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
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DiffMIC | DiffMIC-main/diffusion_trainer.py | import logging
import time
import gc
import matplotlib.pyplot as plt
import statsmodels.api as sm
import numpy as np
import torch
import torch.nn as nn
import torch.utils.data as data
from scipy.stats import ttest_rel
from tqdm import tqdm
from ema import EMA
from model import *
from pretraining.dcg import DCG as Aux... | 31,018 | 51.843271 | 185 | py |
DiffMIC | DiffMIC-main/utils.py | import random
import math
import numpy as np
import argparse
import torch
import torch.optim as optim
import torchvision
from torch import nn
from torchvision import transforms
from dataloader.loading import *
import torch.nn.functional as F
def set_random_seed(seed):
print(f"\n* Set seed {seed}")
torch.manual... | 8,144 | 39.321782 | 104 | py |
DiffMIC | DiffMIC-main/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.resnet import resnet18, resnet50
from torchvision.models.densenet import densenet121
from timm.models import create_model
import numpy as np
class ConditionalLinear(nn.Module):
def __init__(self, num_in, num_out, n_steps):... | 3,986 | 30.393701 | 74 | py |
DiffMIC | DiffMIC-main/ema.py | import torch.nn as nn
class EMA(object):
def __init__(self, mu=0.999):
self.mu = mu
self.shadow = {}
def register(self, module):
for name, param in module.named_parameters():
if param.requires_grad:
self.shadow[name] = param.data.clone()
def update(self... | 1,053 | 30 | 103 | py |
DiffMIC | DiffMIC-main/diffusion_utils.py | import math
import torch
import numpy as np
def make_beta_schedule(schedule="linear", num_timesteps=1000, start=1e-5, end=1e-2):
if schedule == "linear":
betas = torch.linspace(start, end, num_timesteps)
elif schedule == "const":
betas = end * torch.ones(num_timesteps)
elif schedule == "qua... | 7,115 | 41.357143 | 117 | py |
DiffMIC | DiffMIC-main/pretraining/dcg.py | import torch
import torch.nn as nn
import numpy as np
import pretraining.tools as tools
import pretraining.modules as m
class DCG(nn.Module):
def __init__(self, parameters):
super(DCG, self).__init__()
# save parameters
self.experiment_parameters = {
"device_type": 'gpu',
... | 5,533 | 41.569231 | 147 | py |
DiffMIC | DiffMIC-main/pretraining/modules.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pretraining.tools as tools
from torchvision.models.resnet import conv3x3, resnet18, resnet50
class BasicBlockV2(nn.Module):
"""
Basic Residual Block of ResNet V2
"""
expansion = 1
def __init__(self, inpl... | 15,698 | 34.679545 | 119 | py |
DiffMIC | DiffMIC-main/pretraining/resnet.py | import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None,
groups=1, base_width=64, dilation=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, k... | 4,706 | 31.6875 | 78 | py |
DiffMIC | DiffMIC-main/pretraining/densenet.py | import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from collections import OrderedDict
from torchvision._internally_replaced_utils import load_state_dict_from_url
from torch import Tensor
from typing import Any, List, Tuple
__all__ = ['DenseNet', 'densenet... | 12,797 | 39.628571 | 105 | py |
DiffMIC | DiffMIC-main/pretraining/tools.py | import numpy as np
import torch
from torch.autograd import Variable
import torch.nn.functional as F
def partition_batch(ls, size):
"""
Partitions a list into buckets of given maximum length.
"""
i = 0
partitioned_lists = []
while i < len(ls):
partitioned_lists.append(ls[i: i+size])
... | 8,163 | 36.62212 | 120 | py |
DiffMIC | DiffMIC-main/dataloader/loading.py | import os, torch, cv2, random
import numpy as np
from torch.utils.data import Dataset, Sampler
import torchvision.transforms as transforms
from scipy.ndimage.morphology import binary_erosion
import torchvision.transforms.functional as TF
from PIL import Image, ImageOps
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED... | 5,506 | 31.976048 | 128 | py |
DiffMIC | DiffMIC-main/dataloader/functional.py | import math
import random
from PIL import Image, ImageEnhance, ImageOps
try:
import accimage
except ImportError:
accimage = None
import collections
import numbers
import types
import warnings
import cv2
import numpy as np
from PIL import Image
_cv2_pad_to_str = {
'constant': cv2.BORDER_CONSTANT,
'ed... | 23,553 | 41.516245 | 122 | py |
mbtr | mbtr-master/docs/source/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
# -- Path setup --------------------------------------------------------------
# If ex... | 2,038 | 31.365079 | 79 | py |
Variational-Inequality-GAN | Variational-Inequality-GAN-master/train_extraadam.py | # MIT License
# Copyright (c) Facebook, Inc. and its affiliates.
# 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... | 11,901 | 37.895425 | 263 | py |
Variational-Inequality-GAN | Variational-Inequality-GAN-master/train_optimisticadam.py | # MIT License
# Copyright (c) Facebook, Inc. and its affiliates.
# 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... | 11,654 | 38.508475 | 259 | py |
Variational-Inequality-GAN | Variational-Inequality-GAN-master/eval_fid.py | # MIT License
# Copyright (c) Facebook, Inc. and its affiliates.
# 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... | 3,742 | 33.027273 | 105 | py |
Variational-Inequality-GAN | Variational-Inequality-GAN-master/utils.py | # MIT License
# Copyright (c) Facebook, Inc. and its affiliates.
# 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... | 2,680 | 39.014925 | 152 | py |
Variational-Inequality-GAN | Variational-Inequality-GAN-master/train_pastextraadam.py | # MIT License
# Copyright (c) Facebook, Inc. and its affiliates.
# 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... | 11,844 | 38.352159 | 259 | py |
Variational-Inequality-GAN | Variational-Inequality-GAN-master/train_adam.py | # MIT License
# Copyright (c) Facebook, Inc. and its affiliates.
# 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... | 12,152 | 37.097179 | 263 | py |
Variational-Inequality-GAN | Variational-Inequality-GAN-master/eval_inception_score.py | # MIT License
# Copyright (c) Facebook, Inc. and its affiliates.
# 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... | 3,176 | 32.797872 | 104 | py |
Variational-Inequality-GAN | Variational-Inequality-GAN-master/models/discriminator.py | # MIT License
# Copyright (c) 2017 Ishaan Gulrajani
# Copyright (c) 2017 Marvin Cao
# Copyright (c) Facebook, Inc. and its affiliates.
# 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 witho... | 2,109 | 43.893617 | 222 | py |
Variational-Inequality-GAN | Variational-Inequality-GAN-master/models/resnet.py | # MIT License
# Copyright (c) Facebook, Inc. and its affiliates.
# 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... | 4,785 | 41.732143 | 109 | py |
Variational-Inequality-GAN | Variational-Inequality-GAN-master/models/dcgan.py | # MIT License
# Copyright (c) Facebook, Inc. and its affiliates.
# 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... | 3,533 | 35.43299 | 92 | py |
Variational-Inequality-GAN | Variational-Inequality-GAN-master/tflib/inception_score.py | # From https://github.com/openai/improved-gan/blob/master/inception_score/model.py
# Code derived from tensorflow/tensorflow/models/image/imagenet/classify_image.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import os.path
import sys
import ta... | 3,708 | 34.32381 | 90 | py |
Variational-Inequality-GAN | Variational-Inequality-GAN-master/optim/extragradient.py | # MIT License
# Copyright (c) Facebook, Inc. and its affiliates.
# 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... | 10,809 | 39.335821 | 199 | py |
Variational-Inequality-GAN | Variational-Inequality-GAN-master/optim/omd.py | # MIT License
# Copyright (c) Facebook, Inc. and its affiliates.
# 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... | 5,917 | 39.534247 | 116 | py |
JamBot | JamBot-master/polyphonic_lstm_training.py | # Author: Jonas Wiesendanger wjonas@student.ethz.ch
from settings import *
from keras.models import Sequential
from keras.layers.recurrent import LSTM
from keras.layers import Dense, Activation
from keras.layers.embeddings import Embedding
from keras.optimizers import RMSprop, Adam
# from keras.utils import to_categori... | 8,195 | 34.025641 | 265 | py |
JamBot | JamBot-master/chord_model.py | from settings import *
from keras.models import load_model
import keras
import numpy as np
from numpy import array
import _pickle as pickle
from keras import backend as K
from data_processing import get_chord_dict
class Chord_Model:
def __init__(self,
model_path,
predi... | 4,054 | 27.356643 | 124 | py |
JamBot | JamBot-master/generation.py | from settings import *
from keras.models import load_model
import numpy as np
from numpy import array
import _pickle as pickle
import os
import data_processing
import chord_model
import midi_functions as mf
import data_class
chord_model_folder = 'models/chords/1523433134-Shifted_True_Lr_1e-05_EmDim_10_opt_Adam_bi_... | 4,976 | 28.625 | 187 | py |
JamBot | JamBot-master/chord_lstm_training.py | # Author: Jonas Wiesendanger, Andres Konrad, Gino Brunner (brunnegi@ethz.ch)
from settings import *
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense, Activation
from keras.layers import Embedding
from keras.optimizers import RMSprop, Adam
import keras.utils
from keras.uti... | 5,989 | 35.975309 | 191 | py |
DSRE | DSRE-main/main.py | # coding:utf-8
import torch
import numpy as np
import json
import sys
import os
import argparse
import logging
import framework
import encoder
import model1
import model2
parser = argparse.ArgumentParser()
parser.add_argument('--pretrain_path', default='bert-base-uncased',
help='Pre-trained ckpt path / model ... | 4,541 | 32.644444 | 147 | py |
DSRE | DSRE-main/encoder/passage_encoder.py | import logging
import torch
import torch.nn as nn
from transformers import BertModel, BertTokenizer
class PassageEncoder(nn.Module):
def __init__(self, pretrain_path, batch_size, blank_padding=True, mask_entity=False):
super().__init__()
self.blank_padding = blank_padding
self.hidden_size =... | 4,027 | 40.958333 | 112 | py |
DSRE | DSRE-main/model1/passage_att.py | import torch
from torch import nn, optim
from torch.nn import functional as F
class PassageAttention(nn.Module):
"""
token-level attention for passage-level relation extraction.
"""
def __init__(self,
passage_encoder,
num_class,
rel2id):
"""
... | 2,684 | 41.619048 | 163 | py |
DSRE | DSRE-main/model2/passage_att.py | import torch
from torch import nn, optim
from torch.nn import functional as F
import pdb
class PassageAttention(nn.Module):
"""
token-level attention for passage-level relation extraction.
"""
def __init__(self,
passage_encoder,
num_class,
rel2id):
... | 3,204 | 41.733333 | 163 | py |
DSRE | DSRE-main/framework/passage_re.py | import torch
from torch import nn, optim
from .data_loader import PassageRELoader
from .utils import AverageMeter
from tqdm import tqdm
import pdb
class PassageRE(nn.Module):
def __init__(self,
model,
train_path,
val_path,
test_path,
... | 7,276 | 39.882022 | 154 | py |
DSRE | DSRE-main/framework/data_loader.py | import torch
import torch.utils.data as data
import os, random, json, logging
import numpy as np
import sklearn.metrics
class PassageREDataset(data.Dataset):
"""
Bag-level relation extraction dataset. Note that relation of NA should be named as 'NA'.
"""
def __init__(self, path, rel2id, tokenizer):
... | 11,370 | 43.592157 | 372 | py |
CmpLoss | CmpLoss-main/run_squad.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Team 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-... | 20,046 | 45.838785 | 119 | py |
CmpLoss | CmpLoss-main/run_glue.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team. 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/LI... | 22,570 | 44.690283 | 119 | py |
CmpLoss | CmpLoss-main/run_hotpot.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Team 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-... | 13,817 | 42.866667 | 120 | py |
CmpLoss | CmpLoss-main/modeling.py | from dataclasses import dataclass
import json
import logging
import os
from typing import Any, Dict, Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers import AutoModel, LongformerModel, RobertaModel, PreTrainedModel
from transformers.... | 28,205 | 41.224551 | 125 | py |
CmpLoss | CmpLoss-main/trainer.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team 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 require... | 49,544 | 46.140818 | 130 | py |
CmpLoss | CmpLoss-main/utils/data.py | from collections import OrderedDict
from dataclasses import dataclass
import json
import logging
import os
import random
from tqdm.auto import tqdm
from typing import Any, List, Dict, Optional, Tuple
import numpy as np
import torch
from torch.utils.data import Dataset
from transformers import LongformerTokenizerFast,... | 21,763 | 45.504274 | 118 | py |
CmpLoss | CmpLoss-main/utils/qa.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team 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 require... | 17,507 | 47.633333 | 119 | py |
CmpLoss | CmpLoss-main/utils/ranking.py | import logging
from itertools import product
from typing import Optional
import torch
import torch.nn.functional as F
logger = logging.getLogger(__name__)
def list_mle(y_pred: torch.Tensor, y_true: torch.Tensor, mask: Optional[torch.Tensor] = None,
reduction: Optional[str] = 'mean', eps: Optional[float... | 4,044 | 37.52381 | 100 | py |
CmpLoss | CmpLoss-main/utils/tensor.py | from typing import List
import torch
def mask_where0(x, m):
"""
Args:
x (torch.Tensor): (*)
m (torch.Tensor): same size as logits
1 for positions that are NOT MASKED, 0 for MASKED positions.
Returns:
torch.Tensor: same size as logits
"""
if x.dtype == torch.f... | 1,471 | 31 | 111 | py |
BPAM | BPAM-master/Baselines/DeepCF/MLP2.py | import numpy as np
import tensorflow as tf
from keras import initializers
from keras.regularizers import l2
from keras.models import Model
from keras.layers import Embedding, Input, Dense, Flatten, concatenate, Lambda, Reshape
from keras.optimizers import Adagrad, Adam, SGD, RMSprop
from keras import backend as K
from ... | 11,843 | 43.19403 | 139 | py |
BPAM | BPAM-master/Baselines/DeepCF/DMF_implicit2.py | import numpy as np
import tensorflow as tf
from keras import initializers
from keras.regularizers import l2
from keras.models import Model
from keras.layers import Embedding, Input, Dense, Flatten, concatenate, Dot, Lambda, multiply, Reshape, merge
from keras.optimizers import Adagrad, Adam, SGD, RMSprop
from keras imp... | 9,721 | 43.801843 | 138 | py |
BPAM | BPAM-master/Baselines/DeepCF/DeepCF4.py | import numpy as np
import tensorflow as tf
from keras import initializers
from keras.regularizers import l2
from keras.models import Model
from keras.layers import Embedding, Input, Dense, Flatten, concatenate, Dot, Lambda, multiply, Reshape, multiply
from keras.optimizers import Adagrad, Adam, SGD, RMSprop
from keras ... | 12,443 | 44.582418 | 139 | py |
BPAM | BPAM-master/Baselines/NMF/GMF.py | '''
Created on Aug 9, 2016
Keras Implementation of Generalized Matrix Factorization (GMF) recommender model in:
He Xiangnan et al. Neural Collaborative Filtering. In WWW 2017.
@author: Xiangnan He (xiangnanhe@gmail.com)
'''
import numpy as np
import theano.tensor as T
from keras import backend as K
from keras impor... | 7,381 | 41.425287 | 106 | py |
BPAM | BPAM-master/Baselines/NMF/NeuMF.py | '''
Created on Aug 9, 2016
Keras Implementation of Neural Matrix Factorization (NeuMF) recommender model in:
He Xiangnan et al. Neural Collaborative Filtering. In WWW 2017.
@author: Xiangnan He (xiangnanhe@gmail.com)
'''
import numpy as np
import theano
import theano.tensor as T
import keras
from keras import backe... | 11,298 | 46.079167 | 157 | py |
BPAM | BPAM-master/Baselines/NMF/MLP.py | '''
Created on Aug 9, 2016
Keras Implementation of Multi-Layer Perceptron (GMF) recommender model in:
He Xiangnan et al. Neural Collaborative Filtering. In WWW 2017.
@author: Xiangnan He (xiangnanhe@gmail.com)
'''
import numpy as np
import theano
import theano.tensor as T
import keras
from keras import backend as ... | 7,721 | 42.139665 | 165 | py |
t3f | t3f-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# t3f documentation build configuration file, created by
# sphinx-quickstart on Sun Mar 12 10:06:09 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All c... | 5,326 | 30.708333 | 107 | py |
t3f | t3f-master/t3f/nn.py | """Utils for simplifying building neural networks with TT-layers"""
from itertools import count
import numpy as np
from tensorflow.keras.layers import Layer
from tensorflow.keras.layers import Activation
import t3f
import tensorflow as tf
class KerasDense(Layer):
_counter = count(0)
def __init__(self, input_dim... | 2,954 | 36.884615 | 79 | py |
lime | lime-master/doc/conf.py | # -*- coding: utf-8 -*-
#
# lime documentation build configuration file, created by
# sphinx-quickstart on Fri Mar 18 16:20:40 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All ... | 10,195 | 31.368254 | 95 | py |
lime | lime-master/lime/lime_tabular.py | """
Functions for explaining classifiers that use tabular data (matrices).
"""
import collections
import copy
from functools import partial
import json
import warnings
import numpy as np
import scipy as sp
import sklearn
import sklearn.preprocessing
from sklearn.utils import check_random_state
from pyDOE2 import lhs
f... | 34,542 | 46.254446 | 100 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/moltrans_dti/helper/utils/paddle_io.py | # Copyright (c) 2021 PaddlePaddle Authors. 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 app... | 4,237 | 29.489209 | 119 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pointwise/GraphDTA/train_kiba.py | """Training scripts for GraphDTA backbone."""
import rdkit
import torch
import sklearn
import numpy as np
import pandas as pd
import sys, os
import os.path
from os import path
import random
from random import shuffle
from time import time
from rdkit import Chem
import torch.nn as nn
from argparse import ArgumentParser... | 8,902 | 35.338776 | 160 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pointwise/GraphDTA/utils_bindingDB.py | """Utils scripts for GraphDTA."""
import os
import numpy as np
from math import sqrt
from scipy import stats
from torch_geometric.data import InMemoryDataset, DataLoader
from torch_geometric import data as DATA
import torch
class TestbedDataset(InMemoryDataset):
"""TestbedDataset."""
def __init__(self, root=... | 3,795 | 31.724138 | 109 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pointwise/GraphDTA/processing.py | """Preprocessing scripts for GraphDTA."""
import pandas as pd
import numpy as np
import os
import rdkit
import sklearn
import torch
import json,pickle
from collections import OrderedDict
from rdkit import Chem
from rdkit.Chem import MolFromSmiles
import networkx as nx
from utils import *
# Global setting
seq_voc = "A... | 3,178 | 33.554348 | 316 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pointwise/GraphDTA/train_davis.py | """Training scripts for GraphDTA backbone."""
import rdkit
import torch
import sklearn
import numpy as np
import pandas as pd
import sys, os
import random
from random import shuffle
from time import time
from rdkit import Chem
import torch.nn as nn
from argparse import ArgumentParser
from models.gat import GATNet
fro... | 7,864 | 37.365854 | 161 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pointwise/GraphDTA/utils.py | """Utils scripts for GraphDTA."""
import os
import numpy as np
from math import sqrt
from scipy import stats
from torch_geometric.data import InMemoryDataset, DataLoader
from torch_geometric import data as DATA
import torch
class TestbedDataset(InMemoryDataset):
"""TestbedDataset."""
def __init__(self, root=... | 3,851 | 29.816 | 109 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pointwise/GraphDTA/train_bindingDB.py | """Training scripts for GraphDTA backbone."""
import rdkit
import torch
import sklearn
import numpy as np
import pandas as pd
import sys, os
import random
from random import shuffle
from time import time
from rdkit import Chem
import torch.nn as nn
from argparse import ArgumentParser
from models.gat import GATNet
fro... | 9,787 | 35.118081 | 160 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pointwise/GraphDTA/preprocess.py | """Preprocessing scripts for GraphDTA."""
import pandas as pd
import numpy as np
import os
import rdkit
import sklearn
import torch
import json,pickle
from collections import OrderedDict
from rdkit import Chem
from rdkit.Chem import MolFromSmiles
import networkx as nx
from utils import *
# Global setting
seq_voc = "A... | 3,938 | 34.809091 | 316 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pointwise/GraphDTA/models/ginconv.py | """GraphDTA_GIN backbone model."""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import GINConv, global_add_pool
from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp
# GINConv backbone model
class GIN... | 3,247 | 34.304348 | 91 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pointwise/GraphDTA/models/gcn.py | """GraphDTA_GCN backbone model."""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, global_max_pool as gmp
# GCN backbone model
class GCNNet(torch.nn.Module):
"""GCN model.
Args:
data: Input data.
Returns:
out: Prediction res... | 2,487 | 30.897436 | 132 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pointwise/GraphDTA/models/gat.py | """GraphDTA_GAT backbone model."""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import GATConv
from torch_geometric.nn import global_max_pool as gmp
# GAT backbone model
class GATNet(torch.nn.Module):
"""GAT model.
A... | 2,428 | 31.386667 | 90 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pointwise/GraphDTA/models/gat_gcn.py | """GraphDTA_GATGCN backbone model."""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import GCNConv, GATConv, GINConv, global_add_pool
from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp
# GATGCN back... | 2,577 | 33.373333 | 91 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pointwise/Moltrans/helper/utils/paddle_io.py | # Copyright (c) 2021 PaddlePaddle Authors. 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 app... | 4,237 | 29.489209 | 119 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pairwise/GraphDTA/run_pairwise_GraphDTA_BindingDB.py | from itertools import combinations
import itertools
import argparse
from random import *
import random
import pdb
from lifelines.utils import concordance_index
import functools
import random
import time
import pandas as pd
import torch.multiprocessing as mp
import torch.distributed as dist
from torch.nn.parallel impor... | 19,537 | 37.385069 | 230 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pairwise/GraphDTA/processing.py | import pandas as pd
import numpy as np
import os
import rdkit
import sklearn
import torch
import json,pickle
from collections import OrderedDict
from rdkit import Chem
from rdkit.Chem import MolFromSmiles
import networkx as nx
from torch_geometric.data import InMemoryDataset, DataLoader
from utils import *
import pdb
i... | 6,546 | 29.593458 | 316 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pairwise/GraphDTA/utils.py | import os
import numpy as np
from math import sqrt
from scipy import stats
from torch_geometric.data import InMemoryDataset, DataLoader
from torch_geometric.data import Dataset
from torch_geometric import data as DATA
import torch
import pdb
class TrainDataset(Dataset):
def __init__(self, root='./', train_x1_index... | 9,638 | 30.603279 | 168 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pairwise/GraphDTA/run_pairwise_GraphDTA_CV.py | from itertools import combinations
import itertools
from random import *
import random
import pdb
from lifelines.utils import concordance_index
from sklearn import preprocessing
import functools
import random
import time
import pandas as pd
import torch.multiprocessing as mp
import torch.distributed as dist
from torch... | 18,993 | 36.243137 | 230 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pairwise/GraphDTA/models/ginconv.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import GINConv, global_add_pool
from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp
# GINConv model
class GINConvNet(torch.nn.Module):
def __init__(sel... | 3,499 | 34.353535 | 101 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pairwise/GraphDTA/models/gcn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, global_max_pool as gmp
# GCN based model
class GCNNet(torch.nn.Module):
def __init__(self, n_output=1, n_filters=32, embed_dim=128,num_features_xd=78, num_features_xt=25, output_dim=128, dropout=0.2):
... | 2,456 | 30.101266 | 132 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pairwise/GraphDTA/models/gat.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import GATConv
from torch_geometric.nn import global_max_pool as gmp
# GAT model
class GATNet(torch.nn.Module):
def __init__(self, num_features_xd=78, n_output=1, num_features_x... | 2,418 | 31.689189 | 90 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pairwise/GraphDTA/models/gat_gcn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import GCNConv, GATConv, GINConv, global_add_pool
from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp
# GCN-CNN based model
class GAT_GCN(torch.nn.Module)... | 2,495 | 33.666667 | 91 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/drug_target_interaction/batchdta/pairwise/Moltrans/helper/utils/paddle_io.py | # Copyright (c) 2021 PaddlePaddle Authors. 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 app... | 4,237 | 29.489209 | 119 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/fewshot_molecular_property/chem_lib/models/relation.py | from collections import OrderedDict
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
class MLP(nn.Layer):
def __init__(self, inp_dim, hidden_dim, num_layers,batch_norm=False, dropout=0.):
super(MLP, self).__init__()
layer_list = OrderedDict()
in_dim = inp_dim
f... | 13,630 | 40.685015 | 142 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/fewshot_molecular_property/chem_lib/models/maml.py |
import paddle.nn as nn
import paddlefsl.utils as utils
class MAML(nn.Layer):
def __init__(
self,
model,
lr,
first_order=False,
allow_unused=None,
allow_nograd=False,
anil=False,
):
super(MAML, self).__init__()
self.layers = model
self.lr = lr
self.first_order = first_order
self.allow... | 1,819 | 26.575758 | 89 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/fewshot_molecular_property/chem_lib/datasets/loader.py | import os
import json
import numpy as np
import paddle
import pgl.graph as G
from pahelix.datasets import InMemoryDataset
try:
from rdkit import Chem
from rdkit.Chem import AllChem
allowable_features = {
'possible_atomic_num_list' : list(range(1, 119)),
'possible_formal_charge_list' : [-5, ... | 15,028 | 39.400538 | 91 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/protein_folding/helixfold-single/alphafold_paddle/model/modules.py | # Copyright (c) 2022 PaddlePaddle Authors. 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 app... | 107,160 | 42.543681 | 136 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/protein_folding/helixfold-single/alphafold_paddle/model/utils.py | # Copyright (c) 2022 PaddlePaddle Authors. 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 app... | 10,196 | 31.578275 | 132 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/protein_folding/helixfold-single/alphafold_paddle/model/model.py | # Copyright (c) 2022 PaddlePaddle Authors. 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 app... | 9,721 | 34.097473 | 84 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/protein_folding/helixfold/train.py | # Copyright (c) 2022 PaddlePaddle Authors. 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 app... | 23,213 | 38.75 | 152 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/protein_folding/helixfold/alphafold_paddle/model/modules.py | # Copyright (c) 2022 PaddlePaddle Authors. 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 app... | 99,940 | 41.080421 | 159 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/protein_folding/helixfold/alphafold_paddle/model/utils.py | # Copyright (c) 2022 PaddlePaddle Authors. 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 app... | 10,211 | 31.626198 | 132 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/protein_folding/helixfold/alphafold_paddle/model/model.py | # Copyright (c) 2022 PaddlePaddle Authors. 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 app... | 11,831 | 36.561905 | 148 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/protein_folding/helixfold/utils/misc.py | # copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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 applica... | 4,773 | 31.040268 | 97 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/pretrained_compound/ChemRL/GEM-2/src/dataset.py | #!/usr/bin/python
#-*-coding:utf-8-*-
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance wi... | 2,678 | 32.911392 | 113 | py |
PaddleHelix-dev | PaddleHelix-dev/apps/protein_function_prediction/PTHL/layers.py | import paddle
from paddle import nn
import paddle.nn.functional as F
from utils import _norm_no_nan
import pgl
import pgl.math as math
class GVP(nn.Layer):
'''
Paddle version of the GVP proposed by https://github.com/drorlab/gvp-pytorch
'''
def __init__(self, in_dims, out_dims, h_dim=None, activatio... | 3,468 | 31.12037 | 104 | py |
PaddleHelix-dev | PaddleHelix-dev/pahelix/utils/metrics/molecular_generation/metrics_.py | #!/usr/bin/python3
#-*-coding:utf-8-*-
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance w... | 12,863 | 33.580645 | 114 | py |
PaddleHelix-dev | PaddleHelix-dev/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
# -- Path setup --------------------------------------------------------------
# If ex... | 3,510 | 30.348214 | 175 | py |
PaddleHelix-dev | PaddleHelix-dev/competition/kddcup2021-PCQM4M-LSC/models/conv.py | import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import pgl
import pgl.nn as gnn
from pgl.utils.logger import log
import models.mol_encoder as ME
import models.layers as L
class LiteGEM(paddle.nn.Layer):
def __init__(self, config, with_efeat=False):
super(LiteGEM, ... | 13,481 | 38.421053 | 182 | py |
PaddleHelix-dev | PaddleHelix-dev/competition/kddcup2021-PCQM4M-LSC/ensemble/ensemble.py | import os
import os.path as osp
import glob
import pickle
import numpy as np
import pandas as pd
import sklearn
import sklearn.linear_model
import torch
def mae(pred, true):
return np.mean(np.abs(pred-true))
model_root = "./model_pred"
max_min_drop_rate = 0.2 # <=0 means no drop, should < 0.5
split_idx = t... | 4,960 | 36.022388 | 92 | py |
PaddleHelix-dev | PaddleHelix-dev/competition/ogbg_molhiv/main.py | # Copyright (c) 2020 PaddlePaddle Authors. 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 applic... | 6,215 | 32.967213 | 88 | py |
interpretability | interpretability-master/context-atlas/preprocess.py |
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 6,574 | 30.45933 | 102 | py |
interpretability | interpretability-master/text-dream/python/dream/mlm.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... | 3,024 | 37.782051 | 80 | py |
interpretability | interpretability-master/text-dream/python/dream/dream.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... | 13,732 | 46.355172 | 80 | py |
interpretability | interpretability-master/text-dream/python/dream/reconstruct_changed_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... | 11,710 | 45.472222 | 82 | py |
interpretability | interpretability-master/text-dream/python/dream/dream_mlm.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... | 14,960 | 46.646497 | 80 | py |
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