mvpa2.clfs.glmnet.GLMNET_C

class mvpa2.clfs.glmnet.GLMNET_C(**kwargs)

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.

Notes

Available conditional attributes:

• calling_time+: Time (in seconds) it took to call the node
• estimates+: Internal classifier estimates the most recent predictions are based on
• predicting_time+: Time (in seconds) which took classifier to predict
• predictions+: Most recent set of predictions
• raw_results: Computed results before invoking postproc. Stored only if postproc is not None.
• trained_dataset: The dataset it has been trained on
• trained_nsamples+: Number of samples it has been trained on
• trained_targets+: Set of unique targets it has been trained on
• training_stats: Confusion matrix of learning performance
• training_time+: Time (in seconds) it took to train the learner

(Conditional attributes enabled by default suffixed with +)

Methods

clone()	Create full copy of the classifier.
generate(ds)	Yield processing results.
get_postproc()	Returns the post-processing node or None.
get_sensitivity_analyzer(**kwargs)	Returns a sensitivity analyzer for GLMNET.
get_space()	Query the processing space name of this node.
is_trained([dataset])	Either classifier was already trained.
predict(obj, data, *args, **kwargs)
repredict(obj, data, *args, **kwargs)
reset()
retrain(dataset, **kwargs)	Helper to avoid check if data was changed actually changed Useful if just some aspects of classifier were changed since its previous training.
set_postproc(node)	Assigns a post-processing node Set to None to disable postprocessing.
set_space(name)	Set the processing space name of this node.
summary()	Providing summary over the classifier
train(ds)	The default implementation calls _pretrain(), _train(), and finally _posttrain().
untrain()	Reverts changes in the state of this node caused by previous training

Initialize GLM-Net multinomial classifier.

See the help in R for further details on the parameters

Parameters:

family :

Response type of your targets (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_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

auto_train : bool

Flag whether the learner will automatically train itself on the input dataset when called untrained.

force_train : bool

Flag whether the learner will enforce training on the input dataset upon every call.

space: str, optional :

Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.

postproc : Node instance, optional

Node to perform post-processing of results. This node is applied in __call__() to perform a final processing step on the to be result dataset. If None, nothing is done.

descr : str

Description of the instance

Methods

clone()	Create full copy of the classifier.
generate(ds)	Yield processing results.
get_postproc()	Returns the post-processing node or None.
get_sensitivity_analyzer(**kwargs)	Returns a sensitivity analyzer for GLMNET.
get_space()	Query the processing space name of this node.
is_trained([dataset])	Either classifier was already trained.
predict(obj, data, *args, **kwargs)
repredict(obj, data, *args, **kwargs)
reset()
retrain(dataset, **kwargs)	Helper to avoid check if data was changed actually changed Useful if just some aspects of classifier were changed since its previous training.
set_postproc(node)	Assigns a post-processing node Set to None to disable postprocessing.
set_space(name)	Set the processing space name of this node.
summary()	Providing summary over the classifier
train(ds)	The default implementation calls _pretrain(), _train(), and finally _posttrain().
untrain()	Reverts changes in the state of this node caused by previous training