updates

Backend/venv/lib/python3.12/site-packages/nltk/tbl/feature.py

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+# Natural Language Toolkit: Transformation-based learning
+#
+# Copyright (C) 2001-2025 NLTK Project
+# Author: Marcus Uneson <marcus.uneson@gmail.com>
+#   based on previous (nltk2) version by
+#   Christopher Maloof, Edward Loper, Steven Bird
+# URL: <https://www.nltk.org/>
+# For license information, see  LICENSE.TXT
+
+from abc import ABCMeta, abstractmethod
+
+
+class Feature(metaclass=ABCMeta):
+    """
+    An abstract base class for Features. A Feature is a combination of
+    a specific property-computing method and a list of relative positions
+    to apply that method to.
+
+    The property-computing method, M{extract_property(tokens, index)},
+    must be implemented by every subclass. It extracts or computes a specific
+    property for the token at the current index. Typical extract_property()
+    methods return features such as the token text or tag; but more involved
+    methods may consider the entire sequence M{tokens} and
+    for instance compute the length of the sentence the token belongs to.
+
+    In addition, the subclass may have a PROPERTY_NAME, which is how
+    it will be printed (in Rules and Templates, etc). If not given, defaults
+    to the classname.
+
+    """
+
+    json_tag = "nltk.tbl.Feature"
+    PROPERTY_NAME = None
+
+    def __init__(self, positions, end=None):
+        """
+        Construct a Feature which may apply at C{positions}.
+
+        >>> # For instance, importing some concrete subclasses (Feature is abstract)
+        >>> from nltk.tag.brill import Word, Pos
+
+        >>> # Feature Word, applying at one of [-2, -1]
+        >>> Word([-2,-1])
+        Word([-2, -1])
+
+        >>> # Positions need not be contiguous
+        >>> Word([-2,-1, 1])
+        Word([-2, -1, 1])
+
+        >>> # Contiguous ranges can alternatively be specified giving the
+        >>> # two endpoints (inclusive)
+        >>> Pos(-3, -1)
+        Pos([-3, -2, -1])
+
+        >>> # In two-arg form, start <= end is enforced
+        >>> Pos(2, 1)
+        Traceback (most recent call last):
+          File "<stdin>", line 1, in <module>
+          File "nltk/tbl/template.py", line 306, in __init__
+            raise TypeError
+        ValueError: illegal interval specification: (start=2, end=1)
+
+        :type positions: list of int
+        :param positions: the positions at which this features should apply
+        :raises ValueError: illegal position specifications
+
+        An alternative calling convention, for contiguous positions only,
+        is Feature(start, end):
+
+        :type start: int
+        :param start: start of range where this feature should apply
+        :type end: int
+        :param end: end of range (NOTE: inclusive!) where this feature should apply
+        """
+        self.positions = None  # to avoid warnings
+        if end is None:
+            self.positions = tuple(sorted({int(i) for i in positions}))
+        else:  # positions was actually not a list, but only the start index
+            try:
+                if positions > end:
+                    raise TypeError
+                self.positions = tuple(range(positions, end + 1))
+            except TypeError as e:
+                # let any kind of erroneous spec raise ValueError
+                raise ValueError(
+                    "illegal interval specification: (start={}, end={})".format(
+                        positions, end
+                    )
+                ) from e
+
+        # set property name given in subclass, or otherwise name of subclass
+        self.PROPERTY_NAME = self.__class__.PROPERTY_NAME or self.__class__.__name__
+
+    def encode_json_obj(self):
+        return self.positions
+
+    @classmethod
+    def decode_json_obj(cls, obj):
+        positions = obj
+        return cls(positions)
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({list(self.positions)!r})"
+
+    @classmethod
+    def expand(cls, starts, winlens, excludezero=False):
+        """
+        Return a list of features, one for each start point in starts
+        and for each window length in winlen. If excludezero is True,
+        no Features containing 0 in its positions will be generated
+        (many tbl trainers have a special representation for the
+        target feature at [0])
+
+        For instance, importing a concrete subclass (Feature is abstract)
+
+        >>> from nltk.tag.brill import Word
+
+        First argument gives the possible start positions, second the
+        possible window lengths
+
+        >>> Word.expand([-3,-2,-1], [1])
+        [Word([-3]), Word([-2]), Word([-1])]
+
+        >>> Word.expand([-2,-1], [1])
+        [Word([-2]), Word([-1])]
+
+        >>> Word.expand([-3,-2,-1], [1,2])
+        [Word([-3]), Word([-2]), Word([-1]), Word([-3, -2]), Word([-2, -1])]
+
+        >>> Word.expand([-2,-1], [1])
+        [Word([-2]), Word([-1])]
+
+        A third optional argument excludes all Features whose positions contain zero
+
+        >>> Word.expand([-2,-1,0], [1,2], excludezero=False)
+        [Word([-2]), Word([-1]), Word([0]), Word([-2, -1]), Word([-1, 0])]
+
+        >>> Word.expand([-2,-1,0], [1,2], excludezero=True)
+        [Word([-2]), Word([-1]), Word([-2, -1])]
+
+        All window lengths must be positive
+
+        >>> Word.expand([-2,-1], [0])
+        Traceback (most recent call last):
+          File "<stdin>", line 1, in <module>
+          File "nltk/tag/tbl/template.py", line 371, in expand
+            :param starts: where to start looking for Feature
+        ValueError: non-positive window length in [0]
+
+        :param starts: where to start looking for Feature
+        :type starts: list of ints
+        :param winlens: window lengths where to look for Feature
+        :type starts: list of ints
+        :param excludezero: do not output any Feature with 0 in any of its positions.
+        :type excludezero: bool
+        :returns: list of Features
+        :raises ValueError: for non-positive window lengths
+        """
+        if not all(x > 0 for x in winlens):
+            raise ValueError(f"non-positive window length in {winlens}")
+        xs = (starts[i : i + w] for w in winlens for i in range(len(starts) - w + 1))
+        return [cls(x) for x in xs if not (excludezero and 0 in x)]
+
+    def issuperset(self, other):
+        """
+        Return True if this Feature always returns True when other does
+
+        More precisely, return True if this feature refers to the same property as other;
+        and this Feature looks at all positions that other does (and possibly
+        other positions in addition).
+
+        #For instance, importing a concrete subclass (Feature is abstract)
+        >>> from nltk.tag.brill import Word, Pos
+
+        >>> Word([-3,-2,-1]).issuperset(Word([-3,-2]))
+        True
+
+        >>> Word([-3,-2,-1]).issuperset(Word([-3,-2, 0]))
+        False
+
+        #Feature subclasses must agree
+        >>> Word([-3,-2,-1]).issuperset(Pos([-3,-2]))
+        False
+
+        :param other: feature with which to compare
+        :type other: (subclass of) Feature
+        :return: True if this feature is superset, otherwise False
+        :rtype: bool
+
+
+        """
+        return self.__class__ is other.__class__ and set(self.positions) >= set(
+            other.positions
+        )
+
+    def intersects(self, other):
+        """
+        Return True if the positions of this Feature intersects with those of other
+
+        More precisely, return True if this feature refers to the same property as other;
+        and there is some overlap in the positions they look at.
+
+        #For instance, importing a concrete subclass (Feature is abstract)
+        >>> from nltk.tag.brill import Word, Pos
+
+        >>> Word([-3,-2,-1]).intersects(Word([-3,-2]))
+        True
+
+        >>> Word([-3,-2,-1]).intersects(Word([-3,-2, 0]))
+        True
+
+        >>> Word([-3,-2,-1]).intersects(Word([0]))
+        False
+
+        #Feature subclasses must agree
+        >>> Word([-3,-2,-1]).intersects(Pos([-3,-2]))
+        False
+
+        :param other: feature with which to compare
+        :type other: (subclass of) Feature
+        :return: True if feature classes agree and there is some overlap in the positions they look at
+        :rtype: bool
+        """
+
+        return bool(
+            self.__class__ is other.__class__
+            and set(self.positions) & set(other.positions)
+        )
+
+    # Rich comparisons for Features. With @functools.total_ordering (Python 2.7+),
+    # it will be enough to define __lt__ and __eq__
+    def __eq__(self, other):
+        return self.__class__ is other.__class__ and self.positions == other.positions
+
+    def __lt__(self, other):
+        return (
+            self.__class__.__name__ < other.__class__.__name__
+            or
+            #    self.positions is a sorted tuple of ints
+            self.positions < other.positions
+        )
+
+    def __ne__(self, other):
+        return not (self == other)
+
+    def __gt__(self, other):
+        return other < self
+
+    def __ge__(self, other):
+        return not self < other
+
+    def __le__(self, other):
+        return self < other or self == other
+
+    @staticmethod
+    @abstractmethod
+    def extract_property(tokens, index):
+        """
+        Any subclass of Feature must define static method extract_property(tokens, index)
+
+        :param tokens: the sequence of tokens
+        :type tokens: list of tokens
+        :param index: the current index
+        :type index: int
+        :return: feature value
+        :rtype: any (but usually scalar)
+        """