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"""
Base class for ensemble-based estimators.
"""
# Authors: Gilles Louppe
# License: BSD 3
from ..base import clone
from ..base import BaseEstimator
class BaseEnsemble(BaseEstimator):
"""Base class for all ensemble classes.
Warning: This class should not be used directly. Use derived classes
instead.
Parameters
----------
base_estimator : object, optional (default=None)
The base estimator from which the ensemble is built.
n_estimators : integer
The number of estimators in the ensemble.
estimator_params : list of strings
The list of attributes to use as parameters when instantiating a
new base estimator. If none are given, default parameters are used.
"""
def __init__(self, base_estimator, n_estimators, estimator_params=[]):
# Check parameters
if not isinstance(base_estimator, BaseEstimator):
raise TypeError("estimator must be a subclass of BaseEstimator")
if n_estimators <= 0:
raise ValueError("n_estimators must be greater than zero.")
# Set parameters
self.base_estimator = base_estimator
self.n_estimators = n_estimators
self.estimator_params = estimator_params
# Don't instantiate estimators now! Parameters of base_estimator might
# still change. Eg., when grid-searching with the nested object syntax.
# This needs to be filled by the derived classes.
self.estimators_ = []
def _make_estimator(self, append=True):
"""Makes, configures and returns a copy of the base estimator.
Warning: This method should be used to properly instantiate new
sub-estimators.
"""
estimator = clone(self.base_estimator)
estimator.set_params(**dict((p, getattr(self, p))
for p in self.estimator_params))
if append:
self.estimators_.append(estimator)
return estimator
def __len__(self):
"""Returns the number of estimators in the ensemble."""
return len(self.estimators_)
def __getitem__(self, index):
"""Returns the index'th estimator in the ensemble."""
return self.estimators_[index]
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