regressor.py 文件源码

def train_lassolars_model(train_x, train_y, predict_x):
    print_title("LassoLars Regressor")
    reg = linear_model.LassoLarsCV(
        cv=10, n_jobs=3, max_iter=2000, normalize=False)
    reg.fit(train_x, train_y)
    print("alphas and cv_alphas: {0} and {1}".format(
        reg.alphas_.shape, reg.cv_alphas_.shape))
    print("alphas[%d]: %s" % (len(reg.cv_alphas_), reg.cv_alphas_))
    print("mse shape: {0}".format(reg.cv_mse_path_.shape))
    # print("mse: %s" % np.mean(_mse, axis=0))
    # print("mse: %s" % np.mean(_mse, axis=1))
    # index = np.where(reg.alphas_ == reg.alpha_)
    # print("itemindex: %s" % index)
    index = np.where(reg.cv_alphas_ == reg.alpha_)
    _mse_v = np.mean(reg.cv_mse_path_[index, :])
    print("mse value: %f" % _mse_v)

    print("best alpha: %f" % reg.alpha_)
    best_alpha = reg.alpha_
    reg = linear_model.LassoLars(alpha=best_alpha)
    reg.fit(train_x, train_y)
    n_nonzeros = (reg.coef_ != 0).sum()
    print("Non-zeros coef: %d" % n_nonzeros)
    predict_y = reg.predict(predict_x)
    return {'y': predict_y, "coef": reg.coef_}