presidio-evaluator 0.1.2
PII dataset generator, model evaluator for Presidio and PII data in general
License: MIT

# Presidio-research

This package features data-science related tasks for developing new recognizers for 
[Presidio](https://github.com/microsoft/presidio).
It is used for the evaluation of the entire system, 
as well as for evaluating specific PII recognizers or PII detection models. 
In addition, it contains a fake data generator which creates fake sentences based on templates and fake PII.

## Who should use it?

- Anyone interested in **developing or evaluating PII detection models**, an existing Presidio instance or a Presidio PII recognizer.
- Anyone interested in **generating new data based on previous datasets or sentence templates** (e.g. to increase the coverage of entity values) for Named Entity Recognition models.

## Getting started

### From PyPI

``` sh
conda create --name presidio python=3.9
conda activate presidio
pip install presidio-evaluator

# Download a spaCy model used by presidio-analyzer
python -m spacy download en_core_web_lg
```

### From source

To install the package:
1. Clone the repo
2. Install all dependencies, preferably in a virtual environment:

``` sh
# Create conda env (optional)
conda create --name presidio python=3.9
conda activate presidio

# Install package+dependencies
pip install poetry
poetry install
# To install with all additional NER dependencies (e.g. Flair, Stanza, CRF), run:
# poetry install --with ner 

# Download a spaCy model used by presidio-analyzer
python -m spacy download en_core_web_lg

# Verify installation
pytest
```

Note that some dependencies (such as Flair and Stanza) are not automatically installed to reduce installation complexity.

## What's in this package?

1. **Fake data generator** for PII recognizers and NER models
2. **Data representation layer** for data generation, modeling and analysis
3. Multiple **Model/Recognizer evaluation** files (e.g. for Spacy, Flair, CRF, Presidio API, Presidio Analyzer python package, specific Presidio recognizers)
4. **Training and modeling code** for multiple models
5. Helper functions for **results analysis**

## 1. Data generation

See [Data Generator README](presidio_evaluator/data_generator/README.md) for more details.

The data generation process receives a file with templates, e.g. `My name is {{name}}`. 
Then, it creates new synthetic sentences by sampling templates and PII values. 
Furthermore, it tokenizes the data, creates tags (either IO/BIO/BILUO) and spans for the newly created samples.

- For information on data generation/augmentation, see the data generator [README](presidio_evaluator/data_generator/README.md).
- For an example for running the generation process, see [this notebook](notebooks/1_Generate_data.ipynb).
- For an understanding of the underlying fake PII data used, see this [exploratory data analysis notebook](notebooks/2_PII_EDA.ipynb).

Once data is generated, it could be split into train/test/validation sets 
while ensuring that each template only exists in one set. 
See [this notebook for more details](notebooks/3_Split_by_pattern_%23.ipynb).

## 2. Data representation

In order to standardize the process, 
we use specific data objects that hold all the information needed for generating, 
analyzing, modeling and evaluating data and models. Specifically, 
see [data_objects.py](presidio_evaluator/data_objects.py).

The standardized structure, `List[InputSample]` could be translated into different formats:
- CONLL
```python
from presidio_evaluator import InputSample
dataset = InputSample.read_dataset_json("data/synth_dataset_v2.json")
conll = InputSample.create_conll_dataset(dataset)
conll.to_csv("dataset.csv", sep="\t")

```

- spaCy v3
```python
from presidio_evaluator import InputSample
dataset = InputSample.read_dataset_json("data/synth_dataset_v2.json")
InputSample.create_spacy_dataset(dataset, output_path="dataset.spacy")
```

- Flair
```python
from presidio_evaluator import InputSample
dataset = InputSample.read_dataset_json("data/synth_dataset_v2.json")
flair = InputSample.create_flair_dataset(dataset)
```

- json
```python
from presidio_evaluator import InputSample
dataset = InputSample.read_dataset_json("data/synth_dataset_v2.json")
InputSample.to_json(dataset, output_file="dataset_json")
```

## 3. PII models evaluation

The presidio-evaluator framework allows you to evaluate Presidio as a system, a NER model, 
or a specific PII recognizer for precision and recall and error-analysis.


### Examples:
- [Evaluate Presidio](notebooks/4_Evaluate_Presidio_Analyzer.ipynb)
- [Evaluate spaCy models](notebooks/models/Evaluate%20spacy%20models.ipynb)
- [Evaluate Stanza models](notebooks/models/Evaluate%20stanza%20models.ipynb)
- [Evaluate CRF models](notebooks/models/Evaluate%20CRF%20models.ipynb)
- [Evaluate Flair models](notebooks/models/Evaluate%20flair%20models.ipynb)
- [Evaluate Azure Text Analytics](notebooks/models/Evaluate%20azure%20text%20analytics.ipynb)

## 4. Training PII detection models

### CRF

To train a vanilla CRF on a new dataset, see [this notebook](notebooks/models/Train%20CRF.ipynb). To evaluate, see [this notebook](notebooks/models/Evaluate%20CRF%20models.ipynb).

### spaCy

To train a new spaCy model, first save the dataset in a spaCy format:
```python
# dataset is a List[InputSample]
InputSample.create_spacy_dataset(dataset ,output_path="dataset.spacy")
```

To evaluate, see [this notebook](notebooks/models/Evaluate%20spacy%20models.ipynb)

### Flair

- To train Flair models, see this [helper class](presidio_evaluator/models/flair_train.py) or this snippet:
```python
from presidio_evaluator.models import FlairTrainer
train_samples = "data/generated_train.json"
test_samples = "data/generated_test.json"
val_samples = "data/generated_validation.json"

trainer = FlairTrainer()
trainer.create_flair_corpus(train_samples, test_samples, val_samples)

corpus = trainer.read_corpus("")
trainer.train(corpus)
```

> Note that the three json files are created using `InputSample.to_json`.

## For more information


- [Blog post on NLP approaches to data anonymization](https://towardsdatascience.com/nlp-approaches-to-data-anonymization-1fb5bde6b929)
- [Conference talk about leveraging Presidio and utilizing NLP approaches for data anonymization](https://youtu.be/Tl773LANRwY)

# Contributing

This project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit <https://cla.opensource.microsoft.com>.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

Copyright notice:

Fake Name Generator identities by the [Fake Name Generator](https://www.fakenamegenerator.com/)
are licensed under a [Creative Commons Attribution-Share Alike 3.0 United States License](http://creativecommons.org/licenses/by-sa/3.0/us/).
Fake Name Generator and the Fake Name Generator logo are trademarks of Corban Works, LLC.