def read_dataset(
self,
dataset: str,
owner: Optional[str],
query: Optional[TokenClassificationQuery] = None,
) -> Iterable[TokenClassificationRecord]:
"""
Scan a dataset records
Parameters
----------
dataset:
The dataset name
owner:
The dataset owner. Optional
query:
If provided, scan will retrieve only records matching
the provided query filters. Optional
"""
dataset = self.__datasets__.find_by_name(dataset, owner=owner)
for db_record in self.__dao__.scan_dataset(
dataset, search=RecordSearch(query=as_elasticsearch(query))
):
yield TokenClassificationRecord.parse_obj(db_record)
def test_too_long_metadata():
text = "On one ones o no"
record = TokenClassificationRecord.parse_obj({
"text": text,
"tokens": text.split(),
"metadata": {
"too_long": "a" * 1000
},
})
assert len(record.metadata["too_long"]) == MAX_KEYWORD_LENGTH
def search(
self,
dataset: str,
owner: Optional[str],
search: TokenClassificationQuery,
record_from: int = 0,
size: int = 100,
) -> TokenClassificationSearchResults:
"""
Run a search in a dataset
Parameters
----------
dataset:
The dataset name
owner:
The dataset owner
search:
The search parameters
record_from:
The record from return results
size:
The max number of records to return
Returns
-------
The matched records with aggregation info for specified task_meta.py
"""
dataset = self.__datasets__.find_by_name(dataset, owner=owner)
results = self.__dao__.search_records(
dataset,
search=RecordSearch(query=as_elasticsearch(search)),
size=size,
record_from=record_from,
)
return TokenClassificationSearchResults(
total=results.total,
records=[
TokenClassificationRecord.parse_obj(r) for r in results.records
],
aggregations=TokenClassificationAggregations(
**results.aggregations,
words=results.words,
metadata=results.metadata or {},
) if results.aggregations else None,
)
def add_records(
self,
dataset: str,
owner: Optional[str],
records: List[CreationTokenClassificationRecord],
):
dataset = self.__datasets__.find_by_name(dataset, owner=owner)
db_records = []
now = datetime.datetime.now()
for record in records:
db_record = TokenClassificationRecord.parse_obj(record)
db_record.last_updated = now
db_records.append(db_record.dict(exclude_none=True))
failed = self.__dao__.add_records(
dataset=dataset,
records=db_records,
)
return BulkResponse(dataset=dataset.name, processed=len(records), failed=failed)
def search(
self,
dataset: str,
owner: Optional[str],
search: TokenClassificationQuery,
record_from: int = 0,
size: int = 100,
) -> TokenClassificationSearchResults:
"""
Run a search in a dataset
Parameters
----------
dataset:
The dataset name
owner:
The dataset owner
search:
The search parameters
record_from:
The record from return results
size:
The max number of records to return
Returns
-------
The matched records with aggregation info for specified task_meta.py
"""
dataset = self.__datasets__.find_by_name(dataset, owner=owner)
results = self.__dao__.search_records(
dataset,
search=RecordSearch(
query=as_elasticsearch(search),
aggregations={
**aggregations.nested_aggregation(
name=PREDICTED_MENTIONS_ES_FIELD_NAME,
nested_path=PREDICTED_MENTIONS_ES_FIELD_NAME,
inner_aggregation=aggregations.bidimentional_terms_aggregations(
name=PREDICTED_MENTIONS_ES_FIELD_NAME,
field_name_x=PREDICTED_MENTIONS_ES_FIELD_NAME + ".entity",
field_name_y=PREDICTED_MENTIONS_ES_FIELD_NAME + ".mention",
),
),
**aggregations.nested_aggregation(
name=MENTIONS_ES_FIELD_NAME,
nested_path=MENTIONS_ES_FIELD_NAME,
inner_aggregation=aggregations.bidimentional_terms_aggregations(
name=MENTIONS_ES_FIELD_NAME,
field_name_x=MENTIONS_ES_FIELD_NAME + ".entity",
field_name_y=MENTIONS_ES_FIELD_NAME + ".mention",
),
),
},
),
size=size,
record_from=record_from,
)
return TokenClassificationSearchResults(
total=results.total,
records=[TokenClassificationRecord.parse_obj(r) for r in results.records],
aggregations=TokenClassificationAggregations(
**results.aggregations,
words=results.words,
metadata=results.metadata or {},
)
if results.aggregations
else None,
)