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import logging
from typing import Optional, List, Tuple, Set
from presidio_analyzer import (
RecognizerResult,
EntityRecognizer,
AnalysisExplanation,
)
from presidio_analyzer.nlp_engine import NlpArtifacts
from flair.data import Sentence
from flair.models import SequenceTagger
logger = logging.getLogger("presidio-analyzer")
class FlairRecognizer(EntityRecognizer):
"""
Wrapper for a flair model, if needed to be used within Presidio Analyzer.
:example:
>from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
>flair_recognizer = FlairRecognizer()
>registry = RecognizerRegistry()
>registry.add_recognizer(flair_recognizer)
>analyzer = AnalyzerEngine(registry=registry)
>results = analyzer.analyze(
> "My name is Christopher and I live in Irbid.",
> language="en",
> return_decision_process=True,
>)
>for result in results:
> print(result)
> print(result.analysis_explanation)
"""
ENTITIES = [
"LOCATION",
"PERSON",
"ORGANIZATION",
# "MISCELLANEOUS" # - There are no direct correlation with Presidio entities.
]
DEFAULT_EXPLANATION = "Identified as {} by Flair's Named Entity Recognition"
CHECK_LABEL_GROUPS = [
({"LOCATION"}, {"LOC", "LOCATION"}),
({"PERSON"}, {"PER", "PERSON"}),
({"ORGANIZATION"}, {"ORG"}),
# ({"MISCELLANEOUS"}, {"MISC"}), # Probably not PII
]
MODEL_LANGUAGES = {
"en": "flair/ner-english-large"
}
PRESIDIO_EQUIVALENCES = {
"PER": "PERSON",
"LOC": "LOCATION",
"ORG": "ORGANIZATION",
# 'MISC': 'MISCELLANEOUS' # - Probably not PII
}
def __init__(
self,
supported_language: str = "en",
supported_entities: Optional[List[str]] = None,
check_label_groups: Optional[Tuple[Set, Set]] = None,
model: SequenceTagger = None,
):
self.check_label_groups = (
check_label_groups if check_label_groups else self.CHECK_LABEL_GROUPS
)
supported_entities = supported_entities if supported_entities else self.ENTITIES
self.model = (
model
if model
else SequenceTagger.load(self.MODEL_LANGUAGES.get(supported_language))
)
super().__init__(
supported_entities=supported_entities,
supported_language=supported_language,
name="Flair Analytics",
)
def load(self) -> None:
"""Load the model, not used. Model is loaded during initialization."""
pass
def get_supported_entities(self) -> List[str]:
"""
Return supported entities by this model.
:return: List of the supported entities.
"""
return self.supported_entities
# Class to use Flair with Presidio as an external recognizer.
def analyze(
self, text: str, entities: List[str], nlp_artifacts: NlpArtifacts = None
) -> List[RecognizerResult]:
"""
Analyze text using Text Analytics.
:param text: The text for analysis.
:param entities: Not working properly for this recognizer.
:param nlp_artifacts: Not used by this recognizer.
:param language: Text language. Supported languages in MODEL_LANGUAGES
:return: The list of Presidio RecognizerResult constructed from the recognized
Flair detections.
"""
results = []
sentences = Sentence(text)
self.model.predict(sentences)
# If there are no specific list of entities, we will look for all of it.
if not entities:
entities = self.supported_entities
for entity in entities:
if entity not in self.supported_entities:
continue
for ent in sentences.get_spans("ner"):
if not self.__check_label(
entity, ent.labels[0].value, self.check_label_groups
):
continue
textual_explanation = self.DEFAULT_EXPLANATION.format(
ent.labels[0].value
)
explanation = self.build_flair_explanation(
round(ent.score, 2), textual_explanation
)
flair_result = self._convert_to_recognizer_result(ent, explanation)
results.append(flair_result)
return results
def _convert_to_recognizer_result(self, entity, explanation) -> RecognizerResult:
entity_type = self.PRESIDIO_EQUIVALENCES.get(entity.tag, entity.tag)
flair_score = round(entity.score, 2)
flair_results = RecognizerResult(
entity_type=entity_type,
start=entity.start_position,
end=entity.end_position,
score=flair_score,
analysis_explanation=explanation,
)
return flair_results
def build_flair_explanation(
self, original_score: float, explanation: str
) -> AnalysisExplanation:
"""
Create explanation for why this result was detected.
:param original_score: Score given by this recognizer
:param explanation: Explanation string
:return:
"""
explanation = AnalysisExplanation(
recognizer=self.__class__.__name__,
original_score=original_score,
textual_explanation=explanation,
)
return explanation
@staticmethod
def __check_label(
entity: str, label: str, check_label_groups: Tuple[Set, Set]
) -> bool:
return any(
[entity in egrp and label in lgrp for egrp, lgrp in check_label_groups]
)
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