12.5 classification_metrics.py 文件源码

def test_precision_recall_curve():

    iris=load_iris()
    X=iris.data
    y=iris.target

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

    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)

    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)

    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()