deepensemble.combiner
Methods Combiner or Mixing Models¶
Base Class Combiner¶
- class deepensemble.combiner.modelcombiner.ModelCombiner(param=None, type_model='regressor')[source]¶
Base class for mixing output of models.
Parameters: param : dict
Parameters of combiner method.
type_model : str
Type of model: regressor or classifier
Attributes
_param (dict) Parameters of combiner method. _type_model (str, “regressor” by default) Type of model: regressor or classifier - get_param(only_values=False)[source]¶
Getter model combinator parameters.
Returns: theano.shared
Returns model parameters.
- get_type_model()[source]¶
Get type of model.
Returns: str
Returns one string with the type of model: “regressor” or “classifier”
- output(ensemble_model, _input, prob)[source]¶
Mixing the output or diversity of ensemble’s models.
Parameters: ensemble_model : EnsembleModel
Ensemble Model it uses for get ensemble’s models.
_input : theano.tensor.matrix or numpy.array
Input sample.
prob : bool
In the case of classifier if is True the output is probability, for False means the output is translated. Is recommended hold True for training because the translate function is non-differentiable.
Returns: theano.tensor.matrix
Returns the mixing diversity of ensemble’s models.
- predict(ensemble_model, _input)[source]¶
Compute the diversity of model.
Parameters: ensemble_model : EnsembleModel
Ensemble model where gets the output.
_input : theano.tensor.matrix or numpy.array
Input sample.
Returns: numpy.array
Return the diversity of model.
- update_parameters(ensemble_model, _input, _target)[source]¶
Update internal parameters.
Parameters: ensemble_model : EnsembleModel
Ensemble Model it uses for get ensemble’s models.
_input : theano.tensor.matrix
Input sample.
_target : theano.tensor.matrix
Target sample.
Returns: OrderedDict
A dictionary mapping each parameter to its update expression.
Methods Combiner Regression¶
- class deepensemble.combiner.averagecombiner.AverageCombiner(**kwargs)[source]¶
Class for compute the average the output models.
- output(ensemble_model, _input, prob)[source]¶
Average the output of the ensemble’s models.
Parameters: ensemble_model : EnsembleModel
Ensemble Model it uses for get ensemble’s models.
_input : theano.tensor.matrix or numpy.array
Input sample.
prob : bool
In the case of classifier if is True the output is probability, for False means the output is translated. Is recommended hold True for training because the translate function is non-differentiable.
Returns: theano.Op
Returns the average of the output models.
- class deepensemble.combiner.weightaveragecombiner.WeightAverageCombiner(n_models, **kwargs)[source]¶
Class for compute the average the output models.
Parameters: n_models : int
Number of models of ensemble.
References
[R12] Zhi-Hua Zhou. (2012), pp 70: Ensemble Methods Foundations and Algorithms Chapman & Hall/CRC Machine Learning & Pattern Recognition Series. [R22] M. P. Perrone and L. N. Cooper. When networks disagree: Ensemble method for neural networks. In R. J.Mammone, editor, Artificial Neural Networks for Spech and Vision, pages 126-142. Chapman & Hall, New York, NY, 1993. Attributes
n_models (int) Number of models in ensemble. params (theano.shared) This parameter contain the weights of method. - output(ensemble_model, _input, prob)[source]¶
Average the output of the ensemble’s models.
Parameters: ensemble_model : EnsembleModel
Ensemble Model it uses for get ensemble’s models.
_input : theano.tensor.matrix or numpy.array
Input sample.
prob : bool
In the case of classifier if is True the output is probability, for False means the output is translated. Is recommended hold True for training because the translate function is non-differentiable.
Returns: theano.Op
Returns the average of the output models.
- update_parameters(ensemble_model, _input, _target)[source]¶
Update internal parameters.
Parameters: ensemble_model : EnsembleModel
Ensemble Model it uses for get ensemble’s models.
_input : theano.tensor.matrix
Input sample.
_target : theano.tensor.matrix
Target sample.
Returns: OrderedDict
A dictionary mapping each parameter to its update expression.
- class deepensemble.combiner.geometriccombiner.GeometricCombiner(**kwargs)[source]¶
Class for compute the average the output models.
- output(ensemble_model, _input, prob)[source]¶
Multiplied the output of the ensemble’s models.
Parameters: ensemble_model : EnsembleModel
Ensemble Model it uses for get ensemble’s models.
_input : theano.tensor.matrix or numpy.array
Input sample.
prob : bool
In the case of classifier if is True the output is probability, for False means the output is translated. Is recommended hold True for training because the translate function is non-differentiable.
Returns: theano.Op
Returns the average of the output models.
Methods Combiner Classification¶
- class deepensemble.combiner.averagecombiner.PluralityVotingCombiner[source]¶
Combiner classifier method where each model in ensemble votes by one class and the class with more votes win.
Plurality voting takes the class label which receives the largest number of votes as the final winner. That is, the output class label of the ensemble.
References
[R55] Zhi-Hua Zhou. (2012), pp 73: Ensemble Methods Foundations and Algorithms Chapman & Hall/CRC Machine Learning & Pattern Recognition Series. - output(ensemble_model, _input, prob)[source]¶
Average the output of the ensemble’s models.
Parameters: ensemble_model : EnsembleModel
Ensemble Model it uses for get ensemble’s models.
_input : theano.tensor.matrix or numpy.array
Input sample.
prob : bool
In the case of classifier if is True the output is probability, for False means the output is translated. Is recommended hold True for training because the translate function is non-differentiable.
Returns: theano.Op
Returns the average of the output models.
- class deepensemble.combiner.weightaveragecombiner.WeightedVotingCombiner(n_models)[source]¶
Class for compute the average the output models.
Parameters: n_models : int
Number of models of ensemble.
References
[R77] Zhi-Hua Zhou. (2012), pp 74: Ensemble Methods Foundations and Algorithms Chapman & Hall/CRC Machine Learning & Pattern Recognition Series. - output(ensemble_model, _input, prob)[source]¶
Average the output of the ensemble’s models.
Parameters: ensemble_model : EnsembleModel
Ensemble Model it uses for get ensemble’s models.
_input : theano.tensor.matrix or numpy.array
Input sample.
prob : bool
In the case of classifier if is True the output is probability, for False means the output is translated. Is recommended hold True for training because the translate function is non-differentiable.
Returns: theano.Op
Returns the average of the output models.
- class deepensemble.combiner.geometriccombiner.GeometricVotingCombiner[source]¶
Combiner classifier method where each model in ensemble votes by one class and the class with more votes win.
- output(ensemble_model, _input, prob)[source]¶
Average the output of the ensemble’s models.
Parameters: ensemble_model : EnsembleModel
Ensemble Model it uses for get ensemble’s models.
_input : theano.tensor.matrix or numpy.array
Input sample.
prob : bool
In the case of classifier if is True the output is probability, for False means the output is translated. Is recommended hold True for training because the translate function is non-differentiable.
Returns: theano.Op
Returns the average of the output models.