langchain/libs/experimental/tests/unit_tests/test_data_anonymizer.py

from typing import Iterator, List

import pytest
from . import is_libcublas_available


@pytest.fixture(scope="module", autouse=True)
def check_spacy_model() -> Iterator[None]:
    import spacy

    if not spacy.util.is_package("en_core_web_lg"):
        pytest.skip(reason="Spacy model 'en_core_web_lg' not installed")
    yield

@pytest.fixture(scope="module", autouse=True)
def check_libcublas() -> Iterator[None]:
    if not is_libcublas_available():
        pytest.skip(reason="libcublas.so is not available")
    yield



@pytest.mark.requires("presidio_analyzer", "presidio_anonymizer", "faker")
@pytest.mark.parametrize(
    "analyzed_fields,should_contain",
    [(["PERSON"], False), (["PHONE_NUMBER"], True), (None, False)],
)
def test_anonymize(analyzed_fields: List[str], should_contain: bool) -> None:
    """Test anonymizing a name in a simple sentence"""
    from langchain_experimental.data_anonymizer import PresidioAnonymizer

    text = "Hello, my name is John Doe."
    anonymizer = PresidioAnonymizer(analyzed_fields=analyzed_fields)
    anonymized_text = anonymizer.anonymize(text)
    assert ("John Doe" in anonymized_text) == should_contain


@pytest.mark.requires("presidio_analyzer", "presidio_anonymizer", "faker")
def test_anonymize_multiple() -> None:
    """Test anonymizing multiple items in a sentence"""
    from langchain_experimental.data_anonymizer import PresidioAnonymizer

    text = "John Smith's phone number is 313-666-7440 and email is johnsmith@gmail.com"
    anonymizer = PresidioAnonymizer()
    anonymized_text = anonymizer.anonymize(text)
    for phrase in ["John Smith", "313-666-7440", "johnsmith@gmail.com"]:
        assert phrase not in anonymized_text


@pytest.mark.requires("presidio_analyzer", "presidio_anonymizer", "faker")
def test_anonymize_with_custom_operator() -> None:
    """Test anonymize a name with a custom operator"""
    from presidio_anonymizer.entities import OperatorConfig

    from langchain_experimental.data_anonymizer import PresidioAnonymizer

    custom_operator = {"PERSON": OperatorConfig("replace", {"new_value": "<name>"})}
    anonymizer = PresidioAnonymizer(operators=custom_operator)

    text = "Jane Doe was here."

    anonymized_text = anonymizer.anonymize(text)
    assert anonymized_text == "<name> was here."


@pytest.mark.requires("presidio_analyzer", "presidio_anonymizer", "faker")
def test_add_recognizer_operator() -> None:
    """
    Test add recognizer and anonymize a new type of entity and with a custom operator
    """
    from presidio_analyzer import PatternRecognizer
    from presidio_anonymizer.entities import OperatorConfig

    from langchain_experimental.data_anonymizer import PresidioAnonymizer

    anonymizer = PresidioAnonymizer(analyzed_fields=[])
    titles_list = ["Sir", "Madam", "Professor"]
    custom_recognizer = PatternRecognizer(
        supported_entity="TITLE", deny_list=titles_list
    )
    anonymizer.add_recognizer(custom_recognizer)

    # anonymizing with custom recognizer
    text = "Madam Jane Doe was here."
    anonymized_text = anonymizer.anonymize(text)
    assert anonymized_text == "<TITLE> Jane Doe was here."

    # anonymizing with custom recognizer and operator
    custom_operator = {"TITLE": OperatorConfig("replace", {"new_value": "Dear"})}
    anonymizer.add_operators(custom_operator)
    anonymized_text = anonymizer.anonymize(text)
    assert anonymized_text == "Dear Jane Doe was here."