# 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)