def __init__(self, normal_vectorizer=None, clean_vectorizer=None):
self.mf_generator = MetaFeatureGenerator()
self.fit_complete = False
if normal_vectorizer and clean_vectorizer:
self.fit_complete = True
if normal_vectorizer:
self.normal_vectorizer = normal_vectorizer
else:
self.normal_vectorizer = Vectorizer()
if clean_vectorizer:
self.clean_vectorizer = clean_vectorizer
else:
self.clean_vectorizer = Vectorizer()
def __init__(self, normal_vectorizer=None, clean_vectorizer=None):
self.mf_generator = MetaFeatureGenerator()
self.fit_complete = False
if normal_vectorizer and clean_vectorizer:
self.fit_complete = True
if normal_vectorizer:
self.normal_vectorizer = normal_vectorizer
else:
self.normal_vectorizer = Vectorizer()
if clean_vectorizer:
self.clean_vectorizer = clean_vectorizer
else:
self.clean_vectorizer = Vectorizer()
class FeatureGenerator(object):
def __init__(self, normal_vectorizer=None, clean_vectorizer=None):
self.mf_generator = MetaFeatureGenerator()
self.fit_complete = False
if normal_vectorizer and clean_vectorizer:
self.fit_complete = True
if normal_vectorizer:
self.normal_vectorizer = normal_vectorizer
else:
self.normal_vectorizer = Vectorizer()
if clean_vectorizer:
self.clean_vectorizer = clean_vectorizer
else:
self.clean_vectorizer = Vectorizer()
def fit(self, input_text, input_scores):
self.normal_vectorizer.fit(input_text, input_scores)
clean_text = [
self.mf_generator.generate_clean_stem_text(t) for t in input_text
]
self.clean_vectorizer.fit(clean_text, input_scores)
def get_features(self, text):
vec_feats = self.generate_vectorizer_features(text)
vec_keys = self.normal_vectorizer.vocab + self.clean_vectorizer.vocab
meta_feats = self.generate_meta_features(text)
meta_keys = meta_feats.keys()
meta_keys.sort()
meta_feat_arr = np.matrix([meta_feats[k] for k in meta_keys])
self.colnames = vec_keys + meta_keys
return np.hstack([vec_feats, meta_feat_arr])
def generate_meta_features(self, text):
feats = self.mf_generator.generate_meta_features(text)
return feats
def generate_vectorizer_features(self, text):
clean_text = self.mf_generator.generate_clean_stem_text(text)
feats = self.normal_vectorizer.get_features([text])
clean_feats = self.clean_vectorizer.get_features([clean_text])
return np.hstack([feats, clean_feats])
class FeatureGenerator(object):
def __init__(self, normal_vectorizer=None, clean_vectorizer=None):
self.mf_generator = MetaFeatureGenerator()
self.fit_complete = False
if normal_vectorizer and clean_vectorizer:
self.fit_complete = True
if normal_vectorizer:
self.normal_vectorizer = normal_vectorizer
else:
self.normal_vectorizer = Vectorizer()
if clean_vectorizer:
self.clean_vectorizer = clean_vectorizer
else:
self.clean_vectorizer = Vectorizer()
def fit(self, input_text, input_scores):
self.normal_vectorizer.fit(input_text, input_scores)
clean_text = [self.mf_generator.generate_clean_stem_text(t) for t in input_text]
self.clean_vectorizer.fit(clean_text, input_scores)
def get_features(self, text):
vec_feats = self.generate_vectorizer_features(text)
vec_keys = self.normal_vectorizer.vocab + self.clean_vectorizer.vocab
meta_feats = self.generate_meta_features(text)
meta_keys = meta_feats.keys()
meta_keys.sort()
meta_feat_arr = np.matrix([meta_feats[k] for k in meta_keys])
self.colnames = vec_keys + meta_keys
return np.hstack([vec_feats, meta_feat_arr])
def generate_meta_features(self, text):
feats = self.mf_generator.generate_meta_features(text)
return feats
def generate_vectorizer_features(self, text):
clean_text = self.mf_generator.generate_clean_stem_text(text)
feats = self.normal_vectorizer.get_features([text])
clean_feats = self.clean_vectorizer.get_features([clean_text])
return np.hstack([feats, clean_feats])
