mvpa2.clfs.glmnet.GLMNET_R

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

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.

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 (or any other space) it has been trained on (if present in the dataset 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 +)

Attributes

auto_train	Whether the Learner performs automatic trainingwhen called untrained.
descr	Description of the object if any
force_train	Whether the Learner enforces training upon every call.
pass_attr	Which attributes of the dataset or self.ca to pass into result dataset upon call
postproc	Node to perform post-processing of results
space	Processing space name of this node
trained	Either classifier was already trained
weights

Methods

__call__(ds)
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
set_postproc(node)	Assigns a post-processing node
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
See

Parameters:

family : {gaussian, multinomial}, optional

Response type of your targets (either ‘gaussian’ for regression or ‘multinomial’ for classification). Constraints: value must be one of (‘gaussian’, ‘multinomial’). [Default: ‘gaussian’]

alpha : float, optional

The elastic net mixing parameter. Larger values will give rise to less L2 regularization, with alpha=1.0 as a true LASSO penalty. Constraints: value must be convertible to type ‘float’, and value must be in range [0.01, 1.0]. [Default: 1.0]

nlambda : int, optional

Maximum number of lambdas to calculate before stopping if not converged. Constraints: value must be convertible to type ‘int’, and value must be in range [1, inf]. [Default: 100]

standardize : bool, optional

Whether to standardize the variables prior to fitting. Constraints: value must be convertible to type bool. [Default: True]

thresh : float, optional

Convergence threshold for coordinate descent. Constraints: value must be convertible to type ‘float’, and value must be in range [1e-10, 1.0]. [Default: 0.0001]

pmax : int or None, optional

Limit the maximum number of variables ever to be nonzero. Constraints: (value must be convertible to type ‘int’, and value must be in range [1, inf]), or value must be None. [Default: None]

maxit : int, optional

Maximum number of outer-loop iterations for ‘multinomial’ families. Constraints: value must be convertible to type ‘int’, and value must be in range [10, inf]. [Default: 100]

model_type : {covariance, naive}, optional

‘covariance’ saves all inner-products ever computed and can be much faster than ‘naive’. The latter can be more efficient for nfeatures>>nsamples situations. Constraints: value must be one of (‘covariance’, ‘naive’). [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.

pass_attr : str, list of str|tuple, optional

Additional attributes to pass on to an output dataset. Attributes can be taken from all three attribute collections of an input dataset (sa, fa, a – see Dataset.get_attr()), or from the collection of conditional attributes (ca) of a node instance. Corresponding collection name prefixes should be used to identify attributes, e.g. ‘ca.null_prob’ for the conditional attribute ‘null_prob’, or ‘fa.stats’ for the feature attribute stats. In addition to a plain attribute identifier it is possible to use a tuple to trigger more complex operations. The first tuple element is the attribute identifier, as described before. The second element is the name of the target attribute collection (sa, fa, or a). The third element is the axis number of a multidimensional array that shall be swapped with the current first axis. The fourth element is a new name that shall be used for an attribute in the output dataset. Example: (‘ca.null_prob’, ‘fa’, 1, ‘pvalues’) will take the conditional attribute ‘null_prob’ and store it as a feature attribute ‘pvalues’, while swapping the first and second axes. Simplified instructions can be given by leaving out consecutive tuple elements starting from the end.

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

Attributes

auto_train	Whether the Learner performs automatic trainingwhen called untrained.
descr	Description of the object if any
force_train	Whether the Learner enforces training upon every call.
pass_attr	Which attributes of the dataset or self.ca to pass into result dataset upon call
postproc	Node to perform post-processing of results
space	Processing space name of this node
trained	Either classifier was already trained
weights

Methods

__call__(ds)
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
set_postproc(node)	Assigns a post-processing node
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