Multivariate Pattern Analysis in Python

# 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+`: None
  - `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`: 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.
  `clf` | Used `Classifier`
  `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

  Methods

  `summary`() |

  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.

  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.
  `clf` | Used `Classifier`
  `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

  Methods

  `summary`() |