吴裕雄 python 机器学习——模型选择分类问题性能度量

Posted 2021-11-26 tszr

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()