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clfs.lars

Module: clfs.lars

Inheritance diagram for mvpa.clfs.lars:

Least angle regression (LARS) classifier.

Classes

LARS

class mvpa.clfs.lars.LARS(model_type='lasso', trace=False, normalize=True, intercept=True, max_steps=None, use_Gram=False, **kwargs)

Bases: mvpa.clfs.base.Classifier

Least angle regression (LARS) Classifier.

LARS is the model selection algorithm from:

Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani, Least Angle Regression Annals of Statistics (with discussion) (2004) 32(2), 407-499. A new method for variable subset selection, with the lasso and ‘epsilon’ forward stagewise methods as special cases.

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.

This classifier behaves more like a ridge regression in that it returns prediction values and it treats the training labels as continuous.

In the true nature of the PyMVPA framework, this algorithm is actually implemented in R by Trevor Hastie and wrapped via RPy. To make use of LARS, you must have R and RPy installed as well as the LARS 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 package by running R as root and calling:

install.packages()

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:

Classifier

Initialize LARS.

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

Parameters:

• model_type (string) – Type of LARS to run. Can be one of (‘lasso’, ‘lar’, ‘forward.stagewise’, ‘stepwise’).
• 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.
• use_Gram (boolean) – Whether to compute the Gram matrix (this should be false if you have more features than samples.)
• 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

getSensitivityAnalyzer(**kwargs)

Returns a sensitivity analyzer for LARS.

weights

LARSWeights

class mvpa.clfs.lars.LARSWeights(clf, force_training=True, **kwargs)

Bases: mvpa.measures.base.Sensitivity

SensitivityAnalyzer that reports the weights LARS 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.