mlens.ensemble.sequential module

ML-ENSEMBLE

author:Sebastian Flennerhag copyright:2017 licence:MIT

Sequential Ensemble class. Fully integrable with Scikit-learn.

class mlens.ensemble.sequential.SequentialEnsemble(shuffle=False, random_state=None, scorer=None, raise_on_exception=True, array_check=2, verbose=False, n_jobs=-1, backend=None, layers=None)

Bases: mlens.ensemble.base.BaseEnsemble

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

BlendEnsemble, Subsemble, SuperLearner

Parameters:

• shuffle (bool (default = True)) – whether to shuffle data before generating folds.
• 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.
• array_check (int (default = 2)) –

  level of strictness in checking input arrays.

  • array_check = 0 will not check X or y
  • array_check = 1 will check X and y for inconsistencies and warn when format looks suspicious, but retain original format.
  • array_check = 2 will impose Scikit-learn array checks, which converts X and y to numpy arrays and raises an error if conversion fails.

• 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 mlens.externals.joblib.Parallel. See Joblib for further documentation. To change global backend, set mlens.config.BACKEND

scores_

dict – if scorer was passed to instance, scores_ contains dictionary with cross-validated scores assembled during fit call. The fold structure used for scoring is determined by folds.

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('subset', [SVR(), Lasso()], n_partitions=10, n_splits=10)
>>>
>>> # Add a super learner as second layer
>>> ensemble.add('stack', [SVR(), Lasso()], n_splits=20)
>>>
>>> # Specify a meta estimator
>>> ensemble.add_meta(SVR())
>>>
>>> ensemble.fit(X, y)
>>> preds = ensemble.predict(X)
>>> rmse(y, preds)
6.5628...

add(cls, estimators, preprocessing=None, **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
  • ‘subset’ : 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 – ensemble instance with layer instantiated.

Return type:

instance

add_meta(estimator, **kwargs)

Meta Learner.

Meta learner to be used for final predictions.

Parameters:

• estimator (instance) – estimator instance.
• **kwargs (optional) – optional keyword arguments.