iris 数据分类

秒速五厘米 2023-06-10 04:25 5阅读 0赞

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#coding=utf-8
    """
    #演示目的：利用鸢尾花数据集画出P-R曲线,mooc
    """
    print(__doc__)
    
    import matplotlib.pyplot as plt
    import numpy as np
    from sklearn import svm, datasets
    from sklearn.metrics import precision_recall_curve
    from sklearn.metrics import average_precision_score
    from sklearn.model_selection import KFold
    from sklearn.model_selection import train_test_split
    
    #from sklearn.cross_validation import train_test_split
    from sklearn.preprocessing import label_binarize
    from sklearn.multiclass import OneVsRestClassifier
    #以iris数据为例，画出P-R曲线
    iris =datasets.load_iris()
    x =iris.data
    y =iris.target
    #print(x)
    print(x.shape,y.shape)
    y=label_binarize(y,classes=[0,1,2])
    n_classes=y.shape[1]
    print(y.shape)
    '''
    #print(y)
    print(x.shape,y.shape)
    '''
    random_state=np.random.RandomState(0)
    
    #print(random_state)
    n_samples,n_features=x.shape# 分别是行数和列数
    print("n_samples:",n_samples,"n_features:",n_features)
    x=np.c_[x,random_state.randn(n_samples,200*n_features)]  # The different of randn and random 
    x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.5,random_state=random_state)
    #print("x_train",x_train,"x_test:",x_test)
    classifier = OneVsRestClassifier(svm.SVC(kernel='linear',probability=True,random_state=random_state))
    y_socre = classifier.fit(x_train,y_train).decision_function(x_test)
    #print("classifier:",classifier)
    #print("y_socre:",y_socre)
    precision=dict()
    recall=dict()
    average_precision=dict()
    
    for i in range(n_classes):
        precision[i],recall[i],_= precision_recall_curve(y_test[:,i],y_socre[:,i])
        average_precision[i]= average_precision_score(y_test[:,i],y_socre[:,i])
    
    precision["micro"],recall["micro"],_=precision_recall_curve(y_test.ravel(),y_socre.ravel())
    average_precision["micro"] =average_precision_score(y_test,y_socre,average="micro")
    
    plt.clf()
    plt.plot(recall["micro"],precision["micro"],label='micro-average Precision-recall'.format(average_precision["micro"]))
    
    for i in range(n_classes):
        plt.plot(recall[i], precision[i],
                 label='Precision-recall curve of class {0} (area = {1:0.2f})'.format(i, average_precision[i]))
    
    
    plt.legend(loc="lower right")#legend 是用于设置图例的函数
    plt.show()

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[https_www.icourse163.org_learn_HIT-1206320802_tid_1206635203_learn_content_type_detail_id_1212706083_sm_1]: https://www.icourse163.org/learn/HIT-1206320802?tid=1206635203#/learn/content?type=detail&id=1212706083&sm=1