吴裕雄 python 机器学习——模型选择分类问题性能度量

import  numpy as np

import  matplotlib.pyplot as plt

from sklearn.svm import  SVC

from sklearn.datasets import load_iris

from sklearn.preprocessing import label_binarize

from sklearn.multiclass import OneVsRestClassifier

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score,fbeta_score,classification_report,confusion_matrix,precision_recall_curve,roc_auc_score,roc_curve

#模型选择分类问题性能度量accuracy_score模型

def test_accuracy_score():

    y_true=[1,1,1,1,1,0,0,0,0,0]

    y_pred=[0,0,1,1,0,0,1,1,0,0]

    print('Accuracy Score(normalize=True):',accuracy_score(y_true,y_pred,normalize=True))

    print('Accuracy Score(normalize=False):',accuracy_score(y_true,y_pred,normalize=False))

#调用test_accuracy_score()

test_accuracy_score()

#模型选择分类问题性能度量precision_score模型

def test_precision_score():

    y_true=[1,1,1,1,1,0,0,0,0,0]

    y_pred=[0,0,1,1,0,0,0,0,0,0]

    print('Accuracy Score:',accuracy_score(y_true,y_pred,normalize=True))

    print('Precision Score:',precision_score(y_true,y_pred))

#调用test_precision_score()

test_precision_score()

#模型选择分类问题性能度量recall_score模型

def test_recall_score():

    y_true=[1,1,1,1,1,0,0,0,0,0]

    y_pred=[0,0,1,1,0,0,0,0,0,0]

    print('Accuracy Score:',accuracy_score(y_true,y_pred,normalize=True))

    print('Precision Score:',precision_score(y_true,y_pred))

    print('Recall Score:',recall_score(y_true,y_pred))

#调用test_recall_score()

test_recall_score()

#模型选择分类问题性能度量f1_score模型

def test_f1_score():

    y_true=[1,1,1,1,1,0,0,0,0,0]

    y_pred=[0,0,1,1,0,0,0,0,0,0]

    print('Accuracy Score:',accuracy_score(y_true,y_pred,normalize=True))

    print('Precision Score:',precision_score(y_true,y_pred))

    print('Recall Score:',recall_score(y_true,y_pred))

    print('F1 Score:',f1_score(y_true,y_pred))

#调用test_f1_score()

test_f1_score()

#模型选择分类问题性能度量fbeta_score模型

def test_fbeta_score():

    y_true=[1,1,1,1,1,0,0,0,0,0]

    y_pred=[0,0,1,1,0,0,0,0,0,0]

    print('Accuracy Score:',accuracy_score(y_true,y_pred,normalize=True))

    print('Precision Score:',precision_score(y_true,y_pred))

    print('Recall Score:',recall_score(y_true,y_pred))

    print('F1 Score:',f1_score(y_true,y_pred))

    print('Fbeta Score(beta=0.001):',fbeta_score(y_true,y_pred,beta=0.001))

    print('Fbeta Score(beta=1):',fbeta_score(y_true,y_pred,beta=1))

    print('Fbeta Score(beta=10):',fbeta_score(y_true,y_pred,beta=10))

    print('Fbeta Score(beta=10000):',fbeta_score(y_true,y_pred,beta=10000))

#调用test_fbeta_score()

test_fbeta_score()

#模型选择分类问题性能度量classification_report模型

def test_classification_report():

    y_true=[1,1,1,1,1,0,0,0,0,0]

    y_pred=[0,0,1,1,0,0,0,0,0,0]

    print('Classification Report:\n',classification_report(y_true,y_pred,target_names=["class_0","class_1"]))

#调用test_classification_report()

test_classification_report()

#模型选择分类问题性能度量confusion_matrix模型

def test_confusion_matrix():

    y_true=[1,1,1,1,1,0,0,0,0,0]

    y_pred=[0,0,1,1,0,0,0,0,0,0]

    print('Confusion Matrix:\n',confusion_matrix(y_true,y_pred,labels=[0,1]))

#调用test_confusion_matrix()

test_confusion_matrix()

#模型选择分类问题性能度量precision_recall_curve模型

def test_precision_recall_curve():

    ### 加载数据

    iris=load_iris()

    X=iris.data

    y=iris.target

    # 二元化标记

    y = label_binarize(y, classes=[0, 1, 2])

    n_classes = y.shape[1]

    #### 添加噪音

    np.random.seed(0)

    n_samples, n_features = X.shape

    X = np.c_[X, np.random.randn(n_samples, 200 * n_features)]

    X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.5,random_state=0)

    ### 训练模型

    clf=OneVsRestClassifier(SVC(kernel='linear', probability=True,random_state=0))

    clf.fit(X_train,y_train)

    y_score = clf.fit(X_train, y_train).decision_function(X_test)

    ### 获取 P-R

    fig=plt.figure()

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

    precision = dict()

    recall = dict()

    for i in range(n_classes):

        precision[i], recall[i], _ = precision_recall_curve(y_test[:, i],y_score[:, i])

        ax.plot(recall[i],precision[i],label="target=%s"%i)

    ax.set_xlabel("Recall Score")

    ax.set_ylabel("Precision Score")

    ax.set_title("P-R")

    ax.legend(loc='best')

    ax.set_xlim(0,1.1)

    ax.set_ylim(0,1.1)

    ax.grid()

    plt.show()

#调用test_precision_recall_curve()

test_precision_recall_curve()

#模型选择分类问题性能度量roc_curve、roc_auc_score模型

def test_roc_auc_score():

    ### 加载数据

    iris=load_iris()

    X=iris.data

    y=iris.target

    # 二元化标记

    y = label_binarize(y, classes=[0, 1, 2])

    n_classes = y.shape[1]

    #### 添加噪音

    np.random.seed(0)

    n_samples, n_features = X.shape

    X = np.c_[X, np.random.randn(n_samples, 200 * n_features)]

    X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.5,random_state=0)

    ### 训练模型

    clf=OneVsRestClassifier(SVC(kernel='linear', probability=True,random_state=0))

    clf.fit(X_train,y_train)

    y_score = clf.fit(X_train, y_train).decision_function(X_test)

    ### 获取 ROC

    fig=plt.figure()

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

    fpr = dict()

    tpr = dict()

    roc_auc=dict()

    for i in range(n_classes):

        fpr[i], tpr[i], _ = roc_curve(y_test[:, i],y_score[:, i])

        roc_auc[i] = roc_auc_score(fpr[i], tpr[i])

        ax.plot(fpr[i],tpr[i],label="target=%s,auc=%s"%(i,roc_auc[i]))

    ax.plot([0, 1], [0, 1], 'k--')

    ax.set_xlabel("FPR")

    ax.set_ylabel("TPR")

    ax.set_title("ROC")

    ax.legend(loc="best")

    ax.set_xlim(0,1.1)

    ax.set_ylim(0,1.1)

    ax.grid()

    plt.show()

#调用test_roc_auc_score()

test_roc_auc_score()