Example 3: Rule based logic recognizer

Taking the numbers recognizer one step further, let's say we also would like to detect numbers within words, e.g. "Number One". We can leverage the underlying spaCy token attributes, or write our own logic to detect such entities.

Notes:

• In this example we would create a new class, which implements EntityRecognizer, the basic recognizer in Presidio. This abstract class requires us to implement the load method and analyze method.

• Each recognizer accepts an object of type NlpArtifacts, which holds pre-computed attributes on the input text.

A new recognizer should have this structure:

from typing import List
from presidio_analyzer import EntityRecognizer, RecognizerResult
from presidio_analyzer.nlp_engine import NlpArtifacts


class MyRecognizer(EntityRecognizer):
    def load(self) -> None:
        """No loading is required."""
        pass

    def analyze(
        self, text: str, entities: List[str], nlp_artifacts: NlpArtifacts
    ) -> List[RecognizerResult]:
        """
        Logic for detecting a specific PII
        """
        pass

For example, detecting numbers in either numerical or alphabetic (e.g. Forty five) form:

from typing import List
from presidio_analyzer import EntityRecognizer, RecognizerResult
from presidio_analyzer.nlp_engine import NlpArtifacts


class NumbersRecognizer(EntityRecognizer):

    expected_confidence_level = 0.7  # expected confidence level for this recognizer

    def load(self) -> None:
        """No loading is required."""
        pass

    def analyze(
        self, text: str, entities: List[str], nlp_artifacts: NlpArtifacts
    ) -> List[RecognizerResult]:
        """
        Analyzes test to find tokens which represent numbers (either 123 or One Two Three).
        """
        results = []

        # iterate over the spaCy tokens, and call `token.like_num`
        for token in nlp_artifacts.tokens:
            if token.like_num:
                result = RecognizerResult(
                    entity_type="NUMBER",
                    start=token.idx,
                    end=token.idx + len(token),
                    score=self.expected_confidence_level,
                )
                results.append(result)
        return results


# Instantiate the new NumbersRecognizer:
new_numbers_recognizer = NumbersRecognizer(supported_entities=["NUMBER"])

Since this recognizer requires the NlpArtifacts, we would have to call it as part of the AnalyzerEngine flow:

from presidio_analyzer import AnalyzerEngine

text3 = "Roberto lives in Five 10 Broad st."
analyzer = AnalyzerEngine()
analyzer.registry.add_recognizer(new_numbers_recognizer)

numbers_results2 = analyzer.analyze(text=text3, language="en")
print("Results:")
print("\n".join([str(res) for res in numbers_results2]))

The analyzer was able to pick up both numeric and alphabetical numbers, including other types of PII entities from other recognizers (PERSON in this case).