regressor.py 文件源码

def train_ridge_linear_model(_train_x, train_y, _predict_x,
                             sample_weight=None):
    print_title("Ridge Regressor")
    train_x, predict_x = \
        standarize_feature(_train_x, _predict_x)

    # using the default CV
    alphas = [0.1, 1, 10, 100, 1e3, 1e4, 2e4, 5e4, 8e4, 1e5, 1e6, 1e7, 1e8]
    reg = linear_model.RidgeCV(alphas=alphas, store_cv_values=True)
    #reg.fit(train_x, train_y, sample_weight=sample_weight)
    reg.fit(train_x, train_y)
    cv_mse = np.mean(reg.cv_values_, axis=0)
    print("alphas: %s" % alphas)
    print("CV MSE: %s" % cv_mse)
    print("Best alpha using built-in RidgeCV: %f" % reg.alpha_)

    # generate the prediction using the best model
    alpha = reg.alpha_
    reg = linear_model.Ridge(alpha=alpha)
    #reg.fit(train_x, train_y, sample_weight=sample_weight)
    reg.fit(train_x, train_y)
    predict_y = reg.predict(predict_x)
    train_y_pred = reg.predict(train_x)

    return {"y": predict_y, "train_y": train_y_pred, "coef": reg.coef_}