本文整理汇总了Python中sklearn.naive_bayes.MultinomialNB.build_vocab方法的典型用法代码示例。如果您正苦于以下问题：Python MultinomialNB.build_vocab方法的具体用法？Python MultinomialNB.build_vocab怎么用？Python MultinomialNB.build_vocab使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.naive_bayes.MultinomialNB的用法示例。
在下文中一共展示了MultinomialNB.build_vocab方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: print
# 需要导入模块: from sklearn.naive_bayes import MultinomialNB [as 别名]
# 或者: from sklearn.naive_bayes.MultinomialNB import build_vocab [as 别名]
if num_classes == 2: model.compile(loss='binary_crossentropy', optimizer='adam', class_mode='binary')
else: model.compile(loss='categorical_crossentropy', optimizer='adam')
model.fit( train_sequences , train_labels , nb_epoch=30, batch_size=32)
results = model.predict_classes( test_sequences )
print ("Accuracy = " + repr( sklearn.metrics.accuracy_score( test_labels , results ) ) )
print (sklearn.metrics.classification_report( test_labels , results ))
print ("Method = Linear SVM with doc2vec features")
np.random.seed(0)
class LabeledLineSentence(object):
def __init__(self, data ): self.data = data
def __iter__(self):
for uid, line in enumerate( self.data ): yield TaggedDocument( line.split(" ") , ["S_%s" % uid] )
model = Doc2Vec( alpha=0.025 , min_alpha=0.025 )
sentences = LabeledLineSentence( train_texts + test_texts )
model.build_vocab( sentences )
model.train( sentences )
for w in model.vocab.keys():
try: model[w] = embeddings[w]
except : continue
for epoch in range(10):
model.train(sentences)
model.alpha -= 0.002
model.min_alpha = model.alpha
train_rep = np.array( [ model.docvecs[i] for i in range( train_matrix.shape[0] ) ] )
test_rep = np.array( [ model.docvecs[i + train_matrix.shape[0]] for i in range( test_matrix.shape[0] ) ] )
model = LinearSVC( random_state=0 )
model.fit( train_rep , train_labels )
results = model.predict( test_rep )
print ("Accuracy = " + repr( sklearn.metrics.accuracy_score( test_labels , results ) ))
print (sklearn.metrics.classification_report( test_labels , results ))