Patra Model Card Toolkit

The Patra Toolkit is a component of the Patra ModelCards framework designed to simplify the process of creating and documenting AI/ML models. It provides a structured schema that guides users in providing essential information about their models, including details about the model's purpose, development process, and performance. The toolkit also includes features for semi-automating the capture of key information, such as fairness and explainability metrics, through integrated analysis tools. By reducing the manual effort involved in creating model cards, the Patra Toolkit encourages researchers and developers to adopt best practices for documenting their models, ultimately contributing to greater transparency and accountability in AI/ML development.

Features

• Structured Schema: The Patra Toolkit offers a structured schema to guide users in providing crucial model information. This includes details such as the model's intended use, development process, and performance metrics.

• Semi-Automated Information Capture: The toolkit supports semi-automatic capture of certain descriptive fields. It achieves this by running a variety of automated scanners, with the results incorporated into the Model Card. These include,

  • Fairness Scanner evaluates the model's fairness by examining its predictions across different groups.
  • Explainability Scanner generates explainability metrics to help understand the model's decision-making process.
  • Model Requirements Scanner captures the Python packages and versions required to run the model.

• Validation and JSON Generation: Once a Model Card is created using the Toolkit, it validates the data against the defined schema to ensure completeness and accuracy. It then generates the Model Card as a JSON file, ready for integration into the Patra Knowledge Base.

• Integration with Patra Knowledge Base: The Model Cards created using the Patra Toolkit are designed to be added to the Patra Knowledge Base, which is a graph database that stores and manages these cards.

The Patra Toolkit plays a crucial role in promoting transparency and accountability in AI/ML development by making it easier for developers to create comprehensive and informative Model Cards. By automating certain aspects of the documentation process and providing a structured schema, the Toolkit reduces the barriers to entry for creating high-quality model documentation.

For more information, please refer to the Patra ModelCards paper.

Getting Started

Installing Patra Model Card

The latest version can be installed from PyPI:

pip install patra-toolkit

For local installation, clone the repository and install using:

pip install -e <local_git_dir>/patra_toolkit

Usage

Create a Model Card

Find the descriptions of the Model Card parameters in the schema descriptions document.

from patra_toolkit import ModelCard

mc = ModelCard(
  name="UCI Adult Data Analysis model using Tensorflow",
  version="0.1",
  short_description="UCI Adult Data analysis using Tensorflow for demonstration of Patra Model Cards.",
  full_description="We have trained a ML model using the tensorflow framework to predict income for the UCI Adult Dataset. We leverage this data to run the Patra model cards to capture metadata about the model as well as fairness and explainability metrics.",
  keywords="uci adult, tensorflow, explainability, fairness, patra",
  author="Sachith Withana",
  input_type="Tabular",
  category="classification",
  foundational_model="None"
)

# Add Model Metadata
mc.input_data = 'https://archive.ics.uci.edu/dataset/2/adult'
mc.output_data = 'https://github.iu.edu/swithana/mcwork/rf_sklearn/adult_model.pkl'

Initialize an AI/ML Model

from patra_toolkit import AIModel

ai_model = AIModel(
  name="UCI Adult Random Forest model",
  version="0.1",
  description="Census classification problem using Random Forest",
  owner="Sachith Withana",
  location="https://github.iu.edu/swithana/mcwork/randomforest/adult_model.pkl",
  license="BSD-3 Clause",
  framework="sklearn",
  model_type="random_forest",
  test_accuracy=accuracy
)

# Populate Model Structure
ai_model.populate_model_structure(trained_model)
mc.ai_model = ai_model

# Add Custom Metrics
ai_model.add_metric("Test loss", loss)
ai_model.add_metric("Epochs", 100)
ai_model.add_metric("Batch Size", 32)
ai_model.add_metric("Optimizer", "Adam")
ai_model.add_metric("Learning Rate", 0.0001)
ai_model.add_metric("Input Shape", "(26048, 100)")

Run Fairness and Explainability Scanners

# To assess fairness, provide the sensitive feature, test data, labels, and predictions
mc.populate_bias(X_test, y_test, predictions, "gender", X_test['sex'], clf)

# To generate explainability metrics, specify the dataset, column names, model, and number of features
mc.populate_xai(X_test, x_columns, model, top_n=10)

Validate and Save the Model Card

# Verify the model card content against the schema
mc.validate()
mc.save(<file_path>)

# Capture Python package dependencies and versions
mc.populate_requirements()

# Upload the model card to the Patra server
mc.submit(<patra_server_url>)

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Examples

Explore the following example notebooks and model cards to learn more about how to use the Patra Model Card Toolkit: Notebook Example, Model Card Example

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License

The Patra Model Card toolkit is developed by Indiana University and distributed under the BSD 3-Clause License. See LICENSE.txt for more details.

Acknowledgements

This research is funded in part through the National Science Foundation under award #2112606, AI Institute for Intelligent CyberInfrastructure with Computational Learning in the Environment (ICICLE), and in part through Data to Insight Center (D2I) at Indiana University.

Reference

S. Withana and B. Plale, "Patra ModelCards: AI/ML Accountability in the Edge-Cloud Continuum," 2024 IEEE 20th International Conference on e-Science (e-Science), Osaka, Japan, 2024, pp. 1-10, doi: 10.1109/e-Science62913.2024.10678710. Keywords: Analytical models, Vectors, Edge-cloud continuum, Model cards, AI/ML accountability, Provenance