def predict(X, clf, vec, feat_obj=None): """ predict() """ # Data -> features if feat_obj == None: feat_obj = FeaturesWrapper() feats = feat_obj.extract_features(X) # predict return predict_vectorized(feats, clf, vec)
def predict( X, clf, vec, feat_obj=None ): """ predict() """ # Data -> features if feat_obj == None: feat_obj = FeaturesWrapper() feats = feat_obj.extract_features(X) # predict return predict_vectorized(feats, clf, vec)
def extract_features(X, feat_obj=None): # Data -> features if feat_obj == None: feat_obj = FeaturesWrapper() return feat_obj.extract_features(X)
def extract_features(X, feat_obj=None): # Data -> features if feat_obj == None: feat_obj = FeaturesWrapper() return feat_obj.extract_features(X)