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from fcntl import LOCK_EX, LOCK_UN, flock
from os import statvfs
from django.conf import settings
from .pathlib import Path
settings.FORUM_LOCKROOT_PATH.mkdir(exist_ok=True)
PATH_TEMPLOCK_CONTEXT = Path(settings.FORUM_LOCKROOT_PATH, 'temp-context-lock')
PATH_TEMPLOCK_CONTEXT.touch(exist_ok=True)
PATH_TEMPLOCK_DIR = ... | {
"repo_name": "karolyi/forum-django",
"path": "backend/forum/utils/locking.py",
"copies": "1",
"size": "2498",
"license": "mit",
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"autogenerated": false,
"ratio": 3.488826815642458,
"config_test... |
from .fdc import FDC
from .classify import CLF
import numpy as np
import pickle
from scipy.cluster.hierarchy import dendrogram as scipydendroed
from scipy.cluster.hierarchy import to_tree
from .hierarchy import compute_linkage_matrix
import copy
from collections import OrderedDict as OD
from collections import Counter
... | {
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"path": "fdc/tree.py",
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"co... |
from fds.auth.common import Common
from fds.galaxy_fds_client_exception import GalaxyFDSClientException
class FDSObjectMetadata(object):
"""
The FDS object metadata class.
"""
USER_DEFINED_METADATA_PREFIX = "x-xiaomi-meta-"
PRE_DEFINED_METADATA = [
Common.CACHE_CONTROL,
Common.CONTENT_ENCODING,
... | {
"repo_name": "XiaoMi/galaxy-fds-sdk-python",
"path": "fds/model/fds_object_metadata.py",
"copies": "1",
"size": "1278",
"license": "apache-2.0",
"hash": 2080177487261055700,
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"autogenerated": false,
"ratio": 3.380952380952381,
"con... |
from .fds_object_summary import FDSObjectSummary
from .permission import Owner
class FDSObjectListing(dict):
'''
The FDS Object Listing class.
'''
def __init__(self, json):
dict.__init__(self, json)
self._objects = []
for obj in self['objects']:
summary = FDSObjectSummary()
summary.bu... | {
"repo_name": "XiaoMi/galaxy-fds-sdk-python",
"path": "fds/model/fds_object_listing.py",
"copies": "1",
"size": "2299",
"license": "apache-2.0",
"hash": -3465371127361400300,
"line_mean": 20.4859813084,
"line_max": 73,
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"autogenerated": false,
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from fea_extractor import FeaExtractor
from fea_extractor import SKETCH, IMAGE
import numpy as np
import os
import cv2
import time
from sklearn.svm import LinearSVC
from sklearn.externals import joblib
from scipy.cluster.vq import *
from sklearn.preprocessing import StandardScaler
train_path = 'resize_img... | {
"repo_name": "Just-CJ/SketchRetrieval",
"path": "python/offline_process.py",
"copies": "1",
"size": "2290",
"license": "mit",
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"alpha_frac": 0.6624454148,
"autogenerated": false,
"ratio": 2.8271604938271606,
"config_test":... |
from fea_extractor import FeaExtractor
from fea_extractor import SKETCH, IMAGE
import argparse as ap
import numpy as np
import os
import cv2
from sklearn.svm import LinearSVC
from sklearn.externals import joblib
from scipy.cluster.vq import *
from scipy.spatial.distance import cdist
from scipy import stats
... | {
"repo_name": "Just-CJ/SketchRetrieval",
"path": "python/online_process.py",
"copies": "1",
"size": "1819",
"license": "mit",
"hash": -7682392263322716000,
"line_mean": 27.8196721311,
"line_max": 84,
"alpha_frac": 0.6866410115,
"autogenerated": false,
"ratio": 2.92443729903537,
"config_test": t... |
from feasta.models import Session,Absent, Student, NonStudent, Meal
from django.utils.timezone import now, timedelta
def currentSession():
session = Session.objects.filter(startdate__lte=now(), enddate__gte=now())
if len(session) > 0:
session = session[0]
else:
session = Session.objects.all()[-1]
return sess... | {
"repo_name": "IIITS/Feasta",
"path": "feasta/methods.py",
"copies": "1",
"size": "1199",
"license": "mit",
"hash": -4225538367844530000,
"line_mean": 24.5106382979,
"line_max": 108,
"alpha_frac": 0.6955796497,
"autogenerated": false,
"ratio": 2.6823266219239374,
"config_test": false,
"has_no... |
from feather.dispatcher import Dispatcher
class InvalidApplication(Exception):
pass
class Application(object):
"""An application is defined as a set of messages that will be generated and
responded to by plugins, and a Dispatcher that handles communication between
plugins.
Applications expect, at... | {
"repo_name": "jdodds/feather",
"path": "feather/application.py",
"copies": "1",
"size": "2058",
"license": "bsd-3-clause",
"hash": 1539938843363058000,
"line_mean": 35.1052631579,
"line_max": 80,
"alpha_frac": 0.6350826045,
"autogenerated": false,
"ratio": 4.604026845637584,
"config_test": fal... |
from featmap import give_features, give_distance, give_direction, give_surrounding_information
import codecs
class Arc:
def __init__(self, mode="sparse", head=None, dependent=None, head_form=None, dependent_form=None, head_lemma=None,
dependent_lemma=None, head_pos=None, dependent_pos=None, s=0.0... | {
"repo_name": "YNedderhoff/cle-dependency-parser",
"path": "modules/graphs.py",
"copies": "1",
"size": "13231",
"license": "mit",
"hash": 2216659244523017700,
"line_mean": 41.0031746032,
"line_max": 120,
"alpha_frac": 0.4866601164,
"autogenerated": false,
"ratio": 4.4911744738628645,
"config_te... |
from FeatureBuilder import FeatureBuilder
#import Stemming.PorterStemmer as PorterStemmer
class TokenFeatureBuilder(FeatureBuilder):
def __init__(self, featureSet):
FeatureBuilder.__init__(self, featureSet)
def buildLinearOrderFeatures(self, tokenIndex, sentenceGraph, rangePos = 999, rangeNeg = 99... | {
"repo_name": "rothadamg/UPSITE",
"path": "ExampleBuilders/FeatureBuilders/TokenFeatureBuilder.py",
"copies": "2",
"size": "3719",
"license": "mit",
"hash": 7033334160445664000,
"line_mean": 49.9589041096,
"line_max": 110,
"alpha_frac": 0.5525678946,
"autogenerated": false,
"ratio": 3.71528471528... |
from FeatureBuilder import FeatureBuilder
# Amino acids from http://www.bio.davidson.edu/courses/genomics/jmol/aatable.html
#amino acid three letter code single letter code
subcomponent = set(["region", "promoter", "upstream", "fragment", "site",
"sequence", "segment", "repeat", "repeat", "eleme... | {
"repo_name": "ashishbaghudana/mthesis-ashish",
"path": "resources/tees/ExampleBuilders/FeatureBuilders/RELFeatureBuilder.py",
"copies": "2",
"size": "6349",
"license": "mit",
"hash": -5608566802513027000,
"line_mean": 41.6174496644,
"line_max": 127,
"alpha_frac": 0.565600882,
"autogenerated": fals... |
from FeatureBuilder import FeatureBuilder
class NodalidaFeatureBuilder(FeatureBuilder):
def __init__(self, featureSet):
FeatureBuilder.__init__(self, featureSet)
def buildShortestPaths(self, graph, tokenPath, position=0, newPath=None):
if newPath == None:
assert(position == 0)
... | {
"repo_name": "ashishbaghudana/mthesis-ashish",
"path": "resources/tees/ExampleBuilders/FeatureBuilders/NodalidaFeatureBuilder.py",
"copies": "2",
"size": "3350",
"license": "mit",
"hash": -7829988480522952000,
"line_mean": 41.417721519,
"line_max": 121,
"alpha_frac": 0.5280597015,
"autogenerated":... |
from feature.extract import ThresholdLogExtractor
from feature.generate import SimpleNGramGenerator
from model.record_manage import EntryTagManager, LabelTagManager
from model.common import PathGetter
from model.data_manage import ArticleManager
class DataMaker:
def __init__(self, feature_generator, feature_extrac... | {
"repo_name": "duckingod/news_analysis",
"path": "data_maker.py",
"copies": "1",
"size": "2834",
"license": "apache-2.0",
"hash": -568398714942177800,
"line_mean": 35.8051948052,
"line_max": 68,
"alpha_frac": 0.6107974594,
"autogenerated": false,
"ratio": 4.320121951219512,
"config_test": false... |
from FeatureExtractionAlgorithms import FEA
from pathlib import Path
import sys
class AutomizedFEA():
"""
Automatically applies FEA on a whole directory with some options
@parameters:
directory string directory for normalized files are stored
target string directory where JSON files will be stored
prefi... | {
"repo_name": "weidler/tyrex",
"path": "fea/AutomizedFEA.py",
"copies": "1",
"size": "2460",
"license": "mit",
"hash": 5559763890895741000,
"line_mean": 31.8,
"line_max": 106,
"alpha_frac": 0.6967479675,
"autogenerated": false,
"ratio": 3.0865746549560855,
"config_test": false,
"has_no_keywor... |
from feature_extraction import feature_extraction_driver
from model_training import training_driver
from util.log import Log
import sys
class CommandEnum:
PRE_PROCESSING = "-pre"
POST_PROCESSING = "-post"
TRAIN_FACTS = '-train'
class Command:
@staticmethod
def execute(command_list):
"""
... | {
"repo_name": "Cyberjusticelab/JusticeAI",
"path": "src/ml_service/main.py",
"copies": "1",
"size": "1143",
"license": "mit",
"hash": 5508604840597966000,
"line_mean": 26.2142857143,
"line_max": 67,
"alpha_frac": 0.624671916,
"autogenerated": false,
"ratio": 4.156363636363636,
"config_test": fa... |
from feature_extraction.post_processing.regex.regex_lib import RegexLib
import re
import datetime
import time
import unicodedata
from util.log import Log
import math
class EntityExtraction:
regex_bin = None
one_month = 86400 * 30 # unix time for 1 month
month_dict = {
'janvier': 1,
'fevri... | {
"repo_name": "Cyberjusticelab/JusticeAI",
"path": "src/ml_service/feature_extraction/post_processing/regex/regex_entity_extraction.py",
"copies": "1",
"size": "7500",
"license": "mit",
"hash": 8145534708138904000,
"line_mean": 35.9458128079,
"line_max": 109,
"alpha_frac": 0.5870666667,
"autogenera... |
from feature_extractor import FeatureExtractor
from data_utils.featured_frame import *
import pywt
class WaveletFeatureExtractor(FeatureExtractor):
def __init__(self, derivative=True):
super(WaveletFeatureExtractor, self).__init__(derivative)
def extract_features(self, frame):
if not isinstan... | {
"repo_name": "BavoGoosens/Gaiter",
"path": "feature_extraction/wavelet_feature_extractor.py",
"copies": "1",
"size": "1033",
"license": "mit",
"hash": -1427550094634096400,
"line_mean": 38.7692307692,
"line_max": 98,
"alpha_frac": 0.6940948693,
"autogenerated": false,
"ratio": 3.840148698884758,... |
from .feature_extractor import FeatureExtractor
from .regressor import Regressor
class FeatureExtractorRegressor(object):
def __init__(self, workflow_element_names=[
'feature_extractor', 'regressor']):
self.element_names = workflow_element_names
self.feature_extractor_workflow = Featur... | {
"repo_name": "paris-saclay-cds/ramp-workflow",
"path": "rampwf/workflows/feature_extractor_regressor.py",
"copies": "1",
"size": "1228",
"license": "bsd-3-clause",
"hash": -2450706008659051000,
"line_mean": 41.3448275862,
"line_max": 75,
"alpha_frac": 0.6473941368,
"autogenerated": false,
"ratio... |
from feature_extractor import *
from data_utils.featured_frame import *
from matplotlib.mlab import entropy as en
import scipy.fftpack as ff
import numpy as np
class FrequencyDomainFeatureExtractor(FeatureExtractor):
def __init__(self, derivative=True):
super(FrequencyDomainFeatureExtractor, self).__init_... | {
"repo_name": "BavoGoosens/Gaiter",
"path": "feature_extraction/frequency_domain_feature_extractor.py",
"copies": "1",
"size": "7795",
"license": "mit",
"hash": -7606390606884634000,
"line_mean": 41.1351351351,
"line_max": 184,
"alpha_frac": 0.6062860808,
"autogenerated": false,
"ratio": 3.458296... |
from featureflow import Feature
from zounds.persistence.arraywithunits import \
ArrayWithUnitsDecoder, ArrayWithUnitsEncoder
from zounds.timeseries import audio_sample_rate, AudioSamples
class AudioSamplesDecoder(ArrayWithUnitsDecoder):
def __init__(self):
super(ArrayWithUnitsDecoder, self).__init__()... | {
"repo_name": "JohnVinyard/zounds",
"path": "zounds/persistence/audiosamples.py",
"copies": "1",
"size": "1081",
"license": "mit",
"hash": 5819348829782147000,
"line_mean": 29.8857142857,
"line_max": 76,
"alpha_frac": 0.5957446809,
"autogenerated": false,
"ratio": 4.619658119658119,
"config_tes... |
from featureflow import Node, Aggregator
import os
from soundfile import SoundFile
import requests
import json
import io
from urllib.parse import urlparse
import featureflow as ff
class AudioMetaData(object):
"""
Encapsulates metadata about a source audio file, including things like
text descriptions and ... | {
"repo_name": "JohnVinyard/zounds",
"path": "zounds/soundfile/audio_metadata.py",
"copies": "1",
"size": "4290",
"license": "mit",
"hash": -8640199707355042000,
"line_mean": 28.1836734694,
"line_max": 78,
"alpha_frac": 0.5722610723,
"autogenerated": false,
"ratio": 4.450207468879668,
"config_te... |
from featureflow import Node, NotEnoughData
from zounds.core import ArrayWithUnits, IdentityDimension
import numpy as np
from multiprocessing.pool import ThreadPool
from os import cpu_count
class Reservoir(object):
def __init__(self, nsamples, dtype=None):
super(Reservoir, self).__init__()
if not... | {
"repo_name": "JohnVinyard/zounds",
"path": "zounds/learn/random_samples.py",
"copies": "1",
"size": "9298",
"license": "mit",
"hash": 329920780244728260,
"line_mean": 32.3261648746,
"line_max": 80,
"alpha_frac": 0.5746397075,
"autogenerated": false,
"ratio": 4.134281903068031,
"config_test": f... |
from featureflow import Node, NotEnoughData
import marshal
import types
from collections import OrderedDict
import hashlib
import numpy as np
from .functional import hyperplanes
from zounds.loudness import log_modulus, inverse_log_modulus
class Op(object):
def __init__(self, func, **kwargs):
super(Op, sel... | {
"repo_name": "JohnVinyard/zounds",
"path": "zounds/learn/preprocess.py",
"copies": "1",
"size": "24462",
"license": "mit",
"hash": 3458871176541839000,
"line_mean": 28.2607655502,
"line_max": 86,
"alpha_frac": 0.5755457444,
"autogenerated": false,
"ratio": 4.08926780341023,
"config_test": fals... |
from featureforge.feature import output_schema
@output_schema({str})
def bag_of_left_entity_IOB_chain(datapoint):
print (hash(datapoint))
eo = datapoint.left_entity_occurrence
return set()
def _bag_of_eo_IOB_chain(datapoint, eo):
tokens = datapoint.segment.tokens
eo_tokens = tokens[eo.segment_of... | {
"repo_name": "machinalis/iepy",
"path": "lex_features.py",
"copies": "2",
"size": "2037",
"license": "bsd-3-clause",
"hash": 7057611381186861000,
"line_mean": 27.6901408451,
"line_max": 64,
"alpha_frac": 0.5920471281,
"autogenerated": false,
"ratio": 3.542608695652174,
"config_test": false,
... |
from FeatureGenerator import *
import cPickle
import pandas as pd
from helpers import *
class TargetFeatureGenerator(FeatureGenerator):
'''
doing nothing other than returning the target variables
'''
def __init__(self, name='targetFeatureGenerator'):
super(TargetFeatureGenerator, sel... | {
"repo_name": "Cisco-Talos/fnc-1",
"path": "tree_model/TargetFeatureGenerator.py",
"copies": "1",
"size": "1562",
"license": "apache-2.0",
"hash": 83005447501958110,
"line_mean": 29.0384615385,
"line_max": 77,
"alpha_frac": 0.6568501921,
"autogenerated": false,
"ratio": 4.326869806094183,
"conf... |
from FeatureGenerator import *
import pandas as pd
import numpy as np
import cPickle
import gensim
from sklearn.preprocessing import normalize
from helpers import *
class Word2VecFeatureGenerator(FeatureGenerator):
def __init__(self, name='word2vecFeatureGenerator'):
super(Word2VecFeatureGenerator, self... | {
"repo_name": "Cisco-Talos/fnc-1",
"path": "tree_model/Word2VecFeatureGenerator.py",
"copies": "1",
"size": "6317",
"license": "apache-2.0",
"hash": 1729871280789272000,
"line_mean": 39.4935897436,
"line_max": 127,
"alpha_frac": 0.6366946335,
"autogenerated": false,
"ratio": 3.746737841043891,
... |
from feature import AbstractFeature
import numpy as np
class FeatureOperator(AbstractFeature):
"""
A FeatureOperator operates on two feature models.
Args:
model1 [AbstractFeature]
model2 [AbstractFeature]
"""
def __init__(self,model1,model2):
if (not isinstance(model1,A... | {
"repo_name": "revan/facerecserver",
"path": "operators.py",
"copies": "1",
"size": "3673",
"license": "bsd-3-clause",
"hash": -8337206848147066000,
"line_mean": 31.2192982456,
"line_max": 121,
"alpha_frac": 0.5750068064,
"autogenerated": false,
"ratio": 3.6693306693306695,
"config_test": false... |
from ..feature import Feature
from .format import Format
import re
class KML(Format):
"""KML Writer"""
url = "/"
layername = "layer"
title_property = None
def encode(self, features, **kwargs):
url = "%s/%s/%s-data.kml" % (self.url, self.layername, self.layername)
results = ["""<... | {
"repo_name": "iocast/vectorformats",
"path": "vectorformats/formats/kml.py",
"copies": "2",
"size": "7800",
"license": "mit",
"hash": -1360154734300992500,
"line_mean": 40.935483871,
"line_max": 139,
"alpha_frac": 0.5558974359,
"autogenerated": false,
"ratio": 4.248366013071895,
"config_test":... |
from ..feature import Feature
from .format import Format
try:
from cjson import encode as json_dumps
from cjson import decode as json_loads
except:
try:
from simplejson import dumps as json_dumps
from simplejson import loads as json_loads
except Exception, E:
raise Exception("si... | {
"repo_name": "iocast/vectorformats",
"path": "vectorformats/formats/geojson.py",
"copies": "2",
"size": "3847",
"license": "mit",
"hash": 5270916192450976000,
"line_mean": 33.3482142857,
"line_max": 154,
"alpha_frac": 0.5469196777,
"autogenerated": false,
"ratio": 4.473255813953489,
"config_te... |
from feature import Feature
from geogig import NULL_ID
TYPE_MODIFIED = "Modified"
TYPE_ADDED = "Added"
TYPE_REMOVED = "Removed"
ATTRIBUTE_DIFF_MODIFIED, ATTRIBUTE_DIFF_ADDED, ATTRIBUTE_DIFF_REMOVED, ATTRIBUTE_DIFF_UNCHANGED = ["M", "A", "R", "U"]
class Diffentry(object):
'''A difference between two referenc... | {
"repo_name": "roscoeZA/GeoGigSync",
"path": "src/geogigpy/diff.py",
"copies": "1",
"size": "1676",
"license": "cc0-1.0",
"hash": -7583681114178966000,
"line_mean": 27.9137931034,
"line_max": 118,
"alpha_frac": 0.5662291169,
"autogenerated": false,
"ratio": 3.6673960612691467,
"config_test": fa... |
from feature import *
from prelib import preprocess
import os
from operator import itemgetter
import time
import sys
import pickle as pickle
imagelocation = "" #Input Image path
indir = "" #Directory Path
class Image(object):
def __init__(self, path):
self.path = path
img = cv2.imread(self.path,0... | {
"repo_name": "devashishp/Content-Based-Image-Retrieval",
"path": "Initial.py",
"copies": "1",
"size": "2535",
"license": "mit",
"hash": 2357126531059848000,
"line_mean": 29.9146341463,
"line_max": 84,
"alpha_frac": 0.6390532544,
"autogenerated": false,
"ratio": 3.050541516245487,
"config_test"... |
from feature import *
from pymongo import MongoClient
from bson.binary import Binary as BsonBinary
import pickle
import os
from operator import itemgetter
import time
import sys
imagelocation = "" #Input Image path
indir = "" #Directory Path
client = MongoClient('mongodb://localhost:27017')
db = client.coil #Inser... | {
"repo_name": "devashishp/Content-Based-Image-Retrieval",
"path": "optimized.py",
"copies": "1",
"size": "3209",
"license": "mit",
"hash": -3684637194031888000,
"line_mean": 28.4403669725,
"line_max": 84,
"alpha_frac": 0.6572140854,
"autogenerated": false,
"ratio": 2.971296296296296,
"config_te... |
from feature import *
@feature_with("my_function")
def my_function():
print "Hello feature"
@feature_with("my_function1")
def my_function1():
print "Hello feature1"
@feature_with("STANDARD")
def my_function2():
print "Hello feature2"
@feature_with("my_function3")
def my_function3():
print "Hello fea... | {
"repo_name": "cyrilthomas/feature",
"path": "feature_test.py",
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"size": "1301",
"license": "mit",
"hash": -8416787259105373000,
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"line_max": 104,
"alpha_frac": 0.6464258263,
"autogenerated": false,
"ratio": 3.441798941798942,
"config_test": false,
"ha... |
from feature import simple
import graph
from nltk.corpus import brown
def calc_readability(corpus):
texts = []
results = []
for fileid in corpus.fileids():
sentlist = brown.sents(fileids=[fileid])
text = ' '.join([ ' '.join(ss) for ss in sentlist ])
texts.append(text)
for text... | {
"repo_name": "worldwise001/stylometry",
"path": "main/readability_graph_brown.py",
"copies": "1",
"size": "2563",
"license": "mit",
"hash": 6360011412488530000,
"line_mean": 37.8484848485,
"line_max": 109,
"alpha_frac": 0.5860319938,
"autogenerated": false,
"ratio": 3.3547120418848166,
"config... |
from feature.model import db
from feature.model.logic import to_json_dump
class Transaction:
def __init__(self):
self.INSERT = 'insert'
self.UPDATE = 'update'
self.DELETE = 'delete'
# map transaction types to actual transaction to commit
# only accessible via co... | {
"repo_name": "parejadan/feature-center",
"path": "feature/model/transaction.py",
"copies": "1",
"size": "1618",
"license": "mit",
"hash": 692930753092179800,
"line_mean": 25.8965517241,
"line_max": 89,
"alpha_frac": 0.5185414091,
"autogenerated": false,
"ratio": 4.60968660968661,
"config_test"... |
from features.cards import prettify_name
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
import urllib
import requests
import discord
import json
import logging
logger = logging.getLogger('discord')
DESCRIPTION_TEXT = 'Click the links for more information'
TITLE_TEXT = 'Rulings from fiveringsdb.com'
def... | {
"repo_name": "MyrionPhoenixmoon/l5r-discord-bot",
"path": "features/rulings.py",
"copies": "1",
"size": "4930",
"license": "mit",
"hash": 1095336481457842600,
"line_mean": 38.126984127,
"line_max": 130,
"alpha_frac": 0.5557809331,
"autogenerated": false,
"ratio": 3.774885145482389,
"config_tes... |
from features_helpers import score_differences
def get_postranscriptional_modification_features(uniprot_exon_indices_location, uniprot_ptm_db_location,
output_file_location):
"""
Reads uniprot PTM file and generates post translational modification site features... | {
"repo_name": "wonjunetai/pulse",
"path": "features/uniprot_ptm.py",
"copies": "1",
"size": "2241",
"license": "mit",
"hash": 7246974326774252000,
"line_mean": 35.1451612903,
"line_max": 112,
"alpha_frac": 0.5586791611,
"autogenerated": false,
"ratio": 3.281112737920937,
"config_test": false,
... |
from features_helpers import score_differences
def get_transmembrane_region_features(uniprot_exon_indices_location, uniprot_tm_indices_db_location,
output_file_location):
"""
Reads uniprot TM indices DB and generates transmembrane scores.
:param uniprot_exon_indices_... | {
"repo_name": "wonjunetai/pulse",
"path": "features/uniprot_transmem.py",
"copies": "1",
"size": "2393",
"license": "mit",
"hash": 6164278022846013000,
"line_mean": 36.390625,
"line_max": 112,
"alpha_frac": 0.5578771417,
"autogenerated": false,
"ratio": 3.3751763046544427,
"config_test": false,... |
from features_helpers import score_differences
def get_uniprot_elm_features(uniprot_exon_indices_location, uniprot_elm_db_location, output_location):
"""
Reads uniprot ELM file and generates ELM features.
:param uniprot_exon_indices_location:
:param uniprot_elm_db_location:
:param output_location... | {
"repo_name": "wonjunetai/pulse",
"path": "features/uniprot_elm_read.py",
"copies": "1",
"size": "2201",
"license": "mit",
"hash": -408927224886734700,
"line_mean": 34.5,
"line_max": 112,
"alpha_frac": 0.5529304861,
"autogenerated": false,
"ratio": 3.2704309063893016,
"config_test": false,
"h... |
from ..features import build_features
from ..visualization import visualize
import numpy as np
import scipy.stats as stats
import scipy.signal
import numpy.matlib
import pdb
import matplotlib.pyplot as plt
from sklearn.covariance import GraphLasso
import sys
def detect_changes(data_retina, params):
res=params['... | {
"repo_name": "curious-abhinav/change-point",
"path": "src/models/train_model.py",
"copies": "1",
"size": "6323",
"license": "mit",
"hash": 1881065939020273000,
"line_mean": 40.3267973856,
"line_max": 217,
"alpha_frac": 0.6893879488,
"autogenerated": false,
"ratio": 3.0679281901989324,
"config_... |
from .features import Dictionary, RegexMatches
name = "catalan"
try:
import enchant
dictionary = enchant.Dict("ca")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'ca'. " +
"Consider installing 'myspell-ca'.")
dictionary = Dic... | {
"repo_name": "he7d3r/revscoring",
"path": "revscoring/languages/catalan.py",
"copies": "2",
"size": "2618",
"license": "mit",
"hash": 8084260928345393000,
"line_mean": 16.7755102041,
"line_max": 82,
"alpha_frac": 0.5407577497,
"autogenerated": false,
"ratio": 2.5542521994134897,
"config_test":... |
from .features import Dictionary, RegexMatches
name = "hindi"
try:
import enchant
dictionary = enchant.Dict("hi")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'hi'. " +
"Consider installing 'aspell-hi'.")
dictionary = Dictio... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/hindi.py",
"copies": "1",
"size": "3648",
"license": "mit",
"hash": -3933073790271755300,
"line_mean": 17.3661971831,
"line_max": 76,
"alpha_frac": 0.4313650307,
"autogenerated": false,
"ratio": 1.84571... |
from .features import Dictionary, RegexMatches
name = "tamil"
try:
import enchant
dictionary = enchant.Dict("ta")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'ta'. " +
"Consider installing 'aspell-ta'.")
dictionary = Dictio... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/tamil.py",
"copies": "1",
"size": "1601",
"license": "mit",
"hash": 3493561712741730000,
"line_mean": 25.2830188679,
"line_max": 76,
"alpha_frac": 0.5577889447,
"autogenerated": false,
"ratio": 2.133231... |
from .features import Dictionary, RegexMatches, Stemmed, Stopwords
from .features.dictionary import MultiDictChecker, load_dict
name = "german"
multi_dict = MultiDictChecker(
load_dict('de_DE', 'myspell-de-de'),
load_dict('de_CH', 'myspell-de-ch'),
load_dict('de_AT', 'myspell-de-at'))
dictionary = Dicti... | {
"repo_name": "he7d3r/revscoring",
"path": "revscoring/languages/german.py",
"copies": "2",
"size": "3953",
"license": "mit",
"hash": -4222095942622481000,
"line_mean": 21,
"line_max": 77,
"alpha_frac": 0.5713560183,
"autogenerated": false,
"ratio": 2.462789243277048,
"config_test": false,
"h... |
from .features import Dictionary, RegexMatches, Stemmed, Stopwords
from .features.dictionary import MultiDictChecker, load_dict, utf16_cleanup
name = "portuguese"
multi_dict = MultiDictChecker(
load_dict('pt_PT', 'myspell-pt-pt'),
load_dict('pt_BR', 'myspell-pt-br'))
def safe_dictionary_check(word):
re... | {
"repo_name": "wiki-ai/revscoring",
"path": "revscoring/languages/portuguese.py",
"copies": "2",
"size": "7303",
"license": "mit",
"hash": -1016060191203864800,
"line_mean": 36.0255102041,
"line_max": 81,
"alpha_frac": 0.5936337329,
"autogenerated": false,
"ratio": 2.3074721780604133,
"config_t... |
from .features import Dictionary, RegexMatches, Stemmed, Stopwords
from .features.dictionary import utf16_cleanup
name = "english"
try:
import enchant
enchant_dict = enchant.Dict("en")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'en'. " +
... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/english.py",
"copies": "1",
"size": "5658",
"license": "mit",
"hash": -5476355383395473000,
"line_mean": 26.0669856459,
"line_max": 79,
"alpha_frac": 0.5105179424,
"autogenerated": false,
"ratio": 2.243... |
from .features import Dictionary, RegexMatches, Stemmed, Stopwords
name = "dutch"
try:
import enchant
dictionary = enchant.Dict("nl")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'nl'. " +
"Consider installing 'myspell-nl'.")... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/dutch.py",
"copies": "1",
"size": "3534",
"license": "mit",
"hash": 6653611434397299000,
"line_mean": 20.5487804878,
"line_max": 77,
"alpha_frac": 0.5738539898,
"autogenerated": false,
"ratio": 2.604274... |
from .features import Dictionary, RegexMatches, Stemmed, Stopwords
name = "french"
try:
import enchant
dictionary = enchant.Dict("fr")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'fr'. " +
"Consider installing 'myspell-fr'."... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/french.py",
"copies": "1",
"size": "3116",
"license": "mit",
"hash": -5546052718434120000,
"line_mean": 22.5151515152,
"line_max": 77,
"alpha_frac": 0.5914948454,
"autogenerated": false,
"ratio": 2.7323... |
from .features import Dictionary, RegexMatches, Stemmed, Stopwords
name = "german"
try:
import enchant
dictionary = enchant.Dict("de")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'de'. " +
"Consider installing 'myspell-de-de... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/german.py",
"copies": "1",
"size": "3956",
"license": "mit",
"hash": -5546473051391101000,
"line_mean": 21.0167597765,
"line_max": 77,
"alpha_frac": 0.5658462319,
"autogenerated": false,
"ratio": 2.5133... |
from .features import Dictionary, RegexMatches, Stemmed, Stopwords
name = "italian"
try:
import enchant
dictionary = enchant.Dict("it")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'it'. " +
"Consider installing 'myspell-it'.... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/italian.py",
"copies": "1",
"size": "3540",
"license": "mit",
"hash": 2190063359505002000,
"line_mean": 21.4050632911,
"line_max": 77,
"alpha_frac": 0.5858757062,
"autogenerated": false,
"ratio": 2.6696... |
from .features import Dictionary, RegexMatches, Stemmed, Stopwords
name = "portuguese"
try:
import enchant
dictionary = enchant.Dict("pt")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'pt'. " +
"Consider installing 'myspell-p... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/portuguese.py",
"copies": "1",
"size": "4890",
"license": "mit",
"hash": -2451336717139108400,
"line_mean": 32.3287671233,
"line_max": 77,
"alpha_frac": 0.5698725853,
"autogenerated": false,
"ratio": 2.... |
from .features import Dictionary, RegexMatches, Stemmed, Stopwords
name = "romanian"
try:
import enchant
dictionary = enchant.Dict("ro")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'ro'. " +
"Consider installing 'aspell-ro'.... | {
"repo_name": "he7d3r/revscoring",
"path": "revscoring/languages/romanian.py",
"copies": "2",
"size": "2379",
"license": "mit",
"hash": -8449267774722949000,
"line_mean": 26.3448275862,
"line_max": 81,
"alpha_frac": 0.6469104666,
"autogenerated": false,
"ratio": 3.003787878787879,
"config_test"... |
from .features import Dictionary, RegexMatches, Stemmed, Stopwords
name = "russian"
try:
import enchant
dictionary = enchant.Dict("ru")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'ru'. " +
"Consider installing 'myspell-ru'.... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/russian.py",
"copies": "1",
"size": "4074",
"license": "mit",
"hash": 8312734406041299000,
"line_mean": 24.7196969697,
"line_max": 77,
"alpha_frac": 0.6120765832,
"autogenerated": false,
"ratio": 2.0853... |
from .features import Dictionary, RegexMatches, Stemmed, Stopwords
name = "spanish"
try:
import enchant
dictionary = enchant.Dict("es")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'es'. " +
"Consider installing 'myspell-es'.... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/spanish.py",
"copies": "1",
"size": "9750",
"license": "mit",
"hash": 7972672393237075000,
"line_mean": 30.7973856209,
"line_max": 77,
"alpha_frac": 0.538643371,
"autogenerated": false,
"ratio": 2.31446... |
from .features import Dictionary, RegexMatches, Stopwords
name = "arabic"
try:
import enchant
dictionary = enchant.Dict("ar")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'ar'. " +
"Consider installing 'aspell-ar'.")
diction... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/arabic.py",
"copies": "1",
"size": "7735",
"license": "mit",
"hash": 3327037906869582300,
"line_mean": 16.2561307902,
"line_max": 76,
"alpha_frac": 0.4834991315,
"autogenerated": false,
"ratio": 1.85555... |
from .features import Dictionary, RegexMatches, Stopwords
name = "bosnian"
try:
import enchant
dictionary = enchant.Dict("bs")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'bs'. " +
"Consider installing 'hunspell-bs'.")
dict... | {
"repo_name": "wiki-ai/revscoring",
"path": "revscoring/languages/bosnian.py",
"copies": "2",
"size": "4482",
"license": "mit",
"hash": -8429181799444585000,
"line_mean": 27.2038216561,
"line_max": 82,
"alpha_frac": 0.5749774164,
"autogenerated": false,
"ratio": 2.3354430379746836,
"config_test... |
from .features import Dictionary, RegexMatches, Stopwords
name = "croatian"
try:
import enchant
dictionary = enchant.Dict("hr")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'hr'. " +
"Consider installing 'myspell-hr'.")
dict... | {
"repo_name": "he7d3r/revscoring",
"path": "revscoring/languages/croatian.py",
"copies": "2",
"size": "5408",
"license": "mit",
"hash": -4275901099600983600,
"line_mean": 24.2735849057,
"line_max": 81,
"alpha_frac": 0.5472191116,
"autogenerated": false,
"ratio": 2.2703389830508476,
"config_test... |
from .features import Dictionary, RegexMatches, Stopwords
name = "czech"
try:
import enchant
dictionary = enchant.Dict("cs")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'cs'. " +
"Consider installing 'myspell-cs'.")
diction... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/czech.py",
"copies": "1",
"size": "8482",
"license": "mit",
"hash": -3000436442441569300,
"line_mean": 14.3508442777,
"line_max": 76,
"alpha_frac": 0.4833781472,
"autogenerated": false,
"ratio": 2.13128... |
from .features import Dictionary, RegexMatches, Stopwords
name = "estonian"
try:
import enchant
dictionary = enchant.Dict("et")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'et'. " +
"Consider installing 'myspell-et'.")
dict... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/estonian.py",
"copies": "1",
"size": "3979",
"license": "mit",
"hash": -489107026439916100,
"line_mean": 33.0603448276,
"line_max": 78,
"alpha_frac": 0.5616299671,
"autogenerated": false,
"ratio": 2.333... |
from .features import Dictionary, RegexMatches, Stopwords
name = "galician"
try:
import enchant
dictionary = enchant.Dict("gl")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'gl'. " +
"Consider installing 'hunspell-gl'.")
d... | {
"repo_name": "wiki-ai/revscoring",
"path": "revscoring/languages/galician.py",
"copies": "2",
"size": "5275",
"license": "mit",
"hash": -3900662200902580000,
"line_mean": 15.1411042945,
"line_max": 83,
"alpha_frac": 0.4986697073,
"autogenerated": false,
"ratio": 2.5506543868153173,
"config_tes... |
from .features import Dictionary, RegexMatches, Stopwords
name = "hungarian"
try:
import enchant
dictionary = enchant.Dict("hu")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'hu'. " +
"Consider installing 'aspell-hu'.")
dict... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/hungarian.py",
"copies": "1",
"size": "5545",
"license": "mit",
"hash": 7921676118048240000,
"line_mean": 15.7975077882,
"line_max": 76,
"alpha_frac": 0.5300445104,
"autogenerated": false,
"ratio": 2.19... |
from .features import Dictionary, RegexMatches, Stopwords
name = "icelandic"
try:
import enchant
dictionary = enchant.Dict("is")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'is'. " +
"Consider installing 'aspell-is'.")
dict... | {
"repo_name": "wiki-ai/revscoring",
"path": "revscoring/languages/icelandic.py",
"copies": "2",
"size": "3556",
"license": "mit",
"hash": 6768823638874780000,
"line_mean": 22.2364864865,
"line_max": 81,
"alpha_frac": 0.5774934574,
"autogenerated": false,
"ratio": 2.26996699669967,
"config_test"... |
from .features import Dictionary, RegexMatches, Stopwords
name = "indonesian"
try:
import enchant
dictionary = enchant.Dict("id")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'id'. " +
"Consider installing 'aspell-id'.")
dic... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/indonesian.py",
"copies": "1",
"size": "7378",
"license": "mit",
"hash": -1372205066020292000,
"line_mean": 40.9204545455,
"line_max": 77,
"alpha_frac": 0.6047709406,
"autogenerated": false,
"ratio": 2.... |
from .features import Dictionary, RegexMatches, Stopwords
name = "latvian"
try:
import enchant
dictionary = enchant.Dict("lv")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'lv'. " +
"Consider installing 'myspell-lv'.")
dicti... | {
"repo_name": "wiki-ai/revscoring",
"path": "revscoring/languages/latvian.py",
"copies": "2",
"size": "5954",
"license": "mit",
"hash": 4817386503650379000,
"line_mean": 14.6166219839,
"line_max": 81,
"alpha_frac": 0.4925321888,
"autogenerated": false,
"ratio": 2.172696754942186,
"config_test":... |
from .features import Dictionary, RegexMatches, Stopwords
name = "norwegian"
try:
import enchant
dictionary = enchant.Dict("nb")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'nb'. " +
"Consider installing 'myspell-nb'.")
dic... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/norwegian.py",
"copies": "1",
"size": "5462",
"license": "mit",
"hash": -4057480912740400000,
"line_mean": 14.3796033994,
"line_max": 76,
"alpha_frac": 0.4866457911,
"autogenerated": false,
"ratio": 2.3... |
from .features import Dictionary, RegexMatches, Stopwords
name = "persian"
try:
import enchant
dictionary = enchant.Dict("fa")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'fa'. " +
"Consider installing 'myspell-fa'.")
dicti... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/persian.py",
"copies": "1",
"size": "11092",
"license": "mit",
"hash": 4754782982746866000,
"line_mean": 36.265560166,
"line_max": 79,
"alpha_frac": 0.5522770293,
"autogenerated": false,
"ratio": 1.7301... |
from .features import Dictionary, RegexMatches, Stopwords
name = "polish"
try:
import enchant
dictionary = enchant.Dict("pl")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'pl'. " +
"Consider installing 'aspell-pl'.")
diction... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/polish.py",
"copies": "1",
"size": "7047",
"license": "mit",
"hash": -6257997089928375000,
"line_mean": 14.4422222222,
"line_max": 76,
"alpha_frac": 0.487120449,
"autogenerated": false,
"ratio": 2.17224... |
from .features import Dictionary, RegexMatches, Stopwords
name = "serbian"
try:
import enchant
dictionary = enchant.Dict("sr")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'sr'. " +
"Consider installing 'hunspell-sr'.")
dict... | {
"repo_name": "wiki-ai/revscoring",
"path": "revscoring/languages/serbian.py",
"copies": "2",
"size": "7803",
"license": "mit",
"hash": -2960437621536685000,
"line_mean": 15.5870786517,
"line_max": 83,
"alpha_frac": 0.5219305673,
"autogenerated": false,
"ratio": 1.692947247706422,
"config_test"... |
from .features import Dictionary, RegexMatches, Stopwords
name = "swedish"
try:
import enchant
dictionary = enchant.Dict("sv")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'sv'. " +
"Consider installing 'aspell-sv'.")
dictio... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/swedish.py",
"copies": "1",
"size": "7589",
"license": "mit",
"hash": 7920417548475634000,
"line_mean": 13.8198019802,
"line_max": 76,
"alpha_frac": 0.4639230358,
"autogenerated": false,
"ratio": 2.3773... |
from .features import Dictionary, RegexMatches, Stopwords
name = "ukrainian"
try:
import enchant
dictionary = enchant.Dict("uk")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'uk'. " +
"Consider installing 'aspell-uk'.")
dict... | {
"repo_name": "wiki-ai/revscoring",
"path": "revscoring/languages/ukrainian.py",
"copies": "2",
"size": "7547",
"license": "mit",
"hash": 8354568600757106000,
"line_mean": 35.0130718954,
"line_max": 82,
"alpha_frac": 0.5468239564,
"autogenerated": false,
"ratio": 1.5573770491803278,
"config_tes... |
from .features import Dictionary, RegexMatches, Stopwords
name = "vietnamese"
try:
import enchant
dictionary = enchant.Dict("vi")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'vi'. " +
"Consider installing 'hunspell-vi'.")
d... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/vietnamese.py",
"copies": "1",
"size": "2546",
"license": "mit",
"hash": -3918965121493440000,
"line_mean": 34.4029850746,
"line_max": 145,
"alpha_frac": 0.6112984823,
"autogenerated": false,
"ratio": 1... |
from features import Features
import sys
def read_data(file_in):
data = []
with open(file_in, 'rb') as f:
for line in f:
line = line.strip()
upper, lower = line.split("\t")
data.append((upper, lower))
return data
Sigma, Sigma_inv = {}, {}
data = read... | {
"repo_name": "se4u/neural_wfst",
"path": "src/python/transducer/src/test_features.py",
"copies": "1",
"size": "1097",
"license": "mit",
"hash": -248822642493512060,
"line_mean": 23.9318181818,
"line_max": 53,
"alpha_frac": 0.5788514129,
"autogenerated": false,
"ratio": 3.324242424242424,
"conf... |
from .features import *
from .castle import *
from maze_builder.util import timed, is_verbose
POV_FILENAME = 'out.pov'
class CastleBuilder(object):
def __init__(self, illustrator, features=None, castle_class=CastleTwoLevel):
self.illustrator = illustrator
self.castle_class = castle_class
... | {
"repo_name": "kcsaff/maze-builder",
"path": "maze_builder/castles/builder.py",
"copies": "1",
"size": "1435",
"license": "mit",
"hash": 5301676582519078000,
"line_mean": 35.7948717949,
"line_max": 90,
"alpha_frac": 0.587456446,
"autogenerated": false,
"ratio": 3.7467362924281984,
"config_test"... |
from .features import overlaps
from .features import contains
from .features import contains_point
from .utils.codons import synonymous
from .utils.codons import nonsynonymous
from .utils.codons import initiator_codon
from .utils.codons import stop_codon
def upstream_transcript_variant(variant, transcript):
upst... | {
"repo_name": "solvebio/veppy",
"path": "veppy/functions.py",
"copies": "1",
"size": "13331",
"license": "mit",
"hash": -2975478017902180000,
"line_mean": 29.0247747748,
"line_max": 89,
"alpha_frac": 0.6048308454,
"autogenerated": false,
"ratio": 3.840679919331605,
"config_test": false,
"has_... |
from features import proportion,coda,clusters
word = u'z\u016bgusyaa'
consonant_proportion = proportion.consonant_proportion(word)
print "consonant proportion for %s: %.2f" % (word, consonant_proportion)
vowel_proportion = proportion.vowel_proportion(word)
print "vowels proportion for %s: %.2f" % (word, vowel_proporti... | {
"repo_name": "russmatney/unicode-classification-engine",
"path": "main.py",
"copies": "1",
"size": "1090",
"license": "mit",
"hash": 322502587595232700,
"line_mean": 39.3703703704,
"line_max": 72,
"alpha_frac": 0.752293578,
"autogenerated": false,
"ratio": 2.665036674816626,
"config_test": fal... |
from .features import RegexMatches
name = "japanese"
# Copied from https://gist.github.com/whym/b5ac3feb2a78797c9d98
# Yusuke Matsubara (CCO)
badword_regexes = [
r"死ね",
r"しね",
r"シネ",
r"あほ",
r"アホ",
r"ばか",
r"バカ",
r"やりまん",
r"ヤリマン",
r"まんこ",
r"マンコ",
r"うんこ",
r"ウンコ",
r... | {
"repo_name": "he7d3r/revscoring",
"path": "revscoring/languages/japanese.py",
"copies": "3",
"size": "2074",
"license": "mit",
"hash": 58761757471332450,
"line_mean": 15.18,
"line_max": 75,
"alpha_frac": 0.4913473424,
"autogenerated": false,
"ratio": 1.8261851015801354,
"config_test": false,
... |
from .features import RegexMatches
name = "tamil"
'''
try:
import enchant
dictionary = enchant.Dict("ta")
except enchant.errors.DictNotFoundError:
raise ImportError("No enchant-compatible dictionary found for 'ta'. " +
"Consider installing 'aspell-ta'.")
dictionary = Dictionary(nam... | {
"repo_name": "wiki-ai/revscoring",
"path": "revscoring/languages/tamil.py",
"copies": "2",
"size": "8839",
"license": "mit",
"hash": -6343661090608819000,
"line_mean": 34.8304347826,
"line_max": 81,
"alpha_frac": 0.6072078631,
"autogenerated": false,
"ratio": 2.1239690721649485,
"config_test":... |
from .features import RegexMatches, Stopwords
name = "finnish"
# No dictionary
# No stemmer
try:
from nltk.corpus import stopwords as nltk_stopwords
stopwords = set(nltk_stopwords.words('finnish'))
except LookupError:
raise ImportError("Could not load stopwords for {0}. ".format(__name__) +
... | {
"repo_name": "wiki-ai/revscoring",
"path": "revscoring/languages/finnish.py",
"copies": "2",
"size": "1984",
"license": "mit",
"hash": -902036748637909900,
"line_mean": 18.66,
"line_max": 78,
"alpha_frac": 0.5910478128,
"autogenerated": false,
"ratio": 2.6389261744966444,
"config_test": false,... |
from .features import RegexMatches, Stopwords
name = "turkish"
try:
from nltk.corpus import stopwords as nltk_stopwords
stopwords = set(nltk_stopwords.words('turkish'))
except LookupError:
raise ImportError("Could not load stopwords for {0}. ".format(__name__) +
"You may need to inst... | {
"repo_name": "yafeunteun/wikipedia-spam-classifier",
"path": "revscoring/revscoring/languages/turkish.py",
"copies": "1",
"size": "5465",
"license": "mit",
"hash": 5482987141369030000,
"line_mean": 25.28,
"line_max": 77,
"alpha_frac": 0.5899923896,
"autogenerated": false,
"ratio": 2.083234244946... |
from ..features import word
class peptide:
def __init__(self, sequence, rt):
self.pre = sequence[0]
self.post = sequence[-1]
self.sequence = sequence[2:-2]
self.rt = rt
self.amino_acids = self.get_amino_acids()
self.aa_indices = []
def build_amino_acid_indices(s... | {
"repo_name": "statisticalbiotechnology/GPTime",
"path": "GPTime/peptides/peptide.py",
"copies": "1",
"size": "1888",
"license": "apache-2.0",
"hash": 8002474304632711000,
"line_mean": 27.6060606061,
"line_max": 53,
"alpha_frac": 0.4846398305,
"autogenerated": false,
"ratio": 3.110378912685338,
... |
from features.models import Attempt, Feature
from jury.models import JudgeRequestAssignment, JudgeRequest
from teams.models import Team
for team in Team.objects.all():
for feature in Feature.objects.all():
request = JudgeRequest.objects.filter(team=team, feature=feature).last()
total, num = 0, 0
... | {
"repo_name": "Kianoosh76/webelopers-scoreboard",
"path": "scripts/check_attempts.py",
"copies": "1",
"size": "1052",
"license": "mit",
"hash": -6735251866529486000,
"line_mean": 46.8181818182,
"line_max": 91,
"alpha_frac": 0.5836501901,
"autogenerated": false,
"ratio": 4.347107438016529,
"conf... |
from features.numpy_sift import SIFTDescriptor
import numpy as np
import features.feature_utils
from features.DetectorDescriptorTemplate import DetectorAndDescriptor
class np_sift(DetectorAndDescriptor):
def __init__(self, peak_thresh=10.0):
super(
np_sift,
self).__init__(
... | {
"repo_name": "spongezhang/vlb",
"path": "python/features/np_sift.py",
"copies": "2",
"size": "1251",
"license": "bsd-2-clause",
"hash": 5817871934851282000,
"line_mean": 30.275,
"line_max": 75,
"alpha_frac": 0.6011191047,
"autogenerated": false,
"ratio": 4.022508038585209,
"config_test": false... |
from FeatureSplitConfig import eshop_better_config_names
from consts import METRICS_MAXIMIZE, METRICS_MINIMIZE
from featuresplitGIA import getBestFeatures, getZ3Feature, \
generateConsumerConstraints, extractWeights, getWeightRanges, \
getConstraintFromFile
from npGIAforZ3 import GuidedImprovementAlgorithm, \
... | {
"repo_name": "ai-se/parGALE",
"path": "epoal_src/distributed_npGIA.py",
"copies": "1",
"size": "2895",
"license": "unlicense",
"hash": 71528078444893060,
"line_mean": 30.1290322581,
"line_max": 91,
"alpha_frac": 0.6835924007,
"autogenerated": false,
"ratio": 3.683206106870229,
"config_test": f... |
from featuretools.entityset.relationship import RelationshipPath
def feature_with_name(features, name):
for f in features:
if f.get_name() == name:
return True
return False
def backward_path(es, entity_ids):
"""
Create a backward RelationshipPath through the given entities. Assu... | {
"repo_name": "Featuretools/featuretools",
"path": "featuretools/tests/testing_utils/features.py",
"copies": "1",
"size": "1255",
"license": "bsd-3-clause",
"hash": -4656735567847818000,
"line_mean": 31.1794871795,
"line_max": 79,
"alpha_frac": 0.6430278884,
"autogenerated": false,
"ratio": 4.141... |
from featuretools.entityset.relationship import Relationship, RelationshipPath
def test_relationship_path(es):
log_to_sessions = Relationship(es['sessions']['id'],
es['log']['session_id'])
sessions_to_customers = Relationship(es['customers']['id'],
... | {
"repo_name": "Featuretools/featuretools",
"path": "featuretools/tests/entityset_tests/test_relationship.py",
"copies": "1",
"size": "3335",
"license": "bsd-3-clause",
"hash": -8617281157929818000,
"line_mean": 40.6875,
"line_max": 95,
"alpha_frac": 0.6113943028,
"autogenerated": false,
"ratio": ... |
from featuretools import list_primitives
from featuretools.primitives import (
Day,
GreaterThan,
Last,
NumCharacters,
get_aggregation_primitives,
get_transform_primitives
)
from featuretools.primitives.utils import _get_descriptions
def test_list_primitives_order():
df = list_primitives()
... | {
"repo_name": "Featuretools/featuretools",
"path": "featuretools/tests/utils_tests/test_list_primitives.py",
"copies": "1",
"size": "1228",
"license": "bsd-3-clause",
"hash": -1712432361529430500,
"line_mean": 34.0857142857,
"line_max": 97,
"alpha_frac": 0.6669381107,
"autogenerated": false,
"rat... |
from featuretools import Relationship, Timedelta, primitives
from featuretools.entityset.relationship import RelationshipPath
from featuretools.primitives.base import (
AggregationPrimitive,
PrimitiveBase,
TransformPrimitive
)
from featuretools.primitives.utils import serialize_primitive
from featuretools.u... | {
"repo_name": "Featuretools/featuretools",
"path": "featuretools/feature_base/feature_base.py",
"copies": "1",
"size": "31464",
"license": "bsd-3-clause",
"hash": 8525751523607173000,
"line_mean": 37.7011070111,
"line_max": 139,
"alpha_frac": 0.6013221459,
"autogenerated": false,
"ratio": 4.49678... |
from featuretools.utils import Trie
def test_get_node():
t = Trie(default=lambda: 'default')
t.get_node([1, 2, 3]).value = '123'
t.get_node([1, 2, 4]).value = '124'
sub = t.get_node([1, 2])
assert sub.get_node([3]).value == '123'
assert sub.get_node([4]).value == '124'
sub.get_node([4, 5... | {
"repo_name": "Featuretools/featuretools",
"path": "featuretools/tests/utils_tests/test_trie.py",
"copies": "1",
"size": "1309",
"license": "bsd-3-clause",
"hash": -3473034067104268000,
"line_mean": 26.2708333333,
"line_max": 63,
"alpha_frac": 0.5225362872,
"autogenerated": false,
"ratio": 2.7384... |
from featuring import db
from featuring.entities.user.models import User
from featuring.entities.client.models import Client
from featuring.entities.product.models import Product
from featuring.entities.ticket.models import Ticket
def insert_mandatory_data():
db.session.add(User(username='root', password='root', ... | {
"repo_name": "ccortezia/featuring",
"path": "featuring-flask-api/featuring/initdata.py",
"copies": "1",
"size": "1972",
"license": "mit",
"hash": 9109742310000129000,
"line_mean": 33.5964912281,
"line_max": 90,
"alpha_frac": 0.6318458418,
"autogenerated": false,
"ratio": 3.502664298401421,
"co... |
from featuring import db
from featuring.utilities.common import iso_tomorrow
from featuring.entities.client.models import Client
from featuring.entities.product.models import Product
class Ticket(db.Model):
__tablename__ = 'tickets'
ticket_id = db.Column(db.Integer, primary_key=True, autoincrement=True)
... | {
"repo_name": "ccortezia/featuring",
"path": "featuring-flask-api/featuring/entities/ticket/models.py",
"copies": "1",
"size": "1043",
"license": "mit",
"hash": 4966855580914204000,
"line_mean": 33.7666666667,
"line_max": 89,
"alpha_frac": 0.6337488015,
"autogenerated": false,
"ratio": 3.99616858... |
from .featurizer import Extractor
import typing
from collections import Counter
class ValueCalculator:
def calculate(self, feature_dict: [typing.Dict[str, float], Counter]) -> typing.Dict[str, float]:
raise NotImplementedError
class BinaryValueCalculator(ValueCalculator):
def calculate(self, feature... | {
"repo_name": "dbracewell/pyHermes",
"path": "hermes/ml/features.py",
"copies": "1",
"size": "1828",
"license": "apache-2.0",
"hash": -6830636876691524000,
"line_mean": 35.56,
"line_max": 107,
"alpha_frac": 0.6690371991,
"autogenerated": false,
"ratio": 4.17351598173516,
"config_test": false,
... |
from federal_spending.usaspending.management.base.importer import BaseImporter
from django.db.models.fields import CharField
from federal_spending.usaspending.utils.ucsv import UnicodeDictReader, UnicodeWriter
from django.conf import settings
import os.path
import re
class BaseUSASpendingConverter(BaseImporter):
... | {
"repo_name": "unitedstates/federal_spending",
"path": "federal_spending/usaspending/management/base/usaspending_importer.py",
"copies": "1",
"size": "5058",
"license": "cc0-1.0",
"hash": -4058004569437011000,
"line_mean": 37.037593985,
"line_max": 125,
"alpha_frac": 0.5889679715,
"autogenerated": ... |
from federal_spending.usaspending.models import Contract, Grant
from federal_spending.usaspending.scripts.usaspending.contracts_loader import Loader
from django.core.management.base import BaseCommand
from federal_spending.usaspending.management.commands.create_indexes import contracts_idx, grants_idx
from federal_spen... | {
"repo_name": "unitedstates/federal_spending",
"path": "federal_spending/usaspending/management/commands/fresh_import.py",
"copies": "1",
"size": "3672",
"license": "cc0-1.0",
"hash": -3036297512889628000,
"line_mean": 39.3516483516,
"line_max": 161,
"alpha_frac": 0.6530501089,
"autogenerated": fal... |
from federal_spending.usaspending.models import Contract, Grant
from federal_spending.usaspending.scripts.usaspending.contracts_loader import Loader
from federal_spending.usaspending.scripts.usaspending.fpds import FIELDS as CONTRACT_FIELDS, CALCULATED_FIELDS as CONTRACT_CALCULATED_FIELDS
from federal_spending.usaspend... | {
"repo_name": "unitedstates/federal_spending",
"path": "federal_spending/usaspending/management/commands/import_updates.py",
"copies": "1",
"size": "9934",
"license": "cc0-1.0",
"hash": -7807684489507780000,
"line_mean": 39.0564516129,
"line_max": 140,
"alpha_frac": 0.5516408295,
"autogenerated": f... |
from federal_spending.usaspending.models import Contract
from federal_spending.usaspending.grants.models import Grant
from federal_spending.usaspending.management.base.importer import BaseImporter
from django.db.models.fields import CharField
import csv
import faads
import fpds
import os
import os.path
import re
import... | {
"repo_name": "unitedstates/federal_spending",
"path": "federal_spending/usaspending/scripts/usaspending/converter.py",
"copies": "1",
"size": "5017",
"license": "cc0-1.0",
"hash": 3750415599564744700,
"line_mean": 33.8402777778,
"line_max": 112,
"alpha_frac": 0.5696631453,
"autogenerated": false,
... |
from federatedml.cipher_compressor.compressor import CipherCompressor, NormalCipherPackage, CipherDecompressor
from federatedml.util import consts
from federatedml.util import LOGGER
from federatedml.ensemble.basic_algorithms.decision_tree.tree_core.splitter import SplitInfo
def get_g_h_info(task_type, max_sample_wei... | {
"repo_name": "FederatedAI/FATE",
"path": "python/federatedml/ensemble/basic_algorithms/decision_tree/tree_core/splitinfo_cipher_compressor.py",
"copies": "1",
"size": "8239",
"license": "apache-2.0",
"hash": -4857751697031869000,
"line_mean": 41.0357142857,
"line_max": 122,
"alpha_frac": 0.637456001... |
from federatedml.transfer_variable.transfer_class.homo_label_encoder_transfer_variable \
import HomoLabelEncoderTransferVariable
from federatedml.util import consts
from federatedml.util import LOGGER
class HomoLabelEncoderClient(object):
def __init__(self):
self.transvar = HomoLabelEncoderTransferVa... | {
"repo_name": "FederatedAI/FATE",
"path": "python/federatedml/util/homo_label_encoder.py",
"copies": "1",
"size": "1607",
"license": "apache-2.0",
"hash": 2622214577926814700,
"line_mean": 40.2051282051,
"line_max": 98,
"alpha_frac": 0.6851275669,
"autogenerated": false,
"ratio": 3.66894977168949... |
from federatedml.util import consts
class PerformanceRecorder(object):
"""
This class record performance(single value metrics during the training process)
"""
def __init__(self):
# all of them are single value metrics
self.allowed_metric = [consts.AUC,
... | {
"repo_name": "FederatedAI/FATE",
"path": "python/federatedml/evaluation/performance_recorder.py",
"copies": "1",
"size": "2724",
"license": "apache-2.0",
"hash": -2136121770146537500,
"line_mean": 33.9230769231,
"line_max": 87,
"alpha_frac": 0.4673274596,
"autogenerated": false,
"ratio": 4.46557... |
from federatedml.util import LOGGER
from federatedml.framework.homo.blocks import secure_sum_aggregator, loss_scatter, has_converged
class HomoBoostArbiterAggregator(object):
def __init__(self,):
"""
Args:
transfer_variable:
converge_type: see federatedml/optim/convergence... | {
"repo_name": "FederatedAI/FATE",
"path": "python/federatedml/ensemble/boosting/boosting_core/homo_boosting_aggregator.py",
"copies": "1",
"size": "1402",
"license": "apache-2.0",
"hash": 2883901769878932000,
"line_mean": 34.075,
"line_max": 96,
"alpha_frac": 0.6690442225,
"autogenerated": false,
... |
__author__ = 'maurizio'
#from fednodes.dummy_classes import DummyFedMessage
import fed_logging
from fed_logging import *
import logging
logger = logging.getLogger("federation.core.federator")
class MessageScheduler(object):
def __init__(self, message_class, producer,configuration):
self._pr=producer
... | {
"repo_name": "INFN-Catania/FedManager",
"path": "fednodes/messaging.py",
"copies": "1",
"size": "2095",
"license": "apache-2.0",
"hash": 429236851188116200,
"line_mean": 37.7962962963,
"line_max": 127,
"alpha_frac": 0.6558472554,
"autogenerated": false,
"ratio": 4.4957081545064375,
"config_tes... |
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