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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