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 |
|---|---|---|---|---|---|---|
SigMaNet | SigMaNet-master/src/node_SigMaNet.py | # external files
import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
from torch_geometric_signed_directed import node_class_split
from torch_geometric_signed_directed.data import load_directed_real_data
import random
... | 11,479 | 42.984674 | 204 | py |
SigMaNet | SigMaNet-master/src/Cheb.py | # external files
import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
from torch_geometric_signed_directed.data import load_directed_real_data
import random
from torch_geometric_signed_directed import node_class_split
... | 12,333 | 43.207885 | 204 | py |
SigMaNet | SigMaNet-master/src/GCN.py | # external files
import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
import random
from torch_geometric_signed_directed.data import load_directed_real_data
from torch_geometric_signed_directed import node_class_split
... | 12,328 | 43.189964 | 204 | py |
SigMaNet | SigMaNet-master/src/Sym_DiGCN.py | # external files
import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
from torch_geometric_signed_directed.data import load_directed_real_data
import random
from torch_geometric_signed_directed import node_class_split
... | 13,046 | 44.618881 | 204 | py |
SigMaNet | SigMaNet-master/src/Edge_SymDiGCN.py | import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse, csv
import torch.nn.functional as F
from torch_geometric_signed_directed.data import load_directed_real_data
import random
import pickle as pk
# internal files
import torch
from layer.DGCN impor... | 10,895 | 44.024793 | 207 | py |
SigMaNet | SigMaNet-master/src/Edge_SAGE.py | # Non c'èla possibilità di mettere matrice pesata
import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
from torch_geometric.utils import to_undirected
from torch_geometric_signed_directed.data import load_directed_rea... | 14,292 | 46.016447 | 207 | py |
SigMaNet | SigMaNet-master/src/sparse_Magnet.py | # external files
import numpy as np
import pickle as pk
from scipy import sparse
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
from torch_geometric_signed_directed import node_class_split
import random
import networkx as nx
import pickle as pk
# int... | 14,935 | 44.398176 | 204 | py |
SigMaNet | SigMaNet-master/src/Edge_GCN.py | import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
from torch_geometric.utils import to_undirected
from torch_geometric_signed_directed.data import load_directed_real_data
import random
import pickle as pk
# intern... | 14,449 | 47.006645 | 207 | py |
SigMaNet | SigMaNet-master/src/Edge_APPNP.py | import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
from torch_geometric.utils import to_undirected
from torch_geometric_signed_directed.data import load_directed_real_data
import random
import pickle as pk
# intern... | 14,773 | 47.759076 | 243 | py |
SigMaNet | SigMaNet-master/src/Edge_GIN.py | # Non c'èla possibilità di mettere matrice pesata
import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
from torch_geometric.utils import to_undirected
from torch_geometric_signed_directed.data import load_directed_rea... | 14,291 | 46.013158 | 207 | py |
SigMaNet | SigMaNet-master/src/Edge_sparseMagnet.py | import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
from torch_geometric_signed_directed.data import load_directed_real_data
import random
import pickle as pk
# internal files
from layer.sparse_magnet import *
from u... | 16,315 | 47.559524 | 207 | py |
SigMaNet | SigMaNet-master/src/Edge_GAT.py | # Controllare se posso fare input weight
import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
from torch_geometric.utils import to_undirected
from torch_geometric_signed_directed.data import load_directed_real_data
im... | 14,543 | 46.220779 | 212 | py |
SigMaNet | SigMaNet-master/src/APPNP.py | # external files
import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
from torch_geometric_signed_directed.data import load_directed_real_data
import random
import networkx as nx
from torch_geometric_signed_directed im... | 12,600 | 43.526502 | 212 | py |
SigMaNet | SigMaNet-master/src/Edge_SigMaNet_sign.py | import numpy as np
import pandas as pd
import torch
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import random
import torch.nn.functional as F
from torch_geometric_signed_directed.data.signed import load_signed_real_data
from scipy.sparse import coo_matrix
from... | 13,901 | 45.494983 | 256 | py |
SigMaNet | SigMaNet-master/src/Digraph.py | # external files
import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
from torch_geometric_signed_directed.data import load_directed_real_data
import random
import networkx as nx
from torch_geometric_signed_directed im... | 13,420 | 44.036913 | 204 | py |
SigMaNet | SigMaNet-master/src/Edge_Digraph.py | import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
from torch_geometric_signed_directed.data import load_directed_real_data
import random
import pickle as pk
# internal files
from layer.DiGCN import *
from layer.ge... | 11,673 | 42.887218 | 207 | py |
SigMaNet | SigMaNet-master/src/SAGE.py | # external files
import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
from torch_geometric_signed_directed import node_class_split
from torch_geometric_signed_directed.data import load_directed_real_data
import random
... | 12,336 | 43.218638 | 204 | py |
SigMaNet | SigMaNet-master/src/Edge_Cheb.py | import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
from torch_geometric.utils import to_undirected
from torch_geometric_signed_directed.data import load_directed_real_data
import random
import pickle as pk
# intern... | 14,387 | 47.120401 | 207 | py |
SigMaNet | SigMaNet-master/src/Edge_SigMaNet.py | import numpy as np
import torch
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
from torch_geometric_signed_directed.data import load_directed_real_data
import random
# internal files
from layer.Signum import SigMaNet_link_predicti... | 16,977 | 46.825352 | 256 | py |
SigMaNet | SigMaNet-master/src/GAT.py | # external files
import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
import random
from torch_geometric_signed_directed.data import load_directed_real_data
from torch_geometric_signed_directed import node_class_split
... | 12,521 | 42.630662 | 208 | py |
SigMaNet | SigMaNet-master/src/GIN.py | # external files
import numpy as np
import pickle as pk
import torch.optim as optim
from datetime import datetime
import os, time, argparse
import torch.nn.functional as F
import random
from torch_geometric_signed_directed.data import load_directed_real_data
from torch_geometric_signed_directed import node_class_split
... | 12,170 | 42.938628 | 204 | py |
SigMaNet | SigMaNet-master/src/utils/telegram_prepare.py | import math
import os
import random
import torch
import numpy as np
import networkx as nx
from scipy import sparse
def to_dataset(A, label, save_path, train_ratio=0.6, test_ratio=0.2):
import pickle as pk
from numpy import linalg as LA
from Citation import train_test_split
from torch_geometric.data i... | 4,071 | 36.703704 | 94 | py |
SigMaNet | SigMaNet-master/src/utils/edge_data.py | import torch
import pickle as pk
import networkx as nx
from scipy.sparse import coo_matrix
from torch_geometric.data import Data
from torch import Tensor
from torch_sparse import SparseTensor, coalesce
#from stellargraph.data import EdgeSplitter
from sklearn.model_selection import train_test_split
from torch_geometric.... | 38,287 | 49.98269 | 295 | py |
SigMaNet | SigMaNet-master/src/utils/preprocess.py | #externel
import torch
import csv, os
import numpy as np
import pickle as pk
import networkx as nx
import scipy.sparse as sp
from torch_geometric.utils import to_undirected
from torch_geometric.datasets import WebKB, WikipediaNetwork
#internel
from utils.hermitian import *
def load_cora(q, path = '../../dataset/cora/... | 12,665 | 38.955836 | 154 | py |
SigMaNet | SigMaNet-master/src/utils/symmetric_distochastic.py | import torch
import numpy as np
import networkx as nx
from scipy import sparse
def desymmetric_stochastic(sizes = [100, 100, 100],
probs = [[0.5, 0.45, 0.45],
[0.45, 0.5, 0.45],
[0.45, 0.45, 0.5]],
seed = 0,
off_diag_prob... | 8,631 | 37.882883 | 140 | py |
SigMaNet | SigMaNet-master/src/utils/SignedDirectedGraphDatasetModified.py | from typing import Optional, Callable
import os
import json
import torch
from torch_geometric.data import (InMemoryDataset, download_url, Data)
dataset_name_url_dic = {
'bitcoin_alpha': '../data/dataset/bitcoin_alpha.csv',
'bitcoin_otc': '../data/dataset/bitcoin_otc.csv',
"epinions": 'https://github.com/... | 3,795 | 34.148148 | 112 | py |
SigMaNet | SigMaNet-master/src/layer/Signum.py | '''
SigMaNet architecture
'''
import torch
from torch.nn import Parameter
from torch_geometric.nn.inits import zeros, glorot
from torch_geometric.nn.conv import MessagePassing
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .src2 import laplacian
class complex_relu_layer(nn.... | 24,376 | 51.764069 | 195 | py |
SigMaNet | SigMaNet-master/src/layer/sparse_magnet.py | #############################################
# Copy and modified from Magnet
# https://github.com/matthew-hirn/magnet/tree/f88f73b8468053dca0967621838ecb9255734797
#############################################
import torch, math
import torch.nn as nn
import torch.nn.functional as F
from torch_sparse import SparseTe... | 6,027 | 35.095808 | 137 | py |
SigMaNet | SigMaNet-master/src/layer/geometric_baselines.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, GATConv, SAGEConv, ChebConv, GINConv, APPNP
####################################################################
# Link Prediction Models
####################################################################
'''
d... | 20,046 | 33.385935 | 94 | py |
SigMaNet | SigMaNet-master/src/layer/cheb.py | import torch, math
import torch.nn as nn
import torch.nn.functional as F
class ChebConv(nn.Module):
"""
The ChebNet convolution operation.
:param in_c: int, number of input channels.
:param out_c: int, number of output channels.
:param K: int, the order of Chebyshev Polynomial.
:param L_norm_r... | 5,465 | 37.223776 | 137 | py |
SigMaNet | SigMaNet-master/src/layer/DiGCN.py | #############################################
# Copy from DiGCN
# https://github.com/flyingtango/DiGCN
#############################################
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter, Linear
from torch_scatter import scatter_add
from torch_geometric.nn.c... | 8,665 | 38.390909 | 115 | py |
SigMaNet | SigMaNet-master/src/layer/DGCN.py | ###############################################
# Modified from pytorch Geometric GCN
###############################################
from typing import Optional, Tuple
from torch_geometric.typing import Adj, OptTensor, PairTensor
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import Parame... | 9,032 | 34.14786 | 103 | py |
SigMaNet | SigMaNet-master/src/layer/src2/activation.py.py | from typing import Callable, List, Optional, Tuple
import math
import torch
from torch.nn.modules import Module
import src.functional as cF
Tensor = torch.Tensor
class CSigmoid(Module):
def forward(self, input: Tensor) -> Tensor:
return cF.c_sigmoid(input)
class CTanh(Module):
def forward(self, inp... | 4,760 | 27.339286 | 84 | py |
SigMaNet | SigMaNet-master/src/layer/src2/functional.py | from typing import Callable, List, Optional, Tuple
import math
import warnings
import numpy as np
import torch
import torch.nn.functional as F
from torch.nn import ParameterList
from torch.nn import _reduction as _Reduction
from torch.overrides import (
has_torch_function, has_torch_function_unary, has_torch_funct... | 4,856 | 41.982301 | 176 | py |
SigMaNet | SigMaNet-master/src/layer/src2/dropout.py | from typing import Callable, List, Optional, Tuple
import math
import torch
import torch.nn.functional as F
from torch.nn.modules import Module
import .functional as cF
Tensor = torch.Tensor
class _DropoutNd(Module):
__constants__ = ['p', 'inplace']
p: float
inplace: bool
def __init__(self, p: float... | 1,560 | 30.22 | 93 | py |
SigMaNet | SigMaNet-master/src/layer/src2/laplacian.py | from platform import node
from typing import Optional
import time
import torch
from torch_scatter import scatter_add
from torch_sparse import coalesce
from torch_geometric.utils import add_self_loops, remove_self_loops, to_scipy_sparse_matrix
from torch_geometric.utils.num_nodes import maybe_num_nodes
import numpy as n... | 7,773 | 43.678161 | 169 | py |
DPFRL | DPFRL-master/code/main.py | import sys
import os
import time
import logging
import collections
import multiprocessing
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from gym.envs.registration import register
from sacred import Experiment
from sacred.utils import apply_backspa... | 25,759 | 34.67867 | 115 | py |
DPFRL | DPFRL-master/code/storage.py | import torch
from torch.utils.data.sampler import BatchSampler, SubsetRandomSampler
class RolloutStorage(object):
def __init__(self, num_steps, num_processes, obs_shape, action_space):
# def __init__(self, num_steps, num_processes, obs_shape, action_space):
self.masks = torch.ones(num_steps + 1, num_p... | 1,804 | 37.404255 | 76 | py |
DPFRL | DPFRL-master/code/pfrnn_model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from policy import Categorical, DiagGaussian
from torch.nn.init import xavier_normal_, orthogonal_
import encoder
import namedlist
from operator import mul
from functools import reduce
from pfrnns import PFGRUCell
imp... | 10,895 | 34.842105 | 114 | py |
DPFRL | DPFRL-master/code/envs.py | import os
import gym
from gym.spaces.box import Box
from baselines import bench
from baselines.common.atari_wrappers import make_atari, wrap_deepmind
try:
import pybullet_envs
except ImportError:
pass
def make_env(env_id, seed, rank, log_dir, frameskips_cases, train):
def _thunk():
mode = 'trai... | 1,951 | 29.984127 | 110 | py |
DPFRL | DPFRL-master/code/utils.py | import torch
import torch.nn as nn
import numpy as np
from torch.autograd import Variable
import errno
import os
import json
import sys
from glob import glob
import os.path as osp
import logging
from docopt import docopt
from sacred.arg_parser import get_config_updates
def safe_make_dirs(path):
"""
Given a pa... | 9,931 | 29.84472 | 108 | py |
DPFRL | DPFRL-master/code/encoder.py | import torch.nn as nn
def get_encoder(observation_type, nr_inputs, cnn_channels, batch_norm=True):
# 84 => 20 => 9 => 7
if observation_type == '84x84':
if batch_norm:
enc = nn.Sequential(
nn.Conv2d(nr_inputs, cnn_channels[0], 8, stride=4),
nn.BatchNorm2d(cn... | 4,368 | 36.663793 | 78 | py |
DPFRL | DPFRL-master/code/policy.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class AddBias(nn.Module):
def __init__(self, bias):
super(AddBias, self).__init__()
self._bias = nn.Parameter(bias.unsqueeze(1))
def forward(self, x):
if x.dim() == 2:
... | 2,743 | 28.505376 | 125 | py |
DPFRL | DPFRL-master/code/pfrnns.py | import torch
from torch import nn
import numpy as np
import ipdb as pdb
import torch.nn.functional as F
class PFRNNBaseCell(nn.Module):
"""parent class for PFRNNs
"""
def __init__(self, num_particles, input_size, hidden_size, resamp_alpha,
use_resampling, activation):
"""init function
... | 5,439 | 33.871795 | 131 | py |
DPFRL | DPFRL-master/code/particle_aggregators.py | import torch
import torch.nn as nn
class Aggregator(nn.Module):
def __init__(self, num_particles, num_features, h_dim, obs_encode_dim):
"""Base class for particle aggregators
Arguments:
nn {[type]} -- [description]
num_particles {int} -- number of particles
... | 3,436 | 36.769231 | 86 | py |
CMPA | CMPA-main/train.py | # import debugpy; debugpy.connect(('127.0.0.1', 5678))
import argparse
import torch
from dassl.utils import setup_logger, set_random_seed, collect_env_info
from dassl.config import get_cfg_default
from dassl.engine import build_trainer
# custom
import datasets.oxford_pets
import datasets.oxford_flowers
import dataset... | 5,575 | 27.020101 | 99 | py |
CMPA | CMPA-main/clip/clip.py | import hashlib
import os
import urllib
import warnings
from typing import Union, List
import torch
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm
from .model import build_model
from .simple_tokenizer import SimpleTokenizer as _Tokenizer
... | 8,256 | 36.193694 | 142 | py |
CMPA | CMPA-main/clip/model.py | from collections import OrderedDict
from typing import Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
import math
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1):
super().__init__()
# all conv layers... | 30,793 | 44.688427 | 159 | py |
CMPA | CMPA-main/interpret_prompts/interpret_prompt.py | import os
import sys
import argparse
import torch
from clip.simple_tokenizer import SimpleTokenizer
from clip import clip
# "ViT-B/16"
# "RN50"
def load_clip_to_cpu(backbone_name="ViT-B/16"):
url = clip._MODELS[backbone_name]
model_path = clip._download(url)
try:
# loading JIT archive
mod... | 2,600 | 29.964286 | 136 | py |
CMPA | CMPA-main/trainers/cmpa.py | import os.path as osp
from collections import OrderedDict
import copy
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.cuda.amp import GradScaler, autocast
from dassl.engine import TRAINER_REGISTRY, TrainerX
from dassl.metrics import compute_accuracy
from dassl.utils import load_pretr... | 14,345 | 38.520661 | 162 | py |
NRG_AI_NeuroOnco_segment | NRG_AI_NeuroOnco_segment-master/wrapper_scripts/segmentation.py | import glob
import logging
import os
import argparse
import nibabel as nib
from keras.models import load_model
from keras_contrib.layers import InstanceNormalization
import numpy as np
import tensorflow as tf
from functools import partial
from pathlib import Path
from medpy.metric.binary import dc, hd, asd, assd, ravd
... | 7,186 | 49.612676 | 151 | py |
NRG_AI_NeuroOnco_segment | NRG_AI_NeuroOnco_segment-master/src/metrics.py | '''
Author: Satrajit Chakrabarty, satrajit.chakrabarty@wustl.edu
Copyright (c) 2021, Computational Imaging Lab, School of Medicine, Washington University in Saint Louis
Redistribution and use in source and binary forms, for any purpose, with or without modification, are permitted provided that the following conditions... | 2,801 | 50.888889 | 755 | py |
NRG_AI_NeuroOnco_segment | NRG_AI_NeuroOnco_segment-master/src/train_segmenter/model.py | from functools import partial
from keras import backend as K
from keras.layers import *
from keras.engine import Model
from keras.optimizers import Adam
from keras import regularizers
K.set_image_data_format("channels_first")
def create_localization_module(input_layer, n_filters, regularizer = None):
convoluti... | 6,910 | 40.884848 | 166 | py |
NRG_AI_NeuroOnco_segment | NRG_AI_NeuroOnco_segment-master/src/train_segmenter/generator.py | import math
import os
import copy
from random import shuffle
import itertools
import nibabel as nib
import numpy as np
import keras
import pandas as pd
import random
import string
from collections import Counter
from utils import pickle_dump, pickle_load
from augment import augment_data
def get_training_and_validatio... | 9,317 | 41.547945 | 151 | py |
NRG_AI_NeuroOnco_segment | NRG_AI_NeuroOnco_segment-master/src/train_segmenter/train.py | import random
import time
random.seed(9001)
import importlib
from config_utils import *
from data import write_data_to_file, open_data_file
from generator import get_training_and_validation_generators_segmentation
from model import segmentation_model
import pickle
import os
from keras import backend as K
from kera... | 8,626 | 42.570707 | 190 | py |
TextUnderstandingTsetlinMachine | TextUnderstandingTsetlinMachine-master/produce_dataset.py | #!/usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import re
import keras
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_selection import mutual_info_classif
from keras.datasets impo... | 2,829 | 23.396552 | 86 | py |
GBRT-for-TSF | GBRT-for-TSF-main/XGBoost_(W-b)/Univariate/xgboostWB_electricity.py | # -*- coding: utf-8 -*-
"""XGBoostWB_Electricity
"""
import sys
#Import Libraries
#import tensorflow as tf
import pandas as pd
import numpy as np
import os
#import matplotlib
#import matplotlib.pyplot as plt
import random
# %matplotlib inline
import shutil
from random import shuffle
import itertools
from sklearn.multi... | 8,121 | 35.585586 | 161 | py |
GBRT-for-TSF | GBRT-for-TSF-main/XGBoost_(W-b)/Univariate/xgboostWB_rate_exchange.py | # -*- coding: utf-8 -*-
"""XGBoostWB-Rate_Exchange
"""
import sys
sys.version
#Import Libraries
import pandas as pd
import numpy as np
import os
#import matplotlib
#import matplotlib.pyplot as plt
import random
# %matplotlib inline
import shutil
from random import shuffle
import itertools
from sklearn.preprocessing imp... | 8,966 | 35.75 | 161 | py |
GBRT-for-TSF | GBRT-for-TSF-main/XGBoost_(W-b)/Univariate/xgboostWB_trafffic.py | # -*- coding: utf-8 -*-
"""XGBoostWB_Traffic
"""
import sys
#Import Libraries
#import tensorflow as tf
import pandas as pd
import numpy as np
import os
#import matplotlib
#import matplotlib.pyplot as plt
import random
# %matplotlib inline
import shutil
from random import shuffle
import itertools
from sklearn.multiout... | 7,683 | 34.410138 | 161 | py |
GBRT-for-TSF | GBRT-for-TSF-main/XGBoost_(W-b)/Univariate/xgboostWB_solar_energy.py |
"""XGBoostWB_Solar
"""
import sys
#Import Libraries
#import tensorflow as tf
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import Normalizer
from datetime import datetime, timedelta
from sklearn import preprocessing
import pandas as pd
import numpy as np
import os
#import matplotlib
#impo... | 9,161 | 32.195652 | 163 | py |
GBRT-for-TSF | GBRT-for-TSF-main/XGBoost_(W-b)/Univariate/xgboostWB_pemds7.py | # -*- coding: utf-8 -*-
"""XGBoostWB_PeMDS7
"""
import sys
sys.version
#Import Libraries
#import tensorflow as tf
import pandas as pd
import numpy as np
import os
#import matplotlib
#import matplotlib.pyplot as plt
import random
# %matplotlib inline
import shutil
import itertools
from random import shuffle
from sklea... | 8,117 | 35.733032 | 161 | py |
GBRT-for-TSF | GBRT-for-TSF-main/XGBoost_(W-b)/Multivariate/xgboostWB_sequence_to_sequence_dl_framework.py | # -*- coding: utf-8 -*-
"""XGBoostWB Sequence-to-Sequence DL Framework.ipynb
"""
import sys
sys.version
#Import Libraries
import itertools
import pandas as pd
import numpy as np
import os
#import matplotlib
#import matplotlib.pyplot as plt
import random
# %matplotlib inline
import shutil
from random import shuffle
fr... | 7,287 | 31.977376 | 120 | py |
GBRT-for-TSF | GBRT-for-TSF-main/XGBoost_(W-b)/Multivariate/xgboostWB_forecasting_using_hybrid_dl_urban_dataset.py | # -*- coding: utf-8 -*-
"""XGBoostWB_Forecasting_Using_Hybrid_DL_Urban_Dataset.ipynb
"""
import sys
sys.version
#Import Libraries
import itertools
import pandas as pd
import numpy as np
import os
#import matplotlib
#import matplotlib.pyplot as plt
import random
# %matplotlib inline
import shutil
from random import shu... | 8,229 | 32.591837 | 163 | py |
GBRT-for-TSF | GBRT-for-TSF-main/XGBoost_(W-b)/Multivariate/xgboostWB_forecasting_using_hybrid_dl_framework_pm2_5.py | # -*- coding: utf-8 -*-
"""XGBoostWB_Forecasting_Using_Hybrid_DL_Framework_Pm2.5_(1,6)
"""
import sys
sys.version
#Import Libraries
import itertools
import pandas as pd
import numpy as np
import os
#import matplotlib
#import matplotlib.pyplot as plt
import random
# %matplotlib inline
import shutil
from random import s... | 7,967 | 32.062241 | 119 | py |
GBRT-for-TSF | GBRT-for-TSF-main/XGBoost_(W-b)/Multivariate/xgboostWB_sml2010.py | # -*- coding: utf-8 -*-
"""DARNN_SML2010.ipynb
"""
import sys
sys.version
#Import Libraries
import pandas as pd
import os
#import matplotlib
#import matplotlib.pyplot as plt
import random
# %matplotlib inline
import shutil
import itertools
import numpy as np
import re
from random import shuffle
from sklearn.multioutp... | 9,125 | 35.358566 | 136 | py |
GBRT-for-TSF | GBRT-for-TSF-main/XGBoost_(W-b)/Multivariate/xgboostWB_nasdaq.py | # -*- coding: utf-8 -*-
"""DARNN_NASDAQ.ipynb
"""
import sys
sys.version
#Import Libraries
import pandas as pd
import numpy as np
import os
#import matplotlib
#import matplotlib.pyplot as plt
import random
# %matplotlib inline
import shutil
import itertools
import re
from random import shuffle
from sklearn.multioutpu... | 8,487 | 34.07438 | 119 | py |
BaitWatcher | BaitWatcher-master/server/clickbait_api/apps/score/utils/history.py | import keras
# define class of loss history
class CustomHistory(keras.callbacks.Callback):
def init(self):
self.train_loss = []
self.val_loss = []
self.train_acc = []
self.val_acc = []
def on_epoch_end(self, batch, logs={}):
self.train_loss.append(logs.get('loss'))
... | 463 | 28 | 50 | py |
BaitWatcher | BaitWatcher-master/server/clickbait_api/apps/score/model/model_2.py | from keras.models import Model, Sequential
from keras.layers import Dense, Dropout, Merge, Embedding, Input, LSTM, Bidirectional, Lambda, Reshape, Conv1D, concatenate
from keras.optimizers import Adadelta, RMSprop
# model - mission_1_model_2
# head - 50, body - 500
def model_selector(args, embedding_matrix):
''... | 12,305 | 38.191083 | 171 | py |
BaitWatcher | BaitWatcher-master/server/clickbait_api/apps/score/source/main.py | import sys
from keras.preprocessing import sequence
from keras.models import load_model
from keras.callbacks import EarlyStopping
import utils.preprocessing as pp
from utils import argumentparser
from utils.history import CustomHistory
from model.model_2 import model_selector
def main(args):
source = args.data... | 7,896 | 33.942478 | 118 | py |
BaitWatcher | BaitWatcher-master/server/clickbait_api/clickbait_api/settings_past_model.py | """
Django settings for clickbait_api project.
Generated by 'django-admin startproject' using Django 2.0.3.
For more information on this file, see
https://docs.djangoproject.com/en/2.0/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.0/ref/settings/
"""
import... | 4,257 | 27.198675 | 162 | py |
BaitWatcher | BaitWatcher-master/server/clickbait_api/clickbait_api/settings_new_model.py | """
Django settings for clickbait_api project.
Generated by 'django-admin startproject' using Django 2.0.3.
For more information on this file, see
https://docs.djangoproject.com/en/2.0/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.0/ref/settings/
"""
import... | 5,158 | 27.983146 | 162 | py |
dm4api | dm4api-master/synthetic_eval/src/run_experiments.py | import sys
sys.path.append("../../dm4api/src")
from experiment_configs import get_config
from dm4api import create_env, create_agent
import itertools
import traceback
import argparse
import pickle
import os
import numpy as np
from collections import Counter, defaultdict
from tensorflow import compat
from gym.spaces i... | 6,881 | 36.402174 | 146 | py |
dm4api | dm4api-master/dm4api/src/test.py | from dm4api import create_env, create_agent
import argparse
import pickle
import os
from tensorflow import compat
from gym.spaces import *
from rl.agents.dqn import DQNAgent, NAFAgent
from rl.policy import *
from rl.memory import *
from rl.random import OrnsteinUhlenbeckProcess
from rl.callbacks import Callback
from ... | 2,709 | 37.714286 | 104 | py |
dm4api | dm4api-master/dm4api/src/train.py | from dm4api import create_env
from models import create_model
import argparse
import pickle
import os
import traceback
from tensorflow import compat
from gym.spaces import *
from rl.agents.dqn import DQNAgent, NAFAgent
from rl.policy import *
from rl.memory import *
from rl.random import OrnsteinUhlenbeckProcess
from... | 4,395 | 36.57265 | 138 | py |
dm4api | dm4api-master/dm4api/src/models/multiinput.py | from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Lambda
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Concatenate
from tensorflow import split
class MultiInput:
def __i... | 1,742 | 35.3125 | 93 | py |
dm4api | dm4api-master/dm4api/src/models/vanilladense.py | from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Flatten
class VanillaDense:
def __init__(self, config):
self.config = config
self.nb_obs = config['nb_obs']
self.nb_actions = config['nb_actions']
self.wlen = config["wlen"]
... | 743 | 31.347826 | 96 | py |
dm4api | dm4api-master/dm4api/src/dm4api/agent.py | from .default_env import DefaultLearnedAgent
from .default_env import DefaultHCAgent
from .default_env import DefaultBaselineAgent
from tensorflow.keras.optimizers import Adam
import os
def create_agent(agenttype=None, env=None, modeldir=None, **kwargs):
agent = None
if agenttype == 'DefaultLearnedAgent':
... | 689 | 33.5 | 68 | py |
dm4api | dm4api-master/dm4api/src/dm4api/default_env/learned_agent.py | import argparse
import pickle
import os
from tensorflow import compat
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import Model
from gym.spaces import *
from rl.agents.dqn import DQNAgent
from rl.policy import *
from rl.memory import *
class DefaultLearnedAgent(DQNAgent):
def __init__(self, ... | 1,020 | 43.391304 | 100 | py |
dm4api | dm4api-master/dm4api/src/dm4api/default_env/hc_agent.py | import argparse
import pickle
import random
import os
from tensorflow import compat
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import Model
from gym.spaces import *
from rl.agents.dqn import DQNAgent
from rl.policy import *
from rl.memory import *
from rl.core import Agent
class DefaultHCAgent(... | 5,688 | 39.347518 | 139 | py |
dm4api | dm4api-master/dm4api/src/dm4api/default_env/baseline_agent.py | import argparse
import pickle
import random
import os
from tensorflow import compat
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import Model
from gym.spaces import *
from rl.agents.dqn import DQNAgent
from rl.policy import *
from rl.memory import *
from rl.core import Agent
class DefaultBaseline... | 1,526 | 22.859375 | 59 | py |
DeepLabV3Plus-Pytorch | DeepLabV3Plus-Pytorch-master/main.py | from tqdm import tqdm
import network
import utils
import os
import random
import argparse
import numpy as np
from torch.utils import data
from datasets import VOCSegmentation, Cityscapes
from utils import ext_transforms as et
from metrics import StreamSegMetrics
import torch
import torch.nn as nn
from utils.visualize... | 16,773 | 42.682292 | 119 | py |
DeepLabV3Plus-Pytorch | DeepLabV3Plus-Pytorch-master/predict.py | from torch.utils.data import dataset
from tqdm import tqdm
import network
import utils
import os
import random
import argparse
import numpy as np
from torch.utils import data
from datasets import VOCSegmentation, Cityscapes, cityscapes
from torchvision import transforms as T
from metrics import StreamSegMetrics
impor... | 5,443 | 38.449275 | 120 | py |
DeepLabV3Plus-Pytorch | DeepLabV3Plus-Pytorch-master/datasets/voc.py | import os
import sys
import tarfile
import collections
import torch.utils.data as data
import shutil
import numpy as np
from PIL import Image
from torchvision.datasets.utils import download_url, check_integrity
DATASET_YEAR_DICT = {
'2012': {
'url': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrain... | 6,061 | 36.190184 | 128 | py |
DeepLabV3Plus-Pytorch | DeepLabV3Plus-Pytorch-master/datasets/cityscapes.py | import json
import os
from collections import namedtuple
import torch
import torch.utils.data as data
from PIL import Image
import numpy as np
class Cityscapes(data.Dataset):
"""Cityscapes <http://www.cityscapes-dataset.com/> Dataset.
**Parameters:**
- **root** (string): Root directory of datase... | 8,370 | 55.945578 | 168 | py |
DeepLabV3Plus-Pytorch | DeepLabV3Plus-Pytorch-master/network/_deeplab.py | import torch
from torch import nn
from torch.nn import functional as F
from .utils import _SimpleSegmentationModel
__all__ = ["DeepLabV3"]
class DeepLabV3(_SimpleSegmentationModel):
"""
Implements DeepLabV3 model from
`"Rethinking Atrous Convolution for Semantic Image Segmentation"
<https://arxiv.o... | 6,709 | 36.696629 | 157 | py |
DeepLabV3Plus-Pytorch | DeepLabV3Plus-Pytorch-master/network/utils.py | import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from collections import OrderedDict
class _SimpleSegmentationModel(nn.Module):
def __init__(self, backbone, classifier):
super(_SimpleSegmentationModel, self).__init__()
self.backbone = backbone
self.class... | 4,237 | 44.085106 | 122 | py |
DeepLabV3Plus-Pytorch | DeepLabV3Plus-Pytorch-master/network/backbone/resnet.py | import torch
import torch.nn as nn
try: # for torchvision<0.4
from torchvision.models.utils import load_state_dict_from_url
except: # for torchvision>=0.4
from torch.hub import load_state_dict_from_url
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x... | 13,687 | 38.446686 | 107 | py |
DeepLabV3Plus-Pytorch | DeepLabV3Plus-Pytorch-master/network/backbone/mobilenetv2.py | from torch import nn
try: # for torchvision<0.4
from torchvision.models.utils import load_state_dict_from_url
except: # for torchvision>=0.4
from torch.hub import load_state_dict_from_url
import torch.nn.functional as F
__all__ = ['MobileNetV2', 'mobilenet_v2']
model_urls = {
'mobilenet_v2': 'https://dow... | 7,035 | 35.837696 | 123 | py |
DeepLabV3Plus-Pytorch | DeepLabV3Plus-Pytorch-master/network/backbone/xception.py |
"""
Xception is adapted from https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/xception.py
Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
@author: tstandley
Adapted by cadene
Creates an Xception Model as defined in:
Francois Chollet
Xceptio... | 9,341 | 38.252101 | 138 | py |
DeepLabV3Plus-Pytorch | DeepLabV3Plus-Pytorch-master/network/backbone/hrnetv2.py | import torch
from torch import nn
import torch.nn.functional as F
import os
__all__ = ['HRNet', 'hrnetv2_48', 'hrnetv2_32']
# Checkpoint path of pre-trained backbone (edit to your path). Download backbone pretrained model hrnetv2-32 @
# https://drive.google.com/file/d/1NxCK7Zgn5PmeS7W1jYLt5J9E0RRZ2oyF/view?usp=sharin... | 14,495 | 40.895954 | 120 | py |
DeepLabV3Plus-Pytorch | DeepLabV3Plus-Pytorch-master/utils/loss.py | import torch.nn as nn
import torch.nn.functional as F
import torch
class FocalLoss(nn.Module):
def __init__(self, alpha=1, gamma=0, size_average=True, ignore_index=255):
super(FocalLoss, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.ignore_index = ignore_index
... | 721 | 33.380952 | 78 | py |
DeepLabV3Plus-Pytorch | DeepLabV3Plus-Pytorch-master/utils/utils.py | from torchvision.transforms.functional import normalize
import torch.nn as nn
import numpy as np
import os
def denormalize(tensor, mean, std):
mean = np.array(mean)
std = np.array(std)
_mean = -mean/std
_std = 1/std
return normalize(tensor, _mean, _std)
class Denormalize(object):
def __init_... | 1,008 | 24.871795 | 84 | py |
DeepLabV3Plus-Pytorch | DeepLabV3Plus-Pytorch-master/utils/scheduler.py | from torch.optim.lr_scheduler import _LRScheduler, StepLR
class PolyLR(_LRScheduler):
def __init__(self, optimizer, max_iters, power=0.9, last_epoch=-1, min_lr=1e-6):
self.power = power
self.max_iters = max_iters # avoid zero lr
self.min_lr = min_lr
super(PolyLR, self).__init__(opt... | 509 | 41.5 | 96 | py |
DeepLabV3Plus-Pytorch | DeepLabV3Plus-Pytorch-master/utils/ext_transforms.py | import collections
import torchvision
import torch
import torchvision.transforms.functional as F
import random
import numbers
import numpy as np
from PIL import Image
#
# Extended Transforms for Semantic Segmentation
#
class ExtRandomHorizontalFlip(object):
"""Horizontally flip the given PIL Image randomly with... | 20,828 | 35.930851 | 150 | py |
SemEval2022-Task-5-Multimedia-Automatic-Misogyny-Identification-MAMI- | SemEval2022-Task-5-Multimedia-Automatic-Misogyny-Identification-MAMI--main/Baselines/Subtask B/Baseline_Flat_Multilabel.py | #pip install -r requirements.txt
'''
requires the same folder:
- script evaluation
-folder 'ref' with truth.txt
-folder 'TRAINING' with images
'''
#path
csv_path_test = './test.csv'
csv_path_train = './train.csv'
image_path = './TRAINING'
import evaluation
import pandas as pd
import tensorflow_hub as hub
import tenso... | 7,743 | 30.225806 | 119 | py |
SemEval2022-Task-5-Multimedia-Automatic-Misogyny-Identification-MAMI- | SemEval2022-Task-5-Multimedia-Automatic-Misogyny-Identification-MAMI--main/Baselines/Subtask B/Baseline_Hierarchical_Multilabel.py | #pip install -r requirements.txt
'''
requires the same folder:
- script evaluation
-folder 'ref' with truth.txt
-folder 'TRAINING' with images
'''
#path
csv_path_test = './test.csv'
csv_path_train = './train.csv'
import evaluation
import pandas as pd
import tensorflow_hub as hub
import tensorflow as tf
import numpy a... | 6,963 | 30.2287 | 151 | py |
SemEval2022-Task-5-Multimedia-Automatic-Misogyny-Identification-MAMI- | SemEval2022-Task-5-Multimedia-Automatic-Misogyny-Identification-MAMI--main/Baselines/Subtask A/Baseline_Image.py | #pip install -r requirements.txt
'''
#requires the same folder:
- script evaluation
-folder 'ref' with truth.txt
-folder 'TRAINING' with images
'''
#path
csv_path_test = './test.csv'
csv_path_train = './train.csv'
image_path = './TRAINING'
import evaluation
import pandas as pd
import tensorflow_hub as hub
import tens... | 4,302 | 28.074324 | 107 | py |
SemEval2022-Task-5-Multimedia-Automatic-Misogyny-Identification-MAMI- | SemEval2022-Task-5-Multimedia-Automatic-Misogyny-Identification-MAMI--main/Baselines/Subtask A/Baseline_Text.py | #pip install -r requirements.txt
'''
requires the same folder:
- script evaluation
-folder 'ref' with truth.txt
'''
#path
csv_path_test = './test.csv'
csv_path_train = './train.csv'
import evaluation
import pandas as pd
import tensorflow_hub as hub
import tensorflow as tf
import numpy as np
import keras
import tensor... | 4,395 | 26.304348 | 107 | py |
SemEval2022-Task-5-Multimedia-Automatic-Misogyny-Identification-MAMI- | SemEval2022-Task-5-Multimedia-Automatic-Misogyny-Identification-MAMI--main/Baselines/Subtask A/Baseline_Image_Text.py | #pip install -r requirements.txt
'''
requires the same folder:
- script evaluation
-folder 'ref' with truth.txt
-folder 'TRAINING' with images
'''
#path
csv_path_test = './test.csv'
csv_path_train = './train.csv'
image_path = './TRAINING'
import evaluation
import pandas as pd
import tensorflow_hub as hub
import tenso... | 7,372 | 28.850202 | 119 | py |
SimXNS | SimXNS-main/SimANS/Doc_training/co_training_doc_train.py | import sys
sys.path += ['../']
import argparse
import json
import logging
import os
import torch
sys.path.append(os.getcwd())
sys.path.append(os.path.abspath(os.path.dirname(os.getcwd())))
#
from torch.utils.data import DataLoader, RandomSampler
from tqdm import tqdm
import torch.distributed as dist
from torch import... | 27,513 | 38.933237 | 126 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.