def learn_feature_matrix(self, triples, person_abs, vectorizer):
"""
Learns a sparse DictVectorizer object representing a feature matrix
that can be used in building machine learning models.
Runs through all the triples, looking up their abstracts in provided
abstract cache (person_abs) and creates feature dictionary for each triple.
Feature dictionaries from all triples in the cup are then used to create a
DictVectorizer object, which represents the abstract-based feature matrix.
"""
def _get_feature_dict(triple):
sub, obj = triple
obj_idx = vectorizer.target_idx_cache.get(obj)
if obj_idx is None or sub not in person_abs:
return dict(
) # no tokens/abstract available for this subject. return empty dictionary
abs_tokens = person_abs[sub]
d = {}
for token in abs_tokens:
if token in vectorizer.top_feature_idx and token not in d:
d[token] = vectorizer.td_mat[
obj_idx, vectorizer.top_feature_idx[token]]
return d
print 'Creating feature dictionaries..',
sys.stdout.flush()
# create list of feature dictionaries, one for each triple
D = []
t1 = time()
person_no_features = 0
for i, triple in enumerate(triples):
d = _get_feature_dict(triple)
if len(d) == 0:
person_no_features += 1
D.append(d)
print '#People w/o features: {}. Time: {:.2f}s'.format(
person_no_features,
time() - t1)
# create a sparse DictVectorizer object, represeting the feature matrix
dvec = DictVectorizer(sparse=True)
dvec.feature_names_ = vectorizer.top_feature_idx.keys()
dvec.dvec_mat = dvec.fit_transform(D)
return dvec
