183 lines
4.9 KiB
Python
183 lines
4.9 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 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 postfix distance between two strings.
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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 or None, 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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"""
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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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sim = similarity(s1, s2)
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dist = maximum - sim
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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 postfix similarity between two strings.
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This is calculated as ``len1 - 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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distance : int
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distance 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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sim = 0
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for ch1, ch2 in zip(reversed(s1), reversed(s2)):
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if ch1 != ch2:
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break
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sim += 1
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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 postfix similarity in the range [1, 0].
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This is calculated as ``distance / (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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norm_sim = normalized_similarity(s1, s2, processor=processor)
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norm_dist = 1.0 - norm_sim
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return norm_dist if (score_cutoff is None or norm_dist <= score_cutoff) else 1.0
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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 postfix 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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maximum = max(len(s1), len(s2))
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sim = similarity(s1, s2)
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norm_sim = sim / maximum if maximum else 1.0
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return norm_sim if (score_cutoff is None or norm_sim >= score_cutoff) else 0.0
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