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venv/lib/python3.10/site-packages/rapidfuzz/distance/Indel_py.py

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+# SPDX-License-Identifier: MIT
+# Copyright (C) 2022 Max Bachmann
+from __future__ import annotations
+
+from rapidfuzz._common_py import conv_sequences
+from rapidfuzz._utils import is_none, setupPandas
+from rapidfuzz.distance.LCSseq_py import (
+    _block_similarity as lcs_seq_block_similarity,
+    editops as lcs_seq_editops,
+    opcodes as lcs_seq_opcodes,
+    similarity as lcs_seq_similarity,
+)
+
+
+def distance(
+    s1,
+    s2,
+    *,
+    processor=None,
+    score_cutoff=None,
+):
+    """
+    Calculates the minimum number of insertions and deletions
+    required to change one sequence into the other. This is equivalent to the
+    Levenshtein distance with a substitution weight of 2.
+
+    Parameters
+    ----------
+    s1 : Sequence[Hashable]
+        First string to compare.
+    s2 : Sequence[Hashable]
+        Second string to compare.
+    processor: callable, optional
+        Optional callable that is used to preprocess the strings before
+        comparing them. Default is None, which deactivates this behaviour.
+    score_cutoff : int, optional
+        Maximum distance between s1 and s2, that is
+        considered as a result. If the distance is bigger than score_cutoff,
+        score_cutoff + 1 is returned instead. Default is None, which deactivates
+        this behaviour.
+
+    Returns
+    -------
+    distance : int
+        distance between s1 and s2
+
+    Examples
+    --------
+    Find the Indel distance between two strings:
+
+    >>> from rapidfuzz.distance import Indel
+    >>> Indel.distance("lewenstein", "levenshtein")
+    3
+
+    Setting a maximum distance allows the implementation to select
+    a more efficient implementation:
+
+    >>> Indel.distance("lewenstein", "levenshtein", score_cutoff=1)
+    2
+
+    """
+    if processor is not None:
+        s1 = processor(s1)
+        s2 = processor(s2)
+
+    s1, s2 = conv_sequences(s1, s2)
+    maximum = len(s1) + len(s2)
+    lcs_sim = lcs_seq_similarity(s1, s2)
+    dist = maximum - 2 * lcs_sim
+    return dist if (score_cutoff is None or dist <= score_cutoff) else score_cutoff + 1
+
+
+def _block_distance(
+    block,
+    s1,
+    s2,
+    score_cutoff=None,
+):
+    maximum = len(s1) + len(s2)
+    lcs_sim = lcs_seq_block_similarity(block, s1, s2)
+    dist = maximum - 2 * lcs_sim
+    return dist if (score_cutoff is None or dist <= score_cutoff) else score_cutoff + 1
+
+
+def similarity(
+    s1,
+    s2,
+    *,
+    processor=None,
+    score_cutoff=None,
+):
+    """
+    Calculates the Indel similarity in the range [max, 0].
+
+    This is calculated as ``(len1 + len2) - distance``.
+
+    Parameters
+    ----------
+    s1 : Sequence[Hashable]
+        First string to compare.
+    s2 : Sequence[Hashable]
+        Second string to compare.
+    processor: callable, optional
+        Optional callable that is used to preprocess the strings before
+        comparing them. Default is None, which deactivates this behaviour.
+    score_cutoff : int, optional
+        Maximum distance between s1 and s2, that is
+        considered as a result. If the similarity is smaller than score_cutoff,
+        0 is returned instead. Default is None, which deactivates
+        this behaviour.
+
+    Returns
+    -------
+    similarity : int
+        similarity between s1 and s2
+    """
+    if processor is not None:
+        s1 = processor(s1)
+        s2 = processor(s2)
+
+    s1, s2 = conv_sequences(s1, s2)
+    maximum = len(s1) + len(s2)
+    dist = distance(s1, s2)
+    sim = maximum - dist
+    return sim if (score_cutoff is None or sim >= score_cutoff) else 0
+
+
+def normalized_distance(
+    s1,
+    s2,
+    *,
+    processor=None,
+    score_cutoff=None,
+):
+    """
+    Calculates a normalized levenshtein similarity in the range [1, 0].
+
+    This is calculated as ``distance / (len1 + len2)``.
+
+    Parameters
+    ----------
+    s1 : Sequence[Hashable]
+        First string to compare.
+    s2 : Sequence[Hashable]
+        Second string to compare.
+    processor: callable, optional
+        Optional callable that is used to preprocess the strings before
+        comparing them. Default is None, which deactivates this behaviour.
+    score_cutoff : float, optional
+        Optional argument for a score threshold as a float between 0 and 1.0.
+        For norm_dist > score_cutoff 1.0 is returned instead. Default is 1.0,
+        which deactivates this behaviour.
+
+    Returns
+    -------
+    norm_dist : float
+        normalized distance between s1 and s2 as a float between 0 and 1.0
+    """
+    setupPandas()
+    if is_none(s1) or is_none(s2):
+        return 1.0
+
+    if processor is not None:
+        s1 = processor(s1)
+        s2 = processor(s2)
+
+    s1, s2 = conv_sequences(s1, s2)
+    maximum = len(s1) + len(s2)
+    dist = distance(s1, s2)
+    norm_dist = dist / maximum if maximum else 0
+    return norm_dist if (score_cutoff is None or norm_dist <= score_cutoff) else 1
+
+
+def _block_normalized_distance(
+    block,
+    s1,
+    s2,
+    score_cutoff=None,
+):
+    maximum = len(s1) + len(s2)
+    dist = _block_distance(block, s1, s2)
+    norm_dist = dist / maximum if maximum else 0
+    return norm_dist if (score_cutoff is None or norm_dist <= score_cutoff) else 1
+
+
+def normalized_similarity(
+    s1,
+    s2,
+    *,
+    processor=None,
+    score_cutoff=None,
+):
+    """
+    Calculates a normalized indel similarity in the range [0, 1].
+
+    This is calculated as ``1 - normalized_distance``
+
+    Parameters
+    ----------
+    s1 : Sequence[Hashable]
+        First string to compare.
+    s2 : Sequence[Hashable]
+        Second string to compare.
+    processor: callable, optional
+        Optional callable that is used to preprocess the strings before
+        comparing them. Default is None, which deactivates this behaviour.
+    score_cutoff : float, optional
+        Optional argument for a score threshold as a float between 0 and 1.0.
+        For norm_sim < score_cutoff 0 is returned instead. Default is 0,
+        which deactivates this behaviour.
+
+    Returns
+    -------
+    norm_sim : float
+        normalized similarity between s1 and s2 as a float between 0 and 1.0
+
+    Examples
+    --------
+    Find the normalized Indel similarity between two strings:
+
+    >>> from rapidfuzz.distance import Indel
+    >>> Indel.normalized_similarity("lewenstein", "levenshtein")
+    0.85714285714285
+
+    Setting a score_cutoff allows the implementation to select
+    a more efficient implementation:
+
+    >>> Indel.normalized_similarity("lewenstein", "levenshtein", score_cutoff=0.9)
+    0.0
+
+    When a different processor is used s1 and s2 do not have to be strings
+
+    >>> Indel.normalized_similarity(["lewenstein"], ["levenshtein"], processor=lambda s: s[0])
+    0.8571428571428572
+    """
+    setupPandas()
+    if is_none(s1) or is_none(s2):
+        return 0.0
+
+    if processor is not None:
+        s1 = processor(s1)
+        s2 = processor(s2)
+
+    s1, s2 = conv_sequences(s1, s2)
+    norm_dist = normalized_distance(s1, s2)
+    norm_sim = 1.0 - norm_dist
+    return norm_sim if (score_cutoff is None or norm_sim >= score_cutoff) else 0
+
+
+def _block_normalized_similarity(
+    block,
+    s1,
+    s2,
+    score_cutoff=None,
+):
+    norm_dist = _block_normalized_distance(block, s1, s2)
+    norm_sim = 1.0 - norm_dist
+    return norm_sim if (score_cutoff is None or norm_sim >= score_cutoff) else 0
+
+
+def editops(
+    s1,
+    s2,
+    *,
+    processor=None,
+):
+    """
+    Return Editops describing how to turn s1 into s2.
+
+    Parameters
+    ----------
+    s1 : Sequence[Hashable]
+        First string to compare.
+    s2 : Sequence[Hashable]
+        Second string to compare.
+    processor: callable, optional
+        Optional callable that is used to preprocess the strings before
+        comparing them. Default is None, which deactivates this behaviour.
+
+    Returns
+    -------
+    editops : Editops
+        edit operations required to turn s1 into s2
+
+    Notes
+    -----
+    The alignment is calculated using an algorithm of Heikki Hyyrö, which is
+    described [6]_. It has a time complexity and memory usage of ``O([N/64] * M)``.
+
+    References
+    ----------
+    .. [6] Hyyrö, Heikki. "A Note on Bit-Parallel Alignment Computation."
+           Stringology (2004).
+
+    Examples
+    --------
+    >>> from rapidfuzz.distance import Indel
+    >>> for tag, src_pos, dest_pos in Indel.editops("qabxcd", "abycdf"):
+    ...    print(("%7s s1[%d] s2[%d]" % (tag, src_pos, dest_pos)))
+     delete s1[0] s2[0]
+     delete s1[3] s2[2]
+     insert s1[4] s2[2]
+     insert s1[6] s2[5]
+    """
+    return lcs_seq_editops(s1, s2, processor=processor)
+
+
+def opcodes(
+    s1,
+    s2,
+    *,
+    processor=None,
+):
+    """
+    Return Opcodes describing how to turn s1 into s2.
+
+    Parameters
+    ----------
+    s1 : Sequence[Hashable]
+        First string to compare.
+    s2 : Sequence[Hashable]
+        Second string to compare.
+    processor: callable, optional
+        Optional callable that is used to preprocess the strings before
+        comparing them. Default is None, which deactivates this behaviour.
+
+    Returns
+    -------
+    opcodes : Opcodes
+        edit operations required to turn s1 into s2
+
+    Notes
+    -----
+    The alignment is calculated using an algorithm of Heikki Hyyrö, which is
+    described [7]_. It has a time complexity and memory usage of ``O([N/64] * M)``.
+
+    References
+    ----------
+    .. [7] Hyyrö, Heikki. "A Note on Bit-Parallel Alignment Computation."
+           Stringology (2004).
+
+    Examples
+    --------
+    >>> from rapidfuzz.distance import Indel
+
+    >>> a = "qabxcd"
+    >>> b = "abycdf"
+    >>> for tag, i1, i2, j1, j2 in Indel.opcodes(a, b):
+    ...    print(("%7s a[%d:%d] (%s) b[%d:%d] (%s)" %
+    ...           (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2])))
+     delete a[0:1] (q) b[0:0] ()
+      equal a[1:3] (ab) b[0:2] (ab)
+     delete a[3:4] (x) b[2:2] ()
+     insert a[4:4] () b[2:3] (y)
+      equal a[4:6] (cd) b[3:5] (cd)
+     insert a[6:6] () b[5:6] (f)
+    """
+    return lcs_seq_opcodes(s1, s2, processor=processor)