Source code for langchain_experimental.data_anonymizer.deanonymizer_mapping

import re
from collections import defaultdict
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Dict, List

if TYPE_CHECKING:
    from presidio_analyzer import RecognizerResult
    from presidio_anonymizer.entities import EngineResult

MappingDataType = Dict[str, Dict[str, str]]


[docs]def format_duplicated_operator(operator_name: str, count: int) -> str: """Format the operator name with the count""" clean_operator_name = re.sub(r"[<>]", "", operator_name) clean_operator_name = re.sub(r"_\d+$", "", clean_operator_name) if operator_name.startswith("<") and operator_name.endswith(">"): return f"<{clean_operator_name}_{count}>" else: return f"{clean_operator_name}_{count}"
[docs]@dataclass class DeanonymizerMapping: mapping: MappingDataType = field( default_factory=lambda: defaultdict(lambda: defaultdict(str)) ) @property def data(self) -> MappingDataType: """Return the deanonymizer mapping""" return {k: dict(v) for k, v in self.mapping.items()}
[docs] def update(self, new_mapping: MappingDataType) -> None: """Update the deanonymizer mapping with new values Duplicated values will not be added If there are multiple entities of the same type, the mapping will include a count to differentiate them. For example, if there are two names in the input text, the mapping will include NAME_1 and NAME_2. """ seen_values = set() for entity_type, values in new_mapping.items(): count = len(self.mapping[entity_type]) + 1 for key, value in values.items(): if ( value not in seen_values and value not in self.mapping[entity_type].values() ): new_key = ( format_duplicated_operator(key, count) if key in self.mapping[entity_type] else key ) self.mapping[entity_type][new_key] = value seen_values.add(value) count += 1
[docs]def create_anonymizer_mapping( original_text: str, analyzer_results: List["RecognizerResult"], anonymizer_results: "EngineResult", is_reversed: bool = False, ) -> MappingDataType: """Creates or updates the mapping used to anonymize and/or deanonymize text. This method exploits the results returned by the analysis and anonymization processes. If is_reversed is True, it constructs a mapping from each original entity to its anonymized value. If is_reversed is False, it constructs a mapping from each anonymized entity back to its original text value. If there are multiple entities of the same type, the mapping will include a count to differentiate them. For example, if there are two names in the input text, the mapping will include NAME_1 and NAME_2. Example of mapping: { "PERSON": { "<original>": "<anonymized>", "John Doe": "Slim Shady" }, "PHONE_NUMBER": { "111-111-1111": "555-555-5555" } ... } """ # We are able to zip and loop through both lists because we expect # them to return corresponding entities for each identified piece # of analyzable data from our input. # We sort them by their 'start' attribute because it allows us to # match corresponding entities by their position in the input text. analyzer_results.sort(key=lambda d: d.start) anonymizer_results.items.sort(key=lambda d: d.start) mapping: MappingDataType = defaultdict(dict) count: dict = defaultdict(int) for analyzed, anonymized in zip(analyzer_results, anonymizer_results.items): original_value = original_text[analyzed.start : analyzed.end] entity_type = anonymized.entity_type if is_reversed: cond = original_value in mapping[entity_type].values() else: cond = original_value in mapping[entity_type] if cond: continue if ( anonymized.text in mapping[entity_type].values() or anonymized.text in mapping[entity_type] ): anonymized_value = format_duplicated_operator( anonymized.text, count[entity_type] + 2 ) count[entity_type] += 1 else: anonymized_value = anonymized.text mapping_key, mapping_value = ( (anonymized_value, original_value) if is_reversed else (original_value, anonymized_value) ) mapping[entity_type][mapping_key] = mapping_value return mapping