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deduplicate categories/qualifiers

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Sarah Hoffmann
2023-11-26 17:11:15 +01:00
parent 580a7b032f
commit 47ca56f21b
2 changed files with 19 additions and 11 deletions
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15
nominatim/api/search/db_search_builder.py
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@@ -7,7 +7,7 @@
""" """
Convertion from token assignment to an abstract DB search. Convertion from token assignment to an abstract DB search.
""" """
from typing import Optional, List, Tuple, Iterator from typing import Optional, List, Tuple, Iterator, Dict
import heapq import heapq
from nominatim.api.types import SearchDetails, DataLayer from nominatim.api.types import SearchDetails, DataLayer
@@ -339,12 +339,13 @@ class SearchBuilder:
Returns None if no category search is requested. Returns None if no category search is requested.
""" """
if assignment.category: if assignment.category:
tokens = [t for t in self.query.get_tokens(assignment.category, tokens: Dict[Tuple[str, str], float] = {}
TokenType.CATEGORY) for t in self.query.get_tokens(assignment.category, TokenType.CATEGORY):
if not self.details.categories cat = t.get_category()
or t.get_category() in self.details.categories] if (not self.details.categories or cat in self.details.categories)\
return dbf.WeightedCategories([t.get_category() for t in tokens], and t.penalty < tokens.get(cat, 1000.0):
[t.penalty for t in tokens]) tokens[cat] = t.penalty
return dbf.WeightedCategories(list(tokens.keys()), list(tokens.values()))
if self.details.categories: if self.details.categories:
return dbf.WeightedCategories(self.details.categories, return dbf.WeightedCategories(self.details.categories,

15
nominatim/api/search/db_search_fields.py
View File

@@ -7,7 +7,7 @@
""" """
Data structures for more complex fields in abstract search descriptions. Data structures for more complex fields in abstract search descriptions.
""" """
from typing import List, Tuple, Iterator, cast from typing import List, Tuple, Iterator, cast, Dict
import dataclasses import dataclasses
import sqlalchemy as sa import sqlalchemy as sa
@@ -195,10 +195,17 @@ class SearchData:
""" Set the qulaifier field from the given tokens. """ Set the qulaifier field from the given tokens.
""" """
if tokens: if tokens:
min_penalty = min(t.penalty for t in tokens) categories: Dict[Tuple[str, str], float] = {}
min_penalty = 1000.0
for t in tokens:
if t.penalty < min_penalty:
min_penalty = t.penalty
cat = t.get_category()
if t.penalty < categories.get(cat, 1000.0):
categories[cat] = t.penalty
self.penalty += min_penalty self.penalty += min_penalty
self.qualifiers = WeightedCategories([t.get_category() for t in tokens], self.qualifiers = WeightedCategories(list(categories.keys()),
[t.penalty - min_penalty for t in tokens]) list(categories.values()))
def set_ranking(self, rankings: List[FieldRanking]) -> None: def set_ranking(self, rankings: List[FieldRanking]) -> None: