196 lines
6.3 KiB
Python
196 lines
6.3 KiB
Python
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# Natural Language Toolkit: Classifier Interface
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#
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# Copyright (C) 2001-2023 NLTK Project
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# Author: Edward Loper <edloper@gmail.com>
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# Steven Bird <stevenbird1@gmail.com> (minor additions)
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# URL: <https://www.nltk.org/>
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# For license information, see LICENSE.TXT
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"""
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Interfaces for labeling tokens with category labels (or "class labels").
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``ClassifierI`` is a standard interface for "single-category
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classification", in which the set of categories is known, the number
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of categories is finite, and each text belongs to exactly one
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category.
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``MultiClassifierI`` is a standard interface for "multi-category
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classification", which is like single-category classification except
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that each text belongs to zero or more categories.
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"""
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from nltk.internals import overridden
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##//////////////////////////////////////////////////////
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# { Classification Interfaces
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##//////////////////////////////////////////////////////
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class ClassifierI:
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"""
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A processing interface for labeling tokens with a single category
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label (or "class"). Labels are typically strs or
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ints, but can be any immutable type. The set of labels
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that the classifier chooses from must be fixed and finite.
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Subclasses must define:
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- ``labels()``
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- either ``classify()`` or ``classify_many()`` (or both)
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Subclasses may define:
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- either ``prob_classify()`` or ``prob_classify_many()`` (or both)
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"""
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def labels(self):
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"""
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:return: the list of category labels used by this classifier.
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:rtype: list of (immutable)
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"""
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raise NotImplementedError()
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def classify(self, featureset):
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"""
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:return: the most appropriate label for the given featureset.
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:rtype: label
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"""
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if overridden(self.classify_many):
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return self.classify_many([featureset])[0]
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else:
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raise NotImplementedError()
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def prob_classify(self, featureset):
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"""
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:return: a probability distribution over labels for the given
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featureset.
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:rtype: ProbDistI
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"""
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if overridden(self.prob_classify_many):
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return self.prob_classify_many([featureset])[0]
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else:
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raise NotImplementedError()
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def classify_many(self, featuresets):
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"""
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Apply ``self.classify()`` to each element of ``featuresets``. I.e.:
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return [self.classify(fs) for fs in featuresets]
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:rtype: list(label)
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"""
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return [self.classify(fs) for fs in featuresets]
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def prob_classify_many(self, featuresets):
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"""
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Apply ``self.prob_classify()`` to each element of ``featuresets``. I.e.:
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return [self.prob_classify(fs) for fs in featuresets]
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:rtype: list(ProbDistI)
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"""
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return [self.prob_classify(fs) for fs in featuresets]
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class MultiClassifierI:
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"""
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A processing interface for labeling tokens with zero or more
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category labels (or "labels"). Labels are typically strs
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or ints, but can be any immutable type. The set of labels
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that the multi-classifier chooses from must be fixed and finite.
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Subclasses must define:
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- ``labels()``
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- either ``classify()`` or ``classify_many()`` (or both)
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Subclasses may define:
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- either ``prob_classify()`` or ``prob_classify_many()`` (or both)
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"""
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def labels(self):
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"""
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:return: the list of category labels used by this classifier.
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:rtype: list of (immutable)
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"""
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raise NotImplementedError()
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def classify(self, featureset):
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"""
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:return: the most appropriate set of labels for the given featureset.
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:rtype: set(label)
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"""
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if overridden(self.classify_many):
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return self.classify_many([featureset])[0]
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else:
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raise NotImplementedError()
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def prob_classify(self, featureset):
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"""
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:return: a probability distribution over sets of labels for the
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given featureset.
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:rtype: ProbDistI
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"""
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if overridden(self.prob_classify_many):
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return self.prob_classify_many([featureset])[0]
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else:
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raise NotImplementedError()
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def classify_many(self, featuresets):
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"""
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Apply ``self.classify()`` to each element of ``featuresets``. I.e.:
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return [self.classify(fs) for fs in featuresets]
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:rtype: list(set(label))
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"""
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return [self.classify(fs) for fs in featuresets]
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def prob_classify_many(self, featuresets):
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"""
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Apply ``self.prob_classify()`` to each element of ``featuresets``. I.e.:
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return [self.prob_classify(fs) for fs in featuresets]
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:rtype: list(ProbDistI)
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"""
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return [self.prob_classify(fs) for fs in featuresets]
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# # [XX] IN PROGRESS:
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# class SequenceClassifierI:
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# """
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# A processing interface for labeling sequences of tokens with a
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# single category label (or "class"). Labels are typically
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# strs or ints, but can be any immutable type. The set
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# of labels that the classifier chooses from must be fixed and
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# finite.
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# """
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# def labels(self):
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# """
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# :return: the list of category labels used by this classifier.
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# :rtype: list of (immutable)
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# """
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# raise NotImplementedError()
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# def prob_classify(self, featureset):
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# """
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# Return a probability distribution over labels for the given
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# featureset.
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# If ``featureset`` is a list of featuresets, then return a
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# corresponding list containing the probability distribution
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# over labels for each of the given featuresets, where the
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# *i*\ th element of this list is the most appropriate label for
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# the *i*\ th element of ``featuresets``.
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# """
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# raise NotImplementedError()
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# def classify(self, featureset):
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# """
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# Return the most appropriate label for the given featureset.
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# If ``featureset`` is a list of featuresets, then return a
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# corresponding list containing the most appropriate label for
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# each of the given featuresets, where the *i*\ th element of
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# this list is the most appropriate label for the *i*\ th element
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# of ``featuresets``.
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# """
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# raise NotImplementedError()
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