def prepare_unigram(path,out):
data_input = pd.read_csv(path)
data_ouput = DataFrame(columns=['sen1_unigram','sen2_unigram'])
for index,row in data_input.iterrows():
s1 = str(row['sen1']).split()
s2 = str(row['sen2']).split()
s1 = ' '.join(getUnigram(s1))
s2 = ' '.join(getUnigram(s2))
data_ouput.loc[index] = [s1,s2]
data_ouput.to_csv(out,index=False)
def generateNGram(df):
# unigram
df['query_unigram'] = df['query'].apply(lambda x: ngram.getUnigram(x))
df['title_unigram'] = df['product_title'].apply(
lambda x: ngram.getUnigram(x))
df['description_unigram'] = df['product_description'].apply(
lambda x: ngram.getUnigram(x))
# bigram
df['query_bigram'] = df['query'].apply(lambda x: ngram.getBigram(x, '_'))
df['title_bigram'] = df['product_title'].apply(
lambda x: ngram.getBigram(x, '_'))
df['description_bigram'] = df['product_description'].apply(
lambda x: ngram.getBigram(x, '_'))
# trigram
df['query_trigram'] = df['query'].apply(lambda x: ngram.getTrigram(x, '_'))
df['title_trigram'] = df['product_title'].apply(
lambda x: ngram.getTrigram(x, '_'))
df['description_trigram'] = df['product_description'].apply(
lambda x: ngram.getTrigram(x, '_'))
return df
def __iter__(self):
for item in DictReader(open(self.tsvFile, "rb"), delimiter='\t', quotechar='"'):
self.counter += 1
item = {featureName:featureValue.decode('utf-8') \
for featureName,featureValue in item.items() \
if featureValue is not None}
description = [ str(self.wordIndex[w]) for w in getWords(item["description"]) ]
if self.ngram == 1:
yield getUnigram(description)
elif self.ngram == 2:
yield getBigram(description, "_")
if self.counter%100000 == 0:
print(( " Process %s" % self.counter ))
def __iter__(self):
for item in DictReader(open(self.tsvFile, "rb"), delimiter='\t', quotechar='"'):
self.counter += 1
item = {featureName:featureValue.decode('utf-8') \
for featureName,featureValue in item.iteritems() \
if featureValue is not None}
description = [ str(self.wordIndex[w]) for w in getWords(item["description"]) ]
if self.ngram == 1:
yield getUnigram(description)
elif self.ngram == 2:
yield getBigram(description, "_")
if self.counter%100000 == 0:
print( " Process %s" % self.counter )
def prepare_unigram(path, out):
print path
c = 0
start = datetime.now()
with open(out, 'w') as outfile:
outfile.write('question1_unigram,question2_unigram\n')
for t, row in enumerate(DictReader(open(path), delimiter=',')):
if c % 100000 == 0:
print 'finished', c
q1 = remove_punctuation(str(
row['question1_porter']).lower()).split(' ')
q2 = remove_punctuation(str(
row['question2_porter']).lower()).lower().split(' ')
q1_bigram = getUnigram(q1)
q2_bigram = getUnigram(q2)
q1_bigram = ' '.join(q1_bigram)
q2_bigram = ' '.join(q2_bigram)
outfile.write('%s,%s\n' % (q1_bigram, q2_bigram))
c += 1
end - start = datetime.now()
print 'times:', end - start
def getVWFile(tsvFile, vwFile, dataIndex, dictionary, tfidf_model, train=True):
""" Generate the overall features in VW format."""
start = datetime.now()
# extract all the index
catIndex = dataIndex["catIndex"]
subCatIndex = dataIndex["subCatIndex"]
attrsKeyIndex = dataIndex["attrsKeyIndex"]
attrsValIndex = dataIndex["attrsValIndex"]
wordIndex = dataIndex["wordIndex"]
with open(vwFile, "wb") as vwWriter:
with open(tsvFile, "rb") as tsvReader:
itemReader = DictReader(tsvReader, delimiter='\t', quotechar='"')
for i, item in enumerate(itemReader):
item = {featureName:featureValue.decode('utf-8') \
for featureName,featureValue in item.items() \
if featureValue is not None}
# get header
itemid = int(item["itemid"])
label = int(item["is_blocked"]) if train else 1
header = "%s '%s " % (int(2*label - 1), itemid)
# category
categoryFeat = "|C %s " % catIndex[ item["category"] ]
# subcategory
subcategoryFeat = "|SC %s " % subCatIndex[ item["subcategory"] ]
# title
title = [ str(wordIndex[w]) for w in getWords(item["title"]) ]
# first-gram
title_start = title[0] if len(title)>0 else "0"
# end-gram
title_end = title[-1] if len(title)>0 else "0"
# naming is a pain for me
titleFeat = "|T %s |bT %s |cT %s " % (" ".join(title), title_start, title_end)
titleStatsFeat = "|t %s " % getTextStatsFeat(item["title"])
# description
description = [ str(wordIndex[w]) for w in getWords(item["description"]) ]
# first-gram
description_start = description[0] if len(description)>0 else "0"
# end-gram
description_end = description[-1] if len(description)>0 else "0"
descriptionFeat = "|D %s |fD %s |gD %s " % (" ".join(description), description_start, description_end)
descriptionStatsFeat = "|d %s " % getTextStatsFeat(item["description"])
tfidf_feat1 = getTfidfFeat(getUnigram(description), dictionary[1], tfidf_model[1])
# 2gram tfidf seem to harm the performance, you are save to drop it here
tfidf_feat2 = getTfidfFeat(getBigram(description, "_"), dictionary[2], tfidf_model[2])
descriptionFeat += "|iD %s |jD %s " % (tfidf_feat1, tfidf_feat2)
# attrs
attrsFeat = ""
countAttrs = 0
if "attrs" in item:
attrsDict = getAttrsDict(item["attrs"])
#print attrs
for k,v in list(attrsDict.items()):
countAttrs += 1
attrsFeat += "|A%s %s " % (attrsKeyIndex[k], attrsValIndex[v])
attrsFeat += "|a "
for k,v in list(attrsDict.items()):
attrsFeat += "%s " % (attrsKeyIndex[k])
if len(attrsFeat) == 0:
attrsFeat = "|NA 1 "
attrsFeat += "|hAC %s " % countAttrs
# price
priceFeat = "|P %s " % item["price"]
# phones_cnt
phonesCntFeat = "|p %s " % item["phones_cnt"]
# emails_cnt
emailsCntFeat = "|e %s " % item["emails_cnt"]
# urls_cnt
urlsCntFeat = "|u %s " % item["urls_cnt"]
# output
vwLine = header \
+ categoryFeat \
+ subcategoryFeat \
+ titleFeat \
+ titleStatsFeat \
+ descriptionFeat \
+ descriptionStatsFeat \
+ attrsFeat \
+ priceFeat \
+ phonesCntFeat \
+ emailsCntFeat \
+ urlsCntFeat[:-1] + "\n"
vwWriter.write( vwLine )
# report progress
if (i+1)%10000 == 0:
print(( "\n%s\t%s"%((i+1),str(datetime.now() - start)) ))
print(( "Sample output:\n%s" % vwLine ))
def getVWFile(tsvFile, vwFile, dataIndex, dictionary, tfidf_model, train=True):
""" Generate the overall features in VW format."""
start = datetime.now()
# extract all the index
catIndex = dataIndex["catIndex"]
subCatIndex = dataIndex["subCatIndex"]
attrsKeyIndex = dataIndex["attrsKeyIndex"]
attrsValIndex = dataIndex["attrsValIndex"]
wordIndex = dataIndex["wordIndex"]
with open(vwFile, "wb") as vwWriter:
with open(tsvFile, "rb") as tsvReader:
itemReader = DictReader(tsvReader, delimiter='\t', quotechar='"')
for i, item in enumerate(itemReader):
item = {featureName:featureValue.decode('utf-8') \
for featureName,featureValue in item.iteritems() \
if featureValue is not None}
# get header
itemid = int(item["itemid"])
label = int(item["is_blocked"]) if train else 1
header = "%s '%s " % (int(2*label - 1), itemid)
# category
categoryFeat = "|C %s " % catIndex[ item["category"] ]
# subcategory
subcategoryFeat = "|SC %s " % subCatIndex[ item["subcategory"] ]
# title
title = [ str(wordIndex[w]) for w in getWords(item["title"]) ]
# first-gram
title_start = title[0] if len(title)>0 else "0"
# end-gram
title_end = title[-1] if len(title)>0 else "0"
# naming is a pain for me
titleFeat = "|T %s |bT %s |cT %s " % (" ".join(title), title_start, title_end)
titleStatsFeat = "|t %s " % getTextStatsFeat(item["title"])
# description
description = [ str(wordIndex[w]) for w in getWords(item["description"]) ]
# first-gram
description_start = description[0] if len(description)>0 else "0"
# end-gram
description_end = description[-1] if len(description)>0 else "0"
descriptionFeat = "|D %s |fD %s |gD %s " % (" ".join(description), description_start, description_end)
descriptionStatsFeat = "|d %s " % getTextStatsFeat(item["description"])
tfidf_feat1 = getTfidfFeat(getUnigram(description), dictionary[1], tfidf_model[1])
# 2gram tfidf seem to harm the performance, you are save to drop it here
tfidf_feat2 = getTfidfFeat(getBigram(description, "_"), dictionary[2], tfidf_model[2])
descriptionFeat += "|iD %s |jD %s " % (tfidf_feat1, tfidf_feat2)
# attrs
attrsFeat = ""
countAttrs = 0
if item.has_key("attrs"):
attrsDict = getAttrsDict(item["attrs"])
#print attrs
for k,v in attrsDict.items():
countAttrs += 1
attrsFeat += "|A%s %s " % (attrsKeyIndex[k], attrsValIndex[v])
attrsFeat += "|a "
for k,v in attrsDict.items():
attrsFeat += "%s " % (attrsKeyIndex[k])
if len(attrsFeat) == 0:
attrsFeat = "|NA 1 "
attrsFeat += "|hAC %s " % countAttrs
# price
priceFeat = "|P %s " % item["price"]
# phones_cnt
phonesCntFeat = "|p %s " % item["phones_cnt"]
# emails_cnt
emailsCntFeat = "|e %s " % item["emails_cnt"]
# urls_cnt
urlsCntFeat = "|u %s " % item["urls_cnt"]
# output
vwLine = header \
+ categoryFeat \
+ subcategoryFeat \
+ titleFeat \
+ titleStatsFeat \
+ descriptionFeat \
+ descriptionStatsFeat \
+ attrsFeat \
+ priceFeat \
+ phonesCntFeat \
+ emailsCntFeat \
+ urlsCntFeat[:-1] + "\n"
vwWriter.write( vwLine )
# report progress
if (i+1)%10000 == 0:
print( "\n%s\t%s"%((i+1),str(datetime.now() - start)) )
print( "Sample output:\n%s" % vwLine )
