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# coding: utf-8
"""
Public API for fuzzy matches for fatcat.
Match methods return candidates, verify methods return a match status.
match_containar_fuzzy -> List[ContainerEntity]
match_release_fuzzy -> List[ReleaseEntity]
verify_serial_name -> MatchStatus
verify_container_name -> MatchStatus
verify_container_fuzzy -> MatchStatus
verify_release_fuzzy -> MatchStatus
Candidate generation will use external data from search and hence is expensive. Verification is fast.
"""
from typing import List, Optional, Union, Set
import elasticsearch
from fatcat_openapi_client import (ApiException, ContainerEntity, DefaultApi, ReleaseEntity,
ReleaseExtIds, WorkEntity)
from fatcat_openapi_client.api.default_api import DefaultApi
from fuzzycat.fatcat.common import MatchStatus, response_to_entity_list
from fuzzycat.serials import serialsdb
from fuzzycat import cleanups
def match_container_fuzzy(container: ContainerEntity,
size: int = 5,
es: Optional[Union[str, elasticsearch.client.Elasticsearch]] = None,
api: Optional[DefaultApi] = None) -> List[ContainerEntity]:
"""
Given a container entity, which can be (very) partial, return a list of
candidate matches. Elasticsearch can be a hostport or the low level client
object.
Random data point: with 20 parallel workers callind match_container_fuzzy,
we get around 40 req/s.
"""
assert isinstance(container, ContainerEntity)
if size is None or size == 0:
size = 10000 # or any large number
if isinstance(es, str):
es = elasticsearch.Elasticsearch([es])
if es is None:
es = elasticsearch.Elasticsearch()
# If we find any match by ISSN-L, we return only those.
if container.issnl:
s = (elasticsearch_dsl.Search(using=es, index="fatcat_container").query(
"term", issns=container.issnl).extra(size=size))
resp = s.execute()
if len(resp) > 0:
return response_to_entity_list(resp, entity_type=ContainerEntity, api=api)
# Do we have an exact QID match?
if container.wikidata_qid:
s = (elasticsearch_dsl.Search(using=es, index="fatcat_container").query(
"term", wikidata_qid=container.wikidata_qid).extra(size=size))
resp = s.execute()
if len(resp) > 0:
return response_to_entity_list(resp, entity_type=ContainerEntity, api=api)
# Start with exact name match.
#
# curl -s https://search.fatcat.wiki/fatcat_container/_mapping | jq .
#
# "name": {
# "type": "text",
# "copy_to": [
# "biblio"
# ],
# "analyzer": "textIcu",
# "search_analyzer": "textIcuSearch"
# },
#
body = {
"query": {
"match": {
"name": {
"query": container.name,
"operator": "AND"
}
}
},
"size": size,
}
resp = es.search(body=body, index="fatcat_container")
if resp["hits"]["total"] > 0:
return response_to_entity_list(resp, entity_type=ContainerEntity, api=api)
# Get fuzzy.
# https://www.elastic.co/guide/en/elasticsearch/reference/current/common-options.html#fuzziness
body = {
"query": {
"match": {
"name": {
"query": container.name,
"operator": "AND",
"fuzziness": "AUTO",
}
}
},
"size": size,
}
resp = es.search(body=body, index="fatcat_container")
if resp["hits"]["total"] > 0:
return response_to_entity_list(resp, entity_type=ContainerEntity, api=api)
return []
def match_release_fuzzy(release: ReleaseEntity,
size: int = 5,
es: Optional[Union[str, elasticsearch.client.Elasticsearch]] = None,
api: Optional[DefaultApi] = None) -> List[ReleaseEntity]:
"""
Given a release entity, return a number similar release entities from
fatcat using Elasticsearch.
"""
assert isinstance(release, ReleaseEntity)
if size is None or size == 0:
size = 10000 # or any large number
if isinstance(es, str):
es = elasticsearch.Elasticsearch([es])
if es is None:
es = elasticsearch.Elasticsearch()
# Try to match by external identifier.
ext_ids = release.ext_ids
attrs = {
"doi": "doi",
"wikidata_qid": "wikidata_qid",
"isbn13": "isbn13",
"pmid": "pmid",
"pmcid": "pmcid",
"core": "code_id",
"arxiv": "arxiv_id",
"jstor": "jstor_id",
"ark": "ark_id",
"mag": "mag_id",
}
for attr, es_field in attrs.items():
value = getattr(ext_ids, attr)
if not value:
continue
s = (elasticsearch_dsl.Search(using=es,
index="fatcat_release").query("term", **{
es_field: value
}).extra(size=size))
resp = s.execute()
if len(resp) > 0:
return response_to_entity_list(resp, entity_type=ReleaseEntity, api=api)
body = {
"query": {
"match": {
"title": {
"query": release.title,
"operator": "AND"
}
}
},
"size": size,
}
resp = es.search(body=body, index="fatcat_release")
if resp["hits"]["total"] > 0:
return response_to_entity_list(resp, entity_type=ReleaseEntity, api=api)
# Get fuzzy.
# https://www.elastic.co/guide/en/elasticsearch/reference/current/common-options.html#fuzziness
body = {
"query": {
"match": {
"title": {
"query": release.title,
"operator": "AND",
"fuzziness": "AUTO",
}
}
},
"size": size,
}
resp = es.search(body=body, index="fatcat_release")
if resp["hits"]["total"] > 0:
return response_to_entity_list(resp, entity_type=ReleaseEntity, api=api)
return []
def verify_serial_name(a: str, b: str) -> MatchStatus:
"""
Serial name verification. Serial names are a subset of container names.
There are about 2M serials.
"""
def verify(a : Set[str], b : Set[str]) -> MatchStatus:
# If any name yields multiple ISSN-L, we cannot decide.
if len(a) > 1:
return MatchStatus.AMBIGIOUS
if len(b) > 1:
return MatchStatus.AMBIGIOUS
# If both names point the same ISSN-L, it is an exact match.
if len(a) > 0 and len(a) == len(b):
if len(a & b) == len(a):
return MatchStatus.EXACT
else:
return MatchStatus.DIFFERENT
# Multiple names possible, but there is overlap.
if len(a & b) > 0:
return MatchStatus.STRONG
issnls_for_a = serialsdb.get(a, set())
issnls_for_b = serialsdb.get(b, set())
status = verify(issnls_for_a, issnls_for_b)
if status != MatchStatus.AMBIGIOUS:
return status
# Try value cleanup.
issnls_for_a = serialsdb.get(a, set(), cleanup_pipeline=cleanups.basic)
issnls_for_b = serialsdb.get(b, set(), cleanup_pipeline=cleanups.basic)
return verify(issnls_for_a, issnls_for_b)
def verify_container_name(a: str, b: str) -> MatchStatus:
pass
def verify_container_match(a: ContainerEntity, b: ContainerEntity) -> MatchStatus:
pass
def verify_release_match(a: ReleaseEntity, b: ReleaseEntity) -> MatchStatus:
pass
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