from itertools import product

from re import finditer

import ngram

from fuzzycomp import fuzzycomp

from gensim.models import KeyedVectors

from nltk.corpus import wordnet

from py_stringmatching.similarity_measure.affine import Affine

from py_stringmatching.similarity_measure.bag_distance import BagDistance

from py_stringmatching.similarity_measure.cosine import Cosine

from py_stringmatching.similarity_measure.dice import Dice

from py_stringmatching.similarity_measure.editex import Editex

from py_stringmatching.similarity_measure.generalized_jaccard import \

GeneralizedJaccard

from py_stringmatching.similarity_measure.jaccard import Jaccard

from py_stringmatching.similarity_measure.jaro import Jaro

from py_stringmatching.similarity_measure.jaro_winkler import JaroWinkler

from py_stringmatching.similarity_measure.levenshtein import Levenshtein

from py_stringmatching.similarity_measure.monge_elkan import MongeElkan

from py_stringmatching.similarity_measure.needleman_wunsch import \

NeedlemanWunsch

from py_stringmatching.similarity_measure.overlap_coefficient import \

OverlapCoefficient

from py_stringmatching.similarity_measure.partial_ratio import PartialRatio

from py_stringmatching.similarity_measure.partial_token_sort import \

PartialTokenSort

from py_stringmatching.similarity_measure.ratio import Ratio

from py_stringmatching.similarity_measure.smith_waterman import SmithWaterman

from py_stringmatching.similarity_measure.soft_tfidf import SoftTfIdf

from py_stringmatching.similarity_measure.soundex import Soundex

from py_stringmatching.similarity_measure.tfidf import TfIdf

from py_stringmatching.similarity_measure.token_sort import TokenSort

from py_stringmatching.similarity_measure.tversky_index import TverskyIndex

from tqdm import tqdm

af = Affine()

me = MongeElkan()

nw = NeedlemanWunsch()

sw = SmithWaterman()

bd = BagDistance()

cos = Cosine()

pr = PartialRatio()

sf = SoftTfIdf()

edx = Editex()

gj = GeneralizedJaccard()

jw = JaroWinkler()

lev = Levenshtein()

dice = Dice()

jac = Jaccard()

jaro = Jaro()

pts = PartialTokenSort()

rat = Ratio()

sound = Soundex()

tfidf = TfIdf()

ts = TokenSort()

tv_ind = TverskyIndex()

over_coef = OverlapCoefficient()

# It's long

print('Loading word2vec model...')

model = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin',

binary=True)

print('Word2vec model are loaded.')

def camel_case_split(identifier):

matches = finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)',

identifier)

return [m.group(0) for m in matches]

def get_word2vec_sim(row_set1, row_set2):

return sum_sim

def get_words(text):

return row_set

def calculate_features(dataset, string_type):

return dataset