吴裕雄 python 机器学习——数据预处理嵌入式特征选择

import numpy as np

import  matplotlib.pyplot as plt

from sklearn.svm import LinearSVC

from sklearn.linear_model import Lasso

from sklearn.model_selection import train_test_split

from sklearn.feature_selection import SelectFromModel

from sklearn.datasets import load_digits,load_diabetes

#数据预处理嵌入式特征选择SelectFromModel模型

def test_SelectFromModel():

    digits=load_digits()

    X=digits.data

    y=digits.target

    estimator=LinearSVC(penalty='l1',dual=False)

    selector=SelectFromModel(estimator=estimator,threshold='mean')

    selector.fit(X,y)

    selector.transform(X)

    print("Threshold %s"%selector.threshold_)

    print("Support is %s"%selector.get_support(indices=True))

#调用test_SelectFromModel()

test_SelectFromModel()

def load_diabetes():

    digits=load_digits()

    X=digits.data

    y=digits.target

    return X,y

def test_Lasso(*data):

    '''

    测试 alpha 与稀疏性的关系

    '''

    X,y=data

    alphas=np.logspace(-2,2)

    zeros=[]

    for alpha in alphas:

        regr=Lasso(alpha=alpha)

        regr.fit(X,y)

        ### 计算零的个数 ###

        num=0

        for ele in regr.coef_:

            if abs(ele) < 1e-5:num+=1

        zeros.append(num)

    ##### 绘图

    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()

X,y = load_diabetes()

test_Lasso(X,y)

def test_LinearSVC(*data):

    '''

    测试 C  与 稀疏性的关系

    '''

    X,y=data

    Cs=np.logspace(-2,2)

    zeros=[]

    for C in Cs:

        clf=LinearSVC(C=C,penalty='l1',dual=False)

        clf.fit(X,y)

     ### 计算零的个数 ###

        num=0

        for row in clf.coef_:

            for ele in row:

                if abs(ele) < 1e-5:num+=1

        zeros.append(num)

    ##### 绘图

    fig=plt.figure()

    ax=fig.add_subplot(1,1,1)

    ax.plot(Cs,zeros)

    ax.set_xlabel("C")

    ax.set_xscale("log")

    ax.set_ylabel("zeros in coef")

    ax.set_title("Sparsity In SVM")

    plt.show()

X,y = load_diabetes()

test_LinearSVC(X,y)