mvpa.clfs.lars.LARS¶
- class mvpa.clfs.lars.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+: 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
- 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 +)
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.
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
- get_sensitivity_analyzer(**kwargs)¶
Returns a sensitivity analyzer for LARS.
- weights¶
