吴裕雄 python 机器学习——模型选择分类问题性能度量

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import  numpy as npimport  matplotlib.pyplot as pltfrom sklearn.svm import  SVCfrom sklearn.datasets import load_irisfrom sklearn.preprocessing import label_binarizefrom sklearn.multiclass import OneVsRestClassifierfrom sklearn.model_selection import train_test_splitfrom 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()

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#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()

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#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()

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#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()

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#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()

 

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