File size: 6,244 Bytes
3477655
 
547518c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3477655
547518c
 
 
 
 
 
3477655
 
 
 
 
 
 
 
 
 
 
547518c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
## Taken from https://github.com/microsoft/presidio/blob/main/docs/samples/python/flair_recognizer.py

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,
        model_path: Optional[str] = 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

        if model and model_path:
            raise ValueError("Only one of model or model_path should be provided.")
        elif model and not model_path:
            self.model = model
        elif not model and model_path:
            print(f"Loading model from {model_path}")
            self.model = SequenceTagger.load(model_path)
        else:
            print(f"Loading model for language {supported_language}")
            self.model = 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]
        )