def rank(training_set, paradigm_lengths, category_description):
transfomer = DataTransformer(training_set, paradigm_lengths, category_description)
headlines, matrix, targets = transfomer.get_training_data_matrix(normalize=True)
matrix = matrix.toarray()
estimator = svm.SVC(C=1, kernel='linear')
selector = RFE(estimator, 1, step=1)
selector = selector.fit(matrix, targets)
for i in range(len(headlines)):
print headlines[i], selector.ranking_[i]
def get_feature_percentage(training_set, paradigm_lengths, category_description):
transfomer = DataTransformer(training_set, paradigm_lengths, category_description)
headlines, matrix, targets = transfomer.get_training_data_matrix(normalize=True)
matrix = matrix.toarray()
forest = ExtraTreesClassifier(n_estimators=10)
forest.fit(matrix, targets)
importances = forest.feature_importances_
std = np.std([tree.feature_importances_ for tree in forest.estimators_],
axis=0)
indices = np.argsort(importances)[::-1]
dict = {}
for f in range(matrix.shape[1]):
dict[headlines[indices[f]]] = importances[indices[f]]
return dict
