apply_ml1.py 文件源码

def performSVMClass(X_train, y_train, X_test, y_test):
    classifier = svm.SVC()
    classifier.fit(X_train, y_train)
    results = classifier.predict(X_test)

    # colors = {1:'red', 0:'blue'}
    # df = pd.DataFrame(dict(adj=X_test[:,5], return_=X_test[:,50], label=results))

    # fig, ax = plt.subplots()
    # colors = {1:'red', 0:'blue'}
    # ax.scatter(df['adj'],df['return_'], c=df['label'].apply(lambda x: colors[x]))
    # # ax.scatter(X_test[:,5], X_test[:,50], c=y_test_list.apply(lambda x: colors[x]))
    # plt.show()
    # print y_pred
    # cm = confusion_matrix(y_test, results)
    # print cm
    # plt.figure()
    # plot_confusion_matrix(cm)
    # plt.show()

    num_correct = (results == y_test).sum()
    recall = num_correct / len(y_test)
    # print "SVM model accuracy (%): ", recall * 100, "%"

    return recall*100