def detect_field_value(
cls, log: ProcessLogger, doc: Document, field: DocumentField,
field_code_to_value: Dict[str, Any]) -> Optional[FieldValueDTO]:
python_coded_field = PYTHON_CODED_FIELDS_REGISTRY.get(
field.python_coded_field) # type: PythonCodedField
if not python_coded_field:
raise RuntimeError('Unknown python-coded field: {0}'.format(
field.python_coded_field))
typed_field = TypedField.by(field) # type: TypedField
if python_coded_field.type != typed_field.type_code:
raise RuntimeError(
f'Python-coded field {python_coded_field.__class__.__name__} is '
f'for fields of type {python_coded_field.type} and field {field.code} '
f'is of type {typed_field.type_code}')
log.debug('detect_field_value: python_coded_field_detection, ' +
f'field {field.code}({field.pk}), document #{doc.pk}')
field_value_dto = python_coded_field.get_value(
log=log,
field=field,
doc=doc,
cur_field_code_to_value=field_code_to_value)
if not typed_field.is_json_field_value_ok(field_value_dto.field_value):
raise ValueError(
f'Python coded field class {field.python_coded_field} returned value not suitable for '
f'field {field.code} ({typed_field.type_code})')
return field_value_dto
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]:
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]:
log.debug('detect_field_value: csv_regexps_field_detection, ' +
f'field {field.code}({field.pk}), document #{doc.pk}')
detectors = cls.detecting_cache.get_detectors(
field.pk,
lambda msg, er: log.error(msg, field_code=field.code, exc_info=er))
if not detectors:
return None
is_multichoice = field.type == MultiChoiceField.type_code
doc_text = cls.get_document_text(doc)
annotations = []
for detector in detectors:
found_item = detector.find_value(doc_text)
if not found_item:
continue
# TODO: implement reading values from full text (TextParts.FULL.value)
# as it is done now, or from text units - paragraphs or sentences
# based on field.text_unit_type - for other detector.text_part options
"""
if detector.text_part == TextParts.BEFORE_REGEXP.value:
return matching_string[:begin], 0, begin
elif detector.text_part == TextParts.AFTER_REGEXP.value:
return matching_string[end:], end, len(text)
elif detector.text_part == TextParts.INSIDE_REGEXP.value:
return matching_string[begin:end], begin, end
else:
return text, 0, len(text)
"""
# starting position has to be shifted backward by 1 symbol for FE
ant = AnnotationDTO(annotation_value=found_item[0],
location_in_doc_start=max(
found_item[1] - 1, 0),
location_in_doc_end=found_item[2],
extraction_hint_name='')
if not is_multichoice:
return FieldValueDTO(field_value=found_item[0],
annotations=[ant])
else:
annotations.append(ant)
if annotations:
f_val = [a.annotation_value for a in annotations]
return FieldValueDTO(field_value=f_val, annotations=annotations)
return None
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)
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:
log.debug('detect_field_value: regexps_and_text_based_ml_field_value, ' +
f'field {field.code}({field.pk}), document #{doc.pk}')
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 detect_field_value(
cls, log: ProcessLogger, doc: Document, field: DocumentField,
field_code_to_value: Dict[str, Any]) -> Optional[FieldValueDTO]:
ants: List[AnnotationDTO] = []
typed_field: TypedField = TypedField.by(field)
text_unit_repo = cls.text_unit_repo
if field.mlflow_detect_on_document_level:
log.debug(
'detect_field_value: mlflow_field_detection on doc level, ' +
f'field {field.code}({field.pk}), document #{doc.pk}')
text = doc.text
model_input = dict(field_code_to_value)
model_input['text'] = text
model_input_df = pd.DataFrame([model_input])
model_output = MLFlowModelManager().predict(
field.mlflow_model_uri, model_input_df)
value = model_output[0]
if not value:
return None
hint_name = ValueExtractionHint.TAKE_FIRST.name
value, hint_name = typed_field \
.get_or_extract_value(doc, value, hint_name, text)
if not value:
return None
return FieldValueDTO(field_value=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)
log.debug(
'detect_field_value: mlflow_field_detection on text unit level, ' +
f'field {field.code}({field.pk}), document #{doc.pk}')
for text_unit in qs_text_units.iterator():
model_input = dict(field_code_to_value)
model_input['text'] = text_unit.text
model_input_df = pd.DataFrame([model_input])
model_output = MLFlowModelManager().predict(
field.mlflow_model_uri, model_input_df)
value = model_output[0]
if value is None:
continue
ant = None
if typed_field.requires_value:
# For the field types expecting a value the mlflow model must return either a value or None.
hint_name = ValueExtractionHint.TAKE_FIRST.name
value, hint_name = typed_field.get_or_extract_value(
doc, value, hint_name, text_unit.text)
if not typed_field.is_python_annotation_value_ok(value):
raise ValueError(
f'ML model of field {field.code} ({typed_field.type_code}) returned '
f'annotation value not suitable for this field:\n'
f'{value}')
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)
elif value:
# For the related-info fields the mlflow model must return 0 or 1
# where 1 means the text unit matches the field.
ant = AnnotationDTO(
annotation_value=None,
location_in_doc_start=text_unit.location_start,
location_in_doc_end=text_unit.location_end,
extraction_hint_name=None)
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)
def detect_field_value(cls,
log: ProcessLogger,
doc: Document,
field: DocumentField,
field_code_to_value: Dict[str, Any]) -> Optional[FieldValueDTO]:
typed_field = TypedField.by(field) # type: TypedField
if field.mlflow_detect_on_document_level:
log.debug('detect_field_value: mlflow_field_detection on doc level, ' +
f'field {field.code}({field.pk}), document #{doc.pk}')
text = doc.text
model_input = dict(field_code_to_value)
model_input['text'] = text
model_input_df = pd.DataFrame([model_input])
model_output = MLFlowModelManager().predict(field.mlflow_model_uri, model_input_df)
value = model_output[0]
if not value:
return None
hint_name = ValueExtractionHint.TAKE_FIRST.name
value, hint_name = typed_field \
.get_or_extract_value(doc, value, hint_name, text)
if not value:
return None
return FieldValueDTO(field_value=value)
ants = list() # type: List[AnnotationDTO]
qs_text_units = TextUnit.objects \
.filter(document=doc) \
.filter(unit_type=field.text_unit_type) \
.select_related('textunittext') \
.order_by('location_start', 'pk') \
.defer('textunittext__text_tsvector')
units_counted = 0
log.debug('detect_field_value: mlflow_field_detection on text unit level, ' +
f'field {field.code}({field.pk}), document #{doc.pk}')
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
model_input = dict(field_code_to_value)
model_input['text'] = text_unit.text
model_input_df = pd.DataFrame([model_input])
model_output = MLFlowModelManager().predict(field.mlflow_model_uri, model_input_df)
value = model_output[0]
if value is None:
continue
ant = None
if typed_field.requires_value:
# For the field types expecting a value the mlflow model must return either a value or None.
hint_name = ValueExtractionHint.TAKE_FIRST.name
value, hint_name = typed_field \
.get_or_extract_value(doc, value, hint_name, text_unit.text)
if not typed_field.is_python_annotation_value_ok(value):
raise ValueError(f'ML model of field {field.code} ({typed_field.type_code}) returned '
f'annotation value not suitable for this field:\n'
f'{value}')
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)
elif value:
# For the related-info fields the mlflow model must return 0 or 1
# where 1 means the text unit matches the field.
ant = AnnotationDTO(annotation_value=None,
location_in_doc_start=text_unit.location_start,
location_in_doc_end=text_unit.location_end,
extraction_hint_name=None)
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)
