def __init__(self, x, y, stop_words=None):
super(vsm, self).__init__()
'''
:x : pd.Series; trainset, each element as a list of words after pre-processing
:y : pd.Series; labels
:stop_words : list; stop words
'''
self.x, self.cv, self.tfidf = to_tfidf(x, stop_words)
self.y = y
def __init__(self, x, y, stop_words=None): super(vsm, self).__init__() ''' :x : pd.Series; trainset, each element as a list of words after pre-processing :y : pd.Series; labels :stop_words : list; stop words ''' self.x, self.cv, self.tfidf = to_tfidf(x, stop_words) self.y = y
def predict(self, x):
'''
:x : pd.Series; testset, each element as a list of words after pre-processing
:return : np.array; predicted labels
'''
tfidf_matrix = to_tfidf(x, self.cv, self.tfidf)
scores = pd.DataFrame(tfidf_matrix.dot(self.x.transpose()))
max_scores_idx = scores.idmax(axis=0)
label_idx = dict(zip(range(len(self.y)), list(self.y)))
prediction = max_scores_idx.map(label_idx)
return prediction
def predict(self, x): ''' :x : pd.Series; testset, each element as a list of words after pre-processing :return : np.array; predicted labels ''' tfidf_matrix = to_tfidf(x, self.cv, self.tfidf) scores = pd.DataFrame(tfidf_matrix.dot(self.x.transpose())) max_scores_idx = scores.idmax(axis=0) label_idx = dict(zip(range(len(self.y)),list(self.y))) prediction = max_scores_idx.map(label_idx) return prediction
