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llmeval-env/lib/python3.10/site-packages/nltk/classify/__init__.py

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+# Natural Language Toolkit: Classifiers
+#
+# Copyright (C) 2001-2023 NLTK Project
+# Author: Edward Loper <[email protected]>
+# URL: <https://www.nltk.org/>
+# For license information, see LICENSE.TXT
+
+"""
+Classes and interfaces for labeling tokens with category labels (or
+"class labels").  Typically, labels are represented with strings
+(such as ``'health'`` or ``'sports'``).  Classifiers can be used to
+perform a wide range of classification tasks.  For example,
+classifiers can be used...
+
+- to classify documents by topic
+- to classify ambiguous words by which word sense is intended
+- to classify acoustic signals by which phoneme they represent
+- to classify sentences by their author
+
+Features
+========
+In order to decide which category label is appropriate for a given
+token, classifiers examine one or more 'features' of the token.  These
+"features" are typically chosen by hand, and indicate which aspects
+of the token are relevant to the classification decision.  For
+example, a document classifier might use a separate feature for each
+word, recording how often that word occurred in the document.
+
+Featuresets
+===========
+The features describing a token are encoded using a "featureset",
+which is a dictionary that maps from "feature names" to "feature
+values".  Feature names are unique strings that indicate what aspect
+of the token is encoded by the feature.  Examples include
+``'prevword'``, for a feature whose value is the previous word; and
+``'contains-word(library)'`` for a feature that is true when a document
+contains the word ``'library'``.  Feature values are typically
+booleans, numbers, or strings, depending on which feature they
+describe.
+
+Featuresets are typically constructed using a "feature detector"
+(also known as a "feature extractor").  A feature detector is a
+function that takes a token (and sometimes information about its
+context) as its input, and returns a featureset describing that token.
+For example, the following feature detector converts a document
+(stored as a list of words) to a featureset describing the set of
+words included in the document:
+
+    >>> # Define a feature detector function.
+    >>> def document_features(document):
+    ...     return dict([('contains-word(%s)' % w, True) for w in document])
+
+Feature detectors are typically applied to each token before it is fed
+to the classifier:
+
+    >>> # Classify each Gutenberg document.
+    >>> from nltk.corpus import gutenberg
+    >>> for fileid in gutenberg.fileids(): # doctest: +SKIP
+    ...     doc = gutenberg.words(fileid) # doctest: +SKIP
+    ...     print(fileid, classifier.classify(document_features(doc))) # doctest: +SKIP
+
+The parameters that a feature detector expects will vary, depending on
+the task and the needs of the feature detector.  For example, a
+feature detector for word sense disambiguation (WSD) might take as its
+input a sentence, and the index of a word that should be classified,
+and return a featureset for that word.  The following feature detector
+for WSD includes features describing the left and right contexts of
+the target word:
+
+    >>> def wsd_features(sentence, index):
+    ...     featureset = {}
+    ...     for i in range(max(0, index-3), index):
+    ...         featureset['left-context(%s)' % sentence[i]] = True
+    ...     for i in range(index, max(index+3, len(sentence))):
+    ...         featureset['right-context(%s)' % sentence[i]] = True
+    ...     return featureset
+
+Training Classifiers
+====================
+Most classifiers are built by training them on a list of hand-labeled
+examples, known as the "training set".  Training sets are represented
+as lists of ``(featuredict, label)`` tuples.
+"""
+
+from nltk.classify.api import ClassifierI, MultiClassifierI
+from nltk.classify.decisiontree import DecisionTreeClassifier
+from nltk.classify.maxent import (
+    BinaryMaxentFeatureEncoding,
+    ConditionalExponentialClassifier,
+    MaxentClassifier,
+    TypedMaxentFeatureEncoding,
+)
+from nltk.classify.megam import call_megam, config_megam
+from nltk.classify.naivebayes import NaiveBayesClassifier
+from nltk.classify.positivenaivebayes import PositiveNaiveBayesClassifier
+from nltk.classify.rte_classify import RTEFeatureExtractor, rte_classifier, rte_features
+from nltk.classify.scikitlearn import SklearnClassifier
+from nltk.classify.senna import Senna
+from nltk.classify.textcat import TextCat
+from nltk.classify.util import accuracy, apply_features, log_likelihood
+from nltk.classify.weka import WekaClassifier, config_weka