# -*- encoding: utf-8 -*-
from h2o.exceptions import H2OValueError
from h2o.model import ModelBase
from h2o.model.extensions import has_extension
# noinspection PyUnresolvedReferences
from h2o.utils.compatibility import * # NOQA
from h2o.utils.shared_utils import _colmean
from h2o.utils.typechecks import assert_is_type
[docs]class H2ORegressionModel(ModelBase):
[docs] def plot(self, timestep="AUTO", metric="AUTO", save_plot_path=None, **kwargs):
"""
Plots training set (and validation set if available) scoring history for an H2ORegressionModel. The ``timestep``
and ``metric`` arguments are restricted to what is available in its scoring history.
:param timestep: A unit of measurement for the x-axis.
:param metric: A unit of measurement for the y-axis.
:param save_plot_path: a path to save the plot via using matplotlib function savefig.
:returns: Object that contains the resulting scoring history plot (can be accessed using ``result.figure()``).
:examples:
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> r = cars[0].runif()
>>> train = cars[r > .2]
>>> valid = cars[r <= .2]
>>> response_col = "economy"
>>> distribution = "gaussian"
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> gbm = H2OGradientBoostingEstimator(nfolds=3,
... distribution=distribution,
... fold_assignment="Random")
>>> gbm.train(x=predictors,
... y=response_col,
... training_frame=train,
... validation_frame=valid)
>>> gbm.plot(timestep="AUTO", metric="AUTO",)
"""
if not has_extension(self, 'ScoringHistory'):
raise H2OValueError("Scoring history plot is not available for this type of model (%s)." % self.algo)
valid_metrics = self._allowed_metrics('regression')
if valid_metrics is not None:
assert_is_type(metric, 'AUTO', *valid_metrics), "metric for H2ORegressionModel must be one of %s" % valid_metrics
if metric == "AUTO":
metric = self._default_metric('regression') or 'AUTO'
self.scoring_history_plot(timestep=timestep, metric=metric, save_plot_path=save_plot_path, **kwargs)
def _mean_var(frame, weights=None):
"""
Compute the (weighted) mean and variance.
:param frame: Single column H2OFrame.
:param weights: optional weights column.
:returns: The (weighted) mean and variance.
"""
return _colmean(frame), frame.var()
[docs]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.
:returns: mean absolute error loss (best is 0.0).
"""
ModelBase._check_targets(y_actual, y_predicted)
return _colmean((y_predicted - y_actual).abs())
[docs]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)
[docs]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
[docs]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().flatten()
denominator = (weights * (y_actual - _colmean(y_actual)) ** 2).sum().flatten()
if denominator == 0.0:
return 1. if numerator == 0. else 0. # 0/0 => 1, else 0
return 1 - numerator / denominator