def detect_field_value(cls,
log: ProcessLogger,
doc: Document,
field: DocumentField,
field_code_to_value: Dict[str, Any]) -> Optional[FieldValueDTO]:
depends_on_full_text = doc.full_text
detected_with_stop_words, detected_value = detect_with_stop_words_by_field_and_full_text(field,
depends_on_full_text)
if detected_with_stop_words:
return FieldValueDTO(field_value=detected_value)
try:
classifier_model = ClassifierModel.objects.get(document_field=field)
sklearn_model = classifier_model.get_trained_model_obj()
typed_field = TypedField.by(field) # type: TypedField
ants = list() # type: List[AnnotationDTO]
qs_text_units = TextUnit.objects \
.filter(document=doc) \
.filter(unit_type=field.text_unit_type) \
.order_by('location_start', 'pk')
units_counted = 0
for text_unit in qs_text_units.iterator():
if field.detect_limit_count:
units_counted = FieldDetectionStrategy.update_units_counted(
field, units_counted, text_unit)
if units_counted > field.detect_limit_count:
break
ant = cls.predict_and_extract_value(sklearn_model=sklearn_model,
typed_field=typed_field,
document=doc,
field=field,
text_unit=text_unit)
if ant is None:
continue
if field.detect_limit_count and field.detect_limit_unit == DocumentField.DETECT_LIMIT_CHAR:
if ant.location_in_doc_start > field.detect_limit_count:
break
ants.append(ant)
if not isinstance(typed_field, MultiValueField):
return FieldValueDTO(field_value=ant.annotation_value, annotations=ants)
if field.detect_limit_count and field.detect_limit_unit == DocumentField.DETECT_LIMIT_CHAR:
units_counted += len(text_unit.text)
if not ants:
return None
return FieldValueDTO(field_value=typed_field.build_json_field_value_from_json_ant_values([a.annotation_value
for a in ants]),
annotations=ants)
except ClassifierModel.DoesNotExist as e:
log.info(f'Classifier model does not exist for field: {field.code}')
raise e
def maybe_detect_with_stop_words(
cls, field: DocumentField, doc: Document,
cached_fields: Dict[str, Any]) -> Optional[FieldValueDTO]:
if field.stop_words:
depends_on_field_codes = list(
field.depends_on_fields.all().values_list(
'code', flat=True)) # type: List[str]
depends_on_full_text = []
if not any(cached_fields):
return None
for field_code in depends_on_field_codes:
v = cached_fields.get(field_code)
if v:
depends_on_full_text.append(str(v))
detected_with_stop_words, detected_field_value = \
detect_with_stop_words_by_field_and_full_text(field=field,
doc=doc,
full_text='\n'.join(depends_on_full_text))
if detected_with_stop_words:
return FieldValueDTO(field_value=TypedField.by(
field).field_value_python_to_json(detected_field_value))
return None
def detect_field_value(
cls, log: ProcessLogger, doc: Document, field: DocumentField,
field_code_to_value: Dict[str, Any]) -> FieldValueDTO:
formula = field.formula
if not formula:
raise ValueError(
f'No formula specified for field {field.code} (#{field.uid})')
depends_on_field_codes = field.get_depends_on_codes() or set()
field_code_to_value = {
c: v
for c, v in field_code_to_value.items()
if c in depends_on_field_codes
}
if field.stop_words:
depends_on_full_text = '\n'.join(
[str(v) for v in field_code_to_value.values()])
log.debug(
'detect_field_value: formula_based_field_detection, checking stop words, '
+ f'field {field.code}({field.pk}), document #{doc.pk}')
detected_with_stop_words, detected_values \
= detect_with_stop_words_by_field_and_full_text(field,
doc,
depends_on_full_text)
if detected_with_stop_words:
return detected_values or list()
else:
log.debug('detect_field_value: formula_based_field_detection, ' +
f'field {field.code}({field.pk}), document #{doc.pk}')
v = cls.calc_formula(field_code=field.code,
formula=formula,
depends_on_field_to_value=field_code_to_value,
convert_decimals_to_floats=field.
convert_decimals_to_floats_in_formula_args)
typed_field = TypedField.by(field)
# We don't accept formulas returning values of wrong type to avoid further confusion and
# creating wrong formulas in future.
# For example for multi-choice fields the formula should return a list and not a string
# to ensure the admin understands that this value will replace the whole set/list of strings and not
# just add one more string to the value.
if typed_field.is_choice_field and typed_field.multi_value:
if v and isinstance(v, str):
# "outdated" formula is incorrect and returns string instead of
# set / list, but we don't warn user: when he updates this formula
# (or other detection method) he'll be forced to write code, returning
# list or set.
v = [v]
if not typed_field.is_python_field_value_ok(v):
raise ValueError(
f'Formula of field {field.code} returned value not suitable for this field:\n{v}'
)
v = typed_field.field_value_python_to_json(v)
return FieldValueDTO(field_value=v)
def detect_field_value(
cls, log: ProcessLogger, doc: Document, field: DocumentField,
field_code_to_value: Dict[str, Any]) -> Optional[FieldValueDTO]:
depends_on_fields = field.get_depends_on_codes()
field_code_to_value = {
c: v
for c, v in field_code_to_value.items() if c in depends_on_fields
}
if field.stop_words:
depends_on_full_text = '\n'.join(
[str(v) for v in field_code_to_value.values()])
detected_with_stop_words, detected_value \
= detect_with_stop_words_by_field_and_full_text(field, doc, depends_on_full_text)
if detected_with_stop_words:
return FieldValueDTO(field_value=detected_value)
field_detectors = DocumentFieldDetector.objects.filter(field=field)
detectors = [DetectorFieldMatcher(d) for d in field_detectors]
typed_field = TypedField.by(field) # type: TypedField
values = list() # type: List
for depends_on_value in field_code_to_value.values():
if not depends_on_value:
continue
depends_on_value = str(depends_on_value)
for detector_field_matcher in detectors: # type: DetectorFieldMatcher
matching_piece = detector_field_matcher.matching_string(
depends_on_value, text_is_sentence=False)
if matching_piece is not None:
matching_string = matching_piece[0]
value = detector_field_matcher.get_validated_detected_value(
field)
if typed_field.requires_value:
hint_name = detector_field_matcher.extraction_hint or ValueExtractionHint.TAKE_FIRST.name
value, hint_name = typed_field \
.get_or_extract_value(doc,
value,
hint_name,
matching_string)
if value is None:
continue
value = typed_field.annotation_value_python_to_json(value)
if not isinstance(typed_field, MultiValueField):
return FieldValueDTO(field_value=value)
else:
values.append(value)
if isinstance(typed_field, MultiValueField):
return FieldValueDTO(
field_value=typed_field.
build_json_field_value_from_json_ant_values(values))
else:
return None
def detect_field_value(
cls, log: ProcessLogger, doc: Document, field: DocumentField,
field_code_to_value: Dict[str, Any]) -> Optional[FieldValueDTO]:
log.debug(
'detect_field_value: regexps_and_text_based_ml_field_value, ' +
f'field {field.code}({field.pk}), document #{doc.pk}')
ants: List[AnnotationDTO] = []
text_unit_repo = cls.text_unit_repo
depends_on_full_text: str = doc.full_text
typed_field: TypedField = TypedField.by(field)
detected_with_stop_words, detected_value = \
detect_with_stop_words_by_field_and_full_text(field, depends_on_full_text)
if detected_with_stop_words:
return FieldValueDTO(field_value=detected_value)
qs_text_units = text_unit_repo.get_doc_text_units(
doc, field.text_unit_type)
qs_text_units = FieldDetectionStrategy.reduce_textunits_by_detection_limit(
qs_text_units, field)
try:
classifier_model = ClassifierModel.objects.get(
document_field=field)
sklearn_model = classifier_model.get_trained_model_obj()
for text_unit in qs_text_units.iterator(): # type: TextUnit
ant = cls.predict_and_extract_value(
sklearn_model=sklearn_model,
typed_field=typed_field,
document=doc,
field=field,
text=text_unit.text,
location_start=text_unit.location_start,
location_end=text_unit.location_end)
if ant is None:
continue
ants.append(ant)
if not isinstance(typed_field, MultiValueField):
return FieldValueDTO(field_value=ant.annotation_value,
annotations=ants)
if not ants:
return None
return FieldValueDTO(field_value=typed_field.
build_json_field_value_from_json_ant_values(
[a.annotation_value for a in ants]),
annotations=ants)
except ClassifierModel.DoesNotExist as e:
log.info(
f'Classifier model does not exist for field: {field.code}')
raise e
def detect_field_value(
cls, log: ProcessLogger, doc: Document, field: DocumentField,
field_code_to_value: Dict[str, Any]) -> Optional[FieldValueDTO]:
try:
log.debug('detect_field_value: regexps_field_detection, ' +
f'field {field.code}({field.pk}), document #{doc.pk}')
except AttributeError:
pass
ants: List[AnnotationDTO] = []
depends_on_full_text: str = doc.full_text
typed_field: TypedField = TypedField.by(field)
text_unit_repo: TextUnitRepository = cls.text_unit_repo
field_detector_repo: FieldDetectorRepository = cls.field_detector_repo
detected_with_stop_words, detected_value = \
detect_with_stop_words_by_field_and_full_text(field, doc, depends_on_full_text)
if detected_with_stop_words:
return FieldValueDTO(field_value=detected_value)
qs_text_units = text_unit_repo.get_doc_text_units(
doc, field.text_unit_type)
qs_text_units = FieldDetectionStrategy.reduce_textunits_by_detection_limit(
qs_text_units, field)
field_detectors = field_detector_repo.get_field_detectors(field)
detectors = [DetectorFieldMatcher(d) for d in field_detectors]
for text_unit in qs_text_units:
unit_ants = cls.extract_from_textunit(text_unit, field, detectors)
if not unit_ants:
continue
if not isinstance(typed_field, MultiValueField):
return FieldValueDTO(field_value=unit_ants[0].annotation_value,
annotations=unit_ants)
else:
ants += unit_ants
if not ants:
return None
if isinstance(typed_field, MultiValueField):
field_value = typed_field.build_json_field_value_from_json_ant_values(
[a.annotation_value for a in ants])
else:
field_value = typed_field.annotation_value_python_to_json(
ants[0].annotation_value)
return FieldValueDTO(field_value=field_value, annotations=ants)
def detect_field_value(
cls, log: ProcessLogger, doc: Document, field: DocumentField,
field_code_to_value: Dict[str, Any]) -> Optional[FieldValueDTO]:
text_unit_repo = cls.text_unit_repo
field_detector_repo = cls.field_detector_repo
log.debug('detect_field_value: regexps_field_detection, ' +
f'field {field.code}({field.pk}), document #{doc.pk}')
depends_on_full_text = doc.full_text
detected_with_stop_words, detected_value = detect_with_stop_words_by_field_and_full_text(
field, depends_on_full_text)
if detected_with_stop_words:
return FieldValueDTO(field_value=detected_value)
qs_text_units = text_unit_repo.get_doc_text_units(
doc, field.text_unit_type)
field_detectors = field_detector_repo.get_field_detectors(field)
detectors = [DetectorFieldMatcher(d) for d in field_detectors]
typed_field = TypedField.by(field) # type: TypedField
ants = list() # type: List[AnnotationDTO]
units_counted = 0
for text_unit in qs_text_units: # type: TextUnit
if field.detect_limit_count:
units_counted = FieldDetectionStrategy.update_units_counted(
field, units_counted, text_unit)
if units_counted > field.detect_limit_count:
break
for field_detector in detectors:
try:
matching_piece = field_detector.matching_string(
text_unit.textunittext.text,
text_is_sentence=text_unit.is_sentence())
if matching_piece is not None:
if field.detect_limit_unit == DocumentField.DETECT_LIMIT_CHAR:
if field.detect_limit_count < units_counted + matching_piece[
1]:
continue
matching_string = matching_piece[0]
value = field_detector.get_validated_detected_value(
field)
hint_name = None
if typed_field.requires_value:
hint_name = field_detector.extraction_hint or ValueExtractionHint.TAKE_FIRST.name
try:
value, hint_name = typed_field \
.get_or_extract_value(doc,
value,
hint_name,
matching_string)
except Exception as e:
raise ValueExtractionFunctionThrownException(
f'Value extraction function has thrown an exception.\n'
f'Document: {doc.name} (#{doc.pk})\n'
f'Value: {value}\n'
f'Extraction hint: {hint_name}\n'
f'Matching string:\n'
f'{matching_string}') from e
if value is None:
continue
annotation_value = typed_field.annotation_value_python_to_json(
value)
ant = AnnotationDTO(
annotation_value=annotation_value,
location_in_doc_start=text_unit.location_start,
location_in_doc_end=text_unit.location_end,
extraction_hint_name=hint_name)
if not isinstance(typed_field, MultiValueField):
return FieldValueDTO(
field_value=ant.annotation_value,
annotations=[ant])
else:
ants.append(ant)
except Exception as e:
raise CaughtErrorWhileApplyingFieldDetector(
f'Exception caught while trying to apply field detector.\n'
f'Document: {doc.name} (#{doc.pk})\n'
f'Field detector: #{field_detector.detector.pk}\n'
f'{field_detector.detector.include_regexps}\n'
f'Text unit: #{text_unit.pk}\n'
f'{text_unit.text[:300]}') from e
if field.detect_limit_count and field.detect_limit_unit == DocumentField.DETECT_LIMIT_CHAR:
units_counted += len(text_unit.text)
if not ants:
return None
return FieldValueDTO(field_value=typed_field.
build_json_field_value_from_json_ant_values(
[a.annotation_value for a in ants]),
annotations=ants)
