def autocorrect(input_word): input_word = input_word.lower() if input_word in V: return 'Your word seems to be correct' else: similarities = [1-(textdistance.Jaccard(qval=2).distance(v,input_word)) for v in freq.keys()] df = pd.DataFrame.from_dict(probs, orient='index').reset_index() df = df.rename(columns={'index':'Word', 0: 'Prob'}) df['Similarity'] = similarities output = df.sort_values(['Similarity', 'Prob'], ascending=False).head() return output
def my_autocorrect(self, input_word): if input_word.lower() in self.V: return (input_word) else: input_word = input_word.lower() similarities = [ 1 - (textdistance.Jaccard(qval=2).distance(v, input_word)) for v in self.word_freq_dict.keys() ] df = pd.DataFrame.from_dict(self.probs, orient='index').reset_index() df = df.rename(columns={'index': 'Word', 0: 'Prob'}) df['Similarity'] = similarities output = df.sort_values(['Similarity', 'Prob'], ascending=False).head() return (output.iloc[0]["Word"])
def get_feature(author_id, paper_id): distance_funcs = [ textdistance.JaroWinkler(), textdistance.Jaccard(), textdistance.Levenshtein(), fuzz.token_sort_ratio ] a = author_data[author_data['Id'] == author_id].iloc[0] p_a = paper_author_data[(paper_author_data['PaperId'] == paper_id) & (paper_author_data['AuthorId'] == author_id)] name_l = [10000 for _ in range(len(distance_funcs))] aff_l = [10000 for _ in range(len(distance_funcs))] for _, row in p_a.iterrows(): for i, f in enumerate(distance_funcs): name_l[i] = min(name_l[i], f(a.Name, row.Name)) aff_l[i] = min(aff_l[i], f(str(a.Affiliation), str(row.Affiliation))) feature = name_l + aff_l return feature
def __init__(self, pa_preprocessor, name, qval=1): super().__init__(pa_preprocessor) self.time_log = [] self.qval = qval self.textdistance_name = name # Edited based: if name == 'Hamming': self.similar_measure = textdistance.Hamming(qval=qval) elif name == 'DamerauLevenshtein': self.similar_measure = textdistance.DamerauLevenshtein(qval=qval) elif name == 'Levenshtein': self.similar_measure = textdistance.Levenshtein(qval=qval) elif name == 'Mlipns': self.similar_measure = textdistance.MLIPNS(qval=qval) elif name == 'Jaro': self.similar_measure = textdistance.Jaro(qval=qval) elif name == 'JaroWinkler': self.similar_measure = textdistance.JaroWinkler(qval=qval) elif name == 'StrCmp95': self.similar_measure = textdistance.StrCmp95() elif name == 'NeedlemanWunsch': self.similar_measure = textdistance.NeedlemanWunsch(qval=qval) elif name == 'Gotoh': self.similar_measure = textdistance.Gotoh(qval=qval) elif name == 'SmithWaterman': self.similar_measure = textdistance.SmithWaterman(qval=qval) # Token based elif name == 'Jaccard': self.similar_measure = textdistance.Jaccard(qval=qval) elif name == 'Sorensen': self.similar_measure = textdistance.Sorensen(qval=qval) elif name == 'Tversky': self.similar_measure = textdistance.Tversky() elif name == 'Overlap': self.similar_measure = textdistance.Overlap(qval=qval) elif name == 'Tanimoto': self.similar_measure = textdistance.Tanimoto(qval=qval) elif name == 'Cosine': self.similar_measure = textdistance.Cosine(qval=qval) elif name == 'MongeElkan': self.similar_measure = textdistance.MongeElkan(qval=qval) elif name == 'Bag': self.similar_measure = textdistance.Bag(qval=qval) # Sequence based elif name == 'LCSSeq': self.similar_measure = textdistance.LCSSeq(qval=qval) elif name == 'LCSStr': self.similar_measure = textdistance.LCSStr(qval=qval) elif name == 'RatcliffObershelp': self.similar_measure = textdistance.RatcliffObershelp(qval=qval) # Compression based elif name == 'ArithNCD': self.similar_measure = textdistance.ArithNCD(qval=qval) elif name == 'RLENCD': self.similar_measure = textdistance.RLENCD(qval=qval) elif name == 'BWTRLENCD': self.similar_measure = textdistance.BWTRLENCD() elif name == 'SqrtNCD': self.similar_measure = textdistance.SqrtNCD(qval=qval) elif name == 'EntropyNCD': self.similar_measure = textdistance.EntropyNCD(qval=qval) # Simple: elif name == 'Prefix': self.similar_measure = textdistance.Prefix(qval=qval) elif name == 'Postfix': self.similar_measure = textdistance.Postfix(qval=qval) elif name == 'Length': self.similar_measure = textdistance.Length(qval=qval) elif name == 'Identity': self.similar_measure = textdistance.Identity(qval=qval) elif name == 'Matrix': self.similar_measure = textdistance.Matrix()
def simple_example(): str1, str2 = 'test', 'text' qval = 2 #-------------------- # Edit-based. if True: print("textdistance.hamming({}, {}) = {}.".format( str1, str2, textdistance.hamming(str1, str2))) print("textdistance.hamming.distance({}, {}) = {}.".format( str1, str2, textdistance.hamming.distance(str1, str2))) print("textdistance.hamming.similarity({}, {}) = {}.".format( str1, str2, textdistance.hamming.similarity(str1, str2))) print("textdistance.hamming.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.hamming.normalized_distance(str1, str2))) print( "textdistance.hamming.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.hamming.normalized_similarity(str1, str2))) print( "textdistance.Hamming(qval={}, test_func=None, truncate=False, external=True).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.Hamming(qval=qval, test_func=None, truncate=False, external=True).distance(str1, str2))) print("textdistance.mlipns({}, {}) = {}.".format( str1, str2, textdistance.mlipns(str1, str2))) print("textdistance.mlipns.distance({}, {}) = {}.".format( str1, str2, textdistance.mlipns.distance(str1, str2))) print("textdistance.mlipns.similarity({}, {}) = {}.".format( str1, str2, textdistance.mlipns.similarity(str1, str2))) print("textdistance.mlipns.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.mlipns.normalized_distance(str1, str2))) print("textdistance.mlipns.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.mlipns.normalized_similarity(str1, str2))) print( "textdistance.MLIPNS(threshold=0.25, maxmismatches=2, qval={}, external=True).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.MLIPNS(threshold=0.25, maxmismatches=2, qval=qval, external=True).distance(str1, str2))) print("textdistance.levenshtein({}, {}) = {}.".format( str1, str2, textdistance.levenshtein(str1, str2))) print("textdistance.levenshtein.distance({}, {}) = {}.".format( str1, str2, textdistance.levenshtein.distance(str1, str2))) print("textdistance.levenshtein.similarity({}, {}) = {}.".format( str1, str2, textdistance.levenshtein.similarity(str1, str2))) print("textdistance.levenshtein.normalized_distance({}, {}) = {}.". format(str1, str2, textdistance.levenshtein.normalized_distance(str1, str2))) print("textdistance.levenshtein.normalized_similarity({}, {}) = {}.". format( str1, str2, textdistance.levenshtein.normalized_similarity(str1, str2))) print( "textdistance.Levenshtein(qval={}, test_func=None, external=True).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.Levenshtein(qval=qval, test_func=None, external=True).distance(str1, str2))) print("textdistance.damerau_levenshtein({}, {}) = {}.".format( str1, str2, textdistance.damerau_levenshtein(str1, str2))) print("textdistance.damerau_levenshtein.distance({}, {}) = {}.".format( str1, str2, textdistance.damerau_levenshtein.distance(str1, str2))) print( "textdistance.damerau_levenshtein.similarity({}, {}) = {}.".format( str1, str2, textdistance.damerau_levenshtein.similarity(str1, str2))) print( "textdistance.damerau_levenshtein.normalized_distance({}, {}) = {}." .format( str1, str2, textdistance.damerau_levenshtein.normalized_distance( str1, str2))) print( "textdistance.damerau_levenshtein.normalized_similarity({}, {}) = {}." .format( str1, str2, textdistance.damerau_levenshtein.normalized_similarity( str1, str2))) print( "textdistance.DamerauLevenshtein(qval={}, test_func=None, external=True).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.DamerauLevenshtein(qval=qval, test_func=None, external=True).distance( str1, str2))) print("textdistance.jaro({}, {}) = {}.".format( str1, str2, textdistance.jaro(str1, str2))) print("textdistance.jaro.distance({}, {}) = {}.".format( str1, str2, textdistance.jaro.distance(str1, str2))) print("textdistance.jaro.similarity({}, {}) = {}.".format( str1, str2, textdistance.jaro.similarity(str1, str2))) print("textdistance.jaro.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.jaro.normalized_distance(str1, str2))) print("textdistance.jaro.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.jaro.normalized_similarity(str1, str2))) print( "textdistance.Jaro(long_tolerance=False, qval={}, external=True).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.Jaro(long_tolerance=False, qval=qval, external=True).distance(str1, str2))) print("textdistance.jaro_winkler({}, {}) = {}.".format( str1, str2, textdistance.jaro_winkler(str1, str2))) print("textdistance.jaro_winkler.distance({}, {}) = {}.".format( str1, str2, textdistance.jaro_winkler.distance(str1, str2))) print("textdistance.jaro_winkler.similarity({}, {}) = {}.".format( str1, str2, textdistance.jaro_winkler.similarity(str1, str2))) print("textdistance.jaro_winkler.normalized_distance({}, {}) = {}.". format(str1, str2, textdistance.jaro_winkler.normalized_distance(str1, str2))) print("textdistance.jaro_winkler.normalized_similarity({}, {}) = {}.". format( str1, str2, textdistance.jaro_winkler.normalized_similarity(str1, str2))) print( "textdistance.JaroWinkler(long_tolerance=False, winklerize=True, qval={}, external=True).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.JaroWinkler(long_tolerance=False, winklerize=True, qval=qval, external=True).distance(str1, str2))) print("textdistance.strcmp95({}, {}) = {}.".format( str1, str2, textdistance.strcmp95(str1, str2))) print("textdistance.strcmp95.distance({}, {}) = {}.".format( str1, str2, textdistance.strcmp95.distance(str1, str2))) print("textdistance.strcmp95.similarity({}, {}) = {}.".format( str1, str2, textdistance.strcmp95.similarity(str1, str2))) print("textdistance.strcmp95.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.strcmp95.normalized_distance(str1, str2))) print( "textdistance.strcmp95.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.strcmp95.normalized_similarity(str1, str2))) print( "textdistance.StrCmp95(long_strings=False, external=True).distance({}, {}) = {}." .format( str1, str2, textdistance.StrCmp95(long_strings=False, external=True).distance(str1, str2))) print("textdistance.needleman_wunsch({}, {}) = {}.".format( str1, str2, textdistance.needleman_wunsch(str1, str2))) print("textdistance.needleman_wunsch.distance({}, {}) = {}.".format( str1, str2, textdistance.needleman_wunsch.distance(str1, str2))) print("textdistance.needleman_wunsch.similarity({}, {}) = {}.".format( str1, str2, textdistance.needleman_wunsch.similarity(str1, str2))) print( "textdistance.needleman_wunsch.normalized_distance({}, {}) = {}.". format( str1, str2, textdistance.needleman_wunsch.normalized_distance(str1, str2))) print( "textdistance.needleman_wunsch.normalized_similarity({}, {}) = {}." .format( str1, str2, textdistance.needleman_wunsch.normalized_similarity( str1, str2))) print( "textdistance.NeedlemanWunsch(gap_cost=1.0, sim_func=None, qval={}, external=True).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.NeedlemanWunsch(gap_cost=1.0, sim_func=None, qval=qval, external=True).distance( str1, str2))) print("textdistance.gotoh({}, {}) = {}.".format( str1, str2, textdistance.gotoh(str1, str2))) print("textdistance.gotoh.distance({}, {}) = {}.".format( str1, str2, textdistance.gotoh.distance(str1, str2))) print("textdistance.gotoh.similarity({}, {}) = {}.".format( str1, str2, textdistance.gotoh.similarity(str1, str2))) print("textdistance.gotoh.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.gotoh.normalized_distance(str1, str2))) print("textdistance.gotoh.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.gotoh.normalized_similarity(str1, str2))) print( "textdistance.Gotoh(gap_open=1, gap_ext=0.4, sim_func=None, qval={}, external=True).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.Gotoh(gap_open=1, gap_ext=0.4, sim_func=None, qval=qval, external=True).distance(str1, str2))) print("textdistance.smith_waterman({}, {}) = {}.".format( str1, str2, textdistance.smith_waterman(str1, str2))) print("textdistance.smith_waterman.distance({}, {}) = {}.".format( str1, str2, textdistance.smith_waterman.distance(str1, str2))) print("textdistance.smith_waterman.similarity({}, {}) = {}.".format( str1, str2, textdistance.smith_waterman.similarity(str1, str2))) print("textdistance.smith_waterman.normalized_distance({}, {}) = {}.". format( str1, str2, textdistance.smith_waterman.normalized_distance(str1, str2))) print( "textdistance.smith_waterman.normalized_similarity({}, {}) = {}.". format( str1, str2, textdistance.smith_waterman.normalized_similarity(str1, str2))) print( "textdistance.SmithWaterman(gap_cost=1.0, sim_func=None, qval={}, external=True).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.SmithWaterman(gap_cost=1.0, sim_func=None, qval=qval, external=True).distance(str1, str2))) #-------------------- # Token-based. if False: print("textdistance.jaccard({}, {}) = {}.".format( str1, str2, textdistance.jaccard(str1, str2))) print("textdistance.jaccard.distance({}, {}) = {}.".format( str1, str2, textdistance.jaccard.distance(str1, str2))) print("textdistance.jaccard.similarity({}, {}) = {}.".format( str1, str2, textdistance.jaccard.similarity(str1, str2))) print("textdistance.jaccard.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.jaccard.normalized_distance(str1, str2))) print( "textdistance.jaccard.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.jaccard.normalized_similarity(str1, str2))) print( "textdistance.Jaccard(qval={}, as_set=False, external=True).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.Jaccard(qval=qval, as_set=False, external=True).distance(str1, str2))) print("textdistance.sorensen({}, {}) = {}.".format( str1, str2, textdistance.sorensen(str1, str2))) print("textdistance.sorensen.distance({}, {}) = {}.".format( str1, str2, textdistance.sorensen.distance(str1, str2))) print("textdistance.sorensen.similarity({}, {}) = {}.".format( str1, str2, textdistance.sorensen.similarity(str1, str2))) print("textdistance.sorensen.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.sorensen.normalized_distance(str1, str2))) print( "textdistance.sorensen.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.sorensen.normalized_similarity(str1, str2))) print( "textdistance.Sorensen(qval={}, as_set=False, external=True).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.Sorensen(qval=qval, as_set=False, external=True).distance(str1, str2))) print("textdistance.sorensen_dice({}, {}) = {}.".format( str1, str2, textdistance.sorensen_dice(str1, str2))) print("textdistance.sorensen_dice.distance({}, {}) = {}.".format( str1, str2, textdistance.sorensen_dice.distance(str1, str2))) print("textdistance.sorensen_dice.similarity({}, {}) = {}.".format( str1, str2, textdistance.sorensen_dice.similarity(str1, str2))) print("textdistance.sorensen_dice.normalized_distance({}, {}) = {}.". format( str1, str2, textdistance.sorensen_dice.normalized_distance(str1, str2))) print("textdistance.sorensen_dice.normalized_similarity({}, {}) = {}.". format( str1, str2, textdistance.sorensen_dice.normalized_similarity(str1, str2))) #print("textdistance.SorensenDice().distance({}, {}) = {}.".format(str1, str2, textdistance.SorensenDice().distance(str1, str2))) print("textdistance.tversky({}, {}) = {}.".format( str1, str2, textdistance.tversky(str1, str2))) print("textdistance.tversky.distance({}, {}) = {}.".format( str1, str2, textdistance.tversky.distance(str1, str2))) print("textdistance.tversky.similarity({}, {}) = {}.".format( str1, str2, textdistance.tversky.similarity(str1, str2))) print("textdistance.tversky.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.tversky.normalized_distance(str1, str2))) print( "textdistance.tversky.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.tversky.normalized_similarity(str1, str2))) print( "textdistance.Tversky(qval={}, ks=None, bias=None, as_set=False, external=True).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.Tversky(qval=qval, ks=None, bias=None, as_set=False, external=True).distance(str1, str2))) print("textdistance.overlap({}, {}) = {}.".format( str1, str2, textdistance.overlap(str1, str2))) print("textdistance.overlap.distance({}, {}) = {}.".format( str1, str2, textdistance.overlap.distance(str1, str2))) print("textdistance.overlap.similarity({}, {}) = {}.".format( str1, str2, textdistance.overlap.similarity(str1, str2))) print("textdistance.overlap.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.overlap.normalized_distance(str1, str2))) print( "textdistance.overlap.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.overlap.normalized_similarity(str1, str2))) print( "textdistance.Overlap(qval={}, as_set=False, external=True).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.Overlap(qval=qval, as_set=False, external=True).distance(str1, str2))) # This is identical to the Jaccard similarity coefficient and the Tversky index for alpha=1 and beta=1. print("textdistance.tanimoto({}, {}) = {}.".format( str1, str2, textdistance.tanimoto(str1, str2))) print("textdistance.tanimoto.distance({}, {}) = {}.".format( str1, str2, textdistance.tanimoto.distance(str1, str2))) print("textdistance.tanimoto.similarity({}, {}) = {}.".format( str1, str2, textdistance.tanimoto.similarity(str1, str2))) print("textdistance.tanimoto.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.tanimoto.normalized_distance(str1, str2))) print( "textdistance.tanimoto.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.tanimoto.normalized_similarity(str1, str2))) print( "textdistance.Tanimoto(qval={}, as_set=False, external=True).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.Tanimoto(qval=qval, as_set=False, external=True).distance(str1, str2))) print("textdistance.cosine({}, {}) = {}.".format( str1, str2, textdistance.cosine(str1, str2))) print("textdistance.cosine.distance({}, {}) = {}.".format( str1, str2, textdistance.cosine.distance(str1, str2))) print("textdistance.cosine.similarity({}, {}) = {}.".format( str1, str2, textdistance.cosine.similarity(str1, str2))) print("textdistance.cosine.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.cosine.normalized_distance(str1, str2))) print("textdistance.cosine.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.cosine.normalized_similarity(str1, str2))) print( "textdistance.Cosine(qval={}, as_set=False, external=True).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.Cosine(qval=qval, as_set=False, external=True).distance(str1, str2))) print("textdistance.monge_elkan({}, {}) = {}.".format( str1, str2, textdistance.monge_elkan(str1, str2))) print("textdistance.monge_elkan.distance({}, {}) = {}.".format( str1, str2, textdistance.monge_elkan.distance(str1, str2))) print("textdistance.monge_elkan.similarity({}, {}) = {}.".format( str1, str2, textdistance.monge_elkan.similarity(str1, str2))) print("textdistance.monge_elkan.normalized_distance({}, {}) = {}.". format(str1, str2, textdistance.monge_elkan.normalized_distance(str1, str2))) print("textdistance.monge_elkan.normalized_similarity({}, {}) = {}.". format( str1, str2, textdistance.monge_elkan.normalized_similarity(str1, str2))) print( "textdistance.MongeElkan(algorithm=textdistance.DamerauLevenshtein(), symmetric=False, qval={}, external=True).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.MongeElkan( algorithm=textdistance.DamerauLevenshtein(), symmetric=False, qval=qval, external=True).distance(str1, str2))) print("textdistance.bag({}, {}) = {}.".format( str1, str2, textdistance.bag(str1, str2))) print("textdistance.bag.distance({}, {}) = {}.".format( str1, str2, textdistance.bag.distance(str1, str2))) print("textdistance.bag.similarity({}, {}) = {}.".format( str1, str2, textdistance.bag.similarity(str1, str2))) print("textdistance.bag.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.bag.normalized_distance(str1, str2))) print("textdistance.bag.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.bag.normalized_similarity(str1, str2))) print("textdistance.Bag(qval={}).distance({}, {}) = {}.".format( qval, str1, str2, textdistance.Bag(qval=qval).distance(str1, str2))) #-------------------- # Sequence-based. if False: print("textdistance.lcsseq({}, {}) = {}.".format( str1, str2, textdistance.lcsseq(str1, str2))) print("textdistance.lcsseq.distance({}, {}) = {}.".format( str1, str2, textdistance.lcsseq.distance(str1, str2))) print("textdistance.lcsseq.similarity({}, {}) = {}.".format( str1, str2, textdistance.lcsseq.similarity(str1, str2))) print("textdistance.lcsseq.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.lcsseq.normalized_distance(str1, str2))) print("textdistance.lcsseq.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.lcsseq.normalized_similarity(str1, str2))) #print("textdistance.LCSSeq(qval={}, test_func=None, external=True).distance({}, {}) = {}.".format(qval, str1, str2, textdistance.LCSSeq(qval=qval, test_func=None, external=True).distance(str1, str2))) print("textdistance.LCSSeq().distance({}, {}) = {}.".format( str1, str2, textdistance.LCSSeq().distance(str1, str2))) print("textdistance.lcsstr({}, {}) = {}.".format( str1, str2, textdistance.lcsstr(str1, str2))) print("textdistance.lcsstr.distance({}, {}) = {}.".format( str1, str2, textdistance.lcsstr.distance(str1, str2))) print("textdistance.lcsstr.similarity({}, {}) = {}.".format( str1, str2, textdistance.lcsstr.similarity(str1, str2))) print("textdistance.lcsstr.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.lcsstr.normalized_distance(str1, str2))) print("textdistance.lcsstr.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.lcsstr.normalized_similarity(str1, str2))) print("textdistance.LCSStr(qval={}).distance({}, {}) = {}.".format( qval, str1, str2, textdistance.LCSStr(qval=qval).distance(str1, str2))) print("textdistance.ratcliff_obershelp({}, {}) = {}.".format( str1, str2, textdistance.ratcliff_obershelp(str1, str2))) print("textdistance.ratcliff_obershelp.distance({}, {}) = {}.".format( str1, str2, textdistance.ratcliff_obershelp.distance(str1, str2))) print( "textdistance.ratcliff_obershelp.similarity({}, {}) = {}.".format( str1, str2, textdistance.ratcliff_obershelp.similarity(str1, str2))) print( "textdistance.ratcliff_obershelp.normalized_distance({}, {}) = {}." .format( str1, str2, textdistance.ratcliff_obershelp.normalized_distance( str1, str2))) print( "textdistance.ratcliff_obershelp.normalized_similarity({}, {}) = {}." .format( str1, str2, textdistance.ratcliff_obershelp.normalized_similarity( str1, str2))) print("textdistance.RatcliffObershelp().distance({}, {}) = {}.".format( str1, str2, textdistance.RatcliffObershelp().distance(str1, str2))) #-------------------- # Compression-based. if False: print("textdistance.arith_ncd({}, {}) = {}.".format( str1, str2, textdistance.arith_ncd(str1, str2))) print("textdistance.arith_ncd.distance({}, {}) = {}.".format( str1, str2, textdistance.arith_ncd.distance(str1, str2))) print("textdistance.arith_ncd.similarity({}, {}) = {}.".format( str1, str2, textdistance.arith_ncd.similarity(str1, str2))) print( "textdistance.arith_ncd.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.arith_ncd.normalized_distance(str1, str2))) print("textdistance.arith_ncd.normalized_similarity({}, {}) = {}.". format(str1, str2, textdistance.arith_ncd.normalized_similarity(str1, str2))) #print("textdistance.ArithNCD(base=2, terminator=None, qval={}).distance({}, {}) = {}.".format(qval, str1, str2, textdistance.ArithNCD(base=2, terminator=None, qval=qval).distance(str1, str2))) print("textdistance.ArithNCD().distance({}, {}) = {}.".format( str1, str2, textdistance.ArithNCD().distance(str1, str2))) print("textdistance.rle_ncd({}, {}) = {}.".format( str1, str2, textdistance.rle_ncd(str1, str2))) print("textdistance.rle_ncd.distance({}, {}) = {}.".format( str1, str2, textdistance.rle_ncd.distance(str1, str2))) print("textdistance.rle_ncd.similarity({}, {}) = {}.".format( str1, str2, textdistance.rle_ncd.similarity(str1, str2))) print("textdistance.rle_ncd.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.rle_ncd.normalized_distance(str1, str2))) print( "textdistance.rle_ncd.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.rle_ncd.normalized_similarity(str1, str2))) print("textdistance.RLENCD().distance({}, {}) = {}.".format( str1, str2, textdistance.RLENCD().distance(str1, str2))) print("textdistance.bwtrle_ncd({}, {}) = {}.".format( str1, str2, textdistance.bwtrle_ncd(str1, str2))) print("textdistance.bwtrle_ncd.distance({}, {}) = {}.".format( str1, str2, textdistance.bwtrle_ncd.distance(str1, str2))) print("textdistance.bwtrle_ncd.similarity({}, {}) = {}.".format( str1, str2, textdistance.bwtrle_ncd.similarity(str1, str2))) print( "textdistance.bwtrle_ncd.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.bwtrle_ncd.normalized_distance(str1, str2))) print("textdistance.bwtrle_ncd.normalized_similarity({}, {}) = {}.". format(str1, str2, textdistance.bwtrle_ncd.normalized_similarity(str1, str2))) print("textdistance.BWTRLENCD(terminator='\0').distance({}, {}) = {}.". format( str1, str2, textdistance.BWTRLENCD(terminator='\0').distance(str1, str2))) print("textdistance.sqrt_ncd({}, {}) = {}.".format( str1, str2, textdistance.sqrt_ncd(str1, str2))) print("textdistance.sqrt_ncd.distance({}, {}) = {}.".format( str1, str2, textdistance.sqrt_ncd.distance(str1, str2))) print("textdistance.sqrt_ncd.similarity({}, {}) = {}.".format( str1, str2, textdistance.sqrt_ncd.similarity(str1, str2))) print("textdistance.sqrt_ncd.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.sqrt_ncd.normalized_distance(str1, str2))) print( "textdistance.sqrt_ncd.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.sqrt_ncd.normalized_similarity(str1, str2))) print("textdistance.SqrtNCD(qval={}).distance({}, {}) = {}.".format( qval, str1, str2, textdistance.SqrtNCD(qval=qval).distance(str1, str2))) print("textdistance.entropy_ncd({}, {}) = {}.".format( str1, str2, textdistance.entropy_ncd(str1, str2))) print("textdistance.entropy_ncd.distance({}, {}) = {}.".format( str1, str2, textdistance.entropy_ncd.distance(str1, str2))) print("textdistance.entropy_ncd.similarity({}, {}) = {}.".format( str1, str2, textdistance.entropy_ncd.similarity(str1, str2))) print("textdistance.entropy_ncd.normalized_distance({}, {}) = {}.". format(str1, str2, textdistance.entropy_ncd.normalized_distance(str1, str2))) print("textdistance.entropy_ncd.normalized_similarity({}, {}) = {}.". format( str1, str2, textdistance.entropy_ncd.normalized_similarity(str1, str2))) print( "textdistance.EntropyNCD(qval={}, coef=1, base=2).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.EntropyNCD(qval=qval, coef=1, base=2).distance(str1, str2))) print("textdistance.bz2_ncd({}, {}) = {}.".format( str1, str2, textdistance.bz2_ncd(str1, str2))) print("textdistance.bz2_ncd.distance({}, {}) = {}.".format( str1, str2, textdistance.bz2_ncd.distance(str1, str2))) print("textdistance.bz2_ncd.similarity({}, {}) = {}.".format( str1, str2, textdistance.bz2_ncd.similarity(str1, str2))) print("textdistance.bz2_ncd.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.bz2_ncd.normalized_distance(str1, str2))) print( "textdistance.bz2_ncd.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.bz2_ncd.normalized_similarity(str1, str2))) print("textdistance.BZ2NCD().distance({}, {}) = {}.".format( str1, str2, textdistance.BZ2NCD().distance(str1, str2))) print("textdistance.lzma_ncd({}, {}) = {}.".format( str1, str2, textdistance.lzma_ncd(str1, str2))) print("textdistance.lzma_ncd.distance({}, {}) = {}.".format( str1, str2, textdistance.lzma_ncd.distance(str1, str2))) print("textdistance.lzma_ncd.similarity({}, {}) = {}.".format( str1, str2, textdistance.lzma_ncd.similarity(str1, str2))) print("textdistance.lzma_ncd.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.lzma_ncd.normalized_distance(str1, str2))) print( "textdistance.lzma_ncd.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.lzma_ncd.normalized_similarity(str1, str2))) print("textdistance.LZMANCD().distance({}, {}) = {}.".format( str1, str2, textdistance.LZMANCD().distance(str1, str2))) print("textdistance.zlib_ncd({}, {}) = {}.".format( str1, str2, textdistance.zlib_ncd(str1, str2))) print("textdistance.zlib_ncd.distance({}, {}) = {}.".format( str1, str2, textdistance.zlib_ncd.distance(str1, str2))) print("textdistance.zlib_ncd.similarity({}, {}) = {}.".format( str1, str2, textdistance.zlib_ncd.similarity(str1, str2))) print("textdistance.zlib_ncd.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.zlib_ncd.normalized_distance(str1, str2))) print( "textdistance.zlib_ncd.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.zlib_ncd.normalized_similarity(str1, str2))) print("textdistance.ZLIBNCD().distance({}, {}) = {}.".format( str1, str2, textdistance.ZLIBNCD().distance(str1, str2))) #-------------------- # Phonetic. if False: print("textdistance.mra({}, {}) = {}.".format( str1, str2, textdistance.mra(str1, str2))) print("textdistance.mra.distance({}, {}) = {}.".format( str1, str2, textdistance.mra.distance(str1, str2))) print("textdistance.mra.similarity({}, {}) = {}.".format( str1, str2, textdistance.mra.similarity(str1, str2))) print("textdistance.mra.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.mra.normalized_distance(str1, str2))) print("textdistance.mra.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.mra.normalized_similarity(str1, str2))) print("textdistance.MRA().distance({}, {}) = {}.".format( str1, str2, textdistance.MRA().distance(str1, str2))) print("textdistance.editex({}, {}) = {}.".format( str1, str2, textdistance.editex(str1, str2))) print("textdistance.editex.distance({}, {}) = {}.".format( str1, str2, textdistance.editex.distance(str1, str2))) print("textdistance.editex.similarity({}, {}) = {}.".format( str1, str2, textdistance.editex.similarity(str1, str2))) print("textdistance.editex.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.editex.normalized_distance(str1, str2))) print("textdistance.editex.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.editex.normalized_similarity(str1, str2))) print( "textdistance.Editex(local=False, match_cost=0, group_cost=1, mismatch_cost=2, groups=None, ungrouped=None, external=True).distance({}, {}) = {}." .format( str1, str2, textdistance.Editex(local=False, match_cost=0, group_cost=1, mismatch_cost=2, groups=None, ungrouped=None, external=True).distance(str1, str2))) #-------------------- # Simple. if False: print("textdistance.prefix({}, {}) = {}.".format( str1, str2, textdistance.prefix(str1, str2))) print("textdistance.prefix.distance({}, {}) = {}.".format( str1, str2, textdistance.prefix.distance(str1, str2))) print("textdistance.prefix.similarity({}, {}) = {}.".format( str1, str2, textdistance.prefix.similarity(str1, str2))) print("textdistance.prefix.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.prefix.normalized_distance(str1, str2))) print("textdistance.prefix.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.prefix.normalized_similarity(str1, str2))) print( "textdistance.Prefix(qval={}, sim_test=None).distance({}, {}) = {}." .format( qval, str1, str2, textdistance.Prefix(qval=qval, sim_test=None).distance(str1, str2))) print("textdistance.postfix({}, {}) = {}.".format( str1, str2, textdistance.postfix(str1, str2))) print("textdistance.postfix.distance({}, {}) = {}.".format( str1, str2, textdistance.postfix.distance(str1, str2))) print("textdistance.postfix.similarity({}, {}) = {}.".format( str1, str2, textdistance.postfix.similarity(str1, str2))) print("textdistance.postfix.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.postfix.normalized_distance(str1, str2))) print( "textdistance.postfix.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.postfix.normalized_similarity(str1, str2))) #print("textdistance.Postfix(qval={}, sim_test=None).distance({}, {}) = {}.".format(qval, str1, str2, textdistance.Postfix(qval=qval, sim_test=None).distance(str1, str2))) print("textdistance.Postfix().distance({}, {}) = {}.".format( str1, str2, textdistance.Postfix().distance(str1, str2))) print("textdistance.length({}, {}) = {}.".format( str1, str2, textdistance.length(str1, str2))) print("textdistance.length.distance({}, {}) = {}.".format( str1, str2, textdistance.length.distance(str1, str2))) print("textdistance.length.similarity({}, {}) = {}.".format( str1, str2, textdistance.length.similarity(str1, str2))) print("textdistance.length.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.length.normalized_distance(str1, str2))) print("textdistance.length.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.length.normalized_similarity(str1, str2))) print("textdistance.Length().distance({}, {}) = {}.".format( str1, str2, textdistance.Length().distance(str1, str2))) print("textdistance.identity({}, {}) = {}.".format( str1, str2, textdistance.identity(str1, str2))) print("textdistance.identity.distance({}, {}) = {}.".format( str1, str2, textdistance.identity.distance(str1, str2))) print("textdistance.identity.similarity({}, {}) = {}.".format( str1, str2, textdistance.identity.similarity(str1, str2))) print("textdistance.identity.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.identity.normalized_distance(str1, str2))) print( "textdistance.identity.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.identity.normalized_similarity(str1, str2))) print("textdistance.Identity().distance({}, {}) = {}.".format( str1, str2, textdistance.Identity().distance(str1, str2))) print("textdistance.matrix({}, {}) = {}.".format( str1, str2, textdistance.matrix(str1, str2))) print("textdistance.matrix.distance({}, {}) = {}.".format( str1, str2, textdistance.matrix.distance(str1, str2))) print("textdistance.matrix.similarity({}, {}) = {}.".format( str1, str2, textdistance.matrix.similarity(str1, str2))) print("textdistance.matrix.normalized_distance({}, {}) = {}.".format( str1, str2, textdistance.matrix.normalized_distance(str1, str2))) print("textdistance.matrix.normalized_similarity({}, {}) = {}.".format( str1, str2, textdistance.matrix.normalized_similarity(str1, str2))) print( "textdistance.Matrix(mat=None, mismatch_cost=0, match_cost=1, symmetric=True, external=True).distance({}, {}) = {}." .format( str1, str2, textdistance.Matrix(mat=None, mismatch_cost=0, match_cost=1, symmetric=True, external=True).distance(str1, str2)))
from flask import Flask, render_template, request, jsonify from flask_cors import CORS import requests import textdistance import os import json from dotenv import load_dotenv load_dotenv() app = Flask(__name__) cors = CORS(app) ##Text-distance Algorithm jac_algo = textdistance.Jaccard() lev_algo = textdistance.Levenshtein() ## translate_kakao function def getKakaoTrans(text, src, dest): def joinContents(text): separator = " " result = separator.join(text) return result API_KEY = os.getenv("KAKAO_API_KEY") kakao_url = "https://dapi.kakao.com" headers = { "Authorization": f"KakaoAK {API_KEY}", "Content-type": "application/x-www-form-urlencoded", }
def metrics(x): a = x[4].strip() b = x[5].strip() al = a.lower() bl = b.lower() a_len = float(len(a)) def tryit(x): try: return x() except Exception as e: return 0.0 tempo = lambda a, b, x: \ sum([ 1 if xi == a else (-1 if xi == b else 0) for xi in x ]) M = [ x[3], tryit(lambda: td.bz2_ncd(a, b)), tryit(lambda: td.zlib_ncd(a, b)), tryit(lambda: td.prefix.normalized_similarity(a, b)), tryit(lambda: td.postfix.normalized_similarity(a, b)), tryit(lambda: td.matrix.normalized_similarity(a, b)), tryit(lambda: td.length.normalized_similarity(a, b)), tryit(lambda: td.Hamming().normalized_similarity(a, b)), tryit(lambda: td.Hamming(qval=2).normalized_similarity(a, b)), tryit(lambda: td.Hamming(qval=3).normalized_similarity(a, b)), tryit(lambda: td.Hamming(qval=4).normalized_similarity(a, b)), tryit(lambda: td.Hamming(qval=5).normalized_similarity(a, b)), tryit(lambda: td.DamerauLevenshtein().normalized_similarity(a, b)), tryit( lambda: td.DamerauLevenshtein(qval=2).normalized_similarity(a, b)), tryit( lambda: td.DamerauLevenshtein(qval=3).normalized_similarity(a, b)), tryit( lambda: td.DamerauLevenshtein(qval=4).normalized_similarity(a, b)), tryit( lambda: td.DamerauLevenshtein(qval=5).normalized_similarity(a, b)), tryit(lambda: td.Jaccard().normalized_similarity(a, b)), tryit(lambda: td.Jaccard().normalized_similarity(al, bl)), tryit(lambda: td.Jaccard(qval=2).normalized_similarity(a, b)), tryit(lambda: td.Jaccard(qval=2).normalized_similarity(al, bl)), tryit(lambda: td.Jaccard(qval=3).normalized_similarity(a, b)), tryit(lambda: td.Jaccard(qval=3).normalized_similarity(al, bl)), tryit(lambda: td.Jaccard(qval=4).normalized_similarity(a, b)), tryit(lambda: td.Jaccard(qval=4).normalized_similarity(al, bl)), tryit(lambda: td.Jaccard(qval=5).normalized_similarity(a, b)), tryit(lambda: td.Jaccard(qval=5).normalized_similarity(al, bl)), tryit(lambda: td.Tversky().normalized_similarity(a, b)), tryit(lambda: td.Tversky(qval=2).normalized_similarity(a, b)), tryit(lambda: td.Tversky(qval=3).normalized_similarity(a, b)), tryit(lambda: td.Tversky(qval=4).normalized_similarity(a, b)), tryit(lambda: td.Tversky(qval=5).normalized_similarity(a, b)), tryit(lambda: td.JaroWinkler().normalized_similarity(a, b)), tryit(lambda: td.JaroWinkler(qval=2).normalized_similarity(a, b)), tryit(lambda: td.JaroWinkler(qval=3).normalized_similarity(a, b)), tryit(lambda: td.JaroWinkler(qval=4).normalized_similarity(a, b)), tryit(lambda: td.JaroWinkler(qval=5).normalized_similarity(a, b)), tryit(lambda: td.StrCmp95().normalized_similarity(a, b)), tryit(lambda: td.StrCmp95().normalized_similarity(al, bl)), 1.0 - (float(abs(tempo('(', ')', a) - tempo('(', ')', b))) / a_len), 1.0 - (float(abs(tempo('[', ']', a) - tempo('[', ']', b))) / a_len), 1.0 - (float(abs(tempo('{', '}', a) - tempo('{', '}', b))) / a_len), 1.0 - (float(abs(tempo('<', '>', a) - tempo('<', '>', b))) / a_len) ] return '{} qid:{} {} # {}'.format( x[0], x[1], ' '.join( ['{}:{:.4f}'.format(k + 1, float(y)) for k, y in enumerate(M)]), x[2])
def get_similarity(self): sim = textdistance.Jaccard(qval = 5).normalized_similarity(self._file1, self._file2) return sim
def Jaccard(txt1, txt2): jac = textdistance.Jaccard() value = jac.similarity(txt1, txt2) return round_toN(value)