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https://github.com/osm-search/Nominatim.git
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115 lines
4.4 KiB
Python
115 lines
4.4 KiB
Python
"""
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Processor for names that are imported into the database based on the
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ICU library.
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"""
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import json
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import itertools
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from icu import Transliterator
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import datrie
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from nominatim.db.properties import set_property, get_property
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DBCFG_IMPORT_NORM_RULES = "tokenizer_import_normalisation"
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DBCFG_IMPORT_TRANS_RULES = "tokenizer_import_transliteration"
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DBCFG_IMPORT_REPLACEMENTS = "tokenizer_import_replacements"
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DBCFG_SEARCH_STD_RULES = "tokenizer_search_standardization"
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class ICUNameProcessorRules:
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""" Data object that saves the rules needed for the name processor.
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The rules can either be initialised through an ICURuleLoader or
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be loaded from a database when a connection is given.
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"""
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def __init__(self, loader=None, conn=None):
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if loader is not None:
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self.norm_rules = loader.get_normalization_rules()
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self.trans_rules = loader.get_transliteration_rules()
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self.replacements = loader.get_replacement_pairs()
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self.search_rules = loader.get_search_rules()
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elif conn is not None:
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self.norm_rules = get_property(conn, DBCFG_IMPORT_NORM_RULES)
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self.trans_rules = get_property(conn, DBCFG_IMPORT_TRANS_RULES)
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self.replacements = json.loads(get_property(conn, DBCFG_IMPORT_REPLACEMENTS))
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self.search_rules = get_property(conn, DBCFG_SEARCH_STD_RULES)
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else:
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assert False, "Parameter loader or conn required."
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# Compute the set of characters used in the replacement list.
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# We need this later when computing the tree.
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chars = set()
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for full, repl in self.replacements:
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chars.update(full)
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for word in repl:
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chars.update(word)
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self.replacement_charset = ''.join(chars)
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def save_rules(self, conn):
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""" Save the rules in the property table of the given database.
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the rules can be loaded again by handing in a connection into
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the constructor of the class.
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"""
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set_property(conn, DBCFG_IMPORT_NORM_RULES, self.norm_rules)
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set_property(conn, DBCFG_IMPORT_TRANS_RULES, self.trans_rules)
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set_property(conn, DBCFG_IMPORT_REPLACEMENTS, json.dumps(self.replacements))
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set_property(conn, DBCFG_SEARCH_STD_RULES, self.search_rules)
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class ICUNameProcessor:
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""" Collects the different transformation rules for normalisation of names
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and provides the functions to aply the transformations.
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"""
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def __init__(self, rules):
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self.normalizer = Transliterator.createFromRules("icu_normalization",
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rules.norm_rules)
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self.to_ascii = Transliterator.createFromRules("icu_to_ascii",
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rules.trans_rules)
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self.search = Transliterator.createFromRules("icu_search",
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rules.search_rules)
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self.replacements = datrie.Trie(rules.replacement_charset)
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for full, repl in rules.replacements:
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self.replacements[full] = repl
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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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variants = ['']
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startpos = 0
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pos = 0
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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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variants = [v + done + r for v, r in itertools.product(variants, repl)]
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startpos = pos + len(full)
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pos = startpos
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else:
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pos += 1
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if startpos == 0:
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return [self.to_ascii.transliterate(norm_name)]
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return [self.to_ascii.transliterate(v + baseform[startpos:pos]).strip() for v in variants]
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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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