h2o-py/h2o/h2o.py
def make_metrics(predicted, actual, domain=None, distribution=None):
    """
    Create Model Metrics from predicted and actual values in H2O.

    :param H2OFrame predicted: an H2OFrame containing predictions.
    :param H2OFrame actuals: an H2OFrame containing actual values.
    :param domain: list of response factors for classification.
    :param distribution: distribution for regression.
    """
    assert_is_type(predicted, H2OFrame)
    assert_is_type(actual, H2OFrame)
    # assert predicted.ncol == 1, "`predicted` frame should have exactly 1 column"
    assert actual.ncol == 1, "`actual` frame should have exactly 1 column"
    assert_is_type(distribution, str, None)
    assert_satisfies(actual.ncol, actual.ncol == 1)
    if domain is None and any(actual.isfactor()):
        domain = actual.levels()[0]
    res = api("POST /3/ModelMetrics/predictions_frame/%s/actuals_frame/%s" % (predicted.frame_id, actual.frame_id),
              data={"domain": domain, "distribution": distribution})
    return res["model_metrics"]
def make_metrics(predicted, actual, domain=None, distribution=None):
    assert_is_type(predicted, H2OFrame)
    assert_is_type(actual, H2OFrame)
    # assert predicted.ncol == 1, "`predicted` frame should have exactly 1 column"
    assert actual.ncol == 1, "`actual` frame should have exactly 1 column"
    assert_is_type(distribution, str, None)
    assert_satisfies(actual.ncol, actual.ncol == 1)
    if domain is None and any(actual.isfactor()):
        domain = actual.levels()[0]
    res = api("POST /3/ModelMetrics/predictions_frame/%s/actuals_frame/%s" % (predicted.frame_id, actual.frame_id),
              data={"domain": domain, "distribution": distribution})
    return res["model_metrics"]
def make_metrics ( predicted , actual , domain = None , distribution = None ) : assert_is_type ( predicted , H2OFrame ) assert_is_type ( actual , H2OFrame ) # assert predicted.ncol == 1, "`predicted` frame should have exactly 1 column" assert actual . ncol == 1 , "`actual` frame should have exactly 1 column" assert_is_type ( distribution , str , None ) assert_satisfies ( actual . ncol , actual . ncol == 1 ) if domain is None and any ( actual . isfactor ( ) ) : domain = actual . levels ( ) [ 0 ] res = api ( "POST /3/ModelMetrics/predictions_frame/%s/actuals_frame/%s" % ( predicted . frame_id , actual . frame_id ) , data = { "domain" : domain , "distribution" : distribution } ) return res [ "model_metrics" ]
Create Model Metrics from predicted and actual values in H2O.

    :param H2OFrame predicted: an H2OFrame containing predictions.
    :param H2OFrame actuals: an H2OFrame containing actual values.
    :param domain: list of response factors for classification.
    :param distribution: distribution for regression.
Create Model Metrics from predicted and actual values in H2O .