repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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hepdata-converter | hepdata-converter-master/hepdata_converter/writers/root_writer.py | # -*- coding: utf-8 -*-
import abc
from hepdata_converter.writers.array_writer import ArrayWriter, ObjectWrapper, ObjectFactory
import ROOT as ROOTModule
import array
import tempfile
import os
from ctypes import c_char_p
from hepdata_converter.writers.utils import error_value_processor
__author__ = 'Michał Szostak'
i... | 20,446 | 43.06681 | 131 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/testsuite/test_csvwriter.py | import os
import hepdata_converter
from hepdata_converter import convert
from hepdata_converter.testsuite import insert_path, insert_data_as_str
from hepdata_converter.testsuite.test_writer import WriterTestSuite
class CSVWriterTestCase(WriterTestSuite):
@insert_path('yaml_full')
def setUp(self, submission_f... | 5,281 | 51.29703 | 106 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/testsuite/test_yamlwriter.py | # -*- encoding: utf-8 -*-
import os
import hepdata_converter
from hepdata_converter.testsuite import insert_data_as_file, insert_data_as_str, insert_path
from hepdata_converter.testsuite.test_writer import WriterTestSuite
__author__ = 'Michał Szostak'
class YAMLWriterTestSuite(WriterTestSuite):
@insert_path('old... | 1,969 | 42.777778 | 108 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/testsuite/test_rootwriter.py | # -*- encoding: utf-8 -*-
import os
import hepdata_converter
from hepdata_converter.testsuite import insert_path, insert_paths
from hepdata_converter.testsuite.test_writer import WriterTestSuite
import ROOT
__author__ = 'Michał Szostak'
def walk(tdirectory,
path=None,
depth=0):
'''Walk the direc... | 4,164 | 40.65 | 98 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/testsuite/test_oldhepdata.py | import hepdata_converter
from hepdata_converter.parsers import yaml_parser
from hepdata_converter.parsers.oldhepdata_parser import OldHEPData
from hepdata_converter.testsuite import insert_data_as_file, insert_path
from hepdata_converter.testsuite.test_writer import WriterTestSuite
class OldHEPDataTestSuite(WriterTes... | 1,812 | 41.162791 | 82 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/testsuite/test_getconcretesubclassmixin.py | import abc
import unittest
from hepdata_converter.common import GetConcreteSubclassMixin
class ParserTestSuite(unittest.TestCase):
"""Test suite for Parser factory class
"""
def test_get_all_subclasses(self):
class A(GetConcreteSubclassMixin):
pass
class AB(A):
pa... | 660 | 22.607143 | 94 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/testsuite/test_parser.py | import unittest
import datetime
from hepdata_converter.parsers import Parser
from hepdata_converter.parsers.oldhepdata_parser import OldHEPData
from hepdata_converter.parsers.yaml_parser import YAML
from hepdata_converter.testsuite import insert_paths
class ParserTestSuite(unittest.TestCase):
"""Test suite for ... | 1,111 | 37.344828 | 97 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/testsuite/test_convert.py | import io
import os
import yaml
import hepdata_converter
from hepdata_converter.testsuite import insert_path
from hepdata_converter.testsuite.test_writer import WriterTestSuite
# We try to load using the CSafeLoader for speed improvements.
try:
from yaml import CSafeLoader as Loader
except ImportError: #pragma: no... | 5,806 | 47.391667 | 193 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/testsuite/test_yodawriter.py | # -*- encoding: utf-8 -*-
import os
import hepdata_converter
from hepdata_converter.testsuite import insert_data_as_file, insert_path, insert_paths
from hepdata_converter.testsuite.test_writer import WriterTestSuite
__author__ = 'Michał Szostak'
class YODAWriterTestSuite(WriterTestSuite):
@insert_path('yaml_full... | 1,652 | 46.228571 | 98 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/testsuite/test_yamlparser.py | # -*- encoding: utf-8 -*-
import hepdata_converter
from hepdata_converter.testsuite import insert_path, insert_data_as_str
from hepdata_converter.testsuite.test_writer import WriterTestSuite
__author__ = 'Michał Szostak'
class YAMLWriterTestSuite(WriterTestSuite):
@insert_path('yaml_qual')
@insert_data_as_st... | 807 | 41.526316 | 98 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/testsuite/test_clitools.py | import os
import hepdata_converter
from hepdata_converter.testsuite import insert_data_as_str, insert_path
from hepdata_converter.testsuite.test_writer import WriterTestSuite
class CLIToolsTestSuite(WriterTestSuite):
def test_wrong_call(self):
self.assertRaises(SystemExit, hepdata_converter.main, [])
... | 1,697 | 42.538462 | 96 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/testsuite/test_optioninitmixin.py | import unittest
from hepdata_converter.common import OptionInitMixin, Option
class OptionInitMixinTestSuite(unittest.TestCase):
def test_dir(self):
class TestClass(OptionInitMixin):
@classmethod
def options(cls):
return {
'testoption': Option('testop... | 623 | 27.363636 | 62 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/testsuite/__init__.py | import os
from random import randint
import tempfile
import unittest
import shutil
import time
import yaml
# We try to load using the CSafeLoader for speed improvements.
try:
from yaml import CSafeLoader as Loader
except ImportError: #pragma: no cover
from yaml import SafeLoader as Loader #pragma: no cover
de... | 4,079 | 32.170732 | 144 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/testsuite/test_writer.py | from hepdata_converter.testsuite import TMPDirMixin, ExtendedTestCase
class WriterTestSuite(TMPDirMixin, ExtendedTestCase):
pass
| 135 | 21.666667 | 69 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/testsuite/test_arraywriter.py | import os
from hepdata_converter import convert
from hepdata_converter.testsuite import insert_path
from hepdata_converter.testsuite.test_writer import WriterTestSuite
class ArrayWriterTestSuite(WriterTestSuite):
@insert_path('yaml_full')
def test_select_table(self, submission_filepath):
csv_content =... | 1,414 | 57.958333 | 123 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/parsers/yaml_parser.py | import yaml
# We try to load using the CSafeLoader for speed improvements.
try:
from yaml import CSafeLoader as Loader
except ImportError: #pragma: no cover
from yaml import SafeLoader as Loader #pragma: no cover
from hepdata_validator import LATEST_SCHEMA_VERSION
from hepdata_validator.submission_file_validat... | 4,770 | 41.981982 | 109 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/parsers/oldhepdata_parser.py | from hepdata_converter.common import OptionInitMixin, Option
from hepdata_converter.parsers import Parser, ParsedData, BadFormat, Table
import copy
import re
class HEPTable(Table):
"""Extension of Table including some place for temporary data needed during conversion
"""
def __init__(self, index=None, dat... | 31,065 | 42.207232 | 269 | py |
hepdata-converter | hepdata-converter-master/hepdata_converter/parsers/__init__.py | import abc
import copy
import os
from hepdata_converter.common import GetConcreteSubclassMixin, OptionInitMixin
__all__ = []
import pkgutil
import inspect
class BadFormat(Exception):
"""Class for exceptions raised if bad formatting of parser's input file prohibits from
parsing the file correctly
"""
... | 5,961 | 31.053763 | 112 | py |
gr-iio | gr-iio-master/python/iio/attr_updater.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2018 Analog Devices Inc.
# Author: Travis Collins <travis.collins@analog.com>
#
# This is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version... | 2,404 | 29.833333 | 83 | py |
gr-iio | gr-iio-master/python/iio/__init__.py | #
# Copyright 2008,2009 Free Software Foundation, Inc.
#
# This application is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# any later version.
#
# This application is di... | 1,350 | 32.775 | 74 | py |
SimPer | SimPer-main/src/augmentation.py | """
Augmentations for SimPer (and other SSL methods).
"""
import tensorflow as tf
import tensorflow_probability as tfp
from typing import Optional, Tuple
def random_apply(func, p, x):
return tf.cond(
tf.less(tf.random.uniform([], minval=0, maxval=1, dtype=tf.float32),
tf.cast(p, tf.float32... | 9,872 | 38.178571 | 87 | py |
SimPer | SimPer-main/src/simper.py | """
Minimal SimPer implementation & example training loops.
"""
import tensorflow as tf
from networks import Featurizer, Classifier
@tf.function
def _max_cross_corr(feats_1, feats_2):
# feats_1: 1 x T(# time stamp)
# feats_2: M(# aug) x T(# time stamp)
feats_2 = tf.cast(feats_2, feats_1.dtype)
feats_1... | 5,269 | 35.344828 | 91 | py |
SimPer | SimPer-main/src/utils.py | import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
def _dot_similarity_dim1(x, y):
# (N, 1, C), (N, C, 1) -> (N, 1, 1)
v = tf.matmul(tf.expand_dims(x, 1), tf.expand_dims(y, 2))
return v
def _dot_similarity_dim2(x, y):
v = tf.tensordot(tf.expand_dims(x, 1), tf.expand_dims... | 999 | 27.571429 | 86 | py |
SimPer | SimPer-main/src/networks.py | """
Example network architectures:
- Featurizer (for representation learning)
- Classifier (for downstream tasks)
"""
import tensorflow as tf
from tensorflow.keras.layers import (Conv2D, Conv3D, Dense, Flatten, BatchNormalization,
TimeDistributed, MaxPool2D, GlobalAveragePooling2D)
... | 2,324 | 26.678571 | 88 | py |
sampling_cf | sampling_cf-main/main.py | import os
import time
import importlib
import datetime as dt
from tqdm import tqdm
from utils import file_write, log_end_epoch, INF, valid_hyper_params
from data_path_constants import get_log_file_path, get_model_file_path
# NOTE: No global-level torch imports as the GPU-ID is set through code
def train(model, crite... | 10,944 | 39.238971 | 114 | py |
sampling_cf | sampling_cf-main/data_genie.py | from data_genie.data_genie_config import *
from data_genie.data_genie_trainers import *
from data_genie.data_genie_data import OracleData
from data_genie.data_genie_model import PointwiseDataGenie, PairwiseDataGenie
# NOTE: Please edit the config in `data_genie/data_genie_config.py` before \
# running this trainer s... | 2,771 | 34.088608 | 124 | py |
sampling_cf | sampling_cf-main/loss.py | import torch
import torch.nn.functional as F
from torch_utils import is_cuda_available
class CustomLoss(torch.nn.Module):
def __init__(self, hyper_params):
super(CustomLoss, self).__init__()
self.forward = {
'explicit': self.mse,
'implicit': self.bpr,
'sequentia... | 3,395 | 36.318681 | 94 | py |
sampling_cf | sampling_cf-main/utils.py | INF = float(1e6)
def get_data_loader_class(hyper_params):
from data_loaders import MF, MVAE, SASRec, SVAE
return {
"pop_rec": (MF.TrainDataset, MF.TestDataset),
"bias_only": (MF.TrainDataset, MF.TestDataset),
"MF_dot": (MF.TrainDataset, MF.TestDataset),
"MF": (MF.TrainDataset, ... | 4,305 | 39.622642 | 157 | py |
sampling_cf | sampling_cf-main/data.py | import os
import h5py
import json
import math
import random
import numpy as np
from collections import defaultdict
import networkx as nx
import networkit as nk
nk.setNumberOfThreads(16)
from graph_sampling.ForestFire import ForestFireSampler
from graph_sampling.RW import RandomWalkWithRestartSampler
class rating_dat... | 15,733 | 40.405263 | 130 | py |
sampling_cf | sampling_cf-main/data_path_constants.py | import os
from utils import get_common_path
# Sampling experiments' constants
BASE_SAMPLING_PATH = "./experiments/sampling_runs/"
# Data-genie experiments' constants
BASE_DATA_GENIE_PATH = "./experiments/data_genie/"
def get_svp_log_file_path(hyper_params):
return BASE_SAMPLING_PATH + "/results/logs/SVP/{}.txt".... | 1,747 | 32.615385 | 97 | py |
sampling_cf | sampling_cf-main/hyper_params.py | hyper_params = {
## Dataset
# [ 'ml-100k', 'magazine', 'software', 'luxury', 'fashion', 'industrial', 'goodreads_comics' ]
'dataset': 'magazine',
## Tasks
# [ 'explicit', 'implicit', 'sequential' ]
'task': 'sequential',
## Sampling
# [ complete_data, us... | 1,302 | 32.410256 | 116 | py |
sampling_cf | sampling_cf-main/torch_utils.py | import torch
is_cuda_available = torch.cuda.is_available()
if is_cuda_available:
print("Using CUDA...\n")
LongTensor = torch.cuda.LongTensor
FloatTensor = torch.cuda.FloatTensor
BoolTensor = torch.cuda.BoolTensor
else:
LongTensor = torch.LongTensor
FloatTensor = torch.FloatTensor
BoolTens... | 827 | 25.709677 | 59 | py |
sampling_cf | sampling_cf-main/grid_search.py | import os
import gc
import copy
import time
import json
import datetime
import traceback
from tqdm import tqdm
import multiprocessing
from main import main
from utils import get_common_path
from data_path_constants import get_index_path, get_log_file_path
# NOTE: Specify all possible combinations of hyper-parameters ... | 5,100 | 30.68323 | 129 | py |
sampling_cf | sampling_cf-main/eval.py | import torch
import numpy as np
from numba import jit, float32, float64, int64
from utils import INF
def evaluate(model, criterion, reader, hyper_params, item_propensity, topk = [ 10, 100 ], test = False):
metrics = {}
# Do a negative sampled item-space evaluation (only on the validation set)
# if the da... | 7,384 | 38.704301 | 137 | py |
sampling_cf | sampling_cf-main/load_data.py | import h5py
import numpy as np
from utils import get_data_loader_class
from data_path_constants import get_data_path, get_index_path
def load_data(hyper_params, track_events = False):
rating_data_path = get_data_path(hyper_params)
index_path = get_index_path(hyper_params)
data_holder = DataHolder(rating_... | 3,038 | 36.060976 | 96 | py |
sampling_cf | sampling_cf-main/__init__.py | 0 | 0 | 0 | py | |
sampling_cf | sampling_cf-main/svp_handler.py | import numpy as np
from collections import defaultdict
from main import main_pytorch
from data_path_constants import get_svp_log_file_path, get_svp_model_file_path
class SVPHandler:
def __init__(self, model_type, loss_type, hyper_params):
hyper_params['model_type'] = model_type
hyper_params['task'... | 7,281 | 40.375 | 110 | py |
sampling_cf | sampling_cf-main/preprocess.py | from initial_data_prep_code import movielens, amazon, goodreads, beeradvocate
from data_path_constants import get_data_path
from svp_handler import SVPHandler
percent_sample = [ 20, 40, 60, 80, 90, 99 ]
# Which datasets to prep?
for dataset in [
'magazine',
'ml-100k',
## Did not download & preprocess the followin... | 5,035 | 41.319328 | 118 | py |
sampling_cf | sampling_cf-main/initial_data_prep_code/amazon.py | from data import rating_data
from utils import remap_items
from data_path_constants import get_data_path
def prep(dataset):
f = open(get_data_path(dataset) + '/data.csv', "r")
users, items, ratings, time = [], [], [], []
user_map, item_map = {}, {}
line = f.readline()
while line:
i, u, r, t = line.strip().spl... | 1,124 | 24 | 69 | py |
sampling_cf | sampling_cf-main/initial_data_prep_code/beeradvocate.py | from data import rating_data
from utils import remap_items
from data_path_constants import get_data_path
def prep(dataset):
f = open(get_data_path(dataset) + '/data.csv', "r")
users, items, ratings, time = [], [], [], []
user_map, item_map = {}, {}
line = f.readline()
temp = line.strip().split(",")
col_map = d... | 1,439 | 25.666667 | 69 | py |
sampling_cf | sampling_cf-main/initial_data_prep_code/__init__.py | 0 | 0 | 0 | py | |
sampling_cf | sampling_cf-main/initial_data_prep_code/movielens.py | from data import rating_data
from utils import remap_items
from data_path_constants import get_data_path
def prep(dataset):
if dataset == "ml-100k": later_path, delim = "/u.data", "\t"
elif dataset == "ml-25m": later_path, delim = "/ratings.csv", ","
f = open(get_data_path(dataset) + later_path, "r")
users, item... | 1,177 | 26.395349 | 69 | py |
sampling_cf | sampling_cf-main/initial_data_prep_code/goodreads.py | import json
from tqdm import tqdm
from datetime import datetime, timezone
from data import rating_data
from utils import remap_items
from data_path_constants import get_data_path
def prep(dataset):
num_lines = sum(1 for line in open(get_data_path(dataset) + '/data.json', "r"))
f = open(get_data_path(dataset) + '/d... | 1,605 | 24.09375 | 80 | py |
sampling_cf | sampling_cf-main/data_genie/data_genie_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class PointwiseLoss(nn.Module):
def __init__(self): super(PointwiseLoss, self).__init__()
def forward(self, output, y, return_mean = True):
loss = torch.pow(output - y, 2)
if return_mean: return torch.mean(loss)
return loss
class PairwiseLoss... | 560 | 27.05 | 63 | py |
sampling_cf | sampling_cf-main/data_genie/data_genie_trainers.py | import time
import torch
import numpy as np
from tqdm import tqdm
from collections import defaultdict
from sklearn.feature_selection import RFE
from sklearn.metrics import roc_auc_score
from xgboost import XGBClassifier, XGBRegressor
from torch.utils.tensorboard import SummaryWriter
from sklearn.linear_model import Rid... | 8,933 | 38.883929 | 144 | py |
sampling_cf | sampling_cf-main/data_genie/get_embeddings.py | import gc
import os
import dgl
import snap
import torch
import numpy as np
from tqdm import tqdm
import networkx as nx
from collections import defaultdict
from data_genie.data_genie_config import *
from data_genie.data_genie_utils import save_numpy, load_numpy
from data_genie.data_genie_utils import EMBEDDINGS_PATH_G... | 7,131 | 33.960784 | 125 | py |
sampling_cf | sampling_cf-main/data_genie/data_genie_data.py | import torch
import numpy as np
from torch_utils import LongTensor, FloatTensor, is_cuda_available
from data_genie.data_genie_config import *
from data_genie.get_data import get_data_pointwise, get_data_pairwise
from data_genie.get_embeddings import get_embeddings
from data_genie.InfoGraph.infograph_dataset import Syn... | 7,077 | 33.526829 | 136 | py |
sampling_cf | sampling_cf-main/data_genie/data_genie_model.py | import dgl
import torch
import torch.nn as nn
from torch_utils import is_cuda_available
from data_genie.data_genie_loss import PointwiseLoss, PairwiseLoss
# NOTE: Below two are the training classes for data-genie: pointwise/pairwise
class PointwiseDataGenie:
def __init__(self, hyper_params, writer, xavier_init):
s... | 3,352 | 31.240385 | 92 | py |
sampling_cf | sampling_cf-main/data_genie/__init__.py | 0 | 0 | 0 | py | |
sampling_cf | sampling_cf-main/data_genie/get_data.py | import os
import random
from tqdm import tqdm
from collections import defaultdict
from data_genie.data_genie_config import *
from data_genie.data_genie_utils import TRAINING_DATA_PATH, CACHED_KENDALL_TAU_PATH, load_obj, save_obj
from data_genie.data_genie_utils import count_performance_retained, get_best_results
from... | 5,240 | 32.596154 | 103 | py |
sampling_cf | sampling_cf-main/data_genie/data_genie_config.py | import copy
TEST_SPLIT = 0.2 # Of total 100%
VAL_SPLIT = 0.15 # Of (100 - TEST_SPLIT)%
NUM_SAMPLES = 10 # Number of samples per degree features e.g. user/item degree, hop-plot distr.
NUM_PURE_FEAUTRES = 7 * NUM_SAMPLES # Graph-based features
NUM_FEAUTRES = NUM_PURE_FEAUTRES + 5 # The remaining 5 are generic feat... | 2,643 | 30.47619 | 124 | py |
sampling_cf | sampling_cf-main/data_genie/data_genie_utils.py | import os
import re
import pickle
import numpy as np
from scipy import stats
from utils import get_common_path, INF
from data_path_constants import BASE_DATA_GENIE_PATH, get_log_base_path
# NOTE: Below is the definition of the directory-structure of data-genie data folder
def append(path):
# Append all relative path... | 4,216 | 35.669565 | 143 | py |
sampling_cf | sampling_cf-main/data_genie/InfoGraph/infograph_dataset.py | from dgl import save_graphs, load_graphs
from dgl.data import DGLDataset
from tqdm import tqdm
import numpy as np
import networkx as nx
import torch
import dgl
import os
from load_data import DataHolder
from data_path_constants import get_data_path, get_index_path
from data_genie.data_genie_config import *
from data_g... | 4,944 | 30.698718 | 104 | py |
sampling_cf | sampling_cf-main/data_genie/InfoGraph/infograph_model.py | ''' Credit https://github.com/hengruizhang98/InfoGraph & https://github.com/fanyun-sun/InfoGraph '''
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, ModuleList, Linear, ReLU, BatchNorm1d
from dgl.nn import GINConv
from dgl.nn.pytorch.glob import SumPooling, Av... | 4,413 | 25.590361 | 110 | py |
sampling_cf | sampling_cf-main/data_genie/InfoGraph/train_infograph.py | import dgl
import time
import argparse
import torch as th
from dgl.dataloading import GraphDataLoader
from tqdm import tqdm
from data_genie.data_genie_utils import INFOGRAPH_MODEL_PATH
from data_genie.InfoGraph.infograph_model import InfoGraph
from data_genie.InfoGraph.infograph_dataset import SyntheticDataset
def ar... | 3,615 | 33.438095 | 121 | py |
sampling_cf | sampling_cf-main/data_genie/InfoGraph/infograph_utils.py | ''' Credit: https://github.com/fanyun-sun/InfoGraph '''
import torch
import torch as th
import torch.nn.functional as F
import math
def local_global_loss_(l_enc, g_enc, graph_id, measure):
num_graphs = g_enc.shape[0]
num_nodes = l_enc.shape[0]
device = g_enc.device
pos_mask = th.zeros((num_nodes, n... | 3,376 | 26.680328 | 82 | py |
sampling_cf | sampling_cf-main/data_genie/InfoGraph/__init__.py | 0 | 0 | 0 | py | |
sampling_cf | sampling_cf-main/pytorch_models/SASRec.py | import torch
import numpy as np
import torch.nn as nn
from torch_utils import LongTensor, BoolTensor, is_cuda_available
class PointWiseFeedForward(nn.Module):
def __init__(self, hidden_units, dropout_rate):
super(PointWiseFeedForward, self).__init__()
self.conv1 = nn.Conv1d(hidden_units, hidden_u... | 5,147 | 40.853659 | 122 | py |
sampling_cf | sampling_cf-main/pytorch_models/NeuMF.py | import torch
import torch.nn as nn
from pytorch_models.MF import BaseMF
class GMF(BaseMF):
def __init__(self, hyper_params):
super(GMF, self).__init__(hyper_params)
self.final = nn.Linear(hyper_params['latent_size'], 1)
self.dropout = nn.Dropout(hyper_params['dropout'])
def g... | 5,009 | 43.732143 | 118 | py |
sampling_cf | sampling_cf-main/pytorch_models/SVAE.py | import torch
import numpy as np
import torch.nn as nn
from torch.autograd import Variable
from torch_utils import is_cuda_available
class Encoder(nn.Module):
def __init__(self, hyper_params):
super(Encoder, self).__init__()
self.linear1 = nn.Linear(
hyper_params['latent_size'], hyper_p... | 3,388 | 38.406977 | 107 | py |
sampling_cf | sampling_cf-main/pytorch_models/MVAE.py | import torch
import numpy as np
import torch.nn as nn
from torch.autograd import Variable
from torch_utils import is_cuda_available
class Encoder(nn.Module):
def __init__(self, hyper_params):
super(Encoder, self).__init__()
self.linear1 = nn.Linear(
hyper_params['total_items'], hyper_p... | 2,331 | 32.797101 | 107 | py |
sampling_cf | sampling_cf-main/pytorch_models/__init__.py | 0 | 0 | 0 | py | |
sampling_cf | sampling_cf-main/pytorch_models/MF.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_utils import LongTensor, FloatTensor
class BaseMF(nn.Module):
def __init__(self, hyper_params, keep_gamma = True):
super(BaseMF, self).__init__()
self.hyper_params = hyper_params
# Declaring alpha, beta, gamma
... | 4,075 | 38.960784 | 108 | py |
sampling_cf | sampling_cf-main/pytorch_models/pop_rec.py | from torch_utils import FloatTensor
class PopRec:
def __init__(self, hyper_params, item_count):
self.hyper_params = hyper_params
self.top_items = FloatTensor([ item_count[i] for i in range(hyper_params['total_items']) ]).unsqueeze(0)
def __call__(self, data, eval = False):
users, _, _ ... | 414 | 28.642857 | 112 | py |
sampling_cf | sampling_cf-main/data_loaders/base.py | import torch
import numpy as np
from collections import defaultdict
from torch.multiprocessing import Process, Queue, Event
class CombinedBase:
def __init__(self): pass
def __len__(self): return (self.num_interactions // self.batch_size) + 1
def __del__(self):
try:
self.p.terminate() ... | 5,688 | 39.347518 | 98 | py |
sampling_cf | sampling_cf-main/data_loaders/SASRec.py | import torch
import numpy as np
from data_loaders.base import BaseTrainDataset, BaseTestDataset
from torch_utils import LongTensor, is_cuda_available
class TrainDataset(BaseTrainDataset):
def __init__(self, data, hyper_params, track_events):
super(TrainDataset, self).__init__(data, hyper_params)
s... | 3,743 | 44.108434 | 110 | py |
sampling_cf | sampling_cf-main/data_loaders/SVAE.py | import torch
import numpy as np
from data_loaders.base import BaseTrainDataset, BaseTestDataset
from torch_utils import LongTensor, FloatTensor, is_cuda_available
class TrainDataset(BaseTrainDataset):
def __init__(self, data, hyper_params, track_events):
super(TrainDataset, self).__init__(data, hyper_para... | 3,329 | 43.4 | 104 | py |
sampling_cf | sampling_cf-main/data_loaders/MVAE.py | import numpy as np
from data_loaders.base import BaseTrainDataset, BaseTestDataset
from torch_utils import LongTensor, FloatTensor
class TrainDataset(BaseTrainDataset):
def __init__(self, data, hyper_params, track_events):
super(TrainDataset, self).__init__(data, hyper_params)
self.shuffle_allowed... | 2,616 | 44.12069 | 104 | py |
sampling_cf | sampling_cf-main/data_loaders/__init__.py | 0 | 0 | 0 | py | |
sampling_cf | sampling_cf-main/data_loaders/MF.py | import torch
import numpy as np
from data_loaders.base import BaseTrainDataset, BaseTestDataset
from torch_utils import LongTensor, FloatTensor, is_cuda_available
class TrainDataset(BaseTrainDataset):
def __init__(self, data, hyper_params, track_events):
super(TrainDataset, self).__init__(data, hyper_para... | 4,575 | 43.862745 | 114 | py |
sampling_cf | sampling_cf-main/graph_sampling/RW.py | import random
class RandomWalkWithRestartSampler:
"""An implementation of node sampling by random walks with restart. The
process is a discrete random walker on nodes which teleports back to the
staring node with a fixed probability. This results in a connected subsample
from the original input graph.... | 2,432 | 34.779412 | 86 | py |
sampling_cf | sampling_cf-main/graph_sampling/ForestFire.py | import random
import numpy as np
from collections import deque
class ForestFireSampler:
"""An implementation of forest fire sampling. The procedure is a stochastic
snowball sampling method where the expansion is proportional to the burning probability.
`"For details about the algorithm see this paper." <h... | 3,031 | 42.314286 | 133 | py |
sampling_cf | sampling_cf-main/graph_sampling/__init__.py | 0 | 0 | 0 | py | |
sampling_cf | sampling_cf-main/data_hyperparams/goodreads_comics.py | hyper_params = {
'weight_decay': float(1e-6),
'epochs': 20,
'batch_size': 128,
'validate_every': 3,
'early_stop': 2,
'max_seq_len': 30,
}
| 183 | 19.444444 | 35 | py |
sampling_cf | sampling_cf-main/data_hyperparams/luxury.py | hyper_params = {
'weight_decay': float(1e-6),
'epochs': 80,
'batch_size': 1024,
'validate_every': 5,
'early_stop': 4,
'max_seq_len': 10,
}
| 184 | 19.555556 | 35 | py |
sampling_cf | sampling_cf-main/data_hyperparams/magazine.py | hyper_params = {
'weight_decay': float(1e-6),
'epochs': 120,
'batch_size': 1024,
'validate_every': 5,
'early_stop': 6,
'max_seq_len': 10,
}
| 167 | 17.666667 | 32 | py |
sampling_cf | sampling_cf-main/data_hyperparams/ml-100k.py | hyper_params = {
'weight_decay': float(1e-6),
'epochs': 100,
'batch_size': 1024,
'validate_every': 5,
'early_stop': 5,
'max_seq_len': 100,
}
| 186 | 19.777778 | 35 | py |
sampling_cf | sampling_cf-main/data_hyperparams/beeradvocate.py | hyper_params = {
'weight_decay': float(1e-6),
'epochs': 30,
'batch_size': 256,
'validate_every': 3,
'early_stop': 3,
'max_seq_len': 10,
}
| 183 | 19.444444 | 35 | py |
sampling_cf | sampling_cf-main/data_hyperparams/__init__.py | 0 | 0 | 0 | py | |
sampling_cf | sampling_cf-main/data_hyperparams/video_games.py | hyper_params = {
'weight_decay': float(1e-6),
'epochs': 25,
'batch_size': 1024,
'validate_every': 3,
'early_stop': 2,
'max_seq_len': 10,
}
| 184 | 19.555556 | 35 | py |
word_forms | word_forms-master/setup.py | from setuptools import setup
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setup(name="word_forms",
version="2.1.0",
description="Generate all possible forms of an English word.",
long_description=long_description,
long_description_content_type="text/mar... | 868 | 33.76 | 71 | py |
word_forms | word_forms-master/test_word_forms.py | #!/usr/bin/env python
# encoding: utf-8
from word_forms.word_forms import get_word_forms
import unittest
class TestWordForms(unittest.TestCase):
"""
Simple TestCase for a specific input to output, one instance generated per test case for use in a TestSuite
"""
def __init__(self, text_input: str, exp... | 1,000 | 24.666667 | 111 | py |
word_forms | word_forms-master/test_values.py | # Test values must be in the form [(text_input, expected_output), (text_input, expected_output), ...]
test_values = [
(
"president",
{
"n": {
"president",
"presidentship",
"presidencies",
"presidency",
"presi... | 19,834 | 23.887077 | 101 | py |
word_forms | word_forms-master/word_forms/word_forms.py | import re
try:
from nltk.corpus import wordnet as wn
raise_lookuperror_if_wordnet_data_absent = wn.synset("python.n.01")
except LookupError:
import nltk
nltk.download("wordnet")
from nltk.stem import WordNetLemmatizer
import inflect
from difflib import SequenceMatcher
from .constants import CONJUGATED... | 4,689 | 36.822581 | 130 | py |
word_forms | word_forms-master/word_forms/constants.py | from collections import defaultdict
from pathlib import Path
import json
class Verb(object):
def __init__(self, verbs=None):
self.verbs = verbs if verbs else set()
def __repr__(self):
return "Verbs" + str(self.verbs)
def update(self, verbs):
self.verbs.update(verbs)
verbs_fh = o... | 797 | 25.6 | 78 | py |
word_forms | word_forms-master/word_forms/lemmatizer.py | from word_forms.word_forms import get_word_forms
def lemmatize(word):
"""
Out of all the related word forms of ``word``, return the smallest form that appears first in the dictionary
"""
forms = [word for pos_form in get_word_forms(word).values() for word in pos_form]
forms.sort()
forms.sort(k... | 449 | 25.470588 | 112 | py |
word_forms | word_forms-master/word_forms/__init__.py | 0 | 0 | 0 | py | |
GUIcandid | GUIcandid-master/candid.py | from __future__ import print_function
import numpy as np
from matplotlib import pyplot as plt
plt.ion() # interactive mode
_fitsLoaded=False
try:
from astropy.io import fits
_fitsLoaded=True
except:
try:
import pyfits as fits
_fitsLoaded=True
except:
pass
if not _fitsLoaded:
... | 203,132 | 42.769231 | 210 | py |
BILP-Q | BILP-Q-main/Utils_CSG.py | #!/usr/bin/env python
# coding: utf-8
import math
import numpy as np
import pandas as pd
from sympy import *
import re
import time
import random
import itertools
################### ########
#Different distributions data generator functions
"""
All different distributions considered as benchmark for the evaluation o... | 9,710 | 30.427184 | 202 | py |
BILP-Q | BILP-Q-main/data_generator.py | from Utils_CSG import *
from Utils_Solvers import *
directory = 'data'
seed = 12
filename = f'data_{seed}.txt'
create_dir(directory, log=False)
distributions = [Agent_based_uniform, Agent_based_normal, Modified_uniform_distribution,
Normal_distribution, SVA_BETA_distribution, Weibull_distribution,... | 1,308 | 28.75 | 105 | py |
BILP-Q | BILP-Q-main/BILP-Q_benchmark.py | from Utils_CSG import *
from Utils_Solvers import *
def running_dwave(linear, quadratic, exact_solution, colnames,
params={'distr':'', 'n':0}, n_runs=1000):
"""
Solve the experimental input instance using the dwave device
:params
linear: dictionary of linear coefficient terms in the ... | 11,869 | 45.18677 | 138 | py |
BILP-Q | BILP-Q-main/Utils_Solvers.py | #!/usr/bin/env python
# coding: utf-8
import warnings
warnings.filterwarnings('ignore')
# Qiskit
from qiskit import BasicAer
from qiskit.algorithms import QAOA, NumPyMinimumEigensolver
from qiskit_optimization.algorithms import MinimumEigenOptimizer, RecursiveMinimumEigenOptimizer
from qiskit_optimization import Qua... | 15,289 | 29.702811 | 201 | py |
neuralTPPs | neuralTPPs-master/setup.py | from setuptools import setup, find_packages
with open('README.md') as f:
readme = f.read()
setup(
name='tpp',
version='0.0.1',
description='Playing around with TPPs.',
long_description=readme,
author='Babylon ML team',
author_email='loss.goes.down@babylonhealth.com',
url='https://gith... | 467 | 23.631579 | 55 | py |
neuralTPPs | neuralTPPs-master/debug/cumulative_attention.py | import torch as th
import matplotlib.pyplot as plt
from torch import nn
from pprint import pprint
from tqdm import tqdm
from tpp.models.base.enc_dec import EncDecProcess
from tpp.models.encoders.mlp_variable import MLPVariableEncoder
from tpp.models.decoders.self_attention_cm import SelfAttentionCmDecoder
from tpp.mo... | 2,491 | 30.544304 | 75 | py |
neuralTPPs | neuralTPPs-master/debug/batchnorm.py | import torch as th
from torch import nn
from tpp.pytorch.layers import NonNegLinear
from tpp.pytorch.layers import BatchNorm1d
def multidim_grad(a, b):
a_split = th.split(a, split_size_or_sections=1, dim=-1)
grads = [th.autograd.grad(
outputs=a_split[i],
inputs=b,
grad_outputs=th.ones... | 1,043 | 22.2 | 59 | py |
neuralTPPs | neuralTPPs-master/debug/layernorm.py | import torch as th
from torch import nn
from tpp.pytorch.layers import LayerNorm
from debug.batchnorm import multidim_grad
th.manual_seed(0)
pytorch_norm = nn.LayerNorm(3)
my_norm = LayerNorm(3, use_running_stats=True)
x = th.rand([3]).reshape(1, -1).float().repeat(2, 1)
x.requires_grad = True
pytorch_y = pytorch_... | 831 | 19.8 | 55 | py |
neuralTPPs | neuralTPPs-master/debug/regression.py | import numpy as np
import torch as th
import torch.nn
import matplotlib.pyplot as plt
from tpp.pytorch.models import MLP
def detach(x: th.Tensor) -> np.ndarray:
return x.detach().cpu().numpy()
th.manual_seed(0)
x_min, x_max, steps = 0., 100., 3000
alpha = 1.
beta = 1.
n_events = 20
epochs = 1000
cumulative =... | 2,239 | 23.086022 | 68 | py |
neuralTPPs | neuralTPPs-master/debug/check_synthea_csvs.py | import os
import datetime as dt
import numpy as np
import pandas as pd
from argparse import ArgumentParser
from tqdm import tqdm
pd.set_option("max.rows", 10)
pd.set_option("max.columns", 50)
pd.set_option("display.width", 1000)
CSV_SUBSET = {"patients", "encounters"}
def main(args):
dfs = get_dfs(synthea_pat... | 2,965 | 30.892473 | 79 | py |
neuralTPPs | neuralTPPs-master/profiling/r_terms_for_pytorch_profile.py | import torch as th
from tpp.processes.hawkes.r_terms_recursive_v import get_r_terms
from tpp.utils.test import get_test_events_query
def run_test():
marks = 3
events, query = get_test_events_query(marks=marks)
beta = th.rand([marks, marks])
get_r_terms(events=events, beta=beta)
if __name__ == '__m... | 343 | 19.235294 | 64 | py |
neuralTPPs | neuralTPPs-master/profiling/get_r_terms_profile.py | import time
import matplotlib.pyplot as plt
import numpy as np
import torch as th
# from tpp.processes.hawkes.r_terms import get_r_terms as naive
from tpp.processes.hawkes.r_terms_recursive import get_r_terms as recursive
from tpp.processes.hawkes.r_terms_recursive_v import get_r_terms as recursive_v
from tpp.utils.te... | 2,568 | 28.872093 | 79 | py |
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