def h2o_median_absolute_error(y_actual, y_predicted):
"""
Median absolute error regression loss
:param y_actual: H2OFrame of actual response.
:param y_predicted: H2OFrame of predicted response.
:return: loss (float) (best is 0.0)
"""
ModelBase._check_targets(y_actual, y_predicted)
return (y_predicted - y_actual).abs().median()
def h2o_median_absolute_error(y_actual, y_predicted):
"""
Median absolute error regression loss
:param y_actual: H2OFrame of actual response.
:param y_predicted: H2OFrame of predicted response.
:returns: median absolute error loss (best is 0.0)
"""
ModelBase._check_targets(y_actual, y_predicted)
return (y_predicted - y_actual).abs().median()
def h2o_mean_squared_error(y_actual, y_predicted, weights=None):
"""
Mean squared error regression loss
:param y_actual: H2OFrame of actual response.
:param y_predicted: H2OFrame of predicted response.
:param weights: (Optional) sample weights
:returns: mean squared error loss (best is 0.0).
"""
ModelBase._check_targets(y_actual, y_predicted)
return _colmean((y_predicted - y_actual)**2)
def h2o_mean_squared_error(y_actual, y_predicted, weights=None):
"""
Mean squared error regression loss
:param y_actual: H2OFrame of actual response.
:param y_predicted: H2OFrame of predicted response.
:param weights: (Optional) sample weights
:return: loss (float) (best is 0.0)
"""
ModelBase._check_targets(y_actual, y_predicted)
return ((y_predicted - y_actual) ** 2).mean()[0]
def h2o_mean_absolute_error(y_actual, y_predicted, weights=None):
"""
Mean absolute error regression loss.
:param y_actual: H2OFrame of actual response.
:param y_predicted: H2OFrame of predicted response.
:param weights: (Optional) sample weights
:return: loss (float) (best is 0.0)
"""
ModelBase._check_targets(y_actual, y_predicted)
return _colmean((y_predicted - y_actual).abs())
def h2o_mean_absolute_error(y_actual, y_predicted, weights=None):
"""
Mean absolute error regression loss.
:param y_actual: H2OFrame of actual response.
:param y_predicted: H2OFrame of predicted response.
:param weights: (Optional) sample weights
:return: loss (float) (best is 0.0)
"""
ModelBase._check_targets(y_actual, y_predicted)
return _colmean((y_predicted - y_actual).abs())
def h2o_explained_variance_score(y_actual, y_predicted, weights=None):
"""
Explained variance regression score function.
:param y_actual: H2OFrame of actual response.
:param y_predicted: H2OFrame of predicted response.
:param weights: (Optional) sample weights
:returns: the explained variance score.
"""
ModelBase._check_targets(y_actual, y_predicted)
_, numerator = _mean_var(y_actual - y_predicted, weights)
_, denominator = _mean_var(y_actual, weights)
if denominator == 0.0:
return 1. if numerator == 0 else 0. # 0/0 => 1, otherwise, 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
def h2o_explained_variance_score(y_actual, y_predicted, weights=None):
"""
Explained variance regression score function
:param y_actual: H2OFrame of actual response.
:param y_predicted: H2OFrame of predicted response.
:param weights: (Optional) sample weights
:return: the explained variance score (float)
"""
ModelBase._check_targets(y_actual, y_predicted)
_, numerator = _mean_var(y_actual - y_predicted, weights)
_, denominator = _mean_var(y_actual, weights)
if denominator == 0.0:
return 1. if numerator == 0 else 0. # 0/0 => 1, otherwise, 0
return 1 - numerator / denominator
def h2o_r2_score(y_actual, y_predicted, weights=1.):
"""
R^2 (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
:return: R^2 (float) (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 - y_actual.mean()[0]) ** 2).sum()
if denominator == 0.0:
return 1. if numerator == 0. else 0. # 0/0 => 1, else 0
return 1 - numerator / denominator
def download_mojo(self, path=".", get_genmodel_jar=False, genmodel_name=""):
"""
Download the leader model in AutoML in MOJO format.
:param path: the path where MOJO file should be saved.
:param get_genmodel_jar: if True, then also download h2o-genmodel.jar and store it in folder ``path``.
:param genmodel_name Custom name of genmodel jar
:returns: name of the MOJO file written.
"""
return ModelBase.download_mojo(self.leader, path, get_genmodel_jar, genmodel_name)
def download_mojo(self, path=".", get_genmodel_jar=False, genmodel_name=""):
"""
Download the leader model in AutoML in MOJO format.
:param path: the path where MOJO file should be saved.
:param get_genmodel_jar: if True, then also download h2o-genmodel.jar and store it in folder ``path``.
:param genmodel_name Custom name of genmodel jar
:returns: name of the MOJO file written.
"""
return ModelBase.download_mojo(self.leader, path, get_genmodel_jar, genmodel_name)
