def vectorizer(txt):
vec = MarisaTfidfVectorizer(min_df=2,
max_features=1000000,
stop_words=None,
smooth_idf=True,
norm='l2',
sublinear_tf=True,
use_idf=True,
ngram_range=(1, 2))
vec.fit(txt)
return vec
def vectorizer(txt):
vec = MarisaTfidfVectorizer(
min_df = 2,
stop_words = None,
max_features=234567,
smooth_idf=True,
norm='l2',
sublinear_tf=True,
use_idf=True,
ngram_range=(1,2))
X = vec.fit_transform(txt)
return (vec,X)
def tfidf_vectorizer(txt):
vec = MarisaTfidfVectorizer(
min_df = 2,
max_features = 1000000,
stop_words = None,
smooth_idf=True,
norm='l2',
sublinear_tf=True,
use_idf=True,
ngram_range=(1,2))
vec.fit(txt)
return vec
def vectorizer(df):
# 1M max_features should fit in memory,
# OvA will be at max 184 classes,
# so we can fit coef_ = 1M*184*8B ~ 1GB in memory easily
vec = MarisaTfidfVectorizer(min_df=1,
stop_words=None,
max_features=1000000,
smooth_idf=True,
norm='l2',
sublinear_tf=True,
use_idf=True,
ngram_range=(1, 3))
vec.fit(iterText(df))
return vec
def vectorizer(df):
# 1M max_features should fit in memory,
# OvA will be at max 184 classes,
# so we can fit coef_ = 1M*184*8B ~ 1GB in memory easily
vec = MarisaTfidfVectorizer(
min_df = 1,
stop_words = None,
max_features=1000000,
smooth_idf=True,
norm='l2',
sublinear_tf=True,
use_idf=True,
ngram_range=(1,3))
vec.fit(iterText(df))
return vec
