def __init__(self, clf):
self.clf = clf
results = list(extract.extract_labeled_tweets())
_ids, _labels, _tweets = zip(*results)
tweets = np.asarray(_tweets)
labels = np.asarray(_labels)
# remap non-negative ('X') to 1, negative 0
labels = convert_labels_to_binary(labels, ['X'])
self.clf.fit(tweets, labels)
def evaluate_revised(scorer):
"""Evaluate a scorer using revised labels."""
y_true = []
y_pred = []
def get_score(text):
score = scorer.get_document_score(text, normalize=False)
if score < 0:
return '-'
else:
return 'X'
res = [(label, get_score(text)) for _id, label, text in
extract.extract_labeled_tweets()]
y_true, y_pred = zip(*res)
p, r, f, s = precision_recall_fscore_support(y_true, y_pred, pos_label='-')
correct = sum(1 for label, pred in zip(y_true, y_pred) if label==pred)
accuracy = float(correct) / len(y_true)
print 'precision=%.3f recall=%.3f fscore=%.3f accuracy=%.3f' % (
p[0], r[0], f[0], accuracy)
analyzer = super(TfidfVectorizer, self).build_analyzer()
return lambda doc: [stemmer.stem(x) for x in analyzer(doc)]
def convert_labels_to_binary(Y, one_label_list):
"""Convert string labels to zero or ones."""
pos = Y == one_label_list[0]
for label in one_label_list[1:]:
pos |= Y == sent_label
X = np.zeros(Y.shape[0], dtype=np.int)
X[pos] = 1
return X
if __name__ == "__main__":
results = list(extract.extract_labeled_tweets())
_ids, _labels, _tweets = zip(*results)
tweets = np.asarray(_tweets)
labels = np.asarray(_labels)
# remap 'X' to 1, everything else to 0
labels = convert_labels_to_binary(labels, ['X'])
X_train, X_test, y_train, y_test = train_test_split(
tweets, labels, test_size=0.2, random_state=0)
clf = create_logistic_regression_classifier()
clf.fit(X_train, y_train)
y_preds = clf.predict(X_test)
target_names = ['Negative', 'Non-negative']