X-Git-Url: https://git.openstreetmap.org./nominatim.git/blobdiff_plain/1f0796778754d8df0dfab9dd01302e26a397f064..31412e06740727695c5d9512e0cd59c0dd683322:/src/nominatim_api/search/icu_tokenizer.py diff --git a/src/nominatim_api/search/icu_tokenizer.py b/src/nominatim_api/search/icu_tokenizer.py index fa14531a..1a449276 100644 --- a/src/nominatim_api/search/icu_tokenizer.py +++ b/src/nominatim_api/search/icu_tokenizer.py @@ -7,59 +7,80 @@ """ Implementation of query analysis for the ICU tokenizer. """ -from typing import Tuple, Dict, List, Optional, NamedTuple, Iterator, Any, cast +from typing import Tuple, Dict, List, Optional, Iterator, Any, cast from collections import defaultdict import dataclasses import difflib +import re +from itertools import zip_longest from icu import Transliterator import sqlalchemy as sa +from ..errors import UsageError from ..typing import SaRow from ..sql.sqlalchemy_types import Json from ..connection import SearchConnection from ..logging import log -from ..search import query as qmod -from ..search.query_analyzer_factory import AbstractQueryAnalyzer +from . import query as qmod +from ..query_preprocessing.config import QueryConfig +from .query_analyzer_factory import AbstractQueryAnalyzer DB_TO_TOKEN_TYPE = { - 'W': qmod.TokenType.WORD, - 'w': qmod.TokenType.PARTIAL, - 'H': qmod.TokenType.HOUSENUMBER, - 'P': qmod.TokenType.POSTCODE, - 'C': qmod.TokenType.COUNTRY + 'W': qmod.TOKEN_WORD, + 'w': qmod.TOKEN_PARTIAL, + 'H': qmod.TOKEN_HOUSENUMBER, + 'P': qmod.TOKEN_POSTCODE, + 'C': qmod.TOKEN_COUNTRY +} + +PENALTY_IN_TOKEN_BREAK = { + qmod.BREAK_START: 0.5, + qmod.BREAK_END: 0.5, + qmod.BREAK_PHRASE: 0.5, + qmod.BREAK_SOFT_PHRASE: 0.5, + qmod.BREAK_WORD: 0.1, + qmod.BREAK_PART: 0.0, + qmod.BREAK_TOKEN: 0.0 } -class QueryPart(NamedTuple): +@dataclasses.dataclass +class QueryPart: """ Normalized and transliterated form of a single term in the query. + When the term came out of a split during the transliteration, the normalized string is the full word before transliteration. - The word number keeps track of the word before transliteration - and can be used to identify partial transliterated terms. + Check the subsequent break type to figure out if the word is + continued. + + Penalty is the break penalty for the break following the token. """ token: str normalized: str - word_number: int + penalty: float QueryParts = List[QueryPart] WordDict = Dict[str, List[qmod.TokenRange]] -def yield_words(terms: List[QueryPart], start: int) -> Iterator[Tuple[str, qmod.TokenRange]]: - """ Return all combinations of words in the terms list after the - given position. +def extract_words(terms: List[QueryPart], start: int, words: WordDict) -> None: + """ Add all combinations of words in the terms list after the + given position to the word list. """ total = len(terms) + base_penalty = PENALTY_IN_TOKEN_BREAK[qmod.BREAK_WORD] for first in range(start, total): word = terms[first].token - yield word, qmod.TokenRange(first, first + 1) + penalty = base_penalty + words[word].append(qmod.TokenRange(first, first + 1, penalty=penalty)) for last in range(first + 1, min(first + 20, total)): word = ' '.join((word, terms[last].token)) - yield word, qmod.TokenRange(first, last + 1) + penalty += terms[last - 1].penalty + words[word].append(qmod.TokenRange(first, last + 1, penalty=penalty)) @dataclasses.dataclass @@ -92,25 +113,25 @@ class ICUToken(qmod.Token): self.penalty += (distance/len(self.lookup_word)) @staticmethod - def from_db_row(row: SaRow) -> 'ICUToken': + def from_db_row(row: SaRow, base_penalty: float = 0.0) -> 'ICUToken': """ Create a ICUToken from the row of the word table. """ count = 1 if row.info is None else row.info.get('count', 1) addr_count = 1 if row.info is None else row.info.get('addr_count', 1) - penalty = 0.0 + penalty = base_penalty if row.type == 'w': - penalty = 0.3 + penalty += 0.3 elif row.type == 'W': if len(row.word_token) == 1 and row.word_token == row.word: - penalty = 0.2 if row.word.isdigit() else 0.3 + penalty += 0.2 if row.word.isdigit() else 0.3 elif row.type == 'H': - penalty = sum(0.1 for c in row.word_token if c != ' ' and not c.isdigit()) + penalty += sum(0.1 for c in row.word_token if c != ' ' and not c.isdigit()) if all(not c.isdigit() for c in row.word_token): penalty += 0.2 * (len(row.word_token) - 1) elif row.type == 'C': if len(row.word_token) == 1: - penalty = 0.3 + penalty += 0.3 if row.info is None: lookup_word = row.word @@ -151,6 +172,8 @@ class ICUQueryAnalyzer(AbstractQueryAnalyzer): self.transliterator = await self.conn.get_cached_value('ICUTOK', 'transliterator', _make_transliterator) + await self._setup_preprocessing() + if 'word' not in self.conn.t.meta.tables: sa.Table('word', self.conn.t.meta, sa.Column('word_id', sa.Integer), @@ -159,15 +182,36 @@ class ICUQueryAnalyzer(AbstractQueryAnalyzer): sa.Column('word', sa.Text), sa.Column('info', Json)) + async def _setup_preprocessing(self) -> None: + """ Load the rules for preprocessing and set up the handlers. + """ + + rules = self.conn.config.load_sub_configuration('icu_tokenizer.yaml', + config='TOKENIZER_CONFIG') + preprocessing_rules = rules.get('query-preprocessing', []) + + self.preprocessors = [] + + for func in preprocessing_rules: + if 'step' not in func: + raise UsageError("Preprocessing rule is missing the 'step' attribute.") + if not isinstance(func['step'], str): + raise UsageError("'step' attribute must be a simple string.") + + module = self.conn.config.load_plugin_module( + func['step'], 'nominatim_api.query_preprocessing') + self.preprocessors.append( + module.create(QueryConfig(func).set_normalizer(self.normalizer))) + async def analyze_query(self, phrases: List[qmod.Phrase]) -> qmod.QueryStruct: """ Analyze the given list of phrases and return the tokenized query. """ log().section('Analyze query (using ICU tokenizer)') - normalized = list(filter(lambda p: p.text, - (qmod.Phrase(p.ptype, self.normalize_text(p.text)) - for p in phrases))) - query = qmod.QueryStruct(normalized) + for func in self.preprocessors: + phrases = func(phrases) + query = qmod.QueryStruct(phrases) + log().var_dump('Normalized query', query.source) if not query.source: return query @@ -177,16 +221,16 @@ class ICUQueryAnalyzer(AbstractQueryAnalyzer): for row in await self.lookup_in_db(list(words.keys())): for trange in words[row.word_token]: - token = ICUToken.from_db_row(row) + token = ICUToken.from_db_row(row, trange.penalty or 0.0) if row.type == 'S': if row.info['op'] in ('in', 'near'): if trange.start == 0: - query.add_token(trange, qmod.TokenType.NEAR_ITEM, token) + query.add_token(trange, qmod.TOKEN_NEAR_ITEM, token) else: if trange.start == 0 and trange.end == query.num_token_slots(): - query.add_token(trange, qmod.TokenType.NEAR_ITEM, token) + query.add_token(trange, qmod.TOKEN_NEAR_ITEM, token) else: - query.add_token(trange, qmod.TokenType.QUALIFIER, token) + query.add_token(trange, qmod.TOKEN_QUALIFIER, token) else: query.add_token(trange, DB_TO_TOKEN_TYPE[row.type], token) @@ -202,7 +246,7 @@ class ICUQueryAnalyzer(AbstractQueryAnalyzer): standardized form search will work with. All information removed at this stage is inevitably lost. """ - return cast(str, self.normalizer.transliterate(text)) + return cast(str, self.normalizer.transliterate(text)).strip('-: ') def split_query(self, query: qmod.QueryStruct) -> Tuple[QueryParts, WordDict]: """ Transliterate the phrases and split them into tokens. @@ -213,26 +257,31 @@ class ICUQueryAnalyzer(AbstractQueryAnalyzer): """ parts: QueryParts = [] phrase_start = 0 - words = defaultdict(list) - wordnr = 0 + words: WordDict = defaultdict(list) for phrase in query.source: query.nodes[-1].ptype = phrase.ptype - for word in phrase.text.split(' '): + phrase_split = re.split('([ :-])', phrase.text) + # The zip construct will give us the pairs of word/break from + # the regular expression split. As the split array ends on the + # final word, we simply use the fillvalue to even out the list and + # add the phrase break at the end. + for word, breakchar in zip_longest(*[iter(phrase_split)]*2, fillvalue=','): + if not word: + continue trans = self.transliterator.transliterate(word) if trans: for term in trans.split(' '): if term: - parts.append(QueryPart(term, word, wordnr)) - query.add_node(qmod.BreakType.TOKEN, phrase.ptype) - query.nodes[-1].btype = qmod.BreakType.WORD - wordnr += 1 - query.nodes[-1].btype = qmod.BreakType.PHRASE + parts.append(QueryPart(term, word, + PENALTY_IN_TOKEN_BREAK[qmod.BREAK_TOKEN])) + query.add_node(qmod.BREAK_TOKEN, phrase.ptype) + query.nodes[-1].btype = breakchar + parts[-1].penalty = PENALTY_IN_TOKEN_BREAK[breakchar] - for word, wrange in yield_words(parts, phrase_start): - words[word].append(wrange) + extract_words(parts, phrase_start, words) phrase_start = len(parts) - query.nodes[-1].btype = qmod.BreakType.END + query.nodes[-1].btype = qmod.BREAK_END return parts, words @@ -247,9 +296,9 @@ class ICUQueryAnalyzer(AbstractQueryAnalyzer): """ Add tokens to query that are not saved in the database. """ for part, node, i in zip(parts, query.nodes, range(1000)): - if len(part.token) <= 4 and part[0].isdigit()\ - and not node.has_tokens(i+1, qmod.TokenType.HOUSENUMBER): - query.add_token(qmod.TokenRange(i, i+1), qmod.TokenType.HOUSENUMBER, + if len(part.token) <= 4 and part.token.isdigit()\ + and not node.has_tokens(i+1, qmod.TOKEN_HOUSENUMBER): + query.add_token(qmod.TokenRange(i, i+1), qmod.TOKEN_HOUSENUMBER, ICUToken(penalty=0.5, token=0, count=1, addr_count=1, lookup_word=part.token, word_token=part.token, info=None)) @@ -258,31 +307,31 @@ class ICUQueryAnalyzer(AbstractQueryAnalyzer): """ Add penalties to tokens that depend on presence of other token. """ for i, node, tlist in query.iter_token_lists(): - if tlist.ttype == qmod.TokenType.POSTCODE: + if tlist.ttype == qmod.TOKEN_POSTCODE: for repl in node.starting: - if repl.end == tlist.end and repl.ttype != qmod.TokenType.POSTCODE \ - and (repl.ttype != qmod.TokenType.HOUSENUMBER + if repl.end == tlist.end and repl.ttype != qmod.TOKEN_POSTCODE \ + and (repl.ttype != qmod.TOKEN_HOUSENUMBER or len(tlist.tokens[0].lookup_word) > 4): repl.add_penalty(0.39) - elif (tlist.ttype == qmod.TokenType.HOUSENUMBER + elif (tlist.ttype == qmod.TOKEN_HOUSENUMBER and len(tlist.tokens[0].lookup_word) <= 3): if any(c.isdigit() for c in tlist.tokens[0].lookup_word): for repl in node.starting: - if repl.end == tlist.end and repl.ttype != qmod.TokenType.HOUSENUMBER: + if repl.end == tlist.end and repl.ttype != qmod.TOKEN_HOUSENUMBER: repl.add_penalty(0.5 - tlist.tokens[0].penalty) - elif tlist.ttype not in (qmod.TokenType.COUNTRY, qmod.TokenType.PARTIAL): + elif tlist.ttype not in (qmod.TOKEN_COUNTRY, qmod.TOKEN_PARTIAL): norm = parts[i].normalized for j in range(i + 1, tlist.end): - if parts[j - 1].word_number != parts[j].word_number: + if node.btype != qmod.BREAK_TOKEN: norm += ' ' + parts[j].normalized for token in tlist.tokens: cast(ICUToken, token).rematch(norm) def _dump_transliterated(query: qmod.QueryStruct, parts: QueryParts) -> str: - out = query.nodes[0].btype.value + out = query.nodes[0].btype for node, part in zip(query.nodes[1:], parts): - out += part.token + node.btype.value + out += part.token + node.btype return out @@ -292,7 +341,7 @@ def _dump_word_tokens(query: qmod.QueryStruct) -> Iterator[List[Any]]: for tlist in node.starting: for token in tlist.tokens: t = cast(ICUToken, token) - yield [tlist.ttype.name, t.token, t.word_token or '', + yield [tlist.ttype, t.token, t.word_token or '', t.lookup_word or '', t.penalty, t.count, t.info]