32692a1ae53d7c5bf36564aeca1b572ddf40f9f0,arviz/stats/stats.py,,r2_score,#Any#Any#Any#,513

Before Change

    if y_true.ndim > 1:
        dimension = 1

    var_y_est = np.var(y_pred, axis=dimension)
    var_e = np.var(y_true - y_pred, axis=dimension)

    r_squared = var_y_est / (var_y_est + var_e)
    return pd.Series([np.median(r_squared), np.mean(r_squared), np.std(r_squared)],
                     index=["r2_median", "r2_mean", "r2_std"]).round(decimals=round_to)

After Change

    y_pred_ = []
    for _ in range(1000):
        idx = np.random.randint(0, len(y_true), size=len(y_true))
        y_pred_.append(_r_square(y_true, y_pred, idx))

    idx = range(len(y_true))
    r_squared = _r_square(y_true, y_pred, idx)
    return pd.Series([r_squared, np.std(y_pred_)],

Instances