name = args.exp_name
+if exp_name is None:
+    exp_name = "non_learnable_schedule" if args.non_learnable_schedule else "learnable_schedule"
+col = args.col
+
+dataname = args.dataname
+
+data_dir = f'data/{dataname}'
+
+real_path = f'{data_dir}/test.csv'
+
+info_path = f'data/{dataname}/info.json'
+with open(info_path, 'r') as f:
+    info = json.load(f)
+task_type = info['task_type']
+
+
+encoder = OneHotEncoder()
+
+real_data = pd.read_csv(real_path)
+target_col = real_data.columns[info['target_col_idx'][0]]
+
+if task_type == "binclass":
+    real_target = real_data[target_col].to_numpy().reshape(-1,1)
+    real_y = encoder.fit_transform(real_target).toarray()
+    
+    syn_y = []
+    for i in range(50):
+        syn_path = f'impute/{dataname}/{exp_name}/{i}.csv'
+        syn_data = pd.read_csv(syn_path)
+        target = syn_data[target_col].to_numpy().reshape(-1, 1)
+        syn_y.append(encoder.transform(target).toarray())
+
+    syn_y_prob = np.stack(syn_y).mean(0)
+    syn_y_oh = np.argmax(syn_y_prob, axis=1)
+    num_classes = np.max(syn_y_oh) + 1
+    syn_y_oh = np.eye(num_classes)[syn_y_oh]
+
+    
+
+
+    micro_f1 = f1_score(real_y.argmax(axis=1), syn_y_prob.argmax(axis=1), average='micro')
+    auc = roc_auc_score(real_y, syn_y_prob, average='micro')
+    auc_argmaxed = roc_auc_score(real_y, syn_y_oh, average='micro')
+    print("AUC: ", round(auc*100, 3))
+else:
+    y_test = real_data[target_col].to_numpy()
+    y_test = np.log(np.clip(y_test, 1, 20000))
+    
+    syn_y_ = []
+    error = []
+    for i in range(50):
+        syn_path = f'impute/{dataname}/{exp_name}/{i}.csv'
+        syn_data = pd.read_csv(syn_path)
+        syn_y = syn_data[target_col].to_numpy()
+        syn_y = np.log(np.clip(syn_y, 1, 20000))
+        syn_y_.append(syn_y)
+        
+    pred = np.stack(syn_y_).mean(0)
+    root_mean_squared = root_mean_squared_error(y_test, pred)   # mean_squared_error with squared=False is deprecated
+    
+    print("RMSE:", round(root_mean_squared, 4))
+    

SynthData0523/hyper_parameter_tuning/n11/tabdiff/runs/tabdiff-n11-20260521_040854/_tabdiff_runtime/images/tabdiff_demo.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:90c8072ab9a7dcc73d7bc3ea32b0e961cae5fcd7d73b6ffc2ffb44e6c291962f
+size 1108599

SynthData0523/hyper_parameter_tuning/n11/tabdiff/runs/tabdiff-n11-20260521_040854/_tabdiff_runtime/main.py

@@ -0,0 +1,46 @@
+import torch
+from tabdiff.main import main as tabdiff_main
+import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Training of TabDiff')
+
+    # General configs
+    parser.add_argument('--dataname', type=str, default='adult', help='Name dataset, one of those in data/ dir')
+    parser.add_argument('--mode', type=str, default='train', help='train or test')
+    parser.add_argument('--method', type=str, default='tabdiff', help='Currently we only release our model TabDiff. Baselines will be released soon.')
+    parser.add_argument('--gpu', type=int, default=0, help='GPU index')
+    parser.add_argument('--debug', action='store_true', help='Enable debug mode')
+    parser.add_argument('--no_wandb', action='store_true', help='disable wandb')
+    parser.add_argument('--exp_name', type=str, default=None, help='Experiment name, used to name log directories and the wandb run name')
+    parser.add_argument('--deterministic', action='store_true', help='Whether to make the entire process deterministic, i.e., fix global random seeds')
+    
+    # Configs for tabdiff
+    parser.add_argument('--y_only', action='store_true', help='Train guidance model that only model the target column')
+    parser.add_argument('--non_learnable_schedule', action='store_true', help='disable learnable noise schedule')
+    
+    # Configs for testing tabdiff
+    parser.add_argument('--num_samples_to_generate', type=int, default=None, help='Number of samples to be generated while testing')
+    parser.add_argument('--ckpt_path', type=str, default=None, help='Path to the model checkpoint to be tested')
+    parser.add_argument('--report', action='store_true', help="Report testing mode: this mode sequentially runs <num_runs> test runs and report the avg and std")
+    parser.add_argument('--num_runs', type=int, default=20, help="Number of runs to be averaged in the report testing mode")
+    
+    # Configs for imputation
+    parser.add_argument('--impute', action='store_true')
+    parser.add_argument('--trial_start', type=int, default=0)
+    parser.add_argument('--trial_size', type=int, default=50)
+    parser.add_argument('--resample_rounds', type=int, default=1)
+    parser.add_argument('--impute_condition', type=str, default="x_t")
+    parser.add_argument('--y_only_model_path', type=str, default=None, help="Path to the y_only model checkpoint that will be used as the unconditional guidance model")
+    parser.add_argument('--w_num', type=float, default=0.6)
+    parser.add_argument('--w_cat', type=float, default=0.6)
+
+    args = parser.parse_args()
+
+    # check cuda
+    if args.gpu != -1 and torch.cuda.is_available():
+        args.device = f'cuda:{args.gpu}'
+    else:
+        args.device = 'cpu'
+    
+    tabdiff_main(args)

SynthData0523/hyper_parameter_tuning/n11/tabdiff/runs/tabdiff-n11-20260521_040854/_tabdiff_runtime/process_dataset.py

@@ -0,0 +1,646 @@
+import numpy as np
+import pandas as pd
+import os
+import sys
+import json
+import argparse
+
+from sklearn.preprocessing import OrdinalEncoder
+from sklearn import model_selection
+
+TYPE_TRANSFORM ={
+    'float', np.float32,
+    'str', str,
+    'int', int
+}
+
+INFO_PATH = 'data/Info'
+
+parser = argparse.ArgumentParser(description='process dataset')
+
+# General configs
+parser.add_argument('--dataname', type=str, default=None, help='Name of dataset.')
+args = parser.parse_args()
+
+def preprocess_beijing():
+    with open(f'{INFO_PATH}/beijing.json', 'r') as f:
+        info = json.load(f)
+    
+    data_path = info['raw_data_path']
+
+    data_df = pd.read_csv(data_path)
+    columns = data_df.columns
+
+    data_df = data_df[columns[1:]]
+
+
+    df_cleaned = data_df.dropna()
+    df_cleaned.to_csv(info['data_path'], index = False)
+    
+def preprocess_beijing_dcr():
+    with open(f'{INFO_PATH}/beijing_dcr.json', 'r') as f:
+        info = json.load(f)
+    
+    data_path = info['raw_data_path']
+
+    data_df = pd.read_csv(data_path)
+    columns = data_df.columns
+
+    data_df = data_df[columns[1:]]
+
+
+    df_cleaned = data_df.dropna()
+    df_cleaned.to_csv(info['data_path'], index = False)
+
+def preprocess_news(remove_cat=False):
+    name = 'news' if not remove_cat else 'news_nocat'
+    with open(f'{INFO_PATH}/{name}.json', 'r') as f:
+        info = json.load(f)
+
+    data_path = info['raw_data_path']
+    data_df = pd.read_csv(data_path)
+    data_df = data_df.drop('url', axis=1)
+
+    columns = np.array(data_df.columns.tolist())
+
+    cat_columns1 = columns[list(range(12,18))]
+    cat_columns2 = columns[list(range(30,38))]
+    
+    if not remove_cat:
+        cat_col1 = data_df[cat_columns1].astype(int).to_numpy().argmax(axis = 1)
+        cat_col2 = data_df[cat_columns2].astype(int).to_numpy().argmax(axis = 1)
+
+    data_df = data_df.drop(cat_columns2, axis=1)
+    data_df = data_df.drop(cat_columns1, axis=1)
+
+    if not remove_cat:
+        data_df['data_channel'] = cat_col1
+        data_df['weekday'] = cat_col2
+    
+    data_save_path = f'data/{name}/{name}.csv'
+    data_df.to_csv(f'{data_save_path}', index = False)
+
+    columns = np.array(data_df.columns.tolist())
+    num_columns = columns[list(range(45))]
+    cat_columns = ['data_channel', 'weekday'] if not remove_cat else []
+    target_columns = columns[[45]]
+
+    info['num_col_idx'] = list(range(45))
+    info['cat_col_idx'] = [46, 47] if not remove_cat else []
+    info['target_col_idx'] = [45]
+    info['data_path'] = data_save_path
+    
+    with open(f'{INFO_PATH}/{name}.json', 'w') as file:
+        json.dump(info, file, indent=4)
+        
+def preprocess_news_dcr(remove_cat=False):
+    name = 'news_dcr' if not remove_cat else 'news_nocat_dcr'
+    with open(f'{INFO_PATH}/{name}.json', 'r') as f:
+        info = json.load(f)
+
+    data_path = info['raw_data_path']
+    data_df = pd.read_csv(data_path)
+    data_df = data_df.drop('url', axis=1)
+
+    columns = np.array(data_df.columns.tolist())
+
+    cat_columns1 = columns[list(range(12,18))]
+    cat_columns2 = columns[list(range(30,38))]
+    
+    if not remove_cat:
+        cat_col1 = data_df[cat_columns1].astype(int).to_numpy().argmax(axis = 1)
+        cat_col2 = data_df[cat_columns2].astype(int).to_numpy().argmax(axis = 1)
+
+    data_df = data_df.drop(cat_columns2, axis=1)
+    data_df = data_df.drop(cat_columns1, axis=1)
+
+    if not remove_cat:
+        data_df['data_channel'] = cat_col1
+        data_df['weekday'] = cat_col2
+    
+    data_save_path = f'data/{name}/{name}.csv'
+    data_df.to_csv(f'{data_save_path}', index = False)
+
+    columns = np.array(data_df.columns.tolist())
+    num_columns = columns[list(range(45))]
+    cat_columns = ['data_channel', 'weekday'] if not remove_cat else []
+    target_columns = columns[[45]]
+
+    info['num_col_idx'] = list(range(45))
+    info['cat_col_idx'] = [46, 47] if not remove_cat else []
+    info['target_col_idx'] = [45]
+    info['data_path'] = data_save_path
+    
+    with open(f'{INFO_PATH}/{name}.json', 'w') as file:
+        json.dump(info, file, indent=4)
+    
+def preprocess_diabetes():
+    """
+    Preprocesses the diabetes dataset is aligned with the concurrent work
+    Continuous Diffusion for Mixed-Type Tabular Data (CDTD):
+    https://github.com/muellermarkus/cdtd
+    """
+    with open(f'{INFO_PATH}/diabetes.json', 'r') as f:
+        info = json.load(f)
+
+    info['num_col_idx'] = list(range(9))
+    info['cat_col_idx'] = list(range(9, 36))
+    info['target_col_idx'] = [36]
+    
+    data_path = info['raw_data_path']
+    df = pd.read_csv(data_path, sep=',')
+    df = df[info['column_names']]
+    df = df.replace(r' ', np.nan)
+    df = df.replace(r'?', np.nan)
+    df = df.replace(r'', np.nan)
+    
+    num_features = [info['column_names'][idx] for idx in info['num_col_idx']]
+    cat_features = [info['column_names'][idx] for idx in info['cat_col_idx']]
+    target = info['column_names'][info['target_col_idx'][0]]
+    df[target] = np.where(df[target] == 'NO', 0, 1)
+    enc = OrdinalEncoder()
+    df['age'] = enc.fit_transform(df['age'].to_numpy().reshape(-1,1))
+    
+    # remove rows with missings in targets
+    idx_target_nan = df[target].isna().to_numpy().nonzero()[0]
+    df.drop(labels = idx_target_nan, axis = 0, inplace = True)
+    
+    # for categorical features, replace missings with 'empty', which will be counted as a new category
+    df[cat_features] = df[cat_features].fillna('empty')
+    
+    # for continuous data, drop missing
+    df.dropna(inplace = True)
+    
+    # ensure correct types
+    X_cat = df[cat_features].to_numpy().astype('str')
+    X_cont = df[num_features].to_numpy().astype('float')
+    y = df[[target]].to_numpy()
+    
+    val_prop, test_prop = 0.2, 0.2
+    prop = val_prop / (1 - test_prop) 
+    
+    stratify = None if info['task_type'] == 'regression' else y
+    X_cat_train, X_cat_test, X_cont_train, X_cont_test, y_train, y_test = \
+        model_selection.train_test_split(X_cat, X_cont, y, test_size = test_prop, 
+                                        stratify = stratify, random_state = 42)
+    if val_prop > 0:
+        stratify = None if info['task_type'] == 'regression' else y_train
+        X_cat_train, X_cat_val, X_cont_train, X_cont_val, y_train, y_val = \
+            model_selection.train_test_split(X_cat_train, X_cont_train, y_train,
+                                            stratify = stratify, test_size = prop, 
+                                            random_state = 42)
+    
+    train_df = pd.DataFrame(np.concatenate([X_cont_train, X_cat_train, y_train], axis = 1), columns = num_features + cat_features + [target])
+    val_df = pd.DataFrame(np.concatenate([X_cont_val, X_cat_val, y_val], axis = 1), columns = num_features + cat_features + [target])
+    test_df = pd.DataFrame(np.concatenate([X_cont_test, X_cat_test, y_test], axis = 1), columns = num_features + cat_features + [target])
+
+    # Save the splited data
+    train_df.to_csv(info['data_path'], index = False)
+    val_df.to_csv(info['val_path'], index = False)
+    test_df.to_csv(info['test_path'], index = False)
+    # Save updated info
+    with open(f'{INFO_PATH}/diabetes.json', 'w') as file:
+        json.dump(info, file, indent=4)
+        
+def preprocess_diabetes_dcr():
+    """
+    Preprocesses the diabetes dataset is aligned with the concurrent work
+    Continuous Diffusion for Mixed-Type Tabular Data (CDTD):
+    https://github.com/muellermarkus/cdtd
+    """
+    with open(f'{INFO_PATH}/diabetes_dcr.json', 'r') as f:
+        info = json.load(f)
+
+    info['num_col_idx'] = list(range(9))
+    info['cat_col_idx'] = list(range(9, 36))
+    info['target_col_idx'] = [36]
+    
+    data_path = info['raw_data_path']
+    df = pd.read_csv(data_path, sep=',')
+    df = df[info['column_names']]
+    df = df.replace(r' ', np.nan)
+    df = df.replace(r'?', np.nan)
+    df = df.replace(r'', np.nan)
+    
+    num_features = [info['column_names'][idx] for idx in info['num_col_idx']]
+    cat_features = [info['column_names'][idx] for idx in info['cat_col_idx']]
+    target = info['column_names'][info['target_col_idx'][0]]
+    df[target] = np.where(df[target] == 'NO', 0, 1)
+    enc = OrdinalEncoder()
+    df['age'] = enc.fit_transform(df['age'].to_numpy().reshape(-1,1))
+    
+    # remove rows with missings in targets
+    idx_target_nan = df[target].isna().to_numpy().nonzero()[0]
+    df.drop(labels = idx_target_nan, axis = 0, inplace = True)
+    
+    # for categorical features, replace missings with 'empty', which will be counted as a new category
+    df[cat_features] = df[cat_features].fillna('empty')
+    
+    # for continuous data, drop missing
+    df.dropna(inplace = True)
+    
+    # ensure correct types
+    X_cat = df[cat_features].to_numpy().astype('str')
+    X_cont = df[num_features].to_numpy().astype('float')
+    y = df[[target]].to_numpy()
+    
+    val_prop, test_prop = 0.0, 0.5      # 50-50 split for dcr eval
+    prop = val_prop / (1 - test_prop) 
+    
+    stratify = None if info['task_type'] == 'regression' else y
+    X_cat_train, X_cat_test, X_cont_train, X_cont_test, y_train, y_test = \
+        model_selection.train_test_split(X_cat, X_cont, y, test_size = test_prop, 
+                                        stratify = stratify, random_state = 42)
+    if val_prop > 0:
+        stratify = None if info['task_type'] == 'regression' else y_train
+        X_cat_train, X_cat_val, X_cont_train, X_cont_val, y_train, y_val = \
+            model_selection.train_test_split(X_cat_train, X_cont_train, y_train,
+                                            stratify = stratify, test_size = prop, 
+                                            random_state = 42)
+    
+    train_df = pd.DataFrame(np.concatenate([X_cont_train, X_cat_train, y_train], axis = 1), columns = num_features + cat_features + [target])
+    if val_prop > 0:
+        val_df = pd.DataFrame(np.concatenate([X_cont_val, X_cat_val, y_val], axis = 1), columns = num_features + cat_features + [target])
+    else:
+        val_df = pd.DataFrame(columns = num_features + cat_features + [target]).astype(train_df.dtypes)
+    test_df = pd.DataFrame(np.concatenate([X_cont_test, X_cat_test, y_test], axis = 1), columns = num_features + cat_features + [target])
+
+    # Save the splited data
+    train_df.to_csv(info['data_path'], index = False)
+    val_df.to_csv(info['val_path'], index = False)
+    test_df.to_csv(info['test_path'], index = False)
+    # Save updated info
+    with open(f'{INFO_PATH}/diabetes_dcr.json', 'w') as file:
+        json.dump(info, file, indent=4)
+    
+
+
+def get_column_name_mapping(data_df, num_col_idx, cat_col_idx, target_col_idx, column_names = None):
+    
+    if not column_names:
+        column_names = np.array(data_df.columns.tolist())
+    
+
+    idx_mapping = {}
+
+    curr_num_idx = 0
+    curr_cat_idx = len(num_col_idx)
+    curr_target_idx = curr_cat_idx + len(cat_col_idx)
+
+    for idx in range(len(column_names)):
+
+        if idx in num_col_idx:
+            idx_mapping[int(idx)] = curr_num_idx
+            curr_num_idx += 1
+        elif idx in cat_col_idx:
+            idx_mapping[int(idx)] = curr_cat_idx
+            curr_cat_idx += 1
+        else:
+            idx_mapping[int(idx)] = curr_target_idx
+            curr_target_idx += 1
+
+
+    inverse_idx_mapping = {}
+    for k, v in idx_mapping.items():
+        inverse_idx_mapping[int(v)] = k
+        
+    idx_name_mapping = {}
+    
+    for i in range(len(column_names)):
+        idx_name_mapping[int(i)] = column_names[i]
+
+    return idx_mapping, inverse_idx_mapping, idx_name_mapping
+
+
+def train_val_test_split(data_df, cat_columns, num_train = 0, num_test = 0):
+    total_num = data_df.shape[0]
+    idx = np.arange(total_num)
+
+
+    seed = 1234
+
+    while True:
+        np.random.seed(seed)
+        np.random.shuffle(idx)
+
+        train_idx = idx[:num_train]
+        test_idx = idx[-num_test:]
+
+
+        train_df = data_df.loc[train_idx]
+        test_df = data_df.loc[test_idx]
+
+
+
+        flag = 0
+        for i in cat_columns:
+            if len(set(train_df[i])) != len(set(data_df[i])):
+                flag = 1
+                break
+
+        if flag == 0:
+            break
+        else:
+            seed += 1
+        
+    return train_df, test_df, seed    
+
+
+def process_data(name):
+
+    if name == 'news':
+        preprocess_news()
+    elif name == 'news_nocat':
+        preprocess_news(remove_cat=True)
+    elif name == 'news_dcr':
+        preprocess_news_dcr()
+    elif name == 'beijing':
+        preprocess_beijing()
+    elif name == 'beijing_dcr':
+        preprocess_beijing_dcr()
+    elif name == 'diabetes':
+        preprocess_diabetes()
+    elif name == 'diabetes_dcr':
+        preprocess_diabetes_dcr()
+
+    with open(f'{INFO_PATH}/{name}.json', 'r') as f:
+        info = json.load(f)
+
+    data_path = info['data_path']
+    if info['file_type'] == 'csv':
+        data_df = pd.read_csv(data_path, header = info['header'])
+
+    elif info['file_type'] == 'xls':
+        data_df = pd.read_excel(data_path, sheet_name='Data', header=1)
+        data_df = data_df.drop('ID', axis=1)
+
+    num_data = data_df.shape[0]
+
+    column_names = info['column_names'] if info['column_names'] else data_df.columns.tolist()
+ 
+    num_col_idx = info['num_col_idx']
+    cat_col_idx = info['cat_col_idx']
+    target_col_idx = info['target_col_idx']
+
+    num_columns = [column_names[i] for i in num_col_idx]
+    cat_columns = [column_names[i] for i in cat_col_idx]
+    target_columns = [column_names[i] for i in target_col_idx]
+    
+    idx_mapping, inverse_idx_mapping, idx_name_mapping = get_column_name_mapping(data_df, num_col_idx, cat_col_idx, target_col_idx, column_names)
+
+    has_val = bool(info['val_path'])
+    val_df = pd.DataFrame(columns=data_df.columns).astype(data_df.dtypes)   # by default (val_path is not provided), set val_Df to be empty
+    if info['test_path']:
+
+        # if testing data is given
+        test_path = info['test_path']
+        
+        if "adult" in name:     # BUG: currently data saved at adult's test_path cannot be directly loaded. Consider integrate the following code to a preprocesing function for adult
+            with open(test_path, 'r') as f:
+                lines = f.readlines()[1:]
+                test_save_path = f'data/{name}/test.data'
+                if not os.path.exists(test_save_path):
+                    with open(test_save_path, 'a') as f1:     
+                        for line in lines:
+                            save_line = line.strip('\n').strip('.')
+                            f1.write(f'{save_line}\n')
+
+            test_df = pd.read_csv(test_save_path, header = None)
+        else:
+            test_df = pd.read_csv(test_path, header = info['header'])
+            
+        if has_val:     # currently you cannot have a val path without a test path
+            val_path = info['val_path']
+            val_df = pd.read_csv(val_path, header = info['header'])
+            
+        train_df = data_df
+        
+        if "dcr" in name and "diabetes" not in name:   # create 50/50 splits for dcr datasets; no need for this for diabetes dataset as it's done in preprocessing
+            complete_df = pd.concat([train_df, test_df, val_df], axis = 0, ignore_index=True)
+            num_data = complete_df.shape[0]
+            num_train = int(num_data*0.5)
+            num_test = num_data - num_train
+            complete_df.rename(columns = idx_name_mapping, inplace=True)
+            train_df, test_df, seed = train_val_test_split(complete_df, cat_columns, num_train, num_test)
+
+    else:  
+        # Train/ Test Split, 90% Training (50% for dcr eval exclusively), 10% Testing (Validation set will be selected from Training set)
+        if "dcr" in name:
+            num_train = int(num_data*0.5)
+        else:
+            num_train = int(num_data*0.9)
+        num_test = num_data - num_train
+
+        train_df, test_df, seed = train_val_test_split(data_df, cat_columns, num_train, num_test)
+    
+    complete_df = pd.concat([train_df, test_df, val_df], axis = 0)
+    name_idx_mapping = {val: key for key, val in idx_name_mapping.items()}
+    int_columns = []
+    int_col_idx = []
+    int_col_idx_wrt_num = []
+    for i, col_idx in enumerate(num_col_idx):
+        col = column_names[col_idx]
+        col_data = complete_df.iloc[:,col_idx]
+        is_int = (col_data%1 == 0).all()
+        if is_int:
+            int_columns.append(col)
+            int_col_idx.append(name_idx_mapping[col])
+            int_col_idx_wrt_num.append(i)
+    info['int_col_idx'] = int_col_idx
+    info['int_columns'] = int_columns
+    info['int_col_idx_wrt_num'] = int_col_idx_wrt_num
+
+    train_df.columns = range(len(train_df.columns))
+    test_df.columns = range(len(test_df.columns))
+    val_df.columns = range(len(val_df.columns))
+
+    print(name, train_df.shape, val_df.shape, test_df.shape, data_df.shape)
+
+    col_info = {}
+    
+    for col_idx in num_col_idx:
+        col_info[col_idx] = {}
+        col_info['type'] = 'numerical'
+        col_info['max'] = float(train_df[col_idx].max())
+        col_info['min'] = float(train_df[col_idx].min())
+     
+    for col_idx in cat_col_idx:
+        col_info[col_idx] = {}
+        col_info['type'] = 'categorical'
+        col_info['categorizes'] = list(set(train_df[col_idx]))    
+
+    for col_idx in target_col_idx:
+        if info['task_type'] == 'regression':
+            col_info[col_idx] = {}
+            col_info['type'] = 'numerical'
+            col_info['max'] = float(train_df[col_idx].max())
+            col_info['min'] = float(train_df[col_idx].min())
+        else:
+            col_info[col_idx] = {}
+            col_info['type'] = 'categorical'
+            col_info['categorizes'] = list(set(train_df[col_idx]))      
+
+    info['column_info'] = col_info
+
+    train_df.rename(columns = idx_name_mapping, inplace=True)
+    test_df.rename(columns = idx_name_mapping, inplace=True)
+    val_df.rename(columns = idx_name_mapping, inplace=True)
+
+    for col in num_columns:
+        if (train_df[col] == ' ?').sum() > 0:
+            print(col)
+            import pdb; pdb.set_trace()
+        if (train_df[col] == '?').sum() > 0:
+            print(col)
+            import pdb; pdb.set_trace()
+        train_df.loc[train_df[col] == '?', col] = np.nan
+    for col in cat_columns:
+        train_df.loc[train_df[col] == '?', col] = 'nan'
+    for col in num_columns:
+        if (test_df[col] == ' ?').sum() > 0:
+            print(col)
+            import pdb; pdb.set_trace()
+        if (test_df[col] == '?').sum() > 0:
+            print(col)
+            import pdb; pdb.set_trace()
+        test_df.loc[test_df[col] == '?', col] = np.nan
+    for col in cat_columns:
+        test_df.loc[test_df[col] == '?', col] = 'nan'
+    for col in num_columns:
+        val_df.loc[val_df[col] == '?', col] = np.nan
+    for col in cat_columns:
+        val_df.loc[val_df[col] == '?', col] = 'nan'
+    
+    if train_df.isna().any().any():
+        print("Training data contains nan in the numerical cols")
+        import pdb; pdb.set_trace()
+
+
+    
+    X_num_train = train_df[num_columns].to_numpy().astype(np.float32)
+    X_cat_train = train_df[cat_columns].to_numpy()
+    y_train = train_df[target_columns].to_numpy()
+
+
+    X_num_test = test_df[num_columns].to_numpy().astype(np.float32)
+    X_cat_test = test_df[cat_columns].to_numpy()
+    y_test = test_df[target_columns].to_numpy()
+
+    X_num_val = val_df[num_columns].to_numpy().astype(np.float32)
+    X_cat_val = val_df[cat_columns].to_numpy()
+    y_val = val_df[target_columns].to_numpy()
+ 
+    save_dir = f'data/{name}'
+    np.save(f'{save_dir}/X_num_train.npy', X_num_train)
+    np.save(f'{save_dir}/X_cat_train.npy', X_cat_train)
+    np.save(f'{save_dir}/y_train.npy', y_train)
+
+    np.save(f'{save_dir}/X_num_test.npy', X_num_test)
+    np.save(f'{save_dir}/X_cat_test.npy', X_cat_test)
+    np.save(f'{save_dir}/y_test.npy', y_test)
+    
+    if has_val:
+        np.save(f'{save_dir}/X_num_val.npy', X_num_val)
+        np.save(f'{save_dir}/X_cat_val.npy', X_cat_val)
+        np.save(f'{save_dir}/y_val.npy', y_val)
+
+    train_df[num_columns] = train_df[num_columns].astype(np.float32)
+    test_df[num_columns] = test_df[num_columns].astype(np.float32)
+    val_df[num_columns] = val_df[num_columns].astype(np.float32)
+
+
+    train_df.to_csv(f'{save_dir}/train.csv', index = False)
+    test_df.to_csv(f'{save_dir}/test.csv', index = False)
+    if has_val:
+        val_df.to_csv(f'{save_dir}/val.csv', index = False)
+
+    if not os.path.exists(f'synthetic/{name}'):
+        os.makedirs(f'synthetic/{name}')
+    
+    train_df.to_csv(f'synthetic/{name}/real.csv', index = False)
+    test_df.to_csv(f'synthetic/{name}/test.csv', index = False)
+    
+    if has_val:
+        val_df.to_csv(f'synthetic/{name}/val.csv', index = False)
+
+    print('Numerical', X_num_train.shape)
+    print('Categorical', X_cat_train.shape)
+
+    info['column_names'] = column_names
+    info['train_num'] = train_df.shape[0]
+    info['test_num'] = test_df.shape[0]
+    info['val_num'] = val_df.shape[0]
+
+    info['idx_mapping'] = idx_mapping
+    info['inverse_idx_mapping'] = inverse_idx_mapping
+    info['idx_name_mapping'] = idx_name_mapping 
+
+    metadata = {'columns': {}}
+    task_type = info['task_type']
+    num_col_idx = info['num_col_idx']
+    cat_col_idx = info['cat_col_idx']
+    target_col_idx = info['target_col_idx']
+
+    for i in num_col_idx:
+        metadata['columns'][i] = {}
+        metadata['columns'][i]['sdtype'] = 'numerical'
+        metadata['columns'][i]['computer_representation'] = 'Float'
+
+    for i in cat_col_idx:
+        metadata['columns'][i] = {}
+        metadata['columns'][i]['sdtype'] = 'categorical'
+
+
+    if task_type == 'regression':
+        
+        for i in target_col_idx:
+            metadata['columns'][i] = {}
+            metadata['columns'][i]['sdtype'] = 'numerical'
+            metadata['columns'][i]['computer_representation'] = 'Float'
+
+    else:
+        for i in target_col_idx:
+            metadata['columns'][i] = {}
+            metadata['columns'][i]['sdtype'] = 'categorical'
+
+    info['metadata'] = metadata
+
+    with open(f'{save_dir}/info.json', 'w') as file:
+        json.dump(info, file, indent=4)
+
+    print(f'Processing and Saving {name} Successfully!')
+
+    print(name)
+    print('Total', info['train_num'] + info['test_num'])
+    print('Train', info['train_num'])
+    print('Val', info['val_num'])
+    print('Test', info['test_num'])
+    if info['task_type'] == 'regression':
+        num = len(info['num_col_idx'] + info['target_col_idx'])
+        cat = len(info['cat_col_idx'])
+    else:
+        cat = len(info['cat_col_idx'] + info['target_col_idx'])
+        num = len(info['num_col_idx'])
+    print('Num', num)
+    print('Int', len(info['int_col_idx']))
+    print('Cat', cat)
+
+
+if __name__ == "__main__":
+
+    if args.dataname:
+        process_data(args.dataname)
+    else:
+        for name in [
+                'adult', 'default', 'shoppers', 'magic', 'beijing', 'news', 'news_nocat', 'diabetes',
+                'adult_dcr',
+                'default_dcr',
+                'shoppers_dcr',
+                'beijing_dcr',
+                'news_dcr', 
+                'diabetes_dcr'
+            ]:    
+            process_data(name)
+
+        
+

SynthData0523/hyper_parameter_tuning/n11/tabdiff/runs/tabdiff-n11-20260521_040854/_tabdiff_runtime/src/__init__.py

@@ -0,0 +1,11 @@
+import torch
+from icecream import install
+
+torch.set_num_threads(1)
+install()
+
+from . import env  # noqa
+from .data import *  # noqa
+from .env import *  # noqa
+from .metrics import *  # noqa
+from .util import *  # noqa

SynthData0523/hyper_parameter_tuning/n11/tabdiff/runs/tabdiff-n11-20260521_040854/_tabdiff_runtime/src/data.py

@@ -0,0 +1,780 @@
+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']
+DEQUANT_DIST = Literal['uniform', 'beta', 'round', 'none']
+
+
+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]:
+    XT = X.T.cpu().tolist() if isinstance(X, torch.Tensor) else X.T.tolist()
+    return [len(set(x)) for x in XT]
+
+
+@dataclass(frozen=False)
+class Dataset:
+    X_num: Optional[ArrayDict]
+    X_cat: Optional[ArrayDict]
+    y: ArrayDict
+    int_col_idx_wrt_num: list
+    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', '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
+        print('No NaNs in numerical features, skipping')
+        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()}
+
+class dequantizer:
+    def __init__(
+        self,
+        dequant_dist: DEQUANT_DIST,
+        int_col_idx_wrt_num: list,
+        int_dequant_factor: float,
+        # return_dequantizer: bool = False
+    ):
+        self.dequant_dist = dequant_dist
+        self.int_col_idx_wrt_num = int_col_idx_wrt_num
+        self.int_dequant_factor = int_dequant_factor
+    def transform(self, X):
+        X_int = X[:, self.int_col_idx_wrt_num]
+        if self.dequant_dist == 'uniform':
+            X[:, self.int_col_idx_wrt_num] = X_int+ np.random.uniform(size=X_int.shape) * self.int_dequant_factor
+        elif self.dequant_dist == 'beta':
+            X[:, self.int_col_idx_wrt_num] = X_int + np.random.beta(self.int_dequant_factor, self.int_dequant_factor, size=X_int.shape) - 0.5
+        elif self.dequant_dist in ['round', 'none']:
+            pass
+        return X
+    def inverse_transform(self, X):
+        X_int = X[:, self.int_col_idx_wrt_num]
+        if self.dequant_dist == 'uniform':
+            X[:, self.int_col_idx_wrt_num] = np.floor(X_int)
+        elif self.dequant_dist == 'beta':
+            X[:, self.int_col_idx_wrt_num] = np.rint(X_int)
+        elif self.dequant_dist == 'round':
+            X[:, self.int_col_idx_wrt_num] = np.rint(X_int)
+        elif self.dequant_dist == 'none':
+            pass
+        return X
+    
+    
+    # if return_dequantizer:
+    #     return {k: transform(v) for k, v in X.items()}, inverse_transform
+    # return {k: 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: v == CAT_MISSING_VALUE for k, v in X.items()}
+    if any(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_output=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'
+    dequant_dist: Optional[DEQUANT_DIST] = None
+    int_dequant_factor: Optional[float] = 0.0
+
+
+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
+    int_transform = None
+    cat_transform = None
+    X_num = dataset.X_num
+
+    int_col_idx_wrt_num = dataset.int_col_idx_wrt_num
+    if X_num is not None and int_col_idx_wrt_num and transformations.dequant_dist is not None:
+        int_transform = dequantizer(
+            transformations.dequant_dist,
+            int_col_idx_wrt_num, 
+            transformations.int_dequant_factor,
+        )
+        X_num = {k: int_transform.transform(v) for k, v in X_num.items()}
+    
+    if X_num is not None and transformations.normalization is not None:
+        has_num = all([x.shape[1]>0 for x in dataset.X_num.values()])
+        if has_num:
+            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:
+        has_cat = all([x.shape[1]>0 for x in dataset.X_cat.values()])
+        if not has_cat:
+            assert transformations.cat_nan_policy is None
+            assert transformations.cat_min_frequency is None
+            X_cat = dataset.X_cat
+            for split in X_cat.keys():   # a patch to make sure that the empty array is transformed into int dtype
+                X_cat[split] = X_cat[split].astype(np.int64)
+        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.int_transform = int_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=int(os.environ.get('TABDIFF_NUM_WORKERS', '0')),
+    )
+    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=int(os.environ.get('TABDIFF_NUM_WORKERS', '0')))
+
+    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
+):
+   
+    X = torch.from_numpy(np.concatenate([D.X_num[split], D.X_cat[split]], axis=1)).float()
+    dataloader = FastTensorDataLoader(X, 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

SynthData0523/hyper_parameter_tuning/n11/tabdiff/runs/tabdiff-n11-20260521_040854/_tabdiff_runtime/src/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)