label_prop_plot_roc_auc_score

label_prop_plot_roc_auc_score.py

import matplotlib.pyplot as plt
def label_prop_test(model, kernel, params_list, X_train, X_test, y_train, y_test):
    plt.figure(figsize=(20,10))
    n, g = 0, 0
    roc_scores = []
    if kernel == 'rbf':
        for g in params_list:
            lp = model(kernel=kernel, n_neighbors=n, gamma=g, max_iter=100000, tol=0.0001)
            lp.fit(X_train, y_train)
            roc_scores.append(roc_auc_score(y_test, lp.predict_proba(X_test), multi_class='ovo'))
    if kernel == 'knn':
        for n in params_list:
            lp = model(kernel=kernel, n_neighbors=n, gamma=g, max_iter=100000, tol=0.0001)
            lp.fit(X_train, y_train)
            roc_scores.append(roc_auc_score(y_test, lp.predict_proba(X_test), multi_class='ovo'))
    plt.figure(figsize=(16,8));
    plt.plot(params_list, roc_scores)
    plt.title('Label Propagation ROC AUC with ' + kernel + ' kernel')
    plt.show()
    print('Best metrics value is at {}'.format(params_list[np.argmax(roc_scores)]))