mvpa2.clfs.warehouse.LARS

class mvpa2.clfs.warehouse.LARS(model_type='lasso', trace=False, normalize=True, intercept=True, max_steps=None, use_Gram=False, **kwargs)

Least angle regression (LARS).

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 learner 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()

Notes

Available conditional attributes:

• calling_time+: None
• 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: None
• trained_dataset: None
• trained_nsamples+: None
• trained_targets+: None
• training_stats: Confusion matrix of learning performance
• training_time+: None

(Conditional attributes enabled by default suffixed with +)

Methods

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.)

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

Methods

get_sensitivity_analyzer(**kwargs)

Returns a sensitivity analyzer for LARS.

weights