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)