Package mvpa :: Package clfs :: Module enet :: Class ENET

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# Class ENET

source code

    Elastic-Net regression (ENET) `Classifier`.

    Elastic-Net is the model selection algorithm from:

    :ref:`Zou and Hastie (2005) <ZH05>` 'Regularization and Variable
    Selection via the Elastic Net' Journal of the Royal Statistical
    Society, Series B, 67, 301-320.

    Similar to SMLR, it performs a feature selection while performing
    classification, but instead of starting with all features, it
    starts with none and adds them in, which is similar to boosting.

    Unlike LARS it has both L1 and L2 regularization (instead of just
    L1).  This means that while it tries to sparsify the features it
    also tries to keep redundant features, which may be very very good
    for fMRI classification.

    In the true nature of the PyMVPA framework, this algorithm was
    actually implemented in R by Zou and Hastie and wrapped via RPy.
    To make use of ENET, you must have R and RPy installed as well as
    both the lars and elasticnet contributed package. You can install
    the R and RPy with the following command on Debian-based machines:

    sudo aptitude install python-rpy python-rpy-doc r-base-dev

    You can then install the lars and elasticnet package by running R
    as root and calling:

    install.packages()

Nested Classes | [hide private]

Inherited from `misc.state.ClassWithCollections`: `__metaclass__`

Instance Methods | [hide private]

__init__(self, lm=1.0, trace=False, normalize=True, intercept=True, max_steps=None, **kwargs) Initialize ENET. | source code

__repr__(self) String summary of the object | source code

_train(self, data) Train the classifier using `data` (Dataset). | source code

_predict(self, data) Predict the output for the provided data. | source code

_getFeatureIds(self) Return ids of the used features | source code

getSensitivityAnalyzer(self, **kwargs) Returns a sensitivity analyzer for ENET. | source code

Inherited from `base.Classifier`: `__str__`, `clone`, `isTrained`, `predict`, `repredict`, `retrain`, `summary`, `train`, `trained`, `untrain`

Inherited from `base.Classifier` (private): `_postpredict`, `_posttrain`, `_prepredict`, `_pretrain`, `_regressionIsBogus`, `_setRetrainable`

Inherited from `misc.state.ClassWithCollections`: `__getattribute__`, `__new__`, `__setattr__`, `reset`

Inherited from `object`: `__delattr__`, `__format__`, `__hash__`, `__reduce__`, `__reduce_ex__`, `__sizeof__`, `__subclasshook__`

Class Variables | [hide private]

| _clf_internals = `['enet', 'regression', 'linear', 'has_sensiti`...`` Describes some specifics about the classifier -- is that it is doing regression for instance....
| weights = `property(lambda self: self.__weights)`
Inherited from `base.Classifier`: `_DEV__doc__`, `feature_ids`, `predicting_time`, `predictions`, `regression`, `retrainable`, `trained_dataset`, `trained_labels`, `trained_nsamples`, `training_confusion`, `training_time`, `values` Inherited from `misc.state.ClassWithCollections`: `descr`

Instance Variables | [hide private]

| __weights The beta weights for each feature.
| __trained_model The model object after training that will be used for predictions.

Properties | [hide private]

Inherited from `object`: `__class__`

Method Details | [hide private]

### __init__(self, lm=1.0, trace=False, normalize=True, intercept=True, max_steps=None, **kwargs)
(Constructor)

source code

Initialize ENET.

See the help in R for further details on the following parameters:

Parameters:
  - `lm` (float) - Penalty parameter. 0 will perform LARS with no ridge regression. Default is 1.0.
  - `trace` (boolean) - Whether to print progress in R as it works.
  - `normalize` (boolean) - Whether to normalize the L2 Norm.
  - `intercept` (boolean) - Whether to add a non-penalized intercept to the model.
  - `max_steps` (None or int) - If not None, specify the total number of iterations to run. Each iteration adds a feature, but leaving it none will add until convergence.
Overrides: object.__init__

### __repr__(self)
(Representation operator)

source code

String summary of the object

Parameters:
  - `fullname` - Either to include full name of the module
  - `prefixes` - What other prefixes to prepend to list of arguments
Overrides: object.__repr__

### _train(self, data)

source code

Train the classifier using `data` (Dataset).

Overrides: base.Classifier._train

### _predict(self, data)

source code

Predict the output for the provided data.

Overrides: base.Classifier._predict

### _getFeatureIds(self)

source code

Return ids of the used features

Overrides: base.Classifier._getFeatureIds

### getSensitivityAnalyzer(self, **kwargs)

source code

Returns a sensitivity analyzer for ENET.

Overrides: base.Classifier.getSensitivityAnalyzer

Class Variable Details | [hide private]

### _clf_internals

Describes some specifics about the classifier -- is that it is doing regression for instance....

Value:

      ['enet', 'regression', 'linear', 'has_sensitivity', 'does_feature_sele
      ction']