def test_valid_input(self):
self.assertEqual(cosine(['data', 'science'], ['data']), 1.0 / (math.sqrt(2) * math.sqrt(1)))
self.assertEqual(cosine(['data', 'science'], ['science', 'good']),
1.0 / (math.sqrt(2) * math.sqrt(2)))
self.assertEqual(cosine([], ['data']), 0.0)
self.assertEqual(cosine(['data', 'data', 'science'], ['data', 'management']),
1.0 / (math.sqrt(2) * math.sqrt(2)))
self.assertEqual(cosine(['data', 'management'], ['data', 'data', 'science']),
1.0 / (math.sqrt(2) * math.sqrt(2)))
self.assertEqual(cosine([], []), 1.0)
self.assertEqual(cosine(set([]), set([])), 1.0)
self.assertEqual(cosine({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8}),
3.0 / (math.sqrt(4) * math.sqrt(7)))
def test_valid_input(self):
self.assertEqual(cosine(['data', 'science'], ['data']),
1.0 / (math.sqrt(2) * math.sqrt(1)))
self.assertEqual(cosine(['data', 'science'], ['science', 'good']),
1.0 / (math.sqrt(2) * math.sqrt(2)))
self.assertEqual(cosine([], ['data']), 0.0)
self.assertEqual(
cosine(['data', 'data', 'science'], ['data', 'management']),
1.0 / (math.sqrt(2) * math.sqrt(2)))
self.assertEqual(
cosine(['data', 'management'], ['data', 'data', 'science']),
1.0 / (math.sqrt(2) * math.sqrt(2)))
self.assertEqual(cosine([], []), 1.0)
self.assertEqual(cosine(set([]), set([])), 1.0)
self.assertEqual(cosine({1, 1, 2, 3, 4}, {2, 3, 4, 5, 6, 7, 7, 8}),
3.0 / (math.sqrt(4) * math.sqrt(7)))
def test_invalid_input3(self):
cosine(None, None)
def test_invalid_input2(self):
cosine(None, ['b'])
def test_invalid_input4(self):
cosine(['a'], None)
def test_invalid_input1(self):
cosine(1, 1)
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 test_invalid_input3(self):
cosine(None, None)
def test_invalid_input2(self):
cosine(None, ['b'])
def test_invalid_input4(self):
cosine(['a'], None)
def test_invalid_input1(self):
cosine(1, 1)
from py_stringmatching import simfunctions, tokenizers
prodNames = []
cosineMeasure = []
with open('set_X.txt', 'r') as f:
for line in f:
line = unicode(line,
errors='ignore') # For character which are not utf-8
data = line.strip().split('?')
pairId = data[0]
prod1_id = data[1]
if (data[2]):
prod1_json = json.loads(data[2])
else:
prod1_json = dict()
prod2_id = data[3]
if (data[4]):
prod2_json = json.loads(data[4])
else:
prod2_json = dict()
label = data[5]
prodNames.append(
(prod1_json['Product Name'][0], prod2_json['Product Name'][0]))
f.close()
for pair in prodNames:
measure = simfunctions.cosine(tokenizers.whitespace(pair[0]),
tokenizers.whitespace(pair[1]))
cosineMeasure.append(measure)
print cosineMeasure
def time_medium_large_wi_rep(self):
simfunctions.cosine(_med_num_tokens_wo_rep, _large_num_tokens_wo_rep)
def time_small_large_wi_rep(self):
simfunctions.cosine(_small_num_tokens_wi_rep, _large_num_tokens_wi_rep)
def time_small_medium_wi_rep(self):
simfunctions.cosine(_small_num_tokens_wi_rep, _med_num_tokens_wi_rep)
def time_small_small_wo_rep(self):
simfunctions.cosine(_small_num_tokens_wo_rep, _small_num_tokens_wo_rep)
