Package nltk :: Package classify :: Module api :: Class MultiClassifierI

type MultiClassifierI

object --+
         |
        MultiClassifierI

A processing interface for labeling tokens with zero or more category labels (or labels). Labels are typically strings or integers, but can be any immutable type. The set of labels that the multi-classifier chooses from must be fixed and finite.

Subclasses must define:

labels()
either classify() or batch_classify() (or both)

Subclasses may define:

either prob_classify() or batch_prob_classify() (or both)

Instance Methods

[hide private]

list of (immutable)

labels(self)
Returns: the list of category labels used by this classifier.

source code

set of label

classify(self, featureset)
Returns: the most appropriate set of labels for the given featureset.

source code

ProbDistI

prob_classify(self, featureset)
Returns: a probability distribution over sets of labels for the given featureset.

source code

list of (set of label)

batch_classify(self, featuresets)
Apply self.classify() to each element of featuresets. source code

list of ProbDistI

batch_prob_classify(self, featuresets)
Apply self.prob_classify() to each element of featuresets. source code

Method Details

[hide private]

labels(self)

source code

Returns: list of (immutable): the list of category labels used by this classifier.

classify(self, featureset)

source code

Returns: set of label: the most appropriate set of labels for the given featureset.

prob_classify(self, featureset)

source code

Returns: ProbDistI: a probability distribution over sets of labels for the given featureset.

batch_classify(self, featuresets)

source code

Apply self.classify() to each element of featuresets. I.e.:

>>> return [self.classify(fs) for fs in featuresets]

Returns: list of (set of label)

batch_prob_classify(self, featuresets)

source code

Apply self.prob_classify() to each element of featuresets. I.e.:

>>> return [self.prob_classify(fs) for fs in featuresets]

Returns: list of ProbDistI