Inheritance diagram of 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:


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.


Available conditional attributes:

  • calling_time+: None
  • raw_results: None

(Conditional attributes enabled by default suffixed with +)



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.

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