mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
2aae1102b0
### Description
Add instance anonymization - if `John Doe` will appear twice in the
text, it will be treated as the same entity.
The difference between `PresidioAnonymizer` and
`PresidioReversibleAnonymizer` is that only the second one has a
built-in memory, so it will remember anonymization mapping for multiple
texts:
```
>>> anonymizer = PresidioAnonymizer()
>>> anonymizer.anonymize("My name is John Doe. Hi John Doe!")
'My name is Noah Rhodes. Hi Noah Rhodes!'
>>> anonymizer.anonymize("My name is John Doe. Hi John Doe!")
'My name is Brett Russell. Hi Brett Russell!'
```
```
>>> anonymizer = PresidioReversibleAnonymizer()
>>> anonymizer.anonymize("My name is John Doe. Hi John Doe!")
'My name is Noah Rhodes. Hi Noah Rhodes!'
>>> anonymizer.anonymize("My name is John Doe. Hi John Doe!")
'My name is Noah Rhodes. Hi Noah Rhodes!'
```
### Twitter handle
@deepsense_ai / @MaksOpp
### Tag maintainer
@baskaryan @hwchase17 @hinthornw
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
120 lines
4.5 KiB
Python
120 lines
4.5 KiB
Python
from typing import Iterator, List
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import pytest
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@pytest.fixture(scope="module", autouse=True)
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def check_spacy_model() -> Iterator[None]:
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import spacy
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if not spacy.util.is_package("en_core_web_lg"):
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pytest.skip(reason="Spacy model 'en_core_web_lg' not installed")
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yield
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@pytest.mark.requires("presidio_analyzer", "presidio_anonymizer", "faker")
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@pytest.mark.parametrize(
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"analyzed_fields,should_contain",
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[(["PERSON"], False), (["PHONE_NUMBER"], True), (None, False)],
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)
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def test_anonymize(analyzed_fields: List[str], should_contain: bool) -> None:
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"""Test anonymizing a name in a simple sentence"""
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from langchain_experimental.data_anonymizer import PresidioAnonymizer
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text = "Hello, my name is John Doe."
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anonymizer = PresidioAnonymizer(analyzed_fields=analyzed_fields)
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anonymized_text = anonymizer.anonymize(text)
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assert ("John Doe" in anonymized_text) == should_contain
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@pytest.mark.requires("presidio_analyzer", "presidio_anonymizer", "faker")
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def test_anonymize_multiple() -> None:
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"""Test anonymizing multiple items in a sentence"""
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from langchain_experimental.data_anonymizer import PresidioAnonymizer
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text = "John Smith's phone number is 313-666-7440 and email is johnsmith@gmail.com"
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anonymizer = PresidioAnonymizer()
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anonymized_text = anonymizer.anonymize(text)
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for phrase in ["John Smith", "313-666-7440", "johnsmith@gmail.com"]:
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assert phrase not in anonymized_text
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@pytest.mark.requires("presidio_analyzer", "presidio_anonymizer", "faker")
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def test_check_instances() -> None:
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"""Test anonymizing multiple items in a sentence"""
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from langchain_experimental.data_anonymizer import PresidioAnonymizer
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text = (
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"This is John Smith. John Smith works in a bakery." "John Smith is a good guy"
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)
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anonymizer = PresidioAnonymizer(["PERSON"], faker_seed=42)
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anonymized_text = anonymizer.anonymize(text)
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assert anonymized_text.count("Connie Lawrence") == 3
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# New name should be generated
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anonymized_text = anonymizer.anonymize(text)
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assert anonymized_text.count("Connie Lawrence") == 0
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@pytest.mark.requires("presidio_analyzer", "presidio_anonymizer", "faker")
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def test_anonymize_with_custom_operator() -> None:
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"""Test anonymize a name with a custom operator"""
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from presidio_anonymizer.entities import OperatorConfig
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from langchain_experimental.data_anonymizer import PresidioAnonymizer
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custom_operator = {"PERSON": OperatorConfig("replace", {"new_value": "NAME"})}
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anonymizer = PresidioAnonymizer(operators=custom_operator)
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text = "Jane Doe was here."
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anonymized_text = anonymizer.anonymize(text)
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assert anonymized_text == "NAME was here."
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@pytest.mark.requires("presidio_analyzer", "presidio_anonymizer", "faker")
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def test_add_recognizer_operator() -> None:
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"""
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Test add recognizer and anonymize a new type of entity and with a custom operator
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"""
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from presidio_analyzer import PatternRecognizer
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from presidio_anonymizer.entities import OperatorConfig
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from langchain_experimental.data_anonymizer import PresidioAnonymizer
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anonymizer = PresidioAnonymizer(analyzed_fields=[])
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titles_list = ["Sir", "Madam", "Professor"]
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custom_recognizer = PatternRecognizer(
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supported_entity="TITLE", deny_list=titles_list
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)
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anonymizer.add_recognizer(custom_recognizer)
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# anonymizing with custom recognizer
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text = "Madam Jane Doe was here."
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anonymized_text = anonymizer.anonymize(text)
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assert anonymized_text == "<TITLE> Jane Doe was here."
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# anonymizing with custom recognizer and operator
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custom_operator = {"TITLE": OperatorConfig("replace", {"new_value": "Dear"})}
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anonymizer.add_operators(custom_operator)
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anonymized_text = anonymizer.anonymize(text)
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assert anonymized_text == "Dear Jane Doe was here."
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@pytest.mark.requires("presidio_analyzer", "presidio_anonymizer", "faker")
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def test_non_faker_values() -> None:
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"""Test anonymizing multiple items in a sentence without faker values"""
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from langchain_experimental.data_anonymizer import PresidioAnonymizer
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text = (
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"My name is John Smith. Your name is Adam Smith. Her name is Jane Smith."
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"Our names are: John Smith, Adam Smith, Jane Smith."
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)
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expected_result = (
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"My name is <PERSON>. Your name is <PERSON_2>. Her name is <PERSON_3>."
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"Our names are: <PERSON>, <PERSON_2>, <PERSON_3>."
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)
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anonymizer = PresidioAnonymizer(add_default_faker_operators=False)
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anonymized_text = anonymizer.anonymize(text)
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assert anonymized_text == expected_result
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