forked from hans/Nominatim
7cfcbacfc7
Adds a mandatory section 'analyzer' to the token-analysis entries which define, which analyser to use. Currently there is exactly one, generic, which implements the former ICUNameProcessor.
111 lines
4.0 KiB
Python
111 lines
4.0 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
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import itertools
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from icu import Transliterator
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import datrie
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### Analysis section
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def create(norm_rules, 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(norm_rules, trans_rules, config['variants'])
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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, norm_rules, trans_rules, replacements):
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self.normalizer = Transliterator.createFromRules("icu_normalization",
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norm_rules)
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self.to_ascii = Transliterator.createFromRules("icu_to_ascii",
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trans_rules +
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";[:Space:]+ > ' '")
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self.search = Transliterator.createFromRules("icu_search",
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norm_rules + trans_rules)
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# Intermediate reorder by source. Also compute required character set.
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immediate = defaultdict(list)
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chars = set()
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for variant in replacements:
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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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# Then copy to datrie
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self.replacements = datrie.Trie(''.join(chars))
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for src, repllist in immediate.items():
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self.replacements[src] = repllist
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def get_normalized(self, name):
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""" Normalize the given name, i.e. remove all elements not relevant
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for search.
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"""
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return self.normalizer.transliterate(name).strip()
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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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def get_search_normalized(self, name):
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""" Return the normalized version of the name (including transliteration)
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to be applied at search time.
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"""
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return self.search.transliterate(' ' + name + ' ').strip()
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