class Combined:
def __init__(self, sub_clf_options, pol_clf_options, train):
train_subjectivity, train_polarity = utils.generate_two_part_dataset(train)
self.subjectivity_clf = BaseMethod(train_subjectivity, **sub_clf_options)
self.polarity_clf = BaseMethod(train_polarity, **pol_clf_options)
self.best_score = (self.subjectivity_clf.best_score + self.polarity_clf.best_score) / 2
def predict(self, arg_input):
orig = arg_input
if isinstance(arg_input, basestring):
orig = [orig]
subjective_predictions = self.subjectivity_clf.predict(orig)
temp = np.array([[i, orig[i]] for i, sub_pred in enumerate(subjective_predictions) if sub_pred != "neutral"])
pol_ids, pol_docs = list(int(i) for i in temp[:,0]), temp[:,1]
polarity_predictions = self.polarity_clf.predict(pol_docs)
for i, pol_id in enumerate(pol_ids):
subjective_predictions[pol_id] = polarity_predictions[i]
return subjective_predictions
def __init__(self, sub_clf_options, pol_clf_options, train):
train_subjectivity, train_polarity = utils.generate_two_part_dataset(train)
self.subjectivity_clf = BaseMethod(train_subjectivity, **sub_clf_options)
self.polarity_clf = BaseMethod(train_polarity, **pol_clf_options)
self.best_score = (self.subjectivity_clf.best_score + self.polarity_clf.best_score) / 2