python线性回归模型之LogisticRegression，LinearDiscriminantAnalysis模型

运行环境：win10 64位 py 3.6 pycharm 2018.1.1

import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets,linear_model,discriminant_analysis,cross_validation
#加载数据
def load_data():
    iris = datasets.load_iris()
    X_train = iris.data
    y_train = iris.target
    return cross_validation.train_test_split(X_train, y_train, test_size=0.25, random_state=0, stratify=y_train)

def test_LogisticRegression(*data):
    X_train, X_test ,y_train , y_test = data
    regr = linear_model.LogisticRegression()
    regr.fit(X_train, y_train)
    print("Coefficients:%s, intercept %s"%(regr.coef_,regr.intercept_))
    print("score:%.2f"% regr.score(X_test,y_test))

X_train, X_test ,y_train , y_test =  load_data()
test_LogisticRegression(X_train, X_test ,y_train , y_test)

#采用one-vs-one处理多分类问题
def test_LogisticRegression_multinomial(*data):
    X_train, X_test, y_train, y_test = data
    regr = linear_model.LogisticRegression(multi_class="multinomial",solver='sag')
    regr.fit(X_train, y_train)
    print("Coefficients:%s, intercept %s" % (regr.coef_, regr.intercept_))
    print("score:%.2f"% regr.score(X_test,y_test))

X_train, X_test ,y_train , y_test =  load_data()
test_LogisticRegression_multinomial(X_train, X_test ,y_train , y_test)

#考察参数C对分类模型的预测能力的影响。C是正则化系数的倒数，它越小正则化的权重越大
def test_LogisticRegression_C(*data):
    X_train, X_test, y_train, y_test = data
    Cs = np.logspace(-2,4,100)
    scores = []
    for c in Cs:
        regr = linear_model.LogisticRegression(C=c)
        regr.fit(X_train,y_train)
        scores.append(regr.score(X_test,y_test))
    fig = plt.figure()
    ax = fig.add_subplot(1,1,1)
    ax.plot(Cs, scores)
    ax.set_xlabel(r"c")
    ax.set_ylabel(r"score")
    ax.set_xscale("log")
    ax.set_title("LogistisRegression")
    plt.show()

X_train, X_test ,y_train , y_test =  load_data()
test_LogisticRegression_C(X_train, X_test ,y_train , y_test)

#线性判别分析
def test_LinearDiscriminantAnalysis(*data):
    X_train, X_test, y_train, y_test = data
    lda = discriminant_analysis.LinearDiscriminantAnalysis()
    lda.fit(X_train, y_train)
    print("Coefficients:%s, intercept %s" % (lda.coef_, lda.intercept_))
    print("score:%.2f" % lda.score(X_test, y_test))

X_train, X_test, y_train, y_test = load_data()
test_LinearDiscriminantAnalysis(X_train, X_test, y_train, y_test)

#现在来检查一下原始数据集在经过线性判别分析LDA之后的数据集的情况，绘制LDA降维之后的数据集函数
def plot_LDA(converted_X,y):
    from mpl_toolkits.mplot3d import Axes3D
    fig = plt.figure()
    ax = Axes3D(fig)
    colors = 'rgb'
    markers = 'o*s'
    for target,color,marker in zip([0,1,2],colors,markers):
        pos=(y==target).ravel()
        X=converted_X[pos,:]
        ax.scatter(X[:,0],X[:,1],X[:,2],color=color,marker=marker,label="Label %d"%target)
    ax.legend(loc="best")
    fig.suptitle("Iris After LDA")
    plt.show()

X_train, X_test, y_train, y_test = load_data()
X=np.vstack((X_train,X_test))
Y=np.vstack((y_train.reshape(y_train.size,1),y_test.reshape(y_test.size,1)))
lda = discriminant_analysis.LinearDiscriminantAnalysis()
lda.fit(X,Y)
converted_X=np.dot(X,np.transpose(lda.coef_))+lda.intercept_
plot_LDA(converted_X,Y)