Multivariate Pattern Analysis in Python

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# mvpa2.clfs.transerror.Confusion

class `mvpa2.clfs.transerror.``Confusion`(attr='targets', labels=None, add_confusion_obj=False, **kwargs)

  Compute a confusion matrix from predictions and targets (Node interface)

  This class is very similar to `ConfusionMatrix` and `ConfusionMatrixError`. However, in contrast to these this class can be used in any place that accepts `Nodes` – most importantly others node’s `postproc` functionality. This makes it very straightforward to compute confusion matrices from classifier output as an intermediate result and continue processing with other nodes. A sketch of a cross-validation setup using this functionality looks like this:

      CrossValidation(some_classifier,
                      some_partitioner,
                      errorfx=None,
                      postproc=Confusion())

  It is vital to set `errorfx` to `None` to preserve raw classifier prediction values in the output dataset to allow for proper data aggregation in a confusion matrix.

  Notes

  Available conditional attributes:

  - `calling_time+`: Time (in seconds) it took to call the node
  - `raw_results`: Computed results before invoking postproc. Stored only if postproc is not None.

  (Conditional attributes enabled by default suffixed with `+`)

  Methods

  `generate`(ds) | Yield processing results.
  `get_postproc`() | Returns the post-processing node or None.
  `get_space`() | Query the processing space name of this node.
  `reset`() |
  `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.

  Parameters:

  attr : str

  > Sample attribute name where classification target values are stored for each prediction.

  labels : list or None

  > Optional list of labels to compute a confusion matrix for. This can be useful if a particular prediction dataset doesn’t have all theoretically possible labels as targets.

  add_confusion_obj : bool

  > If True, the ConfusionMatrix object will be added to the output dataset as attribute ‘confusion_obj’, i.e. ds.a.confusion_obj

  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

  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

  `generate`(ds) | Yield processing results.
  `get_postproc`() | Returns the post-processing node or None.
  `get_space`() | Query the processing space name of this node.
  `reset`() |
  `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.

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