repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
interpretability | interpretability-master/text-dream/python/dream/tokenize_sentence.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 1,506 | 34.046512 | 80 | py |
interpretability | interpretability-master/text-dream/python/dream/similar_embedding_activation.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 6,200 | 39.529412 | 80 | py |
interpretability | interpretability-master/text-dream/python/dream/reconstruct_activation.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 10,106 | 46.228972 | 80 | py |
interpretability | interpretability-master/text-dream/python/dream/reconstruct_shifted_activation.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 12,102 | 45.55 | 82 | py |
interpretability | interpretability-master/text-dream/python/dream/token_search.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 5,144 | 35.489362 | 80 | py |
interpretability | interpretability-master/text-dream/python/helpers/one_hots_helper.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 5,144 | 45.351351 | 80 | py |
interpretability | interpretability-master/text-dream/python/helpers/embeddings_helper.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 8,157 | 42.164021 | 80 | py |
interpretability | interpretability-master/text-dream/python/helpers/optimization_helper.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 4,249 | 45.195652 | 80 | py |
interpretability | interpretability-master/text-dream/python/helpers/activation_helper.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 6,274 | 40.833333 | 80 | py |
interpretability | interpretability-master/text-dream/python/helpers/output_helper.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 10,131 | 36.525926 | 80 | py |
interpretability | interpretability-master/text-dream/python/helpers/classifier_helper.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 1,161 | 37.733333 | 80 | py |
interpretability | interpretability-master/text-dream/python/helpers/inference_helper.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 3,806 | 39.935484 | 80 | py |
interpretability | interpretability-master/text-dream/python/helpers/attention_mask_helper.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 1,836 | 41.72093 | 80 | py |
interpretability | interpretability-master/text-dream/python/helpers/tokenization_helper.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 2,630 | 38.268657 | 80 | py |
interpretability | interpretability-master/text-dream/python/helpers/setup_helper.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 3,457 | 35.4 | 80 | py |
interpretability | interpretability-master/text-dream/python/linear_classifier/classify_token.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 2,754 | 38.357143 | 80 | py |
interpretability | interpretability-master/text-dream/python/linear_classifier/train.py | # Copyright 2018 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 10,428 | 38.059925 | 80 | py |
WeatherBench | WeatherBench-master/src/train_nn.py | from .score import *
import os
import numpy as np
import xarray as xr
import tensorflow as tf
import tensorflow.keras as keras
from tensorflow.keras.layers import Input, Dropout, Conv2D, Lambda, LeakyReLU
import tensorflow.keras.backend as K
from configargparse import ArgParser
def limit_mem():
"""Limit TF GPU mem... | 13,309 | 40.59375 | 122 | py |
BOSS | BOSS-main/Eval_Codes/GenNNs_BOSS_MNIST_fashion_HCs.py | import tensorflow as tf
from keras.utils import np_utils
import glob
#import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
#import PIL
from tensorflow.keras import layers
import time
#from skimage.measure import compare_ssim
from skimage.measure import compare_ssim
import pydot
import graphviz... | 17,240 | 32.938976 | 181 | py |
BOSS | BOSS-main/Eval_Codes/GenNNs_BOSS_GTSRB.py | import tensorflow as tf
from keras.utils import np_utils
import glob
#import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
#import PIL
from tensorflow.keras import layers
import time
#from skimage.measure import compare_ssim
from skimage.measure import compare_ssim
import pydot
import graphviz... | 16,912 | 31.153992 | 178 | py |
BOSS | BOSS-main/Eval_Codes/GenNNs_BOSS_MNIST_digits.py | import tensorflow as tf
from keras.utils import np_utils
import glob
#import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
#import PIL
from tensorflow.keras import layers
import time
#from skimage.measure import compare_ssim
from skimage.measure import compare_ssim
import pydot
import graphviz... | 17,703 | 31.724584 | 180 | py |
BOSS | BOSS-main/Eval_Codes/GenNNs_cifar_10.py | import tensorflow as tf
from keras.utils import np_utils
import glob
#import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
#import PIL
from tensorflow.keras import layers
import time
from keras.datasets import cifar10
from keras.layers import GlobalAveragePooling2D, Lambda, Conv2D, MaxPooling2D, ... | 15,923 | 32.244259 | 158 | py |
BOSS | BOSS-main/Eval_Codes/GenNNs_BOSS_COVID_19.py | import tensorflow as tf
from keras.utils import np_utils
import glob
#import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
#import PIL
from tensorflow.keras import layers
import time
#from skimage.measure import compare_ssim
from skimage.measure import compare_ssim
import pydot
import graphviz... | 15,510 | 31.047521 | 158 | py |
BOSS | BOSS-main/Eval_Codes/applications/GenNNs_MNIST_digits_ensemble_two_models_table_diff_target.py | import tensorflow as tf
from keras.utils import np_utils
import glob
#import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
#import PIL
from tensorflow.keras import layers
import time
#from skimage.measure import compare_ssim
from skimage.measure import compare_ssim
import pydot
import graphviz... | 27,324 | 38.316547 | 201 | py |
BOSS | BOSS-main/Eval_Codes/applications/GenNNs_MNIST_digits_confide_reduction.py | import tensorflow as tf
from keras.utils import np_utils
import glob
#import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
#import PIL
from tensorflow.keras import layers
import time
#from skimage.measure import compare_ssim
from skimage.measure import compare_ssim
import pydot
import graphviz... | 24,839 | 36.185629 | 207 | py |
BOSS | BOSS-main/Eval_Codes/applications/GenNNs_MNIST_digits_targeted_attack.py | import tensorflow as tf
from keras.utils import np_utils
import glob
#import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
#import PIL
from tensorflow.keras import layers
import time
#from skimage.measure import compare_ssim
from skimage.measure import compare_ssim
import pydot
import graphviz... | 23,610 | 35.380586 | 180 | py |
BOSS | BOSS-main/Eval_Codes/applications/GenNNs_MNIST_digits_boudary_examples.py | import tensorflow as tf
from keras.utils import np_utils
import glob
#import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
#import PIL
from tensorflow.keras import layers
import time
#from skimage.measure import compare_ssim
from skimage.measure import compare_ssim
import pydot
import graphviz... | 22,178 | 35.359016 | 180 | py |
BOSS | BOSS-main/Eval_Codes/applications/GenNNs_MNIST_digits_ensemble_two_models_table_same_target.py | import tensorflow as tf
from keras.utils import np_utils
import glob
#import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
#import PIL
from tensorflow.keras import layers
import time
#from skimage.measure import compare_ssim
from skimage.measure import compare_ssim
import pydot
import graphviz... | 25,026 | 38.227273 | 197 | py |
AGCRN | AGCRN-master/model/AGCRN.py | import torch
import torch.nn as nn
from model.AGCRNCell import AGCRNCell
class AVWDCRNN(nn.Module):
def __init__(self, node_num, dim_in, dim_out, cheb_k, embed_dim, num_layers=1):
super(AVWDCRNN, self).__init__()
assert num_layers >= 1, 'At least one DCRNN layer in the Encoder.'
self.node_n... | 3,454 | 44.460526 | 113 | py |
AGCRN | AGCRN-master/model/Run.py |
import os
import sys
file_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
print(file_dir)
sys.path.append(file_dir)
import torch
import numpy as np
import torch.nn as nn
import argparse
import configparser
from datetime import datetime
from model.AGCRN import AGCRN as Network
from model.BasicTrainer... | 6,865 | 43.584416 | 129 | py |
AGCRN | AGCRN-master/model/AGCN.py | import torch
import torch.nn.functional as F
import torch.nn as nn
class AVWGCN(nn.Module):
def __init__(self, dim_in, dim_out, cheb_k, embed_dim):
super(AVWGCN, self).__init__()
self.cheb_k = cheb_k
self.weights_pool = nn.Parameter(torch.FloatTensor(embed_dim, cheb_k, dim_in, dim_out))
... | 1,453 | 54.923077 | 112 | py |
AGCRN | AGCRN-master/model/AGCRNCell.py | import torch
import torch.nn as nn
from model.AGCN import AVWGCN
class AGCRNCell(nn.Module):
def __init__(self, node_num, dim_in, dim_out, cheb_k, embed_dim):
super(AGCRNCell, self).__init__()
self.node_num = node_num
self.hidden_dim = dim_out
self.gate = AVWGCN(dim_in+self.hidden_d... | 1,065 | 40 | 80 | py |
AGCRN | AGCRN-master/model/BasicTrainer.py | import torch
import math
import os
import time
import copy
import numpy as np
from lib.logger import get_logger
from lib.metrics import All_Metrics
class Trainer(object):
def __init__(self, model, loss, optimizer, train_loader, val_loader, test_loader,
scaler, args, lr_scheduler=None):
sup... | 8,935 | 42.590244 | 148 | py |
AGCRN | AGCRN-master/lib/TrainInits.py | import torch
import random
import numpy as np
def init_seed(seed):
'''
Disable cudnn to maximize reproducibility
'''
torch.cuda.cudnn_enabled = False
torch.backends.cudnn.deterministic = True
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)... | 1,818 | 33.980769 | 120 | py |
AGCRN | AGCRN-master/lib/dataloader.py | import torch
import numpy as np
import torch.utils.data
from lib.add_window import Add_Window_Horizon
from lib.load_dataset import load_st_dataset
from lib.normalization import NScaler, MinMax01Scaler, MinMax11Scaler, StandardScaler, ColumnMinMaxScaler
def normalize_dataset(data, normalizer, column_wise=False):
if... | 5,728 | 42.401515 | 154 | py |
AGCRN | AGCRN-master/lib/metrics.py | '''
Always evaluate the model with MAE, RMSE, MAPE, RRSE, PNBI, and oPNBI.
Why add mask to MAE and RMSE?
Filter the 0 that may be caused by error (such as loop sensor)
Why add mask to MAPE and MARE?
Ignore very small values (e.g., 0.5/0.5=100%)
'''
import numpy as np
import torch
def MAE_torch(pred, true, mask... | 7,947 | 34.641256 | 103 | py |
AGCRN | AGCRN-master/lib/normalization.py | import numpy as np
import torch
class NScaler(object):
def transform(self, data):
return data
def inverse_transform(self, data):
return data
class StandardScaler:
"""
Standard the input
"""
def __init__(self, mean, std):
self.mean = mean
self.std = std
de... | 4,047 | 30.138462 | 93 | py |
a3c_continuous | a3c_continuous-master/main.py | from __future__ import print_function, division
import os
os.environ["OMP_NUM_THREADS"] = "1"
import argparse
import torch
import torch.multiprocessing as mp
from environment import create_env
from model import A3C_MLP, A3C_CONV
from train import train
from test import test
from shared_optim import SharedRMSprop, Shar... | 5,128 | 25.713542 | 131 | py |
a3c_continuous | a3c_continuous-master/test.py | from __future__ import division
import os
os.environ["OMP_NUM_THREADS"] = "1"
from setproctitle import setproctitle as ptitle
import numpy as np
import torch
from environment import create_env
from utils import setup_logger
from model import A3C_CONV, A3C_MLP
from player_util import Agent
from torch.autograd import Va... | 4,727 | 35.091603 | 190 | py |
a3c_continuous | a3c_continuous-master/shared_optim.py | from __future__ import division
import math
import torch
import torch.optim as optim
from collections import defaultdict
from math import sqrt
class SharedRMSprop(optim.Optimizer):
"""Implements RMSprop algorithm with shared states."""
def __init__(
self,
params,
lr=7e-4,
alph... | 6,802 | 34.06701 | 100 | py |
a3c_continuous | a3c_continuous-master/player_util.py | from __future__ import division
import os
os.environ["OMP_NUM_THREADS"] = "1"
from math import pi as PI
import numpy as np
from numpy import fromiter, float32
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from utils import normal # , pi
class Agent(object):
def __init__(self, ... | 3,619 | 33.807692 | 77 | py |
a3c_continuous | a3c_continuous-master/utils.py | from __future__ import division
from math import pi as PI
import numpy as np
import torch
from torch.autograd import Variable
import json
import logging
def setup_logger(logger_name, log_file, level=logging.INFO):
l = logging.getLogger(logger_name)
formatter = logging.Formatter("%(asctime)s : %(message)s")
... | 2,514 | 29.670732 | 82 | py |
a3c_continuous | a3c_continuous-master/model.py | from __future__ import division
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.autograd import Variable
from utils import norm_col_init, weights_init, weights_init_mlp
class A3C_CONV(torch.nn.Module):
def __init__(self, num_inputs, action_space, args):
... | 5,526 | 34.203822 | 70 | py |
a3c_continuous | a3c_continuous-master/gym_eval.py | from __future__ import division
import os
os.environ["OMP_NUM_THREADS"] = "1"
import argparse
import torch
from environment import create_env
from utils import setup_logger
from model import A3C_CONV, A3C_MLP
from player_util import Agent
from torch.autograd import Variable
import gym
import logging
import time
gym.l... | 4,450 | 25.337278 | 190 | py |
a3c_continuous | a3c_continuous-master/train.py | from __future__ import division
import os
os.environ["OMP_NUM_THREADS"] = "1"
from setproctitle import setproctitle as ptitle
import numpy as np
import torch
import torch.optim as optim
from environment import create_env
from utils import ensure_shared_grads
from model import A3C_CONV, A3C_MLP
from player_util import ... | 4,686 | 35.333333 | 81 | py |
wikiworkshop2023_imgaccessibility | wikiworkshop2023_imgaccessibility-main/human_alt_quality_exp/study/trial_info/sample_data.py | import pandas as pd
import json
import numpy as np
import random
import shutil
import random
import os
import torch
import csv
import sys
sys.path.insert(0, '../../../models/03_xu2015/code')
import caption
gpu_id = 0
os.environ['KMP_DUPLICATE_LIB_OK']='True'
device = torch.device("cuda:"+str(gpu_id) if torch.cuda.is_a... | 3,722 | 33.155963 | 189 | py |
Meta-RegGNN | Meta-RegGNN-main/evaluators.py | '''
Functions for k-fold evaluation of models.
'''
import random
import pickle
import numpy as np
from sklearn.model_selection import KFold
import torch
import proposed_method.data_utils as data_utils
from proposed_method.MetaRegGNN import MetaRegGNN
from collections import OrderedDict
from config import Config
def ... | 4,454 | 43.108911 | 123 | py |
Meta-RegGNN | Meta-RegGNN-main/demo.py | '''
Main file for creating simulated data or loading real data
and running MetaRegGNN and sample selection methods.
Usage:
For data processing:
python demo.py --mode data
For inferences:
python demo.py --mode infer
For more information:
python demo.py -h
'''
import argparse
impor... | 2,264 | 31.357143 | 114 | py |
Meta-RegGNN | Meta-RegGNN-main/proposed_method/MetaRegGNN.py | '''MetaRegGNN regression model architecture.
torch_geometric needs to be installed.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn.dense import DenseGCNConv
from torchmeta.modules import MetaModule
class MetaRegGNN(MetaModule):
'''Regression using a DenseGCNConv l... | 1,053 | 26.736842 | 68 | py |
Meta-RegGNN | Meta-RegGNN-main/proposed_method/data_utils.py | import copy
import scipy.io # to read .mat files
from scipy.sparse import coo_matrix
from sklearn.model_selection import KFold
import numpy as np
import pandas as pd
import torch
import torch_geometric
from config import Config
DATA_PATH = "/home/latis/Documents/RegGNN/IQ_data_clean/" # this should point to the dir... | 5,565 | 37.386207 | 125 | py |
PAC-Bayesian_Sliced-Wasserstein | PAC-Bayesian_Sliced-Wasserstein-main/swd_pac.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import torch
import torch.nn as nn
from torch import optim
import torch.utils.data as data_utils
from utils import rand_projections, TransformNet, Wasserstein_1D, sliced_wasserstein_distance
from tqdm import tqdm
from vmf_utils import hyperspherical_uni... | 30,509 | 47.816 | 203 | py |
PAC-Bayesian_Sliced-Wasserstein | PAC-Bayesian_Sliced-Wasserstein-main/evaluate_vmf.py | import torch
from torch.distributions.multivariate_normal import MultivariateNormal
from utils import distributional_sliced_wasserstein_distance
from swd_pac import PAC_SWD
import matplotlib.pyplot as plt
import pickle
import os
from vmf_utils import hyperspherical_uniform as unif, von_mises_fisher
torch.manual_seed(... | 12,216 | 47.868 | 186 | py |
PAC-Bayesian_Sliced-Wasserstein | PAC-Bayesian_Sliced-Wasserstein-main/utils.py | import numpy as np
import ot
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torchvision import datasets, transforms
from torch import optim
from vmf_utils import hyperspherical_uniform as unif, von_mises_fisher as vmf
def Wasserstein_1D(mu_n, nu_n, p = 2, power_p = True):
wasserstein... | 13,452 | 39.158209 | 213 | py |
PAC-Bayesian_Sliced-Wasserstein | PAC-Bayesian_Sliced-Wasserstein-main/vmf_utils/von_mises_fisher.py | # Credit: https://github.com/nicola-decao/s-vae-pytorch
import math
import torch
from torch.distributions.kl import register_kl
from vmf_utils.ive import ive, ive_fraction_approx3
from vmf_utils.hyperspherical_uniform import HypersphericalUniform
class VonMisesFisher(torch.distributions.Distribution):
arg_cons... | 7,267 | 34.627451 | 97 | py |
PAC-Bayesian_Sliced-Wasserstein | PAC-Bayesian_Sliced-Wasserstein-main/vmf_utils/hyperspherical_uniform.py | # Credit: https://github.com/nicola-decao/s-vae-pytorch
import math
import torch
class HypersphericalUniform(torch.distributions.Distribution):
arg_constraints = {}
support = torch.distributions.constraints.real
has_rsample = False
_mean_carrier_measure = 0
@property
def dim(self):
... | 1,699 | 27.813559 | 88 | py |
PAC-Bayesian_Sliced-Wasserstein | PAC-Bayesian_Sliced-Wasserstein-main/vmf_utils/ive.py | # Credit: https://github.com/nicola-decao/s-vae-pytorch
import torch
import numpy as np
import scipy.special
from numbers import Number
class IveFunction(torch.autograd.Function):
@staticmethod
def forward(self, v, z):
assert isinstance(v, Number), "v must be a scalar"
self.save_for_backwar... | 2,327 | 26.388235 | 88 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/knowledge_aware/load_data.py | '''
Created on Dec 18, 2018
Tensorflow Implementation of Knowledge Graph Attention Network (KGAT) model in:
Wang Xiang et al. KGAT: Knowledge Graph Attention Network for Recommendation. In KDD 2019.
@author: Xiang Wang (xiangwang@u.nus.edu)
'''
import collections
import os
import numpy as np
import random as rd
import... | 10,100 | 35.204301 | 149 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/knowledge_aware/KGAT/batch_test.py | '''
Created on Dec 18, 2018
Tensorflow Implementation of Knowledge Graph Attention Network (KGAT) model in:
Wang Xiang et al. KGAT: Knowledge Graph Attention Network for Recommendation. In KDD 2019.
@author: Xiang Wang (xiangwang@u.nus.edu)
'''
import models.knowledge_aware.metrics as metrics
from parser import parse_a... | 8,738 | 32.102273 | 101 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/knowledge_aware/CKE/batch_test.py | '''
Created on Dec 18, 2018
Tensorflow Implementation of Knowledge Graph Attention Network (KGAT) model in:
Wang Xiang et al. KGAT: Knowledge Graph Attention Network for Recommendation. In KDD 2019.
@author: Xiang Wang (xiangwang@u.nus.edu)
'''
import models.knowledge_aware.metrics as metrics
from parser import parse_a... | 9,203 | 33.863636 | 110 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/knowledge_aware/CFKG/batch_test.py | '''
Created on Dec 18, 2018
Tensorflow Implementation of Knowledge Graph Attention Network (KGAT) model in:
Wang Xiang et al. KGAT: Knowledge Graph Attention Network for Recommendation. In KDD 2019.
@author: Xiang Wang (xiangwang@u.nus.edu)
'''
import models.knowledge_aware.metrics as metrics
from parser import parse_a... | 8,135 | 30.905882 | 101 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/PGPR/train_agent.py | from __future__ import absolute_import, division, print_function
import warnings
import numpy as np
import torch
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=DeprecationWarning)
import os
import argparse
from colle... | 14,020 | 39.523121 | 129 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/PGPR/transe_model.py | from __future__ import absolute_import, division, print_function
from easydict import EasyDict as edict
import numpy as np
import pickle
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.PGPR.pgpr_utils import *
from models.PGPR.data_utils import Dataset
class KnowledgeEmbedding(nn.Modu... | 7,960 | 41.345745 | 114 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/PGPR/train_transe_model.py | from __future__ import absolute_import, division, print_function
import os
import argparse
import torch
import torch.optim as optim
from data_utils import DataLoader
from models.PGPR.pgpr_utils import *
from models.PGPR.transe_model import KnowledgeEmbedding
logger = None
def train(args, dataset):
dataloader = ... | 5,258 | 41.072 | 112 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/PGPR/test_agent.py | from __future__ import absolute_import, division, print_function
import os
import argparse
from math import log
import numpy as np
import torch
import json
from easydict import EasyDict as edict
from tqdm import tqdm
from functools import reduce
from models.PGPR.kg_env import BatchKGEnvironment
from models.PGPR.train_... | 14,342 | 39.516949 | 125 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/PGPR/pgpr_utils.py | from __future__ import absolute_import, division, print_function
from easydict import EasyDict as edict
import os
import sys
import random
import pickle
import logging
import logging.handlers
import numpy as np
import csv
# import scipy.sparse as sp
import torch
from collections import defaultdict
import shutil
# Data... | 15,735 | 31.715177 | 156 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/embeddings/transe/knowledge_graph.py | from __future__ import absolute_import, division, print_function
import os
import sys
import argparse
from math import log
from tqdm import tqdm
from copy import deepcopy
import pandas as pd
import numpy as np
import gzip
import pickle
import random
from datetime import datetime
# import matplotlib.pyplot as plt
impor... | 4,538 | 32.873134 | 91 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/embeddings/transe/transe_model.py | from __future__ import absolute_import, division, print_function
from easydict import EasyDict as edict
import numpy as np
import pickle
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.embeddings.transe.dataset import Dataset
from models.embeddings.transe.utils import *
class Knowle... | 8,711 | 40.684211 | 114 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/embeddings/transe/utils.py | from __future__ import absolute_import, division, print_function
from easydict import EasyDict as edict
import os
import sys
import random
import pickle
import logging
import logging.handlers
import numpy as np
import csv
# import scipy.sparse as sp
import torch
from collections import defaultdict
# Dataset names.
# ... | 16,750 | 30.077922 | 156 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/embeddings/transe/train_transe.py | from __future__ import absolute_import, division, print_function
import os
import argparse
import torch
import torch.optim as optim
from models.embeddings.transe.dataset import DataLoader, Dataset
from models.embeddings.transe.utils import *
from models.embeddings.transe.transe_model import KnowledgeEmbedding
import j... | 7,161 | 41.129412 | 123 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/CAFE/train_neural_symbol.py | from __future__ import absolute_import, division, print_function
import os
import sys
import numpy as np
import logging
import logging.handlers
import torch
import torch.optim as optim
#from tensorboardX import SummaryWriter
import time
from models.CAFE.knowledge_graph import *
from models.CAFE.data_utils import Onli... | 6,848 | 35.625668 | 131 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/CAFE/cafe_utils.py | from __future__ import absolute_import, division, print_function
import os
import random
import argparse
import pickle
import numpy as np
import gzip
import scipy.sparse as sp
from sklearn.feature_extraction.text import TfidfTransformer
import torch
import sys
import shutil
ML1M = 'ml1m'
LFM1M = 'lfm1m'
CELL = 'cellph... | 9,304 | 34.788462 | 132 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/CAFE/execute_neural_symbol.py | from __future__ import absolute_import, division, print_function
import csv
import sys
from collections import defaultdict
from functools import reduce
import numpy as np
import pickle
import logging
import logging.handlers
import math
from tqdm import tqdm
import torch
from torch.nn import functional as F
import jso... | 16,959 | 35.085106 | 114 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/CAFE/symbolic_model.py | from __future__ import absolute_import, division, print_function
import torch
from torch import nn
from torch.nn import functional as F
from models.CAFE.knowledge_graph import *
from models.CAFE.cafe_utils import *
class EntityEmbeddingModel(nn.Module):
def __init__(self, entity_info, embed_size, init_embed=Non... | 12,938 | 40.07619 | 111 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/traditional/load_data.py |
import collections
import os
import numpy as np
import random as rd
import torch
import torch.utils.data
from torch.utils.data import Dataset
import math
class Data(Dataset):
def __init__(self, args, path, batch_style='list'):
super(Data).__init__()
self.batch_styles = {'list': 0, 'map': 1}
... | 9,960 | 35.756458 | 126 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/traditional/NFM/batch_test.py | '''
Created on Dec 18, 2018
Tensorflow Implementation of Knowledge Graph Attention Network (KGAT) model in:
Wang Xiang et al. KGAT: Knowledge Graph Attention Network for Recommendation. In KDD 2019.
@author: Xiang Wang (xiangwang@u.nus.edu)
'''
import models.traditional.metrics as metrics
from parser import parse_args
... | 7,649 | 31.008368 | 101 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/traditional/FM/batch_test.py | '''
Created on Dec 18, 2018
Tensorflow Implementation of Knowledge Graph Attention Network (KGAT) model in:
Wang Xiang et al. KGAT: Knowledge Graph Attention Network for Recommendation. In KDD 2019.
@author: Xiang Wang (xiangwang@u.nus.edu)
'''
import models.traditional.metrics as metrics
from parser import parse_args
... | 7,646 | 30.995816 | 101 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/traditional/BPRMF/batch_test.py | '''
Created on Dec 18, 2018
Tensorflow Implementation of Knowledge Graph Attention Network (KGAT) model in:
Wang Xiang et al. KGAT: Knowledge Graph Attention Network for Recommendation. In KDD 2019.
@author: Xiang Wang (xiangwang@u.nus.edu)
'''
import models.traditional.metrics as metrics
from parser import parse_args
... | 7,675 | 31.117155 | 101 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/test.py | from __future__ import absolute_import, division, print_function
import os
import argparse
import json
from math import log
from datetime import datetime
from tqdm import tqdm
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.au... | 19,385 | 34.966605 | 248 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/utils.py | from __future__ import absolute_import, division, print_function
from easydict import EasyDict as edict
import os
import sys
import random
import pickle
import logging
import logging.handlers
import numpy as np
import csv
# import scipy.sparse as sp
import torch
from collections import defaultdict
import shutil
# Data... | 17,841 | 29.975694 | 156 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/train.py | from __future__ import absolute_import, division, print_function
import sys
import os
import argparse
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.distributions import Categorical
from m... | 11,947 | 36.930159 | 147 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/src/parser.py | import os
from models.UCPR.utils import *
import argparse
import random
def parse_args():
boolean = lambda x: (str(x).lower() == 'true')
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default=LFM1M, help='One of {clothing, cell, beauty, cd}')
parser.add_argument('--n... | 8,873 | 50 | 116 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/src/test.py | from __future__ import absolute_import, division, print_function
import os
import argparse
import json
from math import log
from datetime import datetime
from tqdm import tqdm
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.au... | 19,385 | 34.966605 | 248 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/src/train.py | from __future__ import absolute_import, division, print_function
import sys
import os
import argparse
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.distributions import Categorical
from m... | 11,947 | 36.930159 | 147 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/src/env/sp_user_tri_set.py | from __future__ import absolute_import, division, print_function
import os
import sys
from tqdm import tqdm
import pickle
import random
import torch
from datetime import datetime
import numpy as np
import collections
from collections import defaultdict
from collections import Counter
import time
import multiprocessi... | 11,671 | 41.59854 | 164 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/src/env/env.py | from __future__ import absolute_import, division, print_function
import os
import sys
sys.path.insert(0,'../preprocess')
from tqdm import tqdm
import pickle
import random
import torch
from datetime import datetime
from collections import defaultdict
from models.UCPR.utils import *
# from preprocess.knowledge_graph im... | 16,021 | 39.768448 | 180 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/src/model/UCPR.py | from __future__ import absolute_import, division, print_function
import sys
import os
import argparse
from collections import namedtuple
import torch
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.distributions import ... | 19,300 | 44.521226 | 149 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/src/model/baseline/baseline.py | from __future__ import absolute_import, division, print_function
import sys
import os
import argparse
from collections import namedtuple
import torch
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.distributions import ... | 10,812 | 38.463504 | 165 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/src/model/lstm_base/model_kg_pre.py |
from __future__ import absolute_import, division, print_function
import sys
import os
import argparse
from collections import namedtuple
import torch
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.distributions import... | 6,028 | 36.918239 | 167 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/src/model/lstm_base/model_lstm_mf_emb.py | from __future__ import absolute_import, division, print_function
import sys
import os
import argparse
from collections import namedtuple
import torch
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.distributions import ... | 10,715 | 37.826087 | 126 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/src/model/lstm_base/backbone_lstm.py | from __future__ import absolute_import, division, print_function
import sys
import os
import argparse
from collections import namedtuple
import torch
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.distributions import ... | 4,204 | 36.212389 | 104 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/src/model/lstm_base/model_kg.py |
from __future__ import absolute_import, division, print_function
import sys
import os
import argparse
from collections import namedtuple
import torch
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.distributions import... | 5,310 | 36.401408 | 148 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/preprocess/knowledge_graph.py | from __future__ import absolute_import, division, print_function
import os
import sys
import argparse
from math import log
from tqdm import tqdm
from copy import deepcopy
import pandas as pd
import numpy as np
import gzip
import pickle
import random
from datetime import datetime
# import matplotlib.pyplot as plt
impor... | 4,556 | 32.755556 | 91 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/preprocess/transe_model.py | from __future__ import absolute_import, division, print_function
from easydict import EasyDict as edict
import numpy as np
import pickle
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.UCPR.preprocess.dataset import Dataset
from models.UCPR.utils import *
class KnowledgeEmbedding(nn... | 8,696 | 40.61244 | 114 | py |
rep-path-reasoning-recsys | rep-path-reasoning-recsys-main/models/UCPR/preprocess/train_transe.py | from __future__ import absolute_import, division, print_function
import os
import argparse
import torch
import torch.optim as optim
from models.UCPR.preprocess.dataset import DataLoader, Dataset
from models.UCPR.utils import *
from models.UCPR.preprocess.transe_model import KnowledgeEmbedding
import json
import sys
lo... | 7,144 | 41.029412 | 123 | py |
shapely | shapely-main/docs/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# set an environment variable for shapely.decorators.requires_geos to see if we
# are i... | 4,366 | 29.326389 | 87 | py |
diffmimic | diffmimic-main/mimic.py | import functools
import numpy as np
import jax.numpy as jnp
from absl import flags, app
import yaml
from brax import envs
from brax.io import metrics
from brax.training.agents.apg import networks as apg_networks
from diffmimic.utils import AttrDict
from diffmimic.mimic_envs import register_mimic_env
import diffmimic.br... | 2,985 | 32.931818 | 109 | py |
diffmimic | diffmimic-main/diffmimic/brax_lib/agent_diffmimic.py | # Copyright 2022 The Brax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | 10,754 | 36.473868 | 123 | py |
diffmimic | diffmimic-main/diffmimic/brax_lib/acting.py | # Copyright 2022 The Brax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | 4,531 | 33.075188 | 98 | py |
diffmimic | diffmimic-main/diffmimic/mimic_envs/humanoid_mimic_train.py | from brax import jumpy as jp
from brax.envs import env
from .humanoid_mimic import HumanoidMimic
from .losses import *
import jax
class HumanoidMimicTrain(HumanoidMimic):
"""Trains a humanoid to mimic reference motion."""
def __init__(self, total_length, rollout_length, early_termination, demo_replay_mode, e... | 2,647 | 43.881356 | 120 | py |
diffmimic | diffmimic-main/diffmimic/utils/rotation6d.py | import jax.numpy as jnp
def quaternion_to_matrix(quaternions):
r, i, j, k = quaternions[..., 0], quaternions[..., 1], quaternions[..., 2], quaternions[..., 3]
two_s = 2.0 / (quaternions * quaternions).sum(-1)
o = jnp.stack(
(
1 - two_s * (j * j + k * k),
two_s * (i * j - k... | 915 | 28.548387 | 99 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.