3.3 Bernoulli NB.py 文件源码

def test_BernoulliNB_alpha(*data):
    '''
    test the performance with different alpha
    :param data: train_data, test_data, train_value, test_value
    :return: None
    '''
    X_train,X_test,y_train,y_test=data
    alphas=np.logspace(-2,5,num=200)
    train_scores=[]
    test_scores=[]
    for alpha in alphas:
        cls=naive_bayes.BernoulliNB(alpha=alpha)
        cls.fit(X_train,y_train)
        train_scores.append(cls.score(X_train,y_train))
        test_scores.append(cls.score(X_test, y_test))

    ## graph
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    ax.plot(alphas,train_scores,label="Training Score")
    ax.plot(alphas,test_scores,label="Testing Score")
    ax.set_xlabel(r"$\alpha$")
    ax.set_ylabel("score")
    ax.set_ylim(0,1.0)
    ax.set_title("BernoulliNB")
    ax.set_xscale("log")
    ax.legend(loc="best")
    plt.show()