from __future__ import absolute_import
from __future__ import division
from past.utils import old_div
from .model_base import ModelBase
[docs]class H2ORegressionModel(ModelBase):
def _make_model(self):
return H2ORegressionModel()
[docs] def plot(self, timestep="AUTO", metric="AUTO", **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.
:return: A scoring history plot.
"""
if self._model_json["algo"] in ("deeplearning", "drf", "gbm"):
if metric == "AUTO": metric = "MSE"
elif metric not in ("MSE","deviance"):
raise ValueError("metric for H2ORegressionModel must be one of: AUTO, MSE, deviance")
self._plot(timestep=timestep, metric=metric, **kwargs)
def _mean_var(frame, weights=None):
"""
Compute the (weighted) mean and variance
:param frame: Single column H2OFrame
:param weights: optional weights column
:return: The (weighted) mean and variance
"""
return frame.mean()[0], 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
:return: loss (float) (best is 0.0)
"""
ModelBase._check_targets(y_actual, y_predicted)
return (y_predicted-y_actual).abs().mean()[0]
[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
:return: loss (float) (best is 0.0)
"""
ModelBase._check_targets(y_actual, y_predicted)
return ((y_predicted-y_actual)**2).mean()[0]
[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
: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 - old_div(numerator, denominator)
[docs]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 - old_div(numerator, denominator)