161 lines
5.4 KiB
Python
161 lines
5.4 KiB
Python
"""
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Base class for ensemble-based estimators.
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"""
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# Authors: Gilles Louppe
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# License: BSD 3 clause
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import numpy as np
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import numbers
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from ..base import clone
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from ..base import BaseEstimator
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from ..base import MetaEstimatorMixin
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from ..utils import _get_n_jobs, check_random_state
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from ..externals import six
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from abc import ABCMeta, abstractmethod
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MAX_RAND_SEED = np.iinfo(np.int32).max
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def _set_random_states(estimator, random_state=None):
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"""Sets fixed random_state parameters for an estimator
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Finds all parameters ending ``random_state`` and sets them to integers
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derived from ``random_state``.
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Parameters
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----------
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estimator : estimator supporting get/set_params
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Estimator with potential randomness managed by random_state
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parameters.
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random_state : int, RandomState instance or None, optional (default=None)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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Notes
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-----
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This does not necessarily set *all* ``random_state`` attributes that
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control an estimator's randomness, only those accessible through
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``estimator.get_params()``. ``random_state``s not controlled include
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those belonging to:
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* cross-validation splitters
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* ``scipy.stats`` rvs
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"""
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random_state = check_random_state(random_state)
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to_set = {}
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for key in sorted(estimator.get_params(deep=True)):
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if key == 'random_state' or key.endswith('__random_state'):
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to_set[key] = random_state.randint(MAX_RAND_SEED)
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if to_set:
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estimator.set_params(**to_set)
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class BaseEnsemble(six.with_metaclass(ABCMeta, BaseEstimator,
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MetaEstimatorMixin)):
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"""Base class for all ensemble classes.
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Warning: This class should not be used directly. Use derived classes
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instead.
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Parameters
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----------
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base_estimator : object, optional (default=None)
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The base estimator from which the ensemble is built.
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n_estimators : integer
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The number of estimators in the ensemble.
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estimator_params : list of strings
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The list of attributes to use as parameters when instantiating a
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new base estimator. If none are given, default parameters are used.
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Attributes
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----------
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base_estimator_ : estimator
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The base estimator from which the ensemble is grown.
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estimators_ : list of estimators
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The collection of fitted base estimators.
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"""
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@abstractmethod
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def __init__(self, base_estimator, n_estimators=10,
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estimator_params=tuple()):
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# Set parameters
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self.base_estimator = base_estimator
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self.n_estimators = n_estimators
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self.estimator_params = estimator_params
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# Don't instantiate estimators now! Parameters of base_estimator might
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# still change. Eg., when grid-searching with the nested object syntax.
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# self.estimators_ needs to be filled by the derived classes in fit.
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def _validate_estimator(self, default=None):
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"""Check the estimator and the n_estimator attribute, set the
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`base_estimator_` attribute."""
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if not isinstance(self.n_estimators, (numbers.Integral, np.integer)):
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raise ValueError("n_estimators must be an integer, "
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"got {0}.".format(type(self.n_estimators)))
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if self.n_estimators <= 0:
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raise ValueError("n_estimators must be greater than zero, "
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"got {0}.".format(self.n_estimators))
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if self.base_estimator is not None:
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self.base_estimator_ = self.base_estimator
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else:
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self.base_estimator_ = default
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if self.base_estimator_ is None:
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raise ValueError("base_estimator cannot be None")
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def _make_estimator(self, append=True, random_state=None):
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"""Make and configure a copy of the `base_estimator_` attribute.
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Warning: This method should be used to properly instantiate new
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sub-estimators.
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"""
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estimator = clone(self.base_estimator_)
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estimator.set_params(**dict((p, getattr(self, p))
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for p in self.estimator_params))
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if random_state is not None:
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_set_random_states(estimator, random_state)
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if append:
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self.estimators_.append(estimator)
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return estimator
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def __len__(self):
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"""Returns the number of estimators in the ensemble."""
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return len(self.estimators_)
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def __getitem__(self, index):
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"""Returns the index'th estimator in the ensemble."""
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return self.estimators_[index]
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def __iter__(self):
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"""Returns iterator over estimators in the ensemble."""
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return iter(self.estimators_)
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def _partition_estimators(n_estimators, n_jobs):
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"""Private function used to partition estimators between jobs."""
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# Compute the number of jobs
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n_jobs = min(_get_n_jobs(n_jobs), n_estimators)
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# Partition estimators between jobs
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n_estimators_per_job = (n_estimators // n_jobs) * np.ones(n_jobs,
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dtype=np.int)
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n_estimators_per_job[:n_estimators % n_jobs] += 1
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starts = np.cumsum(n_estimators_per_job)
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return n_jobs, n_estimators_per_job.tolist(), [0] + starts.tolist()
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