| Multivariate Pattern Analysis in Python

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This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

# clfs.glmnet

## Module: `clfs.glmnet`

Inheritance diagram for `mvpa.clfs.glmnet`:

GLM-Net (GLMNET) regression classifier.

## Classes

### `GLMNETWeights`

class `mvpa.clfs.glmnet.``GLMNETWeights`(clf, force_training=True, **kwargs)

  Bases: `mvpa.measures.base.Sensitivity`

  SensitivityAnalyzer that reports the weights GLMNET trained on a given Dataset.

  Note

  Available state variables:

  - base_sensitivities: Stores basic sensitivities if the sensitivity relies on combining multiple ones
  - null_prob+: State variable
  - null_t: State variable
  - raw_results: Computed results before applying any transformation algorithm

  (States enabled by default are listed with +)

  See also

  Please refer to the documentation of the base class for more information:

  `Sensitivity`

  Initialize the analyzer with the classifier it shall use.

  Parameters:

  - clf (`Classifier`) – classifier to use.
  - force_training (Bool) – if classifier was already trained – do not retrain
  - enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
  - disable_states (None or list of basestring) – Names of the state variables which should be disabled
  - combiner (Functor) – The combiner is only applied if the computed featurewise dataset measure is more than one-dimensional. This is different from a transformer, which is always applied. By default, the sum of absolute values along the second axis is computed.
  - transformer (Functor) – This functor is called in __call__() to perform a final processing step on the to be returned dataset measure. If None, nothing is called
  - null_dist (instance of distribution estimator) – The estimated distribution is used to assign a probability for a certain value of the computed measure.

### `GLMNET_C`

class `mvpa.clfs.glmnet.``GLMNET_C`(**kwargs)

  Bases: `mvpa.clfs.glmnet._GLMNET`

  GLM-NET Multinomial Classifier.

  This is the GLM-NET algorithm from

  Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent. http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf

  parameterized to be a multinomial classifier.

  See GLMNET_Class for the gaussian regression version.

  Note

  Available state variables:

  - feature_ids: Feature IDS which were used for the actual training.
  - predicting_time+: Time (in seconds) which took classifier to predict
  - predictions+: Most recent set of predictions
  - trained_dataset: The dataset it has been trained on
  - trained_labels+: Set of unique labels it has been trained on
  - trained_nsamples+: Number of samples it has been trained on
  - training_confusion: Confusion matrix of learning performance
  - training_time+: Time (in seconds) which took classifier to train
  - values+: Internal classifier values the most recent predictions are based on

  (States enabled by default are listed with +)

  See also

  Please refer to the documentation of the base class for more information:

  `_GLMNET`

  Initialize GLM-Net multinomial classifier.

  See the help in R for further details on the parameters

  Parameters:

  - family – Response type of your labels (either ‘gaussian’ for regression or ‘multinomial’ for classification). (Default: gaussian)
  - alpha – The elastic net mixing parameter. Larger values will give rise to less L2 regularization, with alpha=1.0 as a true LASSO penalty. (Default: 1.0)
  - nlambda – Maximum number of lambdas to calculate before stopping if not converged. (Default: 100)
  - standardize – Whether to standardize the variables prior to fitting. (Default: True)
  - thresh – Convergence threshold for coordinate descent. (Default: 0.0001)
  - pmax – Limit the maximum number of variables ever to be nonzero. (Default: None)
  - maxit – Maximum number of outer-loop iterations for ‘multinomial’ families. (Default: 100)
  - model_type – ‘covariance’ saves all inner-products ever computed and can be much faster than ‘naive’. The latter can be more efficient for nfeatures>>nsamples situations. (Default: covariance)
  - enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
  - disable_states (None or list of basestring) – Names of the state variables which should be disabled

### `GLMNET_R`

class `mvpa.clfs.glmnet.``GLMNET_R`(**kwargs)

  Bases: `mvpa.clfs.glmnet._GLMNET`

  GLM-NET Gaussian Regression Classifier.

  This is the GLM-NET algorithm from

  Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent. http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf

  parameterized to be a regression.

  See GLMNET_C for the multinomial classifier version.

  Note

  Available state variables:

  - feature_ids: Feature IDS which were used for the actual training.
  - predicting_time+: Time (in seconds) which took classifier to predict
  - predictions+: Most recent set of predictions
  - trained_dataset: The dataset it has been trained on
  - trained_labels+: Set of unique labels it has been trained on
  - trained_nsamples+: Number of samples it has been trained on
  - training_confusion: Confusion matrix of learning performance
  - training_time+: Time (in seconds) which took classifier to train
  - values+: Internal classifier values the most recent predictions are based on

  (States enabled by default are listed with +)

  See also

  Please refer to the documentation of the base class for more information:

  `_GLMNET`

  Initialize GLM-Net.

  See the help in R for further details on the parameters

  Parameters:

  - family – Response type of your labels (either ‘gaussian’ for regression or ‘multinomial’ for classification). (Default: gaussian)
  - alpha – The elastic net mixing parameter. Larger values will give rise to less L2 regularization, with alpha=1.0 as a true LASSO penalty. (Default: 1.0)
  - nlambda – Maximum number of lambdas to calculate before stopping if not converged. (Default: 100)
  - standardize – Whether to standardize the variables prior to fitting. (Default: True)
  - thresh – Convergence threshold for coordinate descent. (Default: 0.0001)
  - pmax – Limit the maximum number of variables ever to be nonzero. (Default: None)
  - maxit – Maximum number of outer-loop iterations for ‘multinomial’ families. (Default: 100)
  - model_type – ‘covariance’ saves all inner-products ever computed and can be much faster than ‘naive’. The latter can be more efficient for nfeatures>>nsamples situations. (Default: covariance)
  - regression – Either to use ‘regression’ as regression. By default any Classifier- derived class serves as a classifier, so regression does binary classification. (Default: False)
  - enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
  - disable_states (None or list of basestring) – Names of the state variables which should be disabled

### Navigation

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