Source code for mlens.ensemble.sequential


:author: Sebastian Flennerhag
:copyright: 2017-2018
:licence: MIT

Sequential Ensemble class. Fully integrable with Scikit-learn.

from __future__ import division

from .base import BaseEnsemble
from ..index import INDEXERS
from ..utils import kwarg_parser

[docs]class SequentialEnsemble(BaseEnsemble): r"""Sequential Ensemble class. The Sequential Ensemble class allows users to build ensembles with different classes of layers. The type of layer and its parameters are specified when added to the ensemble. See respective ensemble class for details on parameters. See Also -------- :class:`BlendEnsemble`, :class:`Subsemble`, :class:`SuperLearner` Parameters ---------- shuffle : bool (default = False) whether to shuffle data before before processing each layer. For greater control, specify ``shuffle`` when adding the layer. random_state : int (default = None) random seed if shuffling inputs. scorer : object (default = None) scoring function. If a function is provided, base estimators will be scored on the training set assembled for fitting the meta estimator. Since those predictions are out-of-sample, the scores represent valid test scores. The scorer should be a function that accepts an array of true values and an array of predictions: ``score = f(y_true, y_pred)``. raise_on_exception : bool (default = True) whether to issue warnings on soft exceptions or raise error. Examples include lack of layers, bad inputs, and failed fit of an estimator in a layer. If set to ``False``, warnings are issued instead but estimation continues unless exception is fatal. Note that this can result in unexpected behavior unless the exception is anticipated. verbose : int or bool (default = False) level of verbosity. * ``verbose = 0`` silent (same as ``verbose = False``) * ``verbose = 1`` messages at start and finish (same as ``verbose = True``) * ``verbose = 2`` messages for each layer If ``verbose >= 50`` prints to ``sys.stdout``, else ``sys.stderr``. For verbosity in the layers themselves, use ``fit_params``. n_jobs : int (default = -1) number of CPU cores to use for fitting and prediction. backend : str or object (default = 'threading') backend infrastructure to use during call to :class:`mlens.externals.joblib.Parallel`. See Joblib for further documentation. To change global backend, set ``mlens.config._BACKEND`` model_selection: bool (default=False) Whether to use the ensemble in model selection mode. If ``True``, this will alter the ``transform`` method. When calling ``transform`` on new data, the ensemble will call ``predict``, while calling ``transform`` with the training data reproduces predictions from the ``fit`` call. Hence the ensemble can be used as a pure transformer in a preprocessing pipeline passed to the :class:`Evaluator`, as training folds are faithfully reproduced as during a ``fit``call and test folds are transformed with the ``predict`` method. sample_size: int (default=20) size of training set sample (``[min(sample_size, X.size[0]), min(X.size[1], sample_size)]``) Examples -------- >>> from mlens.ensemble import SequentialEnsemble >>> from mlens.metrics.metrics import rmse >>> from sklearn.datasets import load_boston >>> from sklearn.linear_model import Lasso >>> from sklearn.svm import SVR >>> >>> X, y = load_boston(True) >>> >>> ensemble = SequentialEnsemble() >>> >>> # Add a subsemble with 5 partitions as first layer >>> ensemble.add('subsemble', [SVR(), Lasso()], partitions=10, folds=10) >>> >>> # Add a super learner as second layer >>> ensemble.add('stack', [SVR(), Lasso()], folds=20) >>> >>> # Specify a meta estimator >>> ensemble.add_meta(SVR()) >>> >>>, y) >>> preds = ensemble.predict(X) >>> rmse(y, preds) 6.5628... """ def __init__( self, shuffle=False, random_state=None, scorer=None, raise_on_exception=True, array_check=None, verbose=False, n_jobs=-1, backend=None, model_selection=False, sample_size=20, layers=None): super(SequentialEnsemble, self).__init__( shuffle=shuffle, random_state=random_state, scorer=scorer, raise_on_exception=raise_on_exception, verbose=verbose, n_jobs=n_jobs, layers=layers, array_check=array_check, model_selection=model_selection, sample_size=sample_size, backend=backend)
[docs] def add_meta(self, estimator, **kwargs): """Meta Learner. Meta learner to be used for final predictions. Parameters ---------- estimator : instance estimator instance. **kwargs : optional optional keyword arguments. """ return self.add(cls='full', estimators=estimator, meta=True, **kwargs)
[docs] def add(self, cls, estimators, preprocessing=None, meta=False, **kwargs): """Add layer to ensemble. For full set of optional arguments, see the ensemble API for the specified type. Parameters ---------- cls : str layer class. Accepted types are: * 'blend' : blend ensemble * 'subsemble' : subsemble * 'stack' : super learner estimators: dict of lists or list or instance estimators constituting the layer. If preprocessing is none and the layer is meant to be the meta estimator, it is permissible to pass a single instantiated estimator. If ``preprocessing`` is ``None`` or ``list``, ``estimators`` should be a ``list``. The list can either contain estimator instances, named tuples of estimator instances, or a combination of both. :: option_1 = [estimator_1, estimator_2] option_2 = [("est-1", estimator_1), ("est-2", estimator_2)] option_3 = [estimator_1, ("est-2", estimator_2)] If different preprocessing pipelines are desired, a dictionary that maps estimators to preprocessing pipelines must be passed. The names of the estimator dictionary must correspond to the names of the estimator dictionary. :: preprocessing_cases = {"case-1": [trans_1, trans_2], "case-2": [alt_trans_1, alt_trans_2]} estimators = {"case-1": [est_a, est_b], "case-2": [est_c, est_d]} The lists for each dictionary entry can be any of ``option_1``, ``option_2`` and ``option_3``. preprocessing: dict of lists or list, optional (default = None) preprocessing pipelines for given layer. If the same preprocessing applies to all estimators, ``preprocessing`` should be a list of transformer instances. The list can contain the instances directly, named tuples of transformers, or a combination of both. :: option_1 = [transformer_1, transformer_2] option_2 = [("trans-1", transformer_1), ("trans-2", transformer_2)] option_3 = [transformer_1, ("trans-2", transformer_2)] If different preprocessing pipelines are desired, a dictionary that maps preprocessing pipelines must be passed. The names of the preprocessing dictionary must correspond to the names of the estimator dictionary. :: preprocessing_cases = {"case-1": [trans_1, trans_2], "case-2": [alt_trans_1, alt_trans_2]} estimators = {"case-1": [est_a, est_b], "case-2": [est_c, est_d]} The lists for each dictionary entry can be any of ``option_1``, ``option_2`` and ``option_3``. **kwargs : optional optional keyword arguments to instantiate layer with. See respective ensemble for further details. Returns ------- self : instance ensemble instance with layer instantiated. """ if cls not in INDEXERS: raise NotImplementedError("Layer class not implemented. Select " "one of %r." % sorted(INDEXERS)) if cls == 'subsemble' and 'partition_estimator' in kwargs: cls = 'clusteredsubsemble' # instantiate the indexer indexer = INDEXERS[cls] kwargs_idx, kwargs = kwarg_parser(indexer.__init__, kwargs) indexer = indexer(**kwargs_idx) return super(SequentialEnsemble, self).add( estimators=estimators, indexer=indexer, preprocessing=preprocessing, **kwargs)