Elastic-Net regression (ENET) Classifier.
Elastic-Net is the model selection algorithm from:
Zou and Hastie (2005) ‘Regularization and Variable
Selection via the Elastic Net’ Journal of the Royal Statistical
Society, Series B, 67, 301-320.
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.
Unlike LARS it has both L1 and L2 regularization (instead of just
L1). This means that while it tries to sparsify the features it
also tries to keep redundant features, which may be very very good
for fMRI classification.
In the true nature of the PyMVPA framework, this algorithm was
actually implemented in R by Zou and Hastie and wrapped via RPy.
To make use of ENET, you must have R and RPy installed as well as
both the lars and elasticnet 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 and elasticnet 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
- 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 +)
Initialize ENET.
See the help in R for further details on the following parameters:
Parameters : | lm : float
Penalty parameter. 0 will perform LARS with no ridge regression.
Default is 1.0.
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.
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 ENET.
-
weights