tflearn中计算混淆矩阵方法——需要经过一步转换

def do_rnn_wordbag(trainX, testX, trainY, testY):
    y_test=testY
    #trainX = pad_sequences(trainX, maxlen=100, value=0.)
    #testX = pad_sequences(testX, maxlen=100, value=0.)
    # Converting labels to binary vectors
    trainY = to_categorical(trainY, nb_classes=2)
    testY = to_categorical(testY, nb_classes=2)
  
    # Network building
    net = tflearn.input_data([None, 100]) 
    net = tflearn.embedding(net, input_dim=1000, output_dim=128) 
    net = tflearn.lstm(net, 128, dropout=0.1)
    net = tflearn.fully_connected(net, 2, activation='softmax')
    net = tflearn.regression(net, optimizer='adam', learning_rate=0.005,
                             loss='categorical_crossentropy')
  
    # Training
    model = tflearn.DNN(net, tensorboard_verbose=0)
    model.fit(trainX, trainY, validation_set=0.1, show_metric=True,
              batch_size=1,run_id="uba",n_epoch=10)
  
    y_predict_list = model.predict(testX)
    #print y_predict_list

    y_predict = []
    for i in y_predict_list:
        #print  i[0]
        if i[0] >= 0.5:
            y_predict.append(0)
        else:
            y_predict.append(1)

    print(classification_report(y_test, y_predict))
    print metrics.confusion_matrix(y_test, y_predict)

    print y_train

    print "ture"
    print y_test
    print "pre"
    print y_predict

传统方法贝叶斯：

def do_nb(x_train, x_test, y_train, y_test):
    gnb = GaussianNB()
    gnb.fit(x_train,y_train)
    y_pred=gnb.predict(x_test)
    print(classification_report(y_test, y_pred))
    print metrics.confusion_matrix(y_test, y_pred)

 传统方法hmm：

def do_hmm(trainX, testX, trainY, testY):
    T=-580
    N=2
    lengths=[1]
    X=[[0]]
    print len(trainX)
    for i in trainX:
        z=[]
        for j in i:
            z.append([j])
        #print z
        #X.append(z)
        X=np.concatenate([X,np.array(z)])
        lengths.append(len(i))

    #print lengths
    #print X.shape



    remodel = hmm.GaussianHMM(n_components=N, covariance_type="full", n_iter=100)
    remodel.fit(X, lengths)

    y_predict=[]
    for i in testX:
        z=[]
        for j in i:
            z.append([j])
        y_pred=remodel.score(z)
        print y_pred
        if y_pred < T:
            y_predict.append(1)
        else:
            y_predict.append(0)
    y_predict=np.array(y_predict)

    print(classification_report(testY, y_predict))
    print metrics.confusion_matrix(testY, y_predict)

    print testY
    print y_predict