11.7 feature_selection_embeded.py 文件源码

def test_Lasso(*data):
    '''
    test the correlation between alpha and sparse condition
    :param data: train_data, test_data, train_value, test_value
    :return: None
    '''
    X,y=data
    alphas=np.logspace(-2,2)
    zeros=[]
    for alpha in alphas:
        regr=Lasso(alpha=alpha)
        regr.fit(X,y)
        num=0
        for ele in regr.coef_:
            if abs(ele) < 1e-5:num+=1
        zeros.append(num)
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    ax.plot(alphas,zeros)
    ax.set_xlabel(r"$\alpha$")
    ax.set_xscale("log")
    ax.set_ylim(0,X.shape[1]+1)
    ax.set_ylabel("zeros in coef")
    ax.set_title("Sparsity In Lasso")
    plt.show()