h2o-py/h2o/model/regression.py
def h2o_r2_score(y_actual, y_predicted, weights=1.):
    """
    R-squared (coefficient of determination) regression score function

    :param y_actual: H2OFrame of actual response.
    :param y_predicted: H2OFrame of predicted response.
    :param weights: (Optional) sample weights
    :returns: R-squared (best is 1.0, lower is worse).
    """
    ModelBase._check_targets(y_actual, y_predicted)
    numerator = (weights * (y_actual - y_predicted) ** 2).sum()
    denominator = (weights * (y_actual - _colmean(y_actual)) ** 2).sum()

    if denominator == 0.0:
        return 1. if numerator == 0. else 0.  # 0/0 => 1, else 0
    return 1 - numerator / denominator
def h2o_r2_score(y_actual, y_predicted, weights=1.):
    """
    R-squared (coefficient of determination) regression score function

    :param y_actual: H2OFrame of actual response.
    :param y_predicted: H2OFrame of predicted response.
    :param weights: (Optional) sample weights
    :returns: R-squared (best is 1.0, lower is worse).
    """
    ModelBase._check_targets(y_actual, y_predicted)
    numerator = (weights * (y_actual - y_predicted) ** 2).sum()
    denominator = (weights * (y_actual - _colmean(y_actual)) ** 2).sum()

    if denominator == 0.0:
        return 1. if numerator == 0. else 0.  # 0/0 => 1, else 0
    return 1 - numerator / denominator
R-squared (coefficient of determination) regression score function

    :param y_actual: H2OFrame of actual response.
    :param y_predicted: H2OFrame of predicted response.
    :param weights: (Optional) sample weights
    :returns: R-squared (best is 1.0, lower is worse).