class FeatureWrapper:
# FIXME - Make three objects - one for each classifier
# Instantiate an FeatureWrapper object
def __init__(self, data=None):
# Sentence-level features
self.feat_sent = SentenceFeatures(data)
def extract_IOB_features(self, sentence):
"""
extract_IOB_features()
@param sentence. A list of chunks
@return tuple: boolean (Prose or not), a list of dictionaries of features
>>> fw = FeatureWrapper()
>>> fw.extract_IOB_features(['this', 'is', 'a' 'test']) is not None
True
"""
# Different features depending on whether sentence is 'prose'
isProse = is_prose_sentence(sentence)
if isProse:
features_list = self.feat_sent.IOB_prose_features(sentence)
else:
features_list = self.feat_sent.IOB_nonprose_features(sentence)
# Return features as well as indication of whether it is prose or not
return (isProse, features_list)
def concept_features(self, sentence, chunk_inds):
"""
concept_features()
@param sentence. a list of chunks
@param chunk_inds. a list of important indices of the sentence
@return a list of dictionaries of features
>>> fw = FeatureWrapper()
>>> fw.concept_features(['this', 'is', 'an', 'important', 'test'], [3, 4]) is not None
True
"""
# FIXME - move all of this work to SentenceFeatures object
'''
# VERY basic feature set for sanity check tests during development
features_list = []
for i,ind in enumerate(chunk_inds):
features = {('phrase',sentence[ind]) : 1}
features_list.append(features)
return features_list
'''
# Create a list of feature sets (one per chunk)
features_list = self.feat_sent.concept_features_for_sentence(
sentence, chunk_inds)
return features_list
def __init__(self, data=None):
# Sentence-level features
self.feat_sent = SentenceFeatures(data)
def __init__(self, data=None):
# Sentence-level features
self.feat_sent = SentenceFeatures(data)
class FeatureWrapper:
# FIXME - Make three objects - one for each classifier
# Instantiate an FeatureWrapper object
def __init__(self, data=None):
# Sentence-level features
self.feat_sent = SentenceFeatures(data)
def extract_IOB_features(self, sentence):
"""
extract_IOB_features()
@param sentence. A list of chunks
@return tuple: boolean (Prose or not), a list of dictionaries of features
>>> fw = FeatureWrapper()
>>> fw.extract_IOB_features(['this', 'is', 'a' 'test']) is not None
True
"""
# Different features depending on whether sentence is 'prose'
isProse = is_prose_sentence(sentence)
if isProse:
features_list = self.feat_sent.IOB_prose_features(sentence)
else:
features_list = self.feat_sent.IOB_nonprose_features(sentence)
# Return features as well as indication of whether it is prose or not
return (isProse, features_list)
def concept_features(self, sentence, chunk_inds):
"""
concept_features()
@param sentence. a list of chunks
@param chunk_inds. a list of important indices of the sentence
@return a list of dictionaries of features
>>> fw = FeatureWrapper()
>>> fw.concept_features(['this', 'is', 'an', 'important', 'test'], [3, 4]) is not None
True
"""
# FIXME - move all of this work to SentenceFeatures object
'''
# VERY basic feature set for sanity check tests during development
features_list = []
for i,ind in enumerate(chunk_inds):
features = {('phrase',sentence[ind]) : 1}
features_list.append(features)
return features_list
'''
# Create a list of feature sets (one per chunk)
features_list = self.feat_sent.concept_features_for_sentence(sentence,chunk_inds)
return features_list
