233 lines
6.1 KiB
Python
233 lines
6.1 KiB
Python
# SPDX-License-Identifier: MIT
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# Copyright (C) 2022 Max Bachmann
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from __future__ import annotations
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from rapidfuzz._common_py import conv_sequences
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from rapidfuzz._utils import is_none, setupPandas
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def _osa_distance_hyrroe2003(s1, s2):
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if not s1:
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return len(s2)
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VP = (1 << len(s1)) - 1
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VN = 0
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D0 = 0
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PM_j_old = 0
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currDist = len(s1)
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mask = 1 << (len(s1) - 1)
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block = {}
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block_get = block.get
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x = 1
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for ch1 in s1:
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block[ch1] = block_get(ch1, 0) | x
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x <<= 1
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for ch2 in s2:
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# Step 1: Computing D0
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PM_j = block_get(ch2, 0)
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TR = (((~D0) & PM_j) << 1) & PM_j_old
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D0 = (((PM_j & VP) + VP) ^ VP) | PM_j | VN
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D0 = D0 | TR
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# Step 2: Computing HP and HN
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HP = VN | ~(D0 | VP)
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HN = D0 & VP
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# Step 3: Computing the value D[m,j]
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currDist += (HP & mask) != 0
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currDist -= (HN & mask) != 0
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# Step 4: Computing Vp and VN
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HP = (HP << 1) | 1
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HN = HN << 1
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VP = HN | ~(D0 | HP)
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VN = HP & D0
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PM_j_old = PM_j
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return currDist
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def distance(
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s1,
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s2,
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*,
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processor=None,
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score_cutoff=None,
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):
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"""
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Calculates the optimal string alignment (OSA) distance.
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Parameters
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----------
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s1 : Sequence[Hashable]
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First string to compare.
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s2 : Sequence[Hashable]
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Second string to compare.
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processor: callable, optional
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Optional callable that is used to preprocess the strings before
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comparing them. Default is None, which deactivates this behaviour.
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score_cutoff : int, optional
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Maximum distance between s1 and s2, that is
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considered as a result. If the distance is bigger than score_cutoff,
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score_cutoff + 1 is returned instead. Default is None, which deactivates
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this behaviour.
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Returns
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-------
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distance : int
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distance between s1 and s2
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Examples
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--------
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Find the OSA distance between two strings:
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>>> from rapidfuzz.distance import OSA
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>>> OSA.distance("CA", "AC")
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2
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>>> OSA.distance("CA", "ABC")
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3
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"""
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if processor is not None:
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s1 = processor(s1)
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s2 = processor(s2)
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s1, s2 = conv_sequences(s1, s2)
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dist = _osa_distance_hyrroe2003(s1, s2)
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return dist if (score_cutoff is None or dist <= score_cutoff) else score_cutoff + 1
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def similarity(
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s1,
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s2,
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*,
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processor=None,
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score_cutoff=None,
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):
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"""
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Calculates the optimal string alignment (OSA) similarity in the range [max, 0].
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This is calculated as ``max(len1, len2) - distance``.
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Parameters
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----------
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s1 : Sequence[Hashable]
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First string to compare.
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s2 : Sequence[Hashable]
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Second string to compare.
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processor: callable, optional
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Optional callable that is used to preprocess the strings before
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comparing them. Default is None, which deactivates this behaviour.
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score_cutoff : int, optional
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Maximum distance between s1 and s2, that is
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considered as a result. If the similarity is smaller than score_cutoff,
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0 is returned instead. Default is None, which deactivates
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this behaviour.
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Returns
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-------
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similarity : int
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similarity between s1 and s2
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"""
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if processor is not None:
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s1 = processor(s1)
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s2 = processor(s2)
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s1, s2 = conv_sequences(s1, s2)
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maximum = max(len(s1), len(s2))
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dist = distance(s1, s2)
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sim = maximum - dist
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return sim if (score_cutoff is None or sim >= score_cutoff) else 0
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def normalized_distance(
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s1,
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s2,
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*,
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processor=None,
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score_cutoff=None,
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):
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"""
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Calculates a normalized optimal string alignment (OSA) similarity in the range [1, 0].
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This is calculated as ``distance / max(len1, len2)``.
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Parameters
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----------
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s1 : Sequence[Hashable]
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First string to compare.
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s2 : Sequence[Hashable]
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Second string to compare.
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processor: callable, optional
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Optional callable that is used to preprocess the strings before
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comparing them. Default is None, which deactivates this behaviour.
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score_cutoff : float, optional
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Optional argument for a score threshold as a float between 0 and 1.0.
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For norm_dist > score_cutoff 1.0 is returned instead. Default is 1.0,
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which deactivates this behaviour.
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Returns
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-------
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norm_dist : float
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normalized distance between s1 and s2 as a float between 0 and 1.0
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"""
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setupPandas()
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if is_none(s1) or is_none(s2):
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return 1.0
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if processor is not None:
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s1 = processor(s1)
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s2 = processor(s2)
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s1, s2 = conv_sequences(s1, s2)
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maximum = max(len(s1), len(s2))
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dist = distance(s1, s2)
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norm_dist = dist / maximum if maximum else 0
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return norm_dist if (score_cutoff is None or norm_dist <= score_cutoff) else 1
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def normalized_similarity(
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s1,
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s2,
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*,
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processor=None,
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score_cutoff=None,
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):
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"""
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Calculates a normalized optimal string alignment (OSA) similarity in the range [0, 1].
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This is calculated as ``1 - normalized_distance``
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Parameters
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----------
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s1 : Sequence[Hashable]
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First string to compare.
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s2 : Sequence[Hashable]
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Second string to compare.
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processor: callable, optional
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Optional callable that is used to preprocess the strings before
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comparing them. Default is None, which deactivates this behaviour.
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score_cutoff : float, optional
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Optional argument for a score threshold as a float between 0 and 1.0.
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For norm_sim < score_cutoff 0 is returned instead. Default is 0,
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which deactivates this behaviour.
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Returns
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-------
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norm_sim : float
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normalized similarity between s1 and s2 as a float between 0 and 1.0
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"""
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setupPandas()
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if is_none(s1) or is_none(s2):
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return 0.0
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if processor is not None:
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s1 = processor(s1)
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s2 = processor(s2)
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s1, s2 = conv_sequences(s1, s2)
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norm_dist = normalized_distance(s1, s2)
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norm_sim = 1.0 - norm_dist
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return norm_sim if (score_cutoff is None or norm_sim >= score_cutoff) else 0
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