Task3_PGNS.py 文件源码

def stacking(base_models, X, Y, T):
    models = base_models
    folds = list(KFold(len(Y), n_folds=10, random_state=0))
    S_train = np.zeros((X.shape[0], len(models)))
    S_test = np.zeros((T.shape[0], len(models)))
    for i, bm in enumerate(models):
        clf = bm[1]
        S_test_i = np.zeros((T.shape[0], len(folds)))
        for j, (train_idx, test_idx) in enumerate(folds):
            X_train = X[train_idx]
            y_train = Y[train_idx]
            X_holdout = X[test_idx]
            clf.fit(X_train, y_train)
            y_pred = clf.predict(X_holdout)[:]
            S_train[test_idx, i] = y_pred
            S_test_i[:, j] = clf.predict(T)[:]
        S_test[:, i] = S_test_i.mean(1)
    nuss=NuSVR(kernel='rbf')
    nuss.fit(S_train, Y)
    yp = nuss.predict(S_test)[:]
    return yp

# load train data, the growthrate and log value of train data has been preserved in advance