Multivariate Pattern Analysis in Python

# mvpa2.clfs.enet.ENET

class `mvpa2.clfs.enet.``ENET`(lm=1.0, trace=False, normalize=True, intercept=True, max_steps=None, **kwargs)

  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+`: 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 `+`)

  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 everycalled.
  `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

  `get_sensitivity_analyzer`(**kwargs) | Returns a sensitivity analyzer for ENET.

  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.

  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 everycalled.
  `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

  `get_sensitivity_analyzer`(**kwargs) | Returns a sensitivity analyzer for ENET.

  `get_sensitivity_analyzer`(**kwargs)

    Returns a sensitivity analyzer for ENET.

  `weights`