forked from hans/Nominatim
Implements per-name choice of analyzer. If a non-default analyzer is choosen, then the 'word' identifier is extended with the name of the ana;yzer, so that we still have unique items.
204 lines
6.4 KiB
Python
204 lines
6.4 KiB
Python
"""
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Generic processor for names that creates abbreviation variants.
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"""
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from collections import defaultdict, namedtuple
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import itertools
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import re
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from icu import Transliterator
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import datrie
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from nominatim.config import flatten_config_list
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from nominatim.errors import UsageError
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### Configuration section
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ICUVariant = namedtuple('ICUVariant', ['source', 'replacement'])
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def configure(rules, normalization_rules):
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""" Extract and preprocess the configuration for this module.
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"""
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rules = rules.get('variants')
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immediate = defaultdict(list)
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chars = set()
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if rules:
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vset = set()
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rules = flatten_config_list(rules, 'variants')
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vmaker = _VariantMaker(normalization_rules)
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for section in rules:
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for rule in (section.get('words') or []):
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vset.update(vmaker.compute(rule))
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# Intermediate reorder by source. Also compute required character set.
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for variant in vset:
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if variant.source[-1] == ' ' and variant.replacement[-1] == ' ':
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replstr = variant.replacement[:-1]
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else:
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replstr = variant.replacement
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immediate[variant.source].append(replstr)
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chars.update(variant.source)
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return {'replacements': list(immediate.items()),
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'chars': ''.join(chars)}
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class _VariantMaker:
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""" Generater for all necessary ICUVariants from a single variant rule.
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All text in rules is normalized to make sure the variants match later.
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"""
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def __init__(self, norm_rules):
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self.norm = Transliterator.createFromRules("rule_loader_normalization",
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norm_rules)
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def compute(self, rule):
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""" Generator for all ICUVariant tuples from a single variant rule.
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"""
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parts = re.split(r'(\|)?([=-])>', rule)
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if len(parts) != 4:
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raise UsageError("Syntax error in variant rule: " + rule)
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decompose = parts[1] is None
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src_terms = [self._parse_variant_word(t) for t in parts[0].split(',')]
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repl_terms = (self.norm.transliterate(t.strip()) for t in parts[3].split(','))
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# If the source should be kept, add a 1:1 replacement
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if parts[2] == '-':
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for src in src_terms:
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if src:
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for froms, tos in _create_variants(*src, src[0], decompose):
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yield ICUVariant(froms, tos)
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for src, repl in itertools.product(src_terms, repl_terms):
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if src and repl:
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for froms, tos in _create_variants(*src, repl, decompose):
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yield ICUVariant(froms, tos)
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def _parse_variant_word(self, name):
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name = name.strip()
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match = re.fullmatch(r'([~^]?)([^~$^]*)([~$]?)', name)
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if match is None or (match.group(1) == '~' and match.group(3) == '~'):
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raise UsageError("Invalid variant word descriptor '{}'".format(name))
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norm_name = self.norm.transliterate(match.group(2))
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if not norm_name:
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return None
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return norm_name, match.group(1), match.group(3)
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_FLAG_MATCH = {'^': '^ ',
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'$': ' ^',
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'': ' '}
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def _create_variants(src, preflag, postflag, repl, decompose):
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if preflag == '~':
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postfix = _FLAG_MATCH[postflag]
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# suffix decomposition
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src = src + postfix
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repl = repl + postfix
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yield src, repl
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yield ' ' + src, ' ' + repl
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if decompose:
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yield src, ' ' + repl
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yield ' ' + src, repl
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elif postflag == '~':
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# prefix decomposition
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prefix = _FLAG_MATCH[preflag]
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src = prefix + src
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repl = prefix + repl
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yield src, repl
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yield src + ' ', repl + ' '
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if decompose:
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yield src, repl + ' '
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yield src + ' ', repl
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else:
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prefix = _FLAG_MATCH[preflag]
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postfix = _FLAG_MATCH[postflag]
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yield prefix + src + postfix, prefix + repl + postfix
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### Analysis section
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def create(trans_rules, config):
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""" Create a new token analysis instance for this module.
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"""
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return GenericTokenAnalysis(trans_rules, config)
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class GenericTokenAnalysis:
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""" Collects the different transformation rules for normalisation of names
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and provides the functions to apply the transformations.
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"""
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def __init__(self, to_ascii, config):
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self.to_ascii = to_ascii
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# Set up datrie
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self.replacements = datrie.Trie(config['chars'])
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for src, repllist in config['replacements']:
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self.replacements[src] = repllist
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def get_variants_ascii(self, norm_name):
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""" Compute the spelling variants for the given normalized name
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and transliterate the result.
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"""
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baseform = '^ ' + norm_name + ' ^'
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partials = ['']
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startpos = 0
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pos = 0
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force_space = False
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while pos < len(baseform):
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full, repl = self.replacements.longest_prefix_item(baseform[pos:],
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(None, None))
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if full is not None:
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done = baseform[startpos:pos]
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partials = [v + done + r
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for v, r in itertools.product(partials, repl)
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if not force_space or r.startswith(' ')]
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if len(partials) > 128:
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# If too many variants are produced, they are unlikely
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# to be helpful. Only use the original term.
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startpos = 0
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break
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startpos = pos + len(full)
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if full[-1] == ' ':
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startpos -= 1
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force_space = True
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pos = startpos
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else:
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pos += 1
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force_space = False
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# No variants detected? Fast return.
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if startpos == 0:
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trans_name = self.to_ascii.transliterate(norm_name).strip()
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return [trans_name] if trans_name else []
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return self._compute_result_set(partials, baseform[startpos:])
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def _compute_result_set(self, partials, prefix):
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results = set()
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for variant in partials:
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vname = variant + prefix
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trans_name = self.to_ascii.transliterate(vname[1:-1]).strip()
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if trans_name:
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results.add(trans_name)
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return list(results)
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