def test_valid_input(self): # https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance self.assertAlmostEqual(jaro_winkler('MARTHA', 'MARHTA'), 0.9611111111111111) self.assertAlmostEqual(jaro_winkler('DWAYNE', 'DUANE'), 0.84) self.assertAlmostEqual(jaro_winkler('DIXON', 'DICKSONX'), 0.8133333333333332)
def test_invalid_input3(self): jaro_winkler(None, None)
def test_invalid_input2(self): jaro_winkler('MARHTA', None)
def test_invalid_input1(self): jaro_winkler(None, 'MARHTA')
def generate_feature(file_name): lines = stage3_helper.read_file(file_name) features = [] labels = [] all_names = [] for line in lines: json1, json2, label = stage3_helper.read_jsons_label_from_line(line) string1, string2 = stage3_helper.get_attribute_from_jsons( json1, json2, product_name) all_names.append(tokenizers.whitespace(string1)) all_names.append(tokenizers.whitespace(string2)) for line in lines: json1, json2, label = stage3_helper.read_jsons_label_from_line(line) feature = [] # TODO: Add more features and optimize features. # product_type string1, string2 = stage3_helper.get_attribute_from_jsons( json1, json2, product_type) string1 = string1.lower() string2 = string2.lower() feature.append( simfunctions.jaccard(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append( simfunctions.jaro_winkler(string1, string2, prefix_weight=0.1)) feature.append( simfunctions.jaro( tokenizers.whitespace(string1)[0], tokenizers.whitespace(string2)[0])) # if len(string1) == len(string2): # feature.append(simfunctions.hamming_distance(string1, string2)) # else: # feature.append(5) feature.append( simfunctions.cosine(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append( simfunctions.overlap_coefficient(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append( simfunctions.monge_elkan(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append( simfunctions.tfidf(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append(len(string1)) feature.append(len(string2)) feature.append(len(string1) - len(string2)) feature.append(len(tokenizers.whitespace(string1))) feature.append(len(tokenizers.whitespace(string2))) # product_name string1, string2 = stage3_helper.get_attribute_from_jsons( json1, json2, product_name) string1 = string1.lower() string2 = string2.lower() feature.append( simfunctions.jaccard(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append( simfunctions.jaro_winkler(string1, string2, prefix_weight=0.1)) if len(string1) == len(string2): feature.append(simfunctions.hamming_distance(string1, string2)) else: feature.append(5) feature.append( simfunctions.cosine(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append( simfunctions.overlap_coefficient(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append( simfunctions.monge_elkan(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append( simfunctions.tfidf(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append(len(string1)) feature.append(len(string2)) feature.append(len(string1) - len(string2)) feature.append( simfunctions.jaro( tokenizers.whitespace(string1)[0], tokenizers.whitespace(string2)[0])) feature.append(len(tokenizers.whitespace(string1))) feature.append(len(tokenizers.whitespace(string2))) # product_segment string1, string2 = stage3_helper.get_attribute_from_jsons( json1, json2, product_segment) string1 = string1.lower() string2 = string2.lower() feature.append( simfunctions.jaccard(tokenizers.qgram(string1, 3), tokenizers.qgram(string2, 3))) feature.append( simfunctions.jaro_winkler(string1, string2, prefix_weight=0.1)) # if len(string1) == len(string2): # feature.append(simfunctions.hamming_distance(string1, string2)) # else: # feature.append(5) feature.append( simfunctions.cosine(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append( simfunctions.overlap_coefficient(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append( simfunctions.monge_elkan(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append( simfunctions.tfidf(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append( simfunctions.jaro( tokenizers.whitespace(string1)[0], tokenizers.whitespace(string2)[0])) feature.append(len(string1)) feature.append(len(string2)) feature.append(len(string1) - len(string2)) feature.append(len(tokenizers.whitespace(string1))) feature.append(len(tokenizers.whitespace(string2))) # product_long_description string1, string2 = stage3_helper.get_attribute_from_jsons( json1, json2, product_long_description) if string1 is None or string2 is None: feature.append(0.5) feature.append(0) feature.append(0) feature.append(0) feature.append(0) # feature.append(0) # feature.append(0) # feature.append(0) # feature.append(0) else: string1 = string1.lower() string2 = string2.lower() string1 = stage3_helper.cleanhtml(string1) string2 = stage3_helper.cleanhtml(string2) string1 = stage3_helper.clean_stop_word(string1) string2 = stage3_helper.clean_stop_word(string2) feature.append( simfunctions.jaccard(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) # feature.append(simfunctions.jaro_winkler(string1, string2, prefix_weight=0.1)) feature.append( simfunctions.overlap_coefficient( tokenizers.whitespace(string1), tokenizers.whitespace(string2))) # feature.append(simfunctions.monge_elkan(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) # feature.append(simfunctions.tfidf(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append(len(string1)) feature.append(len(string2)) feature.append(len(string1) - len(string2)) # product_brand string1, string2 = stage3_helper.get_attribute_from_jsons( json1, json2, product_brand) string1_name, string2_name = stage3_helper.get_attribute_from_jsons( json1, json2, product_name) if string1 is None or string1 == '': string1 = get_predict_brand(string1_name) if string2 is None or string2 == '': string2 = get_predict_brand(string2_name) if string1 is None or string2 is None: feature.append(0) feature.append(0) feature.append(0) feature.append(0) feature.append(0) feature.append(0) feature.append(0) feature.append(0) else: feature.append( simfunctions.jaccard(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append( simfunctions.jaro_winkler(string1, string2, prefix_weight=0.1)) feature.append( simfunctions.overlap_coefficient( tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append( simfunctions.monge_elkan(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append( simfunctions.tfidf(tokenizers.whitespace(string1), tokenizers.whitespace(string2))) feature.append(len(string1)) feature.append(len(string2)) feature.append(len(string1) - len(string2)) #feature.append(simfunctions.jaro(tokenizers.whitespace(string1)[0], tokenizers.whitespace(string2)[0])) # Contains similar model names. string1, string2 = stage3_helper.get_attribute_from_jsons( json1, json2, product_name) string1 = string1.lower() string2 = string2.lower() model_strs1 = stage3_helper.find_model_str(string1) model_strs2 = stage3_helper.find_model_str(string2) # share_model_str = False # for model in model_strs1: # if model.lower() in string2.lower(): # share_model_str = True # for model in model_strs2: # if model.lower() in string1.lower(): # share_model_str = True # if share_model_str: # feature.append(1) # else: # feature.append(0) if len(model_strs1) > 0 and len(model_strs2) > 0: feature.append(simfunctions.jaccard(model_strs1, model_strs2)) else: feature.append(0.5) feature.append(len(model_strs1)) feature.append(len(model_strs2)) feature.append(len(model_strs1) - len(string2)) # Other features. common = 0 common_score = 0.0 for item in json1: if item in json2: common += 1 common_score += simfunctions.jaccard( tokenizers.whitespace(json1[item][0]), tokenizers.whitespace(json2[item][0])) common_score = common_score / common feature.append(len(json1)) feature.append(len(json2)) feature.append(len(json1) - len(json2)) feature.append(common) feature.append(common_score) feature.append(len(json.dumps(json1))) feature.append(len(json.dumps(json2))) feature.append(len(json.dumps(json1)) - len(json.dumps(json2))) feature.append( simfunctions.jaccard(tokenizers.whitespace(json.dumps(json1)), tokenizers.whitespace(json.dumps(json2)))) # Add one feature and label. features.append(feature) labels.append(stage3_helper.get_01_from_label(label)) return features, labels, lines
def time_medium_long(self): simfunctions.jaro_winkler(_medium_string_1, _long_string_1)
def time_short_long(self): simfunctions.jaro_winkler(_short_string_1, _long_string_1)
def time_short_medium(self): simfunctions.jaro_winkler(_short_string_1, _medium_string_1)
def time_long_long(self): simfunctions.jaro_winkler(_long_string_1, _long_string_2)
def time_medium_medium(self): simfunctions.jaro_winkler(_medium_string_1, _medium_string_2)
def time_short_short(self): simfunctions.jaro_winkler(_short_string_1, _short_string_2)