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https://github.com/data-privacy-stack/presidio.git
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Two new samples + docs refactoring (#744)
This commit is contained in:
Omri Mendels
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docs/samples/python/batch_processing.ipynb
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489
docs/samples/python/batch_processing.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "gothic-trademark",
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"metadata": {},
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"source": [
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"# Run Presidio on structured / semi-structured data\n",
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"\n",
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"This sample shows how Presidio could be potentially extended to handle the anonymization of a table or data frame.\n",
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"It introduces methods for the analysis and anonymization of both lists and dicts. \n",
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"\n",
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"In this example we create two classes which implement the base Presidio classes:\n",
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"1. `BatchAnalyzerEngine(AnalyzerEngine)`: for the presidio-anlyzer side\n",
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"2. `BatchAnonymizerEngine(AnonymizerEngine`): for the presidio-anonymizer side\n",
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"\n",
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"In addition, we create a `dataclass` (`DictAnalyzerResult`) to serve as the data transfer object between the two.\n",
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"\n",
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"Note: this sample input here is a Pandas DataFrame, but it can be used in other scenarios such as querying SQL data or using Spark DataFrames."
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]
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},
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{
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"cell_type": "markdown",
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"id": "roman-allergy",
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"metadata": {},
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"source": [
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"### Set up imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "extensive-greensboro",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import List, Optional, Dict, Union, Iterator, Iterable\n",
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"import collections\n",
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"from dataclasses import dataclass\n",
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"import pprint\n",
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"\n",
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"import pandas as pd\n",
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"\n",
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"from presidio_analyzer import AnalyzerEngine, RecognizerResult\n",
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"from presidio_anonymizer import AnonymizerEngine\n",
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"from presidio_anonymizer.entities.engine.result import EngineResult\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "specialized-durham",
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"metadata": {},
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"source": [
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"### Set up classes for batch processing"
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]
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},
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{
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"cell_type": "markdown",
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"id": "metropolitan-atlantic",
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"metadata": {},
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"source": [
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"1. Presidio Analyzer: Batch mode"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "medium-ridge",
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"metadata": {},
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"outputs": [],
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"source": [
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"@dataclass\n",
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"class DictAnalyzerResult:\n",
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" \"\"\"Hold the analyzer results per value or list of values.\"\"\"\n",
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" key: str\n",
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" value: Union[str, List[str]]\n",
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" recognizer_results: Union[List[RecognizerResult], List[List[RecognizerResult]]]\n",
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"\n",
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"\n",
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"class BatchAnalyzerEngine(AnalyzerEngine):\n",
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" \"\"\"\n",
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" Class inheriting from AnalyzerEngine and adds the funtionality to analyze lists or dictionaries.\n",
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" \"\"\"\n",
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" \n",
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" def analyze_list(self, list_of_texts: Iterable[str], **kwargs) -> List[List[RecognizerResult]]:\n",
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" \"\"\"\n",
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" Analyze an iterable of strings\n",
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" \n",
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" :param list_of_texts: An iterable containing strings to be analyzed.\n",
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" :param kwargs: Additional parameters for the `AnalyzerEngine.analyze` method.\n",
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" \"\"\"\n",
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" \n",
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" list_results = []\n",
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" for text in list_of_texts:\n",
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" results = self.analyze(text=text, **kwargs) if isinstance(text, str) else []\n",
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" list_results.append(results)\n",
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" return list_results\n",
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"\n",
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" def analyze_dict(\n",
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" self, input_dict: Dict[str, Union[object, Iterable[object]]], **kwargs) -> Iterator[DictAnalyzerResult]:\n",
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" \"\"\"\n",
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" Analyze a dictionary of keys (strings) and values (either object or Iterable[object]). \n",
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" Non-string values are returned as is.\n",
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" \n",
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" :param input_dict: The input dictionary for analysis\n",
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" :param kwargs: Additional keyword arguments for the `AnalyzerEngine.analyze` method\n",
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" \"\"\"\n",
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" \n",
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" for key, value in input_dict.items():\n",
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" if not value:\n",
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" results = []\n",
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" else:\n",
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" if isinstance(value, str):\n",
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" results: List[RecognizerResult] = self.analyze(text=value, **kwargs)\n",
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" elif isinstance(value, collections.Iterable):\n",
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" results: List[List[RecognizerResult]] = self.analyze_list(\n",
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" list_of_texts=value, \n",
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" **kwargs)\n",
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" else:\n",
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" results = []\n",
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" yield DictAnalyzerResult(key=key, value=value, recognizer_results=results)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "obvious-fifty",
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"metadata": {},
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"source": [
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"2. Presidio Anonymizer"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "outstanding-celebration",
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"metadata": {},
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"outputs": [],
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"source": [
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"class BatchAnonymizerEngine(AnonymizerEngine):\n",
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" \"\"\"\n",
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" Class inheriting from the AnonymizerEngine and adding additional functionality \n",
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" for anonymizing lists or dictionaries.\n",
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" \"\"\"\n",
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" \n",
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" def anonymize_list(\n",
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" self, \n",
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" texts:List[str], \n",
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" recognizer_results_list: List[List[RecognizerResult]], \n",
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" **kwargs\n",
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" ) -> List[EngineResult]:\n",
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" \"\"\"\n",
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" Anonymize a list of strings.\n",
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" \n",
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" :param texts: List containing the texts to be anonymized (original texts)\n",
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" :param recognizer_results_list: A list of lists of RecognizerResult, \n",
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" the output of the AnalyzerEngine on each text in the list.\n",
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" :param kwargs: Additional kwargs for the `AnonymizerEngine.anonymize` method\n",
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" \"\"\"\n",
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" return_list = []\n",
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" for text, recognizer_results in zip(texts, recognizer_results_list):\n",
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" if isinstance(text,str):\n",
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" res = self.anonymize(text=text,analyzer_results=recognizer_results,**kwargs)\n",
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" return_list.append(res.text)\n",
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" else:\n",
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" return_list.append(text)\n",
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"\n",
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" return return_list\n",
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"\n",
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"\n",
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" def anonymize_dict(self, analyzer_results: Iterator[DictAnalyzerResult],**kwargs) -> Dict[str, str]:\n",
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"\n",
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" \"\"\"\n",
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" Anonymize values in a dictionary.\n",
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" \n",
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" :param analyzer_results: Iterator of `DictAnalyzerResult` \n",
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" containing the output of the AnalyzerEngine.analyze_dict on the input text.\n",
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" :param kwargs: Additional kwargs for the `AnonymizerEngine.anonymize` method\n",
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" \"\"\"\n",
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" \n",
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" return_dict = {}\n",
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" for result in analyzer_results:\n",
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" if isinstance(result.value, str):\n",
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" resp = self.anonymize(text=result.value, analyzer_results=result.recognizer_results, **kwargs)\n",
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" return_dict[result.key] = resp.text\n",
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" elif isinstance(result.value, collections.Iterable):\n",
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" anonymize_respones = self.anonymize_list(texts=result.value,\n",
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" recognizer_results_list=result.recognizer_results, \n",
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" **kwargs)\n",
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" return_dict[result.key] = anonymize_respones \n",
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" else:\n",
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" return_dict[result.key] = result.value\n",
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"\n",
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" return return_dict"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fiscal-affair",
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"metadata": {},
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"source": [
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"## Example using sample data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "bright-maple",
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"metadata": {},
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"outputs": [],
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"source": [
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"columns = [\"name phrase\",\"phone number phrase\", \"integer\", \"boolean\" ]\n",
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"sample_data = [\n",
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" ('Morris likes this','Please call 212-555-1234 after 2pm', 1, True),\n",
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" ('You should talk to Mike','his number is 978-428-7111', 2, False),\n",
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" ('Mary had a little startup','Phone number: 202-342-1234', 3, False)\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "russian-proceeding",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>name phrase</th>\n",
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" <th>phone number phrase</th>\n",
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" <th>integer</th>\n",
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" <th>boolean</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Morris likes this</td>\n",
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" <td>Please call 212-555-1234 after 2pm</td>\n",
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" <td>1</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>You should talk to Mike</td>\n",
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" <td>his number is 978-428-7111</td>\n",
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" <td>2</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Mary had a little startup</td>\n",
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" <td>Phone number: 202-342-1234</td>\n",
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" <td>3</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" name phrase phone number phrase integer \\\n",
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"0 Morris likes this Please call 212-555-1234 after 2pm 1 \n",
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"1 You should talk to Mike his number is 978-428-7111 2 \n",
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"2 Mary had a little startup Phone number: 202-342-1234 3 \n",
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"\n",
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" boolean \n",
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"0 True \n",
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"1 False \n",
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"2 False "
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Create Pandas DataFrame\n",
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"df = pd.DataFrame(sample_data,columns=columns)\n",
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"\n",
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "brazilian-punch",
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"metadata": {},
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"outputs": [],
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"source": [
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"# DataFrame to dict\n",
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"df_dict = df.to_dict(orient=\"list\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "fixed-commerce",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'boolean': [True, False, False],\n",
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" 'integer': [1, 2, 3],\n",
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" 'name phrase': ['Morris likes this',\n",
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" 'You should talk to Mike',\n",
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" 'Mary had a little startup'],\n",
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" 'phone number phrase': ['Please call 212-555-1234 after 2pm',\n",
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" 'his number is 978-428-7111',\n",
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" 'Phone number: 202-342-1234']}\n"
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]
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}
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],
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"source": [
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"pprint.pprint(df_dict)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "verified-spirituality",
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"metadata": {},
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"outputs": [],
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"source": [
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"batch_analyzer = BatchAnalyzerEngine()\n",
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"batch_anonymizer = BatchAnonymizerEngine()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "narrative-freeze",
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"metadata": {},
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"outputs": [],
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"source": [
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"analyzer_results = batch_analyzer.analyze_dict(df_dict, language=\"en\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "rural-month",
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"metadata": {},
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"outputs": [],
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"source": [
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"anonymizer_results = batch_anonymizer.anonymize_dict(analyzer_results)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "acute-mauritius",
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"metadata": {},
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"outputs": [],
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"source": [
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"scrubbed_df = pd.DataFrame(anonymizer_results)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "irish-phoenix",
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"metadata": {},
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"outputs": [
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{
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||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
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||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
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||||
" vertical-align: top;\n",
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||||
" }\n",
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||||
"\n",
|
||||
" .dataframe thead th {\n",
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||||
" text-align: right;\n",
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||||
" }\n",
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||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
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" <th>name phrase</th>\n",
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" <th>phone number phrase</th>\n",
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" <th>integer</th>\n",
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" <th>boolean</th>\n",
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||||
" </tr>\n",
|
||||
" </thead>\n",
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||||
" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td><PERSON> likes this</td>\n",
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" <td>Please call <PHONE_NUMBER> after <DATE_TIME></td>\n",
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" <td>1</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>You should talk to <PERSON></td>\n",
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" <td>his number is <PHONE_NUMBER></td>\n",
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" <td>2</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td><PERSON> had a little startup</td>\n",
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" <td>Phone number: <PHONE_NUMBER></td>\n",
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" <td>3</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
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||||
],
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||||
"text/plain": [
|
||||
" name phrase \\\n",
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||||
"0 <PERSON> likes this \n",
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"1 You should talk to <PERSON> \n",
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"2 <PERSON> had a little startup \n",
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"\n",
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" phone number phrase integer boolean \n",
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"0 Please call <PHONE_NUMBER> after <DATE_TIME> 1 True \n",
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"1 his number is <PHONE_NUMBER> 2 False \n",
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"2 Phone number: <PHONE_NUMBER> 3 False "
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||||
]
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||||
},
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||||
"execution_count": 12,
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||||
"metadata": {},
|
||||
"output_type": "execute_result"
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||||
}
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||||
],
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"source": [
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"scrubbed_df"
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||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "conceptual-responsibility",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "PyCharm (presidio-research)",
|
||||
"language": "python",
|
||||
"name": "pycharm-c8930cf3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
171
docs/samples/python/getting_entity_values.ipynb
Normal file
171
docs/samples/python/getting_entity_values.ipynb
Normal file
@@ -0,0 +1,171 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "adjusted-jurisdiction",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Getting a list of all identified texts\n",
|
||||
"\n",
|
||||
"This sample illustrates how to get a list of all the identified PII entities using Presidio Analyzer for detection and a custom Presidio Anonymizer operator."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "noted-lounge",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from presidio_analyzer import AnalyzerEngine\n",
|
||||
"from presidio_anonymizer import AnonymizerEngine\n",
|
||||
"from presidio_anonymizer.entities.engine.operator_config import OperatorConfig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "clinical-surface",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"analyzer = AnalyzerEngine()\n",
|
||||
"anonymizer = AnonymizerEngine()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "referenced-argument",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"text_to_analyze = \"Hi my name is Charles Darwin and my email is cdarwin@hmsbeagle.org\"\n",
|
||||
"analyzer_results = analyzer.analyze(text_to_analyze, language=\"en\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "innovative-audio",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"A naive approach for getting the text values:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "congressional-wiring",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[('cdarwin@hmsbeagle.org', 45, 66),\n",
|
||||
" ('hmsbeagle.org', 53, 66),\n",
|
||||
" ('Charles Darwin', 14, 28)]"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"[(text_to_analyze[res.start:res.end], res.start, res.end) for res in analyzer_results]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "informal-evanescence",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Another option is to set up a custom operator* which runs an identity function (`lambda x: x`). This operator doesn't really anonymize, but replaces the identified value with itself. This is useful as the Anonymizer handles the overlaps automatically. \n",
|
||||
"\n",
|
||||
"> In this example, the URL (hmsbeagle.org) is contained in the email address, so it's ommitted from the final result.\n",
|
||||
"\n",
|
||||
"\\* an `Operator` is usually either an `Anonymizer` or `Deanonymizer` on the presidio-anonymizer library/"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "growing-motivation",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"anonymized_results = anonymizer.anonymize(\n",
|
||||
" text=text_to_analyze,\n",
|
||||
" analyzer_results=analyzer_results, \n",
|
||||
" operators={\"DEFAULT\": OperatorConfig(\"custom\", {\"lambda\": lambda x: x})} \n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "hydraulic-association",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The operator defined here is `DEFAULT`, meaning it will be used for all entities. The `OperatorConfig` is a custom one and the labmda is the identity function."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "according-rates",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Output text, start and end locations for each detected entity"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "serial-arcade",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[('cdarwin@hmsbeagle.org', 45, 66), ('Charles Darwin', 14, 28)]"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"[(item.text, item.start, item.end) for item in anonymized_results.items]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "senior-prayer",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "PyCharm (presidio-research)",
|
||||
"language": "python",
|
||||
"name": "pycharm-c8930cf3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -11,3 +11,5 @@ Presidio service can be used as python packages inside python scripts
|
||||
3. [Remote Recognizer](https://github.com/microsoft/presidio/blob/main/docs/samples/python/example_remote_recognizer.py)
|
||||
4. [Azure Text Analytics Integration](text_analytics/index.md)
|
||||
5. [Custom Anonymizer with lambda expression](example_custom_lambda_anonymizer.py)
|
||||
6. [Running Presidio on structured / semi-structured data in batch](batch_processing.ipynb)
|
||||
7. [Getting the detected text value using a custom operator](getting_entity_values.ipynb)
|
||||
|
||||
Reference in New Issue
Block a user