Multivariate Pattern Analysis in Python

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# mvpa2.clfs.meta.RegressionAsClassifier

class `mvpa2.clfs.meta.``RegressionAsClassifier`(clf, centroids=None, distance_measure=None, **kwargs)

  Allows to use arbitrary regression for classification.

  Possible usecases:

  > Binary Classification
  >   Any regression could easily be extended for binary classification. For instance using labels -1 and +1, regression results are quantized into labels depending on their signs
  > Multiclass Classification
  >   Although most of the time classes are not ordered and do not have a corresponding distance matrix among them it might often be the case that there is a hypothesis that classes could be well separated in a projection to single dimension (non-linear manifold, or just linear projection). For such use regression might provide necessary means of classification

  Notes

  Available conditional attributes:

  - `calling_time+`: Time (in seconds) it took to call the node
  - `distances`: Distances obtained during prediction
  - `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 `+`)

  Methods

  `clone`() | Create full copy of the classifier.
  `generate`(ds) | Yield processing results.
  `get_postproc`() | Returns the post-processing node or None.
  `get_sensitivity_analyzer`(*args_, **kwargs_) |
  `get_space`() | Query the processing space name of this node.
  `is_trained`([dataset]) | Either classifier was already trained.
  `predict`(obj, data, *args, **kwargs) |
  `repredict`(obj, data, *args, **kwargs) |
  `reset`() |
  `retrain`(dataset, **kwargs) | Helper to avoid check if data was changed actually changed Useful if just some aspects of classifier were changed since its previous training.
  `set_postproc`(node) | Assigns a post-processing node Set to `None` to disable postprocessing.
  `set_space`(name) | Set the processing space name of this node.
  `summary`() |
  `train`(ds) | The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
  `untrain`() | Reverts changes in the state of this node caused by previous training

  Parameters:

  clf : Classifier XXX Should become learner

  > Regression to be used as a classifier. Although it would accept any Learner, only providing regressions would make sense.

  centroids : None or dict of (float or iterable)

  > Hypothesis or prior information on location/distance of centroids for each category, provide them. If None – during training it will choose equidistant points starting from 0.0. If dict – keys should be a superset of labels of dataset obtained during training and each value should be numeric value or iterable if centroids are multidimensional and regression can do multidimensional regression.

  distance_measure : function or None

  > What distance measure to use to find closest class label from continuous estimates provided by regression. If None, will use Cartesian distance.

  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

  Methods

  `clone`() | Create full copy of the classifier.
  `generate`(ds) | Yield processing results.
  `get_postproc`() | Returns the post-processing node or None.
  `get_sensitivity_analyzer`(*args_, **kwargs_) |
  `get_space`() | Query the processing space name of this node.
  `is_trained`([dataset]) | Either classifier was already trained.
  `predict`(obj, data, *args, **kwargs) |
  `repredict`(obj, data, *args, **kwargs) |
  `reset`() |
  `retrain`(dataset, **kwargs) | Helper to avoid check if data was changed actually changed Useful if just some aspects of classifier were changed since its previous training.
  `set_postproc`(node) | Assigns a post-processing node Set to `None` to disable postprocessing.
  `set_space`(name) | Set the processing space name of this node.
  `summary`() |
  `train`(ds) | The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
  `untrain`() | Reverts changes in the state of this node caused by previous training

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