def go(self):
y_train = self.dfAll["relevance"].values[:TRAIN_SIZE]
for obs_field in self.obs_fields:
if obs_field not in self.dfAll.columns:
self.logger.info("Skip %s"%obs_field)
continue
obs_corpus = self.dfAll[obs_field].values
for target_field in self.target_fields:
if target_field not in self.dfAll.columns:
self.logger.info("Skip %s"%target_field)
continue
target_corpus = self.dfAll[target_field].values
ext = self.generator(obs_corpus, target_corpus, *self.param_list)
x = ext.transform()
if isinstance(ext.__name__(), list):
for i,feat_name in enumerate(ext.__name__()):
dim = 1
fname = "%s_%s_x_%s_%dD"%(feat_name, obs_field, target_field, dim)
pkl_utils._save(os.path.join(self.feat_dir, fname+config.FEAT_FILE_SUFFIX), x[:,i])
corr = np_utils._corr(x[:TRAIN_SIZE,i], y_train)
self.logger.info("%s (%dD): corr = %.6f"%(fname, dim, corr))
else:
dim = np_utils._dim(x)
fname = "%s_%s_x_%s_%dD"%(ext.__name__(), obs_field, target_field, dim)
pkl_utils._save(os.path.join(self.feat_dir, fname+config.FEAT_FILE_SUFFIX), x)
if dim == 1:
corr = np_utils._corr(x[:TRAIN_SIZE], y_train)
self.logger.info("%s (%dD): corr = %.6f"%(fname, dim, corr))
elif self.force_corr:
for j in range(dim):
corr = np_utils._corr(x[:TRAIN_SIZE,j], y_train)
self.logger.info("%s (%d/%dD): corr = %.6f"%(fname, j+1, dim, corr))
def Article2Template(lang="en"):
print "[%s]: generate article2template dict for language %s" % (time_utils._timestamp(), lang)
infile = open(config.ARTICLE_TEMPLATES[lang])
prefix = config.LANG_PREFIX[lang]
len_prefix = len(prefix)
articleDict = {}
for line in infile.readlines():
if line[0] != "<":
continue
row = line.split()
article = row[0][1:-1]
template = row[2][1:-1]
article = article[len_prefix:]
template = template[len_prefix:]
if "/" in template:
continue
if article in articleDict:
articleDict[article].append(template)
else:
articleDict[article] = [template, ]
print "%d articles in total" % len(articleDict)
pkl_utils._save(config.ARTICLE2TEMPLATE[lang], articleDict)
print "[%s]: generation complete" % time_utils._timestamp()
def convert(self):
dfAll = pd.read_csv(self.fname)
columns_to_drop = ["id", "product_uid", "relevance", "search_term", "product_title"]
columns_to_drop = [col for col in columns_to_drop if col in dfAll.columns]
dfAll.drop(columns_to_drop, axis=1, inplace=True)
for col in dfAll.columns:
pkl_utils._save("%s/TuringTest_%s_%s.pkl"%(config.FEAT_DIR, self.name, col), dfAll[col].values)
def main():
dfTrain = pd.read_csv(config.TRAIN_DATA, encoding="ISO-8859-1")
dfTest = pd.read_csv(config.TEST_DATA, encoding="ISO-8859-1")
# splits for level1
splitter = HomedepotSplitter(dfTrain=dfTrain,
dfTest=dfTest,
n_iter=config.N_RUNS,
random_state=config.RANDOM_SEED,
verbose=True,
plot=True,
# tune these params to get a close distribution
split_param=[0.5, 0.25, 0.5],
)
splitter.split()
splitter.save("%s/splits_level1.pkl"%config.SPLIT_DIR)
splits_level1 = splitter.splits
## splits for level2
splits_level1 = pkl_utils._load("%s/splits_level1.pkl"%config.SPLIT_DIR)
splits_level2 = [0]*config.N_RUNS
for run, (trainInd, validInd) in enumerate(splits_level1):
dfValid = dfTrain.iloc[validInd].copy()
splitter2 = HomedepotSplitter(dfTrain=dfValid,
dfTest=dfTest,
n_iter=1,
random_state=run,
verbose=True,
# tune these params to get a close distribution
split_param=[0.5, 0.15, 0.6])
splitter2.split()
splits_level2[run] = splitter2.splits[0]
pkl_utils._save("%s/splits_level2.pkl"%config.SPLIT_DIR, splits_level2)
## splits for level3
splits_level2 = pkl_utils._load("%s/splits_level2.pkl"%config.SPLIT_DIR)
splits_level3 = [0]*config.N_RUNS
for run, (trainInd, validInd) in enumerate(splits_level2):
dfValid = dfTrain.iloc[validInd].copy()
splitter3 = HomedepotSplitter(dfTrain=dfValid,
dfTest=dfTest,
n_iter=1,
random_state=run,
verbose=True,
# tune these params to get a close distribution
split_param=[0.5, 0.15, 0.7])
splitter3.split()
splits_level3[run] = splitter3.splits[0]
pkl_utils._save("%s/splits_level3.pkl"%config.SPLIT_DIR, splits_level3)
def main():
fnames = [
"TSNE_LSA100_Word_Unigram_Pair_search_term_x_product_title_100D",
"TSNE_LSA100_Word_Bigram_Pair_search_term_x_product_title_100D",
"TSNE_LSA100_Word_Obs_Unigram_Target_Unigram_Cooc_search_term_x_product_title_100D",
"TSNE_LSA100_Word_Obs_Unigram_Target_Bigram_Cooc_search_term_x_product_title_100D",
]
fnames = [os.path.join(config.FEAT_DIR, fname+".csv") for fname in fnames]
for fname in fnames:
df = pd.read_csv(fname, index=False)
f = df.values
pkl_utils._save(fname[:-4]+".pkl", f)
def save(self):
data_dict = {
"X_train_basic": self.X_train_basic,
"y_train_cv": self.y_train_cv,
"X_train_cv": self.X_train_cv,
"X_test": self.X_test,
"id_test": self.id_test,
"splitter_prev": self.splitter_prev,
"splitter": self.splitter,
"n_iter": self.n_iter,
"has_basic": self.has_basic,
}
fname = os.path.join(config.FEAT_DIR+"/Combine", self.feature_name+config.FEAT_FILE_SUFFIX)
pkl_utils._save(fname, data_dict)
self.logger.info("Save to %s" % fname)
def main():
logname = "generate_feature_query_quality_%s.log"%time_utils._timestamp()
logger = logging_utils._get_logger(config.LOG_DIR, logname)
obs_corpus = []
query_suffix = []
# raw
dfAll = pkl_utils._load(config.ALL_DATA_RAW)
obs_corpus.append(dfAll["search_term"].values)
query_suffix.append("raw")
# after processing
dfAll = pkl_utils._load(config.ALL_DATA_LEMMATIZED)
obs_corpus.append(dfAll["search_term"].values)
query_suffix.append("lemmatized")
# after extracting product_name in search_term
obs_corpus.append(dfAll["search_term_product_name"].values)
query_suffix.append("product_name")
if "search_term_auto_corrected" in dfAll.columns:
# after auto correction
obs_corpus.append(dfAll["search_term_auto_corrected"].values)
query_suffix.append("corrected")
# after stemming
dfAll = pkl_utils._load(config.ALL_DATA_LEMMATIZED_STEMMED)
obs_corpus.append(dfAll["search_term"].values)
query_suffix.append("stemmed")
y_train = dfAll["relevance"].values[:TRAIN_SIZE]
for i in range(len(query_suffix)-1):
for j in range(i+1, len(query_suffix)):
ext = QueryQuality(obs_corpus[i], obs_corpus[j])
x = ext.transform()
dim = 1
fname = "%s_%s_x_%s_%dD"%(ext._get_feat_name(), query_suffix[i], query_suffix[j], dim)
pkl_utils._save(os.path.join(config.FEAT_DIR, fname+config.FEAT_FILE_SUFFIX), x)
corr = np_utils._corr(x[:TRAIN_SIZE], y_train)
logger.info("%s (%dD): corr = %.6f"%(fname, dim, corr))
# raw
dfAll = pkl_utils._load(config.ALL_DATA_RAW)
obs_fields = ["search_term"]
param_list = []
sf = StandaloneFeatureWrapper(IsInGoogleDict, dfAll, obs_fields, param_list, config.FEAT_DIR, logger)
sf.go()
def go(self):
y_train = self.dfAll["is_duplicate"].values[:TRAIN_SIZE]
for obs_field in self.obs_fields:
if obs_field not in self.dfAll.columns:
self.logger.info("Skip %s" % obs_field)
continue
obs_corpus = self.dfAll[obs_field].values
for target_field in self.target_fields:
if target_field not in self.dfAll.columns:
self.logger.info("Skip %s" % target_field)
continue
target_corpus = self.dfAll[target_field].values
ext = self.generator(obs_corpus, target_corpus,
*self.param_list)
x = ext.transform()
if isinstance(ext.__name__(), list):
for i, feat_name in enumerate(ext.__name__()):
dim = 1
fname = "%s_%s_x_%s_%dD" % (feat_name, obs_field,
target_field, dim)
pkl_utils._save(
os.path.join(self.feat_dir,
fname + config.FEAT_FILE_SUFFIX),
x[:, i])
corr = np_utils._corr(x[:TRAIN_SIZE, i], y_train)
self.logger.info("%s (%dD): corr = %.6f" %
(fname, dim, corr))
else:
dim = np_utils._dim(x)
fname = "%s_%s_x_%s_%dD" % (ext.__name__(), obs_field,
target_field, dim)
pkl_utils._save(
os.path.join(self.feat_dir,
fname + config.FEAT_FILE_SUFFIX), x)
if dim == 1:
corr = np_utils._corr(x[:TRAIN_SIZE], y_train)
self.logger.info("%s (%dD): corr = %.6f" %
(fname, dim, corr))
elif self.force_corr:
for j in range(dim):
corr = np_utils._corr(x[:TRAIN_SIZE, j], y_train)
self.logger.info("%s (%d/%dD): corr = %.6f" %
(fname, j + 1, dim, corr))
def main():
logname = "generate_feature_query_quality_%s.log"%time_utils._timestamp()
logger = logging_utils._get_logger(config.LOG_DIR, logname)
obs_corpus = []
query_suffix = []
# raw
dfAll = pkl_utils._load(config.ALL_DATA_RAW)
obs_corpus.append(dfAll["search_term"].values)
query_suffix.append("raw")
# after processing
dfAll = pkl_utils._load(config.ALL_DATA_LEMMATIZED)
obs_corpus.append(dfAll["search_term"].values)
query_suffix.append("lemmatized")
# after extracting product_name in search_term
obs_corpus.append(dfAll["search_term_product_name"].values)
query_suffix.append("product_name")
if "search_term_auto_corrected" in dfAll.columns:
# after auto correction
obs_corpus.append(dfAll["search_term_auto_corrected"].values)
query_suffix.append("corrected")
# after stemming
dfAll = pkl_utils._load(config.ALL_DATA_LEMMATIZED_STEMMED)
obs_corpus.append(dfAll["search_term"].values)
query_suffix.append("stemmed")
y_train = dfAll["relevance"].values[:TRAIN_SIZE]
for i in range(len(query_suffix)-1):
for j in range(i+1, len(query_suffix)):
ext = QueryQuality(obs_corpus[i], obs_corpus[j])
x = ext.transform()
dim = np_utils._dim(x)
fname = "%s_%s_x_%s_%dD"%(ext.__name__(), query_suffix[i], query_suffix[j], dim)
pkl_utils._save(os.path.join(config.FEAT_DIR, fname+config.FEAT_FILE_SUFFIX), x)
corr = np_utils._corr(x[:TRAIN_SIZE], y_train)
logger.info("%s (%dD): corr = %.6f"%(fname, dim, corr))
# raw
dfAll = pkl_utils._load(config.ALL_DATA_RAW)
obs_fields = ["search_term"]
param_list = []
sf = StandaloneFeatureWrapper(IsInGoogleDict, dfAll, obs_fields, param_list, config.FEAT_DIR, logger)
sf.go()
def main():
dfTrain = pd.read_csv(config.TRAIN_DATA)
dfTest = pd.read_csv(config.TEST_DATA)
# splits for level1
splitter = QuoraSplitter(dfTrain=dfTrain,
dfTest=dfTest,
n_iter=config.N_RUNS,
random_state=config.RANDOM_SEED,
verbose=True)
splitter.split()
splitter.save(config.SPLIT_DIR + "/splits_level1.pkl")
## splits for level2
splits_level1 = pkl_utils._load(config.SPLIT_DIR + "/splits_level1.pkl")
splits_level2 = [0] * config.N_RUNS
for run, (trainInd, validInd) in enumerate(splits_level1):
dfValid = dfTrain.iloc[validInd].copy()
splitter2 = QuoraSplitter(dfTrain=dfValid,
dfTest=dfTest,
n_iter=config.N_RUNS,
random_state=run,
verbose=True)
splitter2.split()
splits_level2[run] = splitter2.splits[-1]
pkl_utils._save(config.SPLIT_DIR + "/splits_level2.pkl", splits_level2)
## splits for level3
splits_level2 = pkl_utils._load(config.SPLIT_DIR + "/splits_level2.pkl")
splits_level3 = [0] * config.N_RUNS
for run, (trainInd, validInd) in enumerate(splits_level2):
dfValid = dfTrain.iloc[validInd].copy()
splitter3 = QuoraSplitter(dfTrain=dfValid,
dfTest=dfTest,
n_iter=config.N_RUNS,
random_state=run,
verbose=True)
splitter3.split()
splits_level3[run] = splitter3.splits[-1]
pkl_utils._save(config.SPLIT_DIR + "/splits_level3.pkl", splits_level3)
def main():
print "[%s]: generate ontology hierarchy tree" % (time_utils._timestamp())
G = g.Graph()
G.parse(config.ONTOLOGY, format="n3")
q = '''
PREFIX rr: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?child ?parent
WHERE {
?child rr:subClassOf ?parent .
}'''
results = G.query(q)
ontologyDict = {}
for row in results:
child = str(row[0])
parent = str(row[1])
if parent in ontologyDict:
ontologyDict[parent].append(child)
else:
ontologyDict[parent] = [child,]
pkl_utils._save(config.ONTOLOGY_TREE, ontologyDict)
print "[%s]: generation complete" % time_utils._timestamp()
def getILL(lang, target):
print "[%s]: generate ILL dict from language %s to language %s" % (time_utils._timestamp(), lang, target)
infile = open(config.ILL[lang])
prefix1 = config.LANG_PREFIX[lang]
prefix2 = config.LANG_PREFIX[target]
len1 = len(prefix1)
len2 = len(prefix2)
linkDict = {}
for line in infile.readlines():
if line[0] != "<":
continue
row = line.split()
lang1 = row[0][1:-1]
lang2 = row[2][1:-1]
if prefix1 not in lang1:
continue
if prefix2 not in lang2:
continue
lang1 = lang1[len1:]
lang2 = lang2[len2:]
linkDict[lang1] = lang2
print "%d links in total" % len(linkDict)
pkl_utils._save(config.ILL_DICT["%s2%s" % (lang, target)], linkDict)
print "[%s]: generation complete" % time_utils._timestamp()
def main():
FNAME = "feature_text"
logname = "%s_%s.log" % (FNAME, now)
logger = logging_utils._get_logger(config.LOG_DIR, logname)
stop_words = set(stopwords.words('russian'))
train, test = dl.load_data()
logger.info("Generating title & description text features ...")
t0 = time()
# Generating text features for title
tfidf_title = TfidfVectorizer(stop_words=stop_words,
max_features=config.MAX_TEXT_FEATURES)
tfidf_description = TfidfVectorizer(stop_words=stop_words,
max_features=config.MAX_TEXT_FEATURES)
train['description'] = train['description'].fillna(' ')
test['description'] = test['description'].fillna(' ')
train['title'] = train['title'].fillna(' ')
test['title'] = test['title'].fillna(' ')
tfidf_title.fit(pd.concat([train['description'], train['description']]))
tfidf_description.fit(pd.concat([test['title'], test['title']]))
train_title_tfidf = tfidf_title.transform(train['title'])
test_title_tfidf = tfidf_title.transform(test['title'])
train_description_tfidf = tfidf_description.transform(train['description'])
test_description_tfidf = tfidf_description.transform(test['description'])
svd_title = TruncatedSVD(n_components=config.SVD_N_COMP,
algorithm='arpack')
svd_title.fit(
tfidf_title.transform(pd.concat([train['title'], test['title']])))
svd_description = TruncatedSVD(n_components=config.SVD_N_COMP,
algorithm='arpack')
svd_description.fit(
tfidf_description.transform(
pd.concat([train['description'], test['description']])))
train_description_svd = pd.DataFrame(
svd_description.transform(train_description_tfidf))
test_description_svd = pd.DataFrame(
svd_description.transform(test_description_tfidf))
train_description_svd.columns = [
'svd_description_' + str(i + 1) for i in range(config.SVD_N_COMP)
]
test_description_svd.columns = [
'svd_description_' + str(i + 1) for i in range(config.SVD_N_COMP)
]
train_title_svd = pd.DataFrame(svd_title.transform(train_title_tfidf))
test_title_svd = pd.DataFrame(svd_title.transform(test_title_tfidf))
train_title_svd.columns = [
'svd_title_' + str(i + 1) for i in range(config.SVD_N_COMP)
]
test_title_svd.columns = [
'svd_title_' + str(i + 1) for i in range(config.SVD_N_COMP)
]
gc.collect()
logger.info(FNAME + ' took: %s minutes' % round((time() - t0) / 60, 1))
logger.info('Train SVD title shape: %s & Test SVD title shape: %s' %
(train_title_svd.shape, test_title_svd.shape))
logger.info(
'Train SVD description shape: %s & Test SVD description shape: %s' %
(train_description_svd.shape, test_description_svd.shape))
# save data
train_fname = os.path.join(config.DATA_FEATURES_DIR,
"train_" + FNAME + config.FEAT_FILE_SUFFIX)
test_fname = os.path.join(config.DATA_FEATURES_DIR,
"test_" + FNAME + config.FEAT_FILE_SUFFIX)
logger.info("Save to %s" % train_fname)
pkl_utils._save(
train_fname, pd.concat([train_title_svd, train_description_svd],
axis=1))
logger.info("Save to %s" % test_fname)
pkl_utils._save(test_fname,
pd.concat([test_title_svd, test_description_svd], axis=1))
gc.collect()
def main():
logname = "generate_feature_group_distance_stat_%s.log"%time_utils._timestamp()
logger = logging_utils._get_logger(config.LOG_DIR, logname)
dfAll = pkl_utils._load(config.ALL_DATA_LEMMATIZED_STEMMED)
y_train = dfAll["relevance"].values[:TRAIN_SIZE]
group_id_names = ["DocId_search_term", "DocId_product_title", "DocIdEcho_product_uid"]
match_list = [
"MatchQueryCount",
"MatchQueryRatio",
"LongestMatchRatio",
]
tfidf_list = [
"StatCoocTF_Unigram_Mean",
"StatCoocTF_Unigram_Max",
"StatCoocTF_Unigram_Min",
# "StatCoocNormTF_Unigram_Mean",
# "StatCoocNormTF_Unigram_Max",
# "StatCoocNormTF_Unigram_Min",
"StatCoocTFIDF_Unigram_Mean",
"StatCoocTFIDF_Unigram_Max",
"StatCoocTFIDF_Unigram_Min",
"StatCoocBM25_Unigram_Mean",
"StatCoocBM25_Unigram_Max",
"StatCoocBM25_Unigram_Min",
# "StatCoocTF_Bigram_Mean",
# "StatCoocTF_Bigram_Max",
# "StatCoocTF_Bigram_Min",
# "StatCoocNormTF_Bigram_Mean",
# "StatCoocNormTF_Bigram_Max",
# "StatCoocNormTF_Bigram_Min",
# "StatCoocTFIDF_Bigram_Mean",
# "StatCoocTFIDF_Bigram_Max",
# "StatCoocTFIDF_Bigram_Min",
# "StatCoocBM25_Bigram_Mean",
# "StatCoocBM25_Bigram_Max",
# "StatCoocBM25_Bigram_Min",
# "StatCoocTF_Trigram_Mean",
# "StatCoocTF_Trigram_Max",
# "StatCoocTF_Trigram_Min",
# "StatCoocNormTF_Trigram_Mean",
# "StatCoocNormTF_Trigram_Max",
# "StatCoocNormTF_Trigram_Min",
# "StatCoocTFIDF_Trigram_Mean",
# "StatCoocTFIDF_Trigram_Max",
# "StatCoocTFIDF_Trigram_Min",
# "StatCoocBM25_Trigram_Mean",
# "StatCoocBM25_Trigram_Max",
# "StatCoocBM25_Trigram_Min",
]
intersect_ngram_count_list = [
"IntersectCount_Unigram",
"IntersectRatio_Unigram",
# "IntersectCount_Bigram",
# "IntersectRatio_Bigram",
# "IntersectCount_Trigram",
# "IntersectRatio_Trigram",
]
first_last_ngram_list = [
"FirstIntersectCount_Unigram",
"FirstIntersectRatio_Unigram",
"LastIntersectCount_Unigram",
"LastIntersectRatio_Unigram",
# "FirstIntersectCount_Bigram",
# "FirstIntersectRatio_Bigram",
# "LastIntersectCount_Bigram",
# "LastIntersectRatio_Bigram",
# "FirstIntersectCount_Trigram",
# "FirstIntersectRatio_Trigram",
# "LastIntersectCount_Trigram",
# "LastIntersectRatio_Trigram",
]
cooccurrence_ngram_count_list = [
"CooccurrenceCount_Unigram",
"CooccurrenceRatio_Unigram",
# "CooccurrenceCount_Bigram",
# "CooccurrenceRatio_Bigram",
# "CooccurrenceCount_Trigram",
# "CooccurrenceRatio_Trigram",
]
ngram_jaccard_list = [
"JaccardCoef_Unigram",
# "JaccardCoef_Bigram",
# "JaccardCoef_Trigram",
"DiceDistance_Unigram",
# "DiceDistance_Bigram",
# "DiceDistance_Trigram",
]
char_dist_sim_list = [
"CharDistribution_CosineSim",
"CharDistribution_KL",
]
tfidf_word_ngram_cosinesim_list = [
"TFIDF_Word_Unigram_CosineSim",
# "TFIDF_Word_Bigram_CosineSim",
# "TFIDF_Word_Trigram_CosineSim",
]
tfidf_char_ngram_cosinesim_list = [
# "TFIDF_Char_Bigram_CosineSim",
# "TFIDF_Char_Trigram_CosineSim",
"TFIDF_Char_Fourgram_CosineSim",
# "TFIDF_Char_Fivegram_CosineSim",
]
lsa_word_ngram_cosinesim_list = [
"LSA100_Word_Unigram_CosineSim",
# "LSA100_Word_Bigram_CosineSim",
# "LSA100_Word_Trigram_CosineSim",
]
lsa_char_ngram_cosinesim_list = [
# "LSA100_Char_Bigram_CosineSim",
# "LSA100_Char_Trigram_CosineSim",
"LSA100_Char_Fourgram_CosineSim",
# "LSA100_Char_Fivegram_CosineSim",
]
doc2vec_list = [
"Doc2Vec_Homedepot_D100_CosineSim",
]
word2vec_list = [
"Word2Vec_N_Similarity",
"Word2Vec_Homedepot_D100_CosineSim_Mean_Mean",
"Word2Vec_Homedepot_D100_CosineSim_Max_Mean",
"Word2Vec_Homedepot_D100_CosineSim_Min_Mean",
]
distance_generator_list = \
match_list + \
tfidf_list + \
intersect_ngram_count_list + \
first_last_ngram_list + \
cooccurrence_ngram_count_list + \
ngram_jaccard_list + \
tfidf_word_ngram_cosinesim_list + \
tfidf_char_ngram_cosinesim_list + \
lsa_word_ngram_cosinesim_list + \
lsa_char_ngram_cosinesim_list + \
char_dist_sim_list + \
word2vec_list + \
doc2vec_list
obs_fields_list = []
target_fields_list = []
## query in document
obs_fields_list.append( ["search_term"] )
target_fields_list.append( ["product_title", "product_title_product_name"] )
aggregation_mode = ["mean", "max", "min"]
for group_id_name in group_id_names:
group_id_list = pkl_utils._load(os.path.join(config.FEAT_DIR, group_id_name+"_1D.pkl"))
for distance_generator in distance_generator_list:
for obs_fields, target_fields in zip(obs_fields_list, target_fields_list):
for obs_field in obs_fields:
for target_field in target_fields:
dist_name = "%s_%s_x_%s"%(distance_generator, obs_field, target_field)
try:
dist_list = pkl_utils._load(os.path.join(config.FEAT_DIR, dist_name+"_1D.pkl"))
ext = GroupDistanceStat(dist_list, group_id_list, dist_name, group_id_name, aggregation_mode)
x = ext.transform()
if isinstance(ext.__name__(), list):
for i,feat_name in enumerate(ext.__name__()):
dim = 1
fname = "%s_%dD"%(feat_name, dim)
pkl_utils._save(os.path.join(config.FEAT_DIR, fname+config.FEAT_FILE_SUFFIX), x[:,i])
corr = np_utils._corr(x[:TRAIN_SIZE,i], y_train)
logger.info("%s (%dD): corr = %.6f"%(fname, dim, corr))
except:
logger.info("Skip %s"%dist_name)
pass
def main():
logname = "generate_feature_group_distance_stat_%s.log" % time_utils._timestamp(
)
logger = logging_utils._get_logger(config.LOG_DIR, logname)
dfAll = pkl_utils._load(config.ALL_DATA_LEMMATIZED_STEMMED)
y_train = dfAll["relevance"].values[:TRAIN_SIZE]
group_id_names = [
"DocId_search_term", "DocId_product_title", "DocIdEcho_product_uid"
]
match_list = [
"MatchQueryCount",
"MatchQueryRatio",
"LongestMatchRatio",
]
tfidf_list = [
"StatCoocTF_Unigram_Mean",
"StatCoocTF_Unigram_Max",
"StatCoocTF_Unigram_Min",
# "StatCoocNormTF_Unigram_Mean",
# "StatCoocNormTF_Unigram_Max",
# "StatCoocNormTF_Unigram_Min",
"StatCoocTFIDF_Unigram_Mean",
"StatCoocTFIDF_Unigram_Max",
"StatCoocTFIDF_Unigram_Min",
"StatCoocBM25_Unigram_Mean",
"StatCoocBM25_Unigram_Max",
"StatCoocBM25_Unigram_Min",
# "StatCoocTF_Bigram_Mean",
# "StatCoocTF_Bigram_Max",
# "StatCoocTF_Bigram_Min",
# "StatCoocNormTF_Bigram_Mean",
# "StatCoocNormTF_Bigram_Max",
# "StatCoocNormTF_Bigram_Min",
# "StatCoocTFIDF_Bigram_Mean",
# "StatCoocTFIDF_Bigram_Max",
# "StatCoocTFIDF_Bigram_Min",
# "StatCoocBM25_Bigram_Mean",
# "StatCoocBM25_Bigram_Max",
# "StatCoocBM25_Bigram_Min",
# "StatCoocTF_Trigram_Mean",
# "StatCoocTF_Trigram_Max",
# "StatCoocTF_Trigram_Min",
# "StatCoocNormTF_Trigram_Mean",
# "StatCoocNormTF_Trigram_Max",
# "StatCoocNormTF_Trigram_Min",
# "StatCoocTFIDF_Trigram_Mean",
# "StatCoocTFIDF_Trigram_Max",
# "StatCoocTFIDF_Trigram_Min",
# "StatCoocBM25_Trigram_Mean",
# "StatCoocBM25_Trigram_Max",
# "StatCoocBM25_Trigram_Min",
]
intersect_ngram_count_list = [
"IntersectCount_Unigram",
"IntersectRatio_Unigram",
# "IntersectCount_Bigram",
# "IntersectRatio_Bigram",
# "IntersectCount_Trigram",
# "IntersectRatio_Trigram",
]
first_last_ngram_list = [
"FirstIntersectCount_Unigram",
"FirstIntersectRatio_Unigram",
"LastIntersectCount_Unigram",
"LastIntersectRatio_Unigram",
# "FirstIntersectCount_Bigram",
# "FirstIntersectRatio_Bigram",
# "LastIntersectCount_Bigram",
# "LastIntersectRatio_Bigram",
# "FirstIntersectCount_Trigram",
# "FirstIntersectRatio_Trigram",
# "LastIntersectCount_Trigram",
# "LastIntersectRatio_Trigram",
]
cooccurrence_ngram_count_list = [
"CooccurrenceCount_Unigram",
"CooccurrenceRatio_Unigram",
# "CooccurrenceCount_Bigram",
# "CooccurrenceRatio_Bigram",
# "CooccurrenceCount_Trigram",
# "CooccurrenceRatio_Trigram",
]
ngram_jaccard_list = [
"JaccardCoef_Unigram",
# "JaccardCoef_Bigram",
# "JaccardCoef_Trigram",
"DiceDistance_Unigram",
# "DiceDistance_Bigram",
# "DiceDistance_Trigram",
]
char_dist_sim_list = [
"CharDistribution_CosineSim",
"CharDistribution_KL",
]
tfidf_word_ngram_cosinesim_list = [
"TFIDF_Word_Unigram_CosineSim",
# "TFIDF_Word_Bigram_CosineSim",
# "TFIDF_Word_Trigram_CosineSim",
]
tfidf_char_ngram_cosinesim_list = [
# "TFIDF_Char_Bigram_CosineSim",
# "TFIDF_Char_Trigram_CosineSim",
"TFIDF_Char_Fourgram_CosineSim",
# "TFIDF_Char_Fivegram_CosineSim",
]
lsa_word_ngram_cosinesim_list = [
"LSA100_Word_Unigram_CosineSim",
# "LSA100_Word_Bigram_CosineSim",
# "LSA100_Word_Trigram_CosineSim",
]
lsa_char_ngram_cosinesim_list = [
# "LSA100_Char_Bigram_CosineSim",
# "LSA100_Char_Trigram_CosineSim",
"LSA100_Char_Fourgram_CosineSim",
# "LSA100_Char_Fivegram_CosineSim",
]
doc2vec_list = [
"Doc2Vec_Homedepot_D100_CosineSim",
]
word2vec_list = [
"Word2Vec_N_Similarity",
"Word2Vec_Homedepot_D100_CosineSim_Mean_Mean",
"Word2Vec_Homedepot_D100_CosineSim_Max_Mean",
"Word2Vec_Homedepot_D100_CosineSim_Min_Mean",
]
distance_generator_list = \
match_list + \
tfidf_list + \
intersect_ngram_count_list + \
first_last_ngram_list + \
cooccurrence_ngram_count_list + \
ngram_jaccard_list + \
tfidf_word_ngram_cosinesim_list + \
tfidf_char_ngram_cosinesim_list + \
lsa_word_ngram_cosinesim_list + \
lsa_char_ngram_cosinesim_list + \
char_dist_sim_list + \
word2vec_list + \
doc2vec_list
obs_fields_list = []
target_fields_list = []
## query in document
obs_fields_list.append(["search_term"])
target_fields_list.append(["product_title", "product_title_product_name"])
aggregation_mode = ["mean", "max", "min"]
for group_id_name in group_id_names:
group_id_list = pkl_utils._load(
os.path.join(config.FEAT_DIR, group_id_name + "_1D.pkl"))
for distance_generator in distance_generator_list:
for obs_fields, target_fields in zip(obs_fields_list,
target_fields_list):
for obs_field in obs_fields:
for target_field in target_fields:
dist_name = "%s_%s_x_%s" % (distance_generator,
obs_field, target_field)
try:
dist_list = pkl_utils._load(
os.path.join(config.FEAT_DIR,
dist_name + "_1D.pkl"))
ext = GroupDistanceStat(dist_list, group_id_list,
dist_name, group_id_name,
aggregation_mode)
x = ext.transform()
if isinstance(ext.__name__(), list):
for i, feat_name in enumerate(ext.__name__()):
dim = 1
fname = "%s_%dD" % (feat_name, dim)
pkl_utils._save(
os.path.join(
config.FEAT_DIR,
fname + config.FEAT_FILE_SUFFIX),
x[:, i])
corr = np_utils._corr(
x[:TRAIN_SIZE, i], y_train)
logger.info("%s (%dD): corr = %.6f" %
(fname, dim, corr))
except:
logger.info("Skip %s" % dist_name)
pass
def parse(lang="en"):
_log.info("starting parsing")
infile = open(config.INSTANCE_TYPES[lang])
rdf_type = "http://www.w3.org/1999/02/22-rdf-syntax-ns#type"
type_entries = []
entitySet = set()
typeSet = set()
for line in infile.readlines():
if line[0] != "<":
continue
row = line.split()
instance = row[0][1:-1]
ontology = row[2][1:-1]
type_entries.append((instance, ontology))
entitySet.add(ontology)
typeSet.add(ontology)
typeDict = {y:x for x, y in enumerate(typeSet)}
infile.close()
cnt_type = len(entitySet)
_log.info("%d types" % cnt_type)
infile = open(config.OBJECTS[lang])
relationDict = {}
instanceSet = set()
for line in infile.readlines():
if line[0] != "<":
continue
row = line.split()
subject = row[0][1:-1]
predicate = row[1][1:-1]
target = row[2][1:-1]
entitySet.add(subject)
entitySet.add(target)
instanceSet.add(subject)
instanceSet.add(target)
if predicate in relationDict:
relationDict[predicate].append((subject, target))
else:
relationDict[predicate] = [(subject, target)]
instanceDict = {y:x for x, y in enumerate(instanceSet)}
entityDict = {y:x for x, y in enumerate(entitySet)}
infile.close()
cnt_ins = len(instanceSet)
N = len(entitySet)
_log.info("%d instanes" % cnt_ins)
_log.info("%d entites" % N)
tensor = []
predicateDict = {}
cnt = 0
for predicate in relationDict:
entries = relationDict[predicate]
rows = [entityDict[entry[0]] for entry in entries]
cols = [entityDict[entry[1]] for entry in entries]
data = [1 for entry in entries]
mat = spsp.csr_matrix((data, (rows, cols)), (N, N))
tensor.append(mat)
predicateDict[predicate] = cnt
cnt += 1
type_entries = [entry for entry in type_entries if entry[0] in instanceSet]
rows = [entityDict[entry[0]] for entry in type_entries]
cols = [entityDict[entry[1]] for entry in type_entries]
data = [1 for entry in type_entries]
mat = spsp.csr_matrix((data, (rows, cols)), (N, N))
tensor.append(mat)
predicateDict[rdf_type] = cnt
_log.info("%d relations" % (cnt+1))
pkl_utils._save(config.TENSOR[lang], tensor)
pkl_utils._save(config.ENTITY[lang], entityDict)
pkl_utils._save(config.PREDICATE[lang], predicateDict)
pkl_utils._save(config.INSTANCE[lang], instanceDict)
pkl_utils._save(config.TYPE[lang], typeDict)
pkl_utils._save(config.TYPE_MATRIX[lang], (rows, cols))
_log.info("parsing complete")
def main():
# load provided data
dfTrain = pd.read_csv(config.TRAIN_DATA, encoding="ISO-8859-1")
dfTest = pd.read_csv(config.TEST_DATA, encoding="ISO-8859-1")
dfAttr = pd.read_csv(config.ATTR_DATA)
dfDesc = pd.read_csv(config.DESC_DATA)
#
print("Train Mean: %.6f" % np.mean(dfTrain["relevance"]))
print("Train Var: %.6f" % np.var(dfTrain["relevance"]))
#
dfTest["relevance"] = np.zeros((config.TEST_SIZE))
dfAttr.dropna(how="all", inplace=True)
dfAttr["value"] = dfAttr["value"].astype(str)
# concat train and test
dfAll = pd.concat((dfTrain, dfTest), ignore_index=True)
del dfTrain
del dfTest
gc.collect()
# merge product description
dfAll = pd.merge(dfAll, dfDesc, on="product_uid", how="left")
dfAll.fillna(config.MISSING_VALUE_STRING, inplace=True)
del dfDesc
gc.collect()
# merge product brand
dfBrand = dfAttr[dfAttr.name == "MFG Brand Name"][[
"product_uid", "value"
]].rename(columns={"value": "product_brand"})
dfAll = pd.merge(dfAll, dfBrand, on="product_uid", how="left")
dfBrand["product_brand"] = dfBrand["product_brand"].values.astype(str)
dfAll.fillna(config.MISSING_VALUE_STRING, inplace=True)
del dfBrand
gc.collect()
# merge product color
color_columns = [
"product_color", "Color Family", "Color/Finish", "Color/Finish Family"
]
dfColor = dfAttr[dfAttr.name.isin(color_columns)][[
"product_uid", "value"
]].rename(columns={"value": "product_color"})
dfColor.dropna(how="all", inplace=True)
_agg_color = lambda df: " ".join(list(set(df["product_color"])))
dfColor = dfColor.groupby("product_uid").apply(_agg_color)
dfColor = dfColor.reset_index(name="product_color")
dfColor["product_color"] = dfColor["product_color"].values.astype(str)
dfAll = pd.merge(dfAll, dfColor, on="product_uid", how="left")
dfAll.fillna(config.MISSING_VALUE_STRING, inplace=True)
del dfColor
gc.collect()
# merge product attribute
_agg_attr = lambda df: config.ATTR_SEPARATOR.join(df[
"name"] + config.ATTR_SEPARATOR + df["value"])
dfAttr = dfAttr.groupby("product_uid").apply(_agg_attr)
dfAttr = dfAttr.reset_index(name="product_attribute_concat")
dfAll = pd.merge(dfAll, dfAttr, on="product_uid", how="left")
dfAll.fillna(config.MISSING_VALUE_STRING, inplace=True)
del dfAttr
gc.collect()
# save data
if config.TASK == "sample":
dfAll = dfAll.iloc[:config.SAMPLE_SIZE].copy()
pkl_utils._save(config.ALL_DATA_RAW, dfAll)
# info
dfInfo = dfAll[["id", "relevance"]].copy()
pkl_utils._save(config.INFO_DATA, dfInfo)
def main():
###########
## Setup ##
###########
logname = "data_processor_%s.log" % now
logger = logging_utils._get_logger(config.LOG_DIR, logname)
# put product_attribute_list, product_attribute and product_description first as they are
# quite time consuming to process
columns_to_proc = [
"question1",
"question2",
]
if config.PLATFORM == "Linux":
config.DATA_PROCESSOR_N_JOBS = len(columns_to_proc)
# clean using a list of processors
processors = [
UnicodeConverter(),
LowerCaseConverter(),
# See LowerUpperCaseSplitter and UnitConverter for why we put UnitConverter here
UnitConverter(),
LowerUpperCaseSplitter(),
# WordReplacer(replace_fname=config.WORD_REPLACER_DATA),
LetterLetterSplitter(),
DigitLetterSplitter(),
DigitCommaDigitMerger(),
NumberDigitMapper(),
UnitConverter(),
QuartetCleaner(),
HtmlCleaner(parser="html.parser"),
Lemmatizer(),
]
stemmers = [
Stemmer(stemmer_type="snowball"),
Stemmer(stemmer_type="porter")
][0:1]
## simple test
text = "1/2 inch rubber lep tips Bullet07"
print("Original:")
print(text)
list_processor = ListProcessor(processors)
print("After:")
print(list_processor.process([text]))
#############
## Process ##
#############
## load raw data
dfAll = pkl_utils._load(config.ALL_DATA_RAW)
columns_to_proc = [col for col in columns_to_proc if col in dfAll.columns]
## clean uisng a list of processors
df_processor = DataFrameParallelProcessor(processors,
config.DATA_PROCESSOR_N_JOBS)
df_processor.process(dfAll, columns_to_proc)
# save data
logger.info("Save to %s" % config.ALL_DATA_LEMMATIZED)
pkl_utils._save(config.ALL_DATA_LEMMATIZED, dfAll)
## clean using stemmers
df_processor = DataFrameParallelProcessor(stemmers,
config.DATA_PROCESSOR_N_JOBS)
df_processor.process(dfAll, columns_to_proc)
# save data
logger.info("Save to %s" % config.ALL_DATA_LEMMATIZED_STEMMED)
pkl_utils._save(config.ALL_DATA_LEMMATIZED_STEMMED, dfAll)
def main():
# load provided data
dfTrain = pd.read_csv(config.TRAIN_DATA, encoding="ISO-8859-1")
dfTest = pd.read_csv(config.TEST_DATA, encoding="ISO-8859-1")
dfAttr = pd.read_csv(config.ATTR_DATA)
dfDesc = pd.read_csv(config.DESC_DATA)
#
print("Train Mean: %.6f"%np.mean(dfTrain["relevance"]))
print("Train Var: %.6f"%np.var(dfTrain["relevance"]))
#
dfTest["relevance"] = np.zeros((config.TEST_SIZE))
dfAttr.dropna(how="all", inplace=True)
dfAttr["value"] = dfAttr["value"].astype(str)
# concat train and test
dfAll = pd.concat((dfTrain, dfTest), ignore_index=True)
del dfTrain
del dfTest
gc.collect()
# merge product description
dfAll = pd.merge(dfAll, dfDesc, on="product_uid", how="left")
dfAll.fillna(config.MISSING_VALUE_STRING, inplace=True)
del dfDesc
gc.collect()
# merge product brand
dfBrand = dfAttr[dfAttr.name=="MFG Brand Name"][["product_uid", "value"]].rename(columns={"value": "product_brand"})
dfAll = pd.merge(dfAll, dfBrand, on="product_uid", how="left")
dfBrand["product_brand"] = dfBrand["product_brand"].values.astype(str)
dfAll.fillna(config.MISSING_VALUE_STRING, inplace=True)
del dfBrand
gc.collect()
# merge product color
color_columns = ["product_color", "Color Family", "Color/Finish", "Color/Finish Family"]
dfColor = dfAttr[dfAttr.name.isin(color_columns)][["product_uid", "value"]].rename(columns={"value": "product_color"})
dfColor.dropna(how="all", inplace=True)
_agg_color = lambda df: " ".join(list(set(df["product_color"])))
dfColor = dfColor.groupby("product_uid").apply(_agg_color)
dfColor = dfColor.reset_index(name="product_color")
dfColor["product_color"] = dfColor["product_color"].values.astype(str)
dfAll = pd.merge(dfAll, dfColor, on="product_uid", how="left")
dfAll.fillna(config.MISSING_VALUE_STRING, inplace=True)
del dfColor
gc.collect()
# merge product attribute
_agg_attr = lambda df: config.ATTR_SEPARATOR.join(df["name"] + config.ATTR_SEPARATOR + df["value"])
dfAttr = dfAttr.groupby("product_uid").apply(_agg_attr)
dfAttr = dfAttr.reset_index(name="product_attribute_concat")
dfAll = pd.merge(dfAll, dfAttr, on="product_uid", how="left")
dfAll.fillna(config.MISSING_VALUE_STRING, inplace=True)
del dfAttr
gc.collect()
# save data
if config.TASK == "sample":
dfAll = dfAll.iloc[:config.SAMPLE_SIZE].copy()
pkl_utils._save(config.ALL_DATA_RAW, dfAll)
# info
dfInfo = dfAll[["id","relevance"]].copy()
pkl_utils._save(config.INFO_DATA, dfInfo)
def group(input_data, output_data, if_sample=False):
df = pd.read_csv(input_data,
sep="\t",
names=["r", "e1", "x1", "y1", "e2", "x2", "y2", "s"])
grouped = df.groupby(["r", "e1", "e2"])
words = []
positions = []
heads = []
tails = []
labels = []
cnt = 0
for name, group in grouped:
if if_sample and cnt > 10000:
break
cnt += 1
if cnt % 1000 == 0:
print(cnt)
group = group.reset_index(drop=True)
label = name[0]
head = name[1]
tail = name[2]
size = group.shape[0]
tmp_words = []
tmp_positions = []
for i in range(size):
tmp_words.append(group.s[i])
tmp_positions.append(
[group.x1[i], group.y1[i], group.x2[i], group.y2[i]])
if size < config.BAG_SIZE:
tmp = size
ans_words = tmp_words[:]
ans_positions = tmp_positions[:]
while tmp + size < config.BAG_SIZE:
tmp += size
ans_words += tmp_words
ans_positions += tmp_positions
ans_words += tmp_words[:config.BAG_SIZE - tmp]
ans_positions += tmp_positions[:config.BAG_SIZE - tmp]
words.append(ans_words)
positions.append(ans_positions)
heads.append(head)
tails.append(tail)
labels.append(label)
else:
tmp = 0
while tmp + config.BAG_SIZE < size:
words.append(tmp_words[tmp:tmp + config.BAG_SIZE])
positions.append(tmp_positions[tmp:tmp + config.BAG_SIZE])
heads.append(head)
tails.append(tail)
labels.append(label)
tmp += config.BAG_SIZE
words.append(tmp_words[-config.BAG_SIZE:])
positions.append(tmp_positions[-config.BAG_SIZE:])
heads.append(head)
tails.append(tail)
labels.append(label)
heads = np.array(heads)
tails = np.array(tails)
labels = np.array(labels)
pkl_utils._save(output_data, (words, positions, heads, tails, labels))
def save(self, model_dir, model_name):
fname = os.path.join(model_dir, model_name)
self.model.save(fname)
pkl_utils._save("%s.sent_label" % fname, self.sentences.sent_label)
def parse(lang="en"):
dataDict = {}
infile = open(config.INSTANCE_TYPES[lang])
rdf_type = "http://www.w3.org/1999/02/22-rdf-syntax-ns#type"
type_entries = []
entitySet = set()
typeSet = set()
for line in infile.readlines():
if line[0] != "<":
continue
row = line.split()
instance = row[0][1:-1]
ontology = row[2][1:-1]
type_entries.append((instance, ontology))
entitySet.add(ontology)
typeSet.add(ontology)
typeDict = {y:x for x, y in enumerate(typeSet)}
infile.close()
cnt_type = len(entitySet)
_log.info("%d types" % cnt_type)
infile = open(config.OBJECTS[lang])
relationDict = {}
instanceSet = set()
for line in infile.readlines():
if line[0] != "<":
continue
row = line.split()
subject = row[0][1:-1]
predicate = row[1][1:-1]
target = row[2][1:-1]
entitySet.add(subject)
entitySet.add(target)
instanceSet.add(subject)
instanceSet.add(target)
if predicate in relationDict:
relationDict[predicate].append((subject, target))
else:
relationDict[predicate] = [(subject, target)]
instanceDict = {y:x for x, y in enumerate(instanceSet)}
entityDict = {y:x for x, y in enumerate(entitySet)}
infile.close()
cnt_ins = len(instanceSet)
N = len(entitySet)
_log.info("%d instanes" % cnt_ins)
_log.info("%d entites" % N)
triples = []
predicateDict = {}
cnt = 0
for predicate in relationDict:
entries = relationDict[predicate]
sub = [entityDict[entry[0]] for entry in entries]
obj = [entityDict[entry[1]] for entry in entries]
pred = [cnt for entry in entries]
triples.extend(zip(sub, obj, pred))
predicateDict[cnt] = predicate
cnt += 1
type_entries = [entry for entry in type_entries if entry[0] in instanceSet]
sub = [entityDict[entry[0]] for entry in type_entries]
obj = [entityDict[entry[1]] for entry in type_entries]
pred = [cnt for entry in type_entries]
triples.extend(zip(sub, obj, pred))
predicateDict[cnt] = rdf_type
triples = pd.Series(triples)
_log.info("%d relations" % (cnt+1))
_log.info("%d triples" % len(triples))
dataDict["entities"] = list(entitySet)
dataDict["relations"] = predicateDict.values()
IDX = list(range(len(triples)))
shuffle(IDX)
dataDict["train_subs"] = list(triples[IDX[:-20000]])
dataDict["valid_subs"] = list(triples[IDX[-20000:-10000]])
dataDict["test_subs"] = list(triples[IDX[-10000:]])
pkl_utils._save(config.DATA_DICT[lang], dataDict)
_log.info("train size: %d" % len(dataDict["train_subs"]))
_log.info("valid size: %d" % len(dataDict["valid_subs"]))
_log.info("test size: %d" % len(dataDict["test_subs"]))
_log.info("parsing complete")
dfAttr = dfAttr.reset_index(name="product_attribute_concat")
dfAll = pd.merge(dfAll, dfAttr, on="product_uid", how="left")
dfAll.fillna(config.MISSING_VALUE_STRING, inplace=True)
del dfAttr
gc.collect()
# In[17]:
dfAll.head()
# In[ ]:
# save data
if config.TASK == 'sample':
dfAll = dfAll.iloc[:config.SAMPLE_SIZE].copy(
) # in this case ".copy" is redundant
pkl_utils._save(config.ALL_DATA_RAW, dfAll)
# info
dfInfo = dfAll[['id', 'relevance']].copy()
pkl_utils._save(config.INFO_DATA, dfInfo)
# In[ ]:
if os.path.isfile('data_preparer.ipynb'):
get_ipython().system('jupyter nbconvert --to script data_preparer.ipynb')
# In[ ]:
# In[ ]:
def main():
###########
## Setup ##
###########
logname = "data_processor_%s.log"%now
logger = logging_utils._get_logger(config.LOG_DIR, logname)
# put product_attribute_list, product_attribute and product_description first as they are
# quite time consuming to process
columns_to_proc = [
# # product_attribute_list is very time consuming to process
# # so we just process product_attribute which is of the form
# # attr_name1 | attr_value1 | attr_name2 | attr_value2 | ...
# # and split it into a list afterwards
# "product_attribute_list",
"product_attribute_concat",
"product_description",
"product_brand",
"product_color",
"product_title",
"search_term",
]
if config.PLATFORM == "Linux":
config.DATA_PROCESSOR_N_JOBS = len(columns_to_proc)
# clean using a list of processors
processors = [
LowerCaseConverter(),
# See LowerUpperCaseSplitter and UnitConverter for why we put UnitConverter here
UnitConverter(),
LowerUpperCaseSplitter(),
WordReplacer(replace_fname=config.WORD_REPLACER_DATA),
LetterLetterSplitter(),
DigitLetterSplitter(),
DigitCommaDigitMerger(),
NumberDigitMapper(),
UnitConverter(),
QuartetCleaner(),
HtmlCleaner(parser="html.parser"),
Lemmatizer(),
]
stemmers = [
Stemmer(stemmer_type="snowball"),
Stemmer(stemmer_type="porter")
][0:1]
## simple test
text = "1/2 inch rubber lep tips Bullet07"
print("Original:")
print(text)
list_processor = ListProcessor(processors)
print("After:")
print(list_processor.process([text]))
#############
## Process ##
#############
## load raw data
dfAll = pkl_utils._load(config.ALL_DATA_RAW)
columns_to_proc = [col for col in columns_to_proc if col in dfAll.columns]
## extract product name from search_term and product_title
ext = ProductNameExtractor()
dfAll["search_term_product_name"] = dfAll["search_term"].apply(ext.transform)
dfAll["product_title_product_name"] = dfAll["product_title"].apply(ext.transform)
if config.TASK == "sample":
print(dfAll[["search_term", "search_term_product_name", "product_title_product_name"]])
## clean using GoogleQuerySpellingChecker
# MUST BE IN FRONT OF ALL THE PROCESSING
if config.GOOGLE_CORRECTING_QUERY:
logger.info("Run GoogleQuerySpellingChecker at search_term")
checker = GoogleQuerySpellingChecker()
dfAll["search_term"] = dfAll["search_term"].apply(checker.correct)
## clean uisng a list of processors
df_processor = DataFrameParallelProcessor(processors, config.DATA_PROCESSOR_N_JOBS)
df_processor.process(dfAll, columns_to_proc)
# split product_attribute_concat into product_attribute and product_attribute_list
dfAll["product_attribute"] = dfAll["product_attribute_concat"].apply(_split_attr_to_text)
dfAll["product_attribute_list"] = dfAll["product_attribute_concat"].apply(_split_attr_to_list)
if config.TASK == "sample":
print(dfAll[["product_attribute", "product_attribute_list"]])
# query expansion
if config.QUERY_EXPANSION:
list_processor = ListProcessor(processors)
base_stopwords = set(list_processor.process(list(config.STOP_WORDS)))
qe = QueryExpansion(dfAll, ngram=3, stopwords_threshold=0.9, base_stopwords=base_stopwords)
dfAll["search_term_alt"] = qe.build()
if config.TASK == "sample":
print(dfAll[["search_term", "search_term_alt"]])
# save data
logger.info("Save to %s"%config.ALL_DATA_LEMMATIZED)
columns_to_save = [col for col in dfAll.columns if col != "product_attribute_concat"]
pkl_utils._save(config.ALL_DATA_LEMMATIZED, dfAll[columns_to_save])
## auto correcting query
if config.AUTO_CORRECTING_QUERY:
logger.info("Run AutoSpellingChecker at search_term")
checker = AutoSpellingChecker(dfAll, exclude_stopwords=False, min_len=4)
dfAll["search_term_auto_corrected"] = list(dfAll["search_term"].apply(checker.correct))
columns_to_proc += ["search_term_auto_corrected"]
if config.TASK == "sample":
print(dfAll[["search_term", "search_term_auto_corrected"]])
# save query_correction_map and spelling checker
fname = "%s/auto_spelling_checker_query_correction_map_%s.log"%(config.LOG_DIR, now)
checker.save_query_correction_map(fname)
# save data
logger.info("Save to %s"%config.ALL_DATA_LEMMATIZED)
columns_to_save = [col for col in dfAll.columns if col != "product_attribute_concat"]
pkl_utils._save(config.ALL_DATA_LEMMATIZED, dfAll[columns_to_save])
## clean using stemmers
df_processor = DataFrameParallelProcessor(stemmers, config.DATA_PROCESSOR_N_JOBS)
df_processor.process(dfAll, columns_to_proc)
# split product_attribute_concat into product_attribute and product_attribute_list
dfAll["product_attribute"] = dfAll["product_attribute_concat"].apply(_split_attr_to_text)
dfAll["product_attribute_list"] = dfAll["product_attribute_concat"].apply(_split_attr_to_list)
# query expansion
if config.QUERY_EXPANSION:
list_processor = ListProcessor(stemmers)
base_stopwords = set(list_processor.process(list(config.STOP_WORDS)))
qe = QueryExpansion(dfAll, ngram=3, stopwords_threshold=0.9, base_stopwords=base_stopwords)
dfAll["search_term_alt"] = qe.build()
if config.TASK == "sample":
print(dfAll[["search_term", "search_term_alt"]])
# save data
logger.info("Save to %s"%config.ALL_DATA_LEMMATIZED_STEMMED)
columns_to_save = [col for col in dfAll.columns if col != "product_attribute_concat"]
pkl_utils._save(config.ALL_DATA_LEMMATIZED_STEMMED, dfAll[columns_to_save])
def main():
### 1. Record Time
now = time_utils._timestamp()
###########
## Setup ##
###########
logname = f'data_processor_{now}.log'
logger = logging_utils._get_logger(config.LOG_DIR, logname)
# Put product_attribute_list, product_attribute and product_description first as they are
# quite time consuming to process.
# Choose the columns by check data_preparer.ipynb. In the end, the notebook will show the clean data frame.
columns_to_proc = [
# # product_attribute_list is very time consuming to process
# # so we just process product_attribute which is of the form
# # attr_name1 | attr_value1 | attr_name2 | attr_value2 | ...
# # and split it into a list afterwards
# 'product_attribute_list',
'product_attribute_concat',
'product_description',
'product_brand',
'product_color',
'product_title',
'search_term',
]
if config.PLATFORM == 'Linux':
config.DATA_PROCESSOR_N_JOBS = len(columns_to_proc)
# clean using a list of processors
processors = [
LowerCaseConverter(),
# See LowerUpperCaseSplitter and UnitConverter for why we put UnitConverter here
# 其實沒差，除非能處理掉數字加介係詞 in 的狀況不被替代成單位 in.(inch)
UnitConverter(),
LowerUpperCaseSplitter(),
WordReplacer(replace_fname=config.WORD_REPLACER_DATA),
LetterLetterSplitter(),
DigitLetterSplitter(),
DigitCommaDigitMerger(),
NumberDigitMapper(),
UnitConverter(),
QuartetCleaner(),
HtmlCleaner(parser='html.parser'),
Lemmatizer(),
]
stemmers = [
Stemmer(stemmer_type='snowball'),
Stemmer(stemmer_type='porter')
][0:1] # means only use Stemmer(stemmer_type='snowball')
## simple test
text = '1/2 inch rubber lep tips Bullet07'
print('Original:')
print(text)
list_processor = ListProcessor(processors)
print('After:')
print(list_processor.process([text]))
#############
## Process ##
#############
## load raw data
dfAll = pkl_utils._load(config.ALL_DATA_RAW)
columns_to_proc = [col for col in columns_to_proc if col in dfAll.columns]
if config.TASK == 'sample':
dfAll = dfAll.iloc[0:config.SAMPLE_SIZE]
print(f'data length: {len(dfAll)}')
## extract product name from search_term and product_title
ext = ProductNameExtractor()
dfAll['search_term_product_name'] = dfAll['search_term'].apply(
ext.transform)
dfAll['product_title_product_name'] = dfAll['product_title'].apply(
ext.transform)
if config.TASK == 'sample':
print(dfAll[[
'search_term', 'search_term_product_name',
'product_title_product_name'
]])
## clean using GoogleQuerySpellingChecker(Chenglong team not used in final submission)
# MUST BE IN FRONT OF ALL THE PROCESSING
if config.GOOGLE_CORRECTING_QUERY:
logger.info('Run GoogleQuerySpellingChecker at search_term')
checker = GoogleQuerySpellingChecker()
dfAll['search_term'] = dfAll['search_term'].apply(checker.correct)
## clean uisng a list of processors
df_processor = DataFrameParallelProcessor(processors,
config.DATA_PROCESSOR_N_JOBS)
df_processor.process(dfAll, columns_to_proc)
# split product_attribute_concat into product_attribute and product_attribute_list
dfAll['product_attribute'] = dfAll['product_attribute_concat'].apply(
_split_attr_to_text)
dfAll['product_attribute_list'] = dfAll['product_attribute_concat'].apply(
_split_attr_to_list)
if config.TASK == 'sample':
print(dfAll[['product_attribute', 'product_attribute_list']])
# query expansion (Chenglong team decided to remove the feature which might be a major cause of overfitting.)
if config.QUERY_EXPANSION:
list_processor = ListProcessor(processors)
# stop words must to access data process. EX. NumberDigitMapper function will replace 'one' to '1'.
# So, if stop word has 'one', it must replace to '1',too.
base_stopwords = set(list_processor.process(list(
config.STOP_WORDS))) # a set of stop word
qe = QueryExpansion(dfAll,
ngram=3,
stopwords_threshold=0.9,
base_stopwords=base_stopwords)
dfAll['search_term_alt'] = qe.build()
if config.TASK == 'sample':
print(dfAll[['search_term', 'search_term_alt']])
# save data
logger.info(f'Save to {config.ALL_DATA_LEMMATIZED}')
columns_to_save = [
col for col in dfAll.columns if col != 'product_attribute_concat'
]
pkl_utils._save(config.ALL_DATA_LEMMATIZED, dfAll[columns_to_save])
## auto correcting query(Chenglong team not used in final submission)
if config.AUTO_CORRECTING_QUERY:
logger.info('Run AutoSpellingChecker at search_term')
checker = AutoSpellingChecker(dfAll,
exclude_stopwords=False,
min_len=4)
dfAll['search_term_auto_corrected'] = list(dfAll['search_term'].apply(
checker.correct))
columns_to_proc += ['search_term_auto_corrected']
if config.TASK == 'sample':
print(dfAll[['search_term', 'search_term_auto_corrected']])
# save query_correction_map and spelling checker
fname = '%s/auto_spelling_checker_query_correction_map_%s.log' % (
config.LOG_DIR, now)
checker.save_query_correction_map(fname)
# save data
logger.info('Save to %s' % config.ALL_DATA_LEMMATIZED)
columns_to_save = [
col for col in dfAll.columns if col != 'product_attribute_concat'
]
pkl_utils._save(config.ALL_DATA_LEMMATIZED, dfAll[columns_to_save])
## clean using stemmers
df_processor = DataFrameParallelProcessor(stemmers,
config.DATA_PROCESSOR_N_JOBS)
df_processor.process(dfAll, columns_to_proc)
# split product_attribute_concat into product_attribute and product_attribute_list
dfAll['product_attribute'] = dfAll['product_attribute_concat'].apply(
_split_attr_to_text)
dfAll['product_attribute_list'] = dfAll['product_attribute_concat'].apply(
_split_attr_to_list)
# query expansion
if config.QUERY_EXPANSION:
list_processor = ListProcessor(stemmers)
base_stopwords = set(list_processor.process(list(config.STOP_WORDS)))
qe = QueryExpansion(dfAll,
ngram=3,
stopwords_threshold=0.9,
base_stopwords=base_stopwords)
dfAll['search_term_alt'] = qe.build()
if config.TASK == 'sample':
print(dfAll[['search_term', 'search_term_alt']])
# save data
logger.info('Save to %s' % config.ALL_DATA_LEMMATIZED_STEMMED)
columns_to_save = [
col for col in dfAll.columns if col != 'product_attribute_concat'
]
pkl_utils._save(config.ALL_DATA_LEMMATIZED_STEMMED, dfAll[columns_to_save])
def save(self, model_dir, model_name):
fname = os.path.join(model_dir, model_name)
self.model.save(fname)
pkl_utils._save("%s.sent_label"%fname, self.sentences.sent_label)
def main():
###########
## Setup ##
###########
logname = "data_processor_%s.log"%now
logger = logging_utils._get_logger(config.LOG_DIR, logname)
# put product_attribute_list, product_attribute and product_description first as they are
# quite time consuming to process
columns_to_proc = [
# # product_attribute_list is very time consuming to process
# # so we just process product_attribute which is of the form
# # attr_name1 | attr_value1 | attr_name2 | attr_value2 | ...
# # and split it into a list afterwards
# "product_attribute_list",
"product_attribute_concat",
"product_description",
"product_brand",
"product_color",
"product_title",
"search_term",
]
if config.PLATFORM == "Linux":
config.DATA_PROCESSOR_N_JOBS = len(columns_to_proc)
# clean using a list of processors
processors = [
LowerCaseConverter(),
# See LowerUpperCaseSplitter and UnitConverter for why we put UnitConverter here
UnitConverter(),
LowerUpperCaseSplitter(),
WordReplacer(replace_fname=config.WORD_REPLACER_DATA),
LetterLetterSplitter(),
DigitLetterSplitter(),
DigitCommaDigitMerger(),
NumberDigitMapper(),
UnitConverter(),
QuartetCleaner(),
HtmlCleaner(parser="html.parser"),
Lemmatizer(),
]
stemmers = [
Stemmer(stemmer_type="snowball"),
Stemmer(stemmer_type="porter")
][0:1]
## simple test
text = "1/2 inch rubber lep tips Bullet07"
print("Original:")
print(text)
list_processor = ListProcessor(processors)
print("After:")
print(list_processor.process([text]))
#############
## Process ##
#############
## load raw data
dfAll = pkl_utils._load(config.ALL_DATA_RAW)
columns_to_proc = [col for col in columns_to_proc if col in dfAll.columns]
## extract product name from search_term and product_title
ext = ProductNameExtractor()
dfAll["search_term_product_name"] = dfAll["search_term"].apply(ext.transform)
dfAll["product_title_product_name"] = dfAll["product_title"].apply(ext.transform)
if config.TASK == "sample":
print(dfAll[["search_term", "search_term_product_name", "product_title_product_name"]])
## clean using GoogleQuerySpellingChecker
# MUST BE IN FRONT OF ALL THE PROCESSING
logger.info("Run GoogleQuerySpellingChecker at search_term")
checker = GoogleQuerySpellingChecker()
dfAll["search_term"] = dfAll["search_term"].apply(checker.correct)
## clean uisng a list of processors
df_processor = DataFrameParallelProcessor(processors, config.DATA_PROCESSOR_N_JOBS)
df_processor.process(dfAll, columns_to_proc)
# split product_attribute_concat into product_attribute and product_attribute_list
dfAll["product_attribute"] = dfAll["product_attribute_concat"].apply(_split_attr_to_text)
dfAll["product_attribute_list"] = dfAll["product_attribute_concat"].apply(_split_attr_to_list)
if config.TASK == "sample":
print(dfAll[["product_attribute", "product_attribute_list"]])
# query expansion
list_processor = ListProcessor(processors)
base_stopwords = set(list_processor.process(list(config.STOP_WORDS)))
qe = QueryExpansion(dfAll, ngram=3, stopwords_threshold=0.9, base_stopwords=base_stopwords)
dfAll["search_term_alt"] = qe.build()
if config.TASK == "sample":
print(dfAll[["search_term", "search_term_alt"]])
# save data
logger.info("Save to %s"%config.ALL_DATA_LEMMATIZED)
columns_to_save = [col for col in dfAll.columns if col != "product_attribute_concat"]
pkl_utils._save(config.ALL_DATA_LEMMATIZED, dfAll[columns_to_save])
## auto correcting query
if config.AUTO_CORRECTING_QUERY:
logger.info("Run AutoSpellingChecker at search_term")
checker = AutoSpellingChecker(dfAll, exclude_stopwords=False, min_len=4)
dfAll['search_term_auto_corrected'] = list(dfAll["search_term"].apply(checker.correct))
columns_to_proc += ['search_term_auto_corrected']
if config.TASK == "sample":
print(dfAll[["search_term", "search_term_auto_corrected"]])
# save query_correction_map and spelling checker
fname = "%s/auto_spelling_checker_query_correction_map_%s.log"%(config.LOG_DIR, now)
checker.save_query_correction_map(fname)
# save data
logger.info("Save to %s"%config.ALL_DATA_LEMMATIZED)
columns_to_save = [col for col in dfAll.columns if col != "product_attribute_concat"]
pkl_utils._save(config.ALL_DATA_LEMMATIZED, dfAll[columns_to_save])
## clean using stemmers
df_processor = DataFrameParallelProcessor(stemmers, config.DATA_PROCESSOR_N_JOBS)
df_processor.process(dfAll, columns_to_proc)
# split product_attribute_concat into product_attribute and product_attribute_list
dfAll["product_attribute"] = dfAll["product_attribute_concat"].apply(_split_attr_to_text)
dfAll["product_attribute_list"] = dfAll["product_attribute_concat"].apply(_split_attr_to_list)
# query expansion
list_processor = ListProcessor(stemmers)
base_stopwords = set(list_processor.process(list(config.STOP_WORDS)))
qe = QueryExpansion(dfAll, ngram=3, stopwords_threshold=0.9, base_stopwords=base_stopwords)
dfAll["search_term_alt"] = qe.build()
if config.TASK == "sample":
print(dfAll[["search_term", "search_term_alt"]])
# save data
logger.info("Save to %s"%config.ALL_DATA_LEMMATIZED_STEMMED)
columns_to_save = [col for col in dfAll.columns if col != "product_attribute_concat"]
pkl_utils._save(config.ALL_DATA_LEMMATIZED_STEMMED, dfAll[columns_to_save])
def save(self, fname):
pkl_utils._save(fname, self.splits)
def save(self, fname):
pkl_utils._save(fname, self.splits)
def parse(lang="en"):
infile = open(config.INSTANCE_TYPES[lang])
rdf_type = "http://www.w3.org/1999/02/22-rdf-syntax-ns#type"
type_entries = []
entitySet = set()
typeSet = set()
for line in infile.readlines():
if line[0] != "<":
continue
row = line.split()
instance = row[0][1:-1]
ontology = row[2][1:-1]
type_entries.append((instance, ontology))
entitySet.add(ontology)
typeSet.add(ontology)
typeDict = {y:x for x, y in enumerate(typeSet)}
infile.close()
cnt_type = len(entitySet)
_log.info("%d types" % cnt_type)
infile = open(config.OBJECTS[lang])
relationDict = {}
instanceSet = set()
for line in infile.readlines():
if line[0] != "<":
continue
row = line.split()
subject = row[0][1:-1]
predicate = row[1][1:-1]
target = row[2][1:-1]
entitySet.add(subject)
entitySet.add(target)
instanceSet.add(subject)
instanceSet.add(target)
if predicate in relationDict:
relationDict[predicate].append((subject, target))
else:
relationDict[predicate] = [(subject, target)]
instanceDict = {y:x for x, y in enumerate(instanceSet)}
entityDict = {y:x for x, y in enumerate(entitySet)}
infile.close()
cnt_ins = len(instanceSet)
N = len(entitySet)
_log.info("%d instanes" % cnt_ins)
_log.info("%d entites" % N)
tensor = []
predicateDict = {}
cnt = 0
for predicate in relationDict:
entries = relationDict[predicate]
rows = [entityDict[entry[0]] for entry in entries]
cols = [entityDict[entry[1]] for entry in entries]
data = [1 for entry in entries]
mat = spsp.csr_matrix((data, (rows, cols)), (N, N))
tensor.append(mat)
predicateDict[cnt] = predicate
cnt += 1
type_entries = [entry for entry in type_entries if entry[0] in instanceSet]
rows = [entityDict[entry[0]] for entry in type_entries]
cols = [entityDict[entry[1]] for entry in type_entries]
data = [1 for entry in type_entries]
mat = spsp.csr_matrix((data, (rows, cols)), (N, N))
tensor.append(mat)
predicateDict[cnt] = rdf_type
_log.info("%d relations" % (cnt+1))
pkl_utils._save(config.TENSOR[lang], tensor)
pkl_utils._save(config.ENTITY[lang], entityDict)
pkl_utils._save(config.PREDICATE[lang], predicateDict)
pkl_utils._save(config.INSTANCE[lang], instanceDict)
pkl_utils._save(config.TYPE[lang], typeDict)
pkl_utils._save(config.TYPE_MATRIX[lang], (rows, cols))
_log.info("parsing complete")
