Quickstart for model code documentation

Welcome! This notebook demonstrates how to use the ValidMind code explainer to automatically generate comprehensive documentation for your codebase. The code explainer analyzes your source code and provides detailed explanations across various aspects of your implementation.

About Code Explainer

The ValidMind code explainer is a powerful tool that automatically analyzes your source code and generates comprehensive documentation. It helps you:

Understand the structure and organization of your codebase
Document dependencies and environment setup
Explain data processing and model implementation details
Document training, evaluation, and inference pipelines
Track configuration, testing, and security measures

This tool is particularly useful for: - Onboarding new team members - Maintaining up-to-date documentation - Ensuring code quality and best practices - Facilitating code reviews and audits

About ValidMind
Install the ValidMind Library
Initialize the client library
- Get your code snippet
Preview the documentation template
Code Analysis Sections

About ValidMind

ValidMind is a suite of tools for managing model risk, including risk associated with AI and statistical models.

You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on model documentation. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model validators.

Before you begin

This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

New to ValidMind?

If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting models and running tests, as well as find code samples and our Python Library API reference.

For access to all features available in this notebook, create a free ValidMind account.

Signing up is FREE — Register with ValidMind

Key concepts

Model documentation: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. It serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.

Documentation template: Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.

Tests: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or model. Tests are the building blocks of ValidMind, used to evaluate and document models and datasets, and can be run individually or as part of a suite defined by your model documentation template.

Custom tests: Custom tests are functions that you define to evaluate your model or dataset. These functions can be registered via the ValidMind Library to be used with the ValidMind Platform.

Inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

model: A single model that has been initialized in ValidMind with vm.init_model().
dataset: Single dataset that has been initialized in ValidMind with vm.init_dataset().
models: A list of ValidMind models - usually this is used when you want to compare multiple models in your custom test.
datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom test. See this example for more information.

Parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.

Outputs: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

Test suites: Collections of tests designed to run together to automate and generate model documentation end-to-end for specific use-cases.

Example: the classifier_full_suite test suite runs tests from the tabular_dataset and classifier test suites to fully document the data and model sections for binary classification model use-cases.

Install the ValidMind Library

To install the library:

%pip install -q validmind

Initialize the ValidMind Library

ValidMind generates a unique code snippet for each registered model to connect with your developer environment. You initialize the ValidMind Library with this code snippet, which ensures that your documentation and tests are uploaded to the correct model when you run the notebook.

Get your code snippet

In a browser, log in to ValidMind.
In the left sidebar, navigate to Model Inventory and click + Register Model.
Enter the model details and click Continue. (Need more help?)

For example, to register a model for use with this notebook, select:
- Documentation template: Model Source Code Documentation
You can fill in other options according to your preference.
Go to Getting Started and click Copy snippet to clipboard.

Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

# Load your model identifier credentials from an `.env` file

%load_ext dotenv
%dotenv .env

# Or replace with your code snippet

import validmind as vm

vm.init(
    # api_host="...",
    # api_key="...",
    # api_secret="...",
    # model="...",
)

Preview the documentation template

A template predefines sections for your model documentation and provides a general outline to follow, making the documentation process much easier.

You will upload documentation and test results into this template later on. For now, take a look at the structure that the template provides with the vm.preview_template() function from the ValidMind library and note the empty sections:

vm.preview_template()

Common function

The code above defines two key functions: 1. A function to read source code from 'customer_churn_full_suite.py' file 2. An 'explain_code' function that uses ValidMind's experimental agents to analyze and explain code.

source_code=""
with open("customer_churn_full_suite.py", "r") as f:
    source_code = f.read()

The vm.experimental.agents.run_task function is used to execute AI agent tasks.

It requires: - task: The type of task to run (e.g. code_explainer) - input: A dictionary containing task-specific parameters - For code_explainer, this includes: - source_code (str): The code to be analyzed - user_instructions (str): Instructions for how to analyze the code

def explain_code(content_id: str, user_instructions: str):
    """Run code explanation task and log the results.
    By default, the code explainer includes sections for:
    - Main Purpose and Overall Functionality
    - Breakdown of Key Functions or Components
    - Potential Risks or Failure Points  
    - Assumptions or Limitations
    If you want default sections, specify user_instructions as an empty string.
    
    Args:
        user_instructions (str): Instructions for how to analyze the code
        content_id (str): ID to use when logging the results
    
    Returns:
        The result object from running the code explanation task
    """
    result = vm.experimental.agents.run_task(
        task="code_explainer",
        input={
            "source_code": source_code,
            "user_instructions": user_instructions
        }
    )
    result.log(content_id=content_id)
    return result

0. Default Behavior

By default, the code explainer includes sections for: - Main Purpose and Overall Functionality - Breakdown of Key Functions or Components - Potential Risks or Failure Points
- Assumptions or Limitations

If you want default sections, specify user_instructions as an empty string. For example:

result = vm.experimental.agents.run_task(
    task="code_explainer",
    input={
        "source_code": source_code,
        "user_instructions": ""
    }
)

1. Codebase Overview

Let's analyze your codebase structure to understand the main modules, components, entry points and their relationships. We'll also examine the technology stack and frameworks that are being utilized in the implementation.

result = explain_code(
    user_instructions="""
        Please provide a summary of the following bullet points only.
        - Describe the overall structure of the source code repository.
        - Identify main modules, folders, and scripts.
        - Highlight entry points for training, inference, and evaluation.
        - State the main programming languages and frameworks used.
        """,
    content_id="code_structure_summary"
)

result = explain_code(
    user_instructions="",
    content_id="code_structure_summary"
)

## 2. Environment and Dependencies ('environment_setup') Let's document the technical requirements and setup needed to run your code, including Python packages, system dependencies, and environment configuration files. Understanding these requirements is essential for proper development environment setup and consistent deployments across different environments.

result = explain_code(
    user_instructions="""
    Please provide a summary of the following bullet points only.
    - List Python packages and system dependencies (OS, compilers, etc.).
    - Reference environment files (requirements.txt, environment.yml, Dockerfile).
    - Include setup instructions using Conda, virtualenv, or containers.
    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
    """,
    content_id="setup_instructions"
)

## 3. Data Ingestion and Preprocessing Let's document how your code handles data, including data sources, validation procedures, and preprocessing steps. We'll examine the data pipeline architecture, covering everything from initial data loading through feature engineering and quality checks.

result = explain_code(
    user_instructions="""
    Please provide a summary of the following bullet points only.
    - Specify data input formats and sources.
    - Document ingestion, validation, and transformation logic.
    - Explain how raw data is preprocessed and features are generated.
    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.    """,
    content_id="data_handling_notes"
)

## 4. Model Implementation Details Let's document the core implementation details of your model, including its architecture, components, and key algorithms. Understanding the technical implementation is crucial for maintenance, debugging, and future improvements to the codebase. We'll examine how theoretical concepts are translated into working code.

result = explain_code(
    user_instructions="""
    Please provide a summary of the following bullet points only.
    - Describe the core model code structure (classes, functions).
    - Link code to theoretical models or equations when applicable.
    - Note custom components like loss functions or feature selectors.
    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
    """,
    content_id="model_code_description"
)

## 5. Model Training Pipeline

Let's document the training pipeline implementation, including how models are trained, optimized and evaluated. We'll examine the training process workflow, hyperparameter tuning approach, and model checkpointing mechanisms. This section provides insights into how the model learns from data and achieves optimal performance.

result = explain_code(
    user_instructions="""
    Please provide a summary of the following bullet points only.
    - Explain the training process, optimization strategy, and hyperparameters.
    - Describe logging, checkpointing, and early stopping mechanisms.
    - Include references to training config files or tuning logic.
    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
    """,
    content_id="training_logic_details"
)

6. Evaluation and Validation Code

Let's examine how the model's validation and evaluation code is implemented, including the metrics calculation and validation processes. We'll explore the diagnostic tools and visualization methods used to assess model performance. This section will also cover how validation results are logged and stored for future reference.

result = explain_code(
    user_instructions="""
    Please provide a summary of the following bullet points only.
    - Describe how validation is implemented and metrics are calculated.
    - Include plots and diagnostic tools (e.g., ROC, SHAP, confusion matrix).
    - State how outputs are logged and persisted.
    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
    """,
    content_id="evaluation_logic_notes"
)

7. Inference and Scoring Logic

Let's examine how the model performs inference and scoring on new data. This section will cover the implementation details of loading trained models, making predictions, and any required pre/post-processing steps. We'll also look at the APIs and interfaces available for both real-time serving and batch scoring scenarios.

result = explain_code(
    user_instructions="""
    Please provide a summary of the following bullet points only.
    - Detail how the trained model is loaded and used for predictions.
    - Explain I/O formats and APIs for serving or batch scoring.
    - Include any preprocessing/postprocessing logic required.
    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
    """,
    content_id="inference_mechanism"
)

8. Configuration and Parameters

Let's explore how configuration and parameters are managed in the codebase. We'll examine the configuration files, command-line arguments, environment variables, and other mechanisms used to control model behavior. This section will also cover parameter versioning and how different configurations are tracked across model iterations.

result = explain_code(
    user_instructions="""
    Please provide a summary of the following bullet points only.
    - Describe configuration management (files, CLI args, env vars).
    - Highlight default parameters and override mechanisms.
    - Reference versioning practices for config files.
    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
    """,
    content_id="config_control_notes"
)

9. Unit and Integration Testing

Let's examine the testing strategy and implementation in the codebase. We'll analyze the unit tests, integration tests, and testing frameworks used to ensure code quality and reliability. This section will also cover test coverage metrics and continuous integration practices.

result = explain_code(
    user_instructions="""
    Please provide a summary of the following bullet points only.
    - List unit and integration tests and what they cover.
    - Mention testing frameworks and coverage tools used.
    - Explain testing strategy for production-readiness.
    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
    """,
    content_id="test_strategy_overview"
)

10. Logging and Monitoring Hooks

Let's analyze how logging and monitoring are implemented in the codebase. We'll examine the logging configuration, monitoring hooks, and key metrics being tracked. This section will also cover any real-time observability integrations and alerting mechanisms in place.

result = explain_code(
    user_instructions="""
    Please provide a summary of the following bullet points only.
    - Describe logging configuration and structure.
    - Highlight real-time monitoring or observability integrations.
    - List key events, metrics, or alerts tracked.
    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
    """,
    content_id="logging_monitoring_notes"
)

11. Code and Model Versioning

Let's examine how code and model versioning is managed in the codebase. This section will cover version control practices, including Git workflows and model artifact versioning tools like DVC or MLflow. We'll also look at how versioning integrates with the CI/CD pipeline.

result = explain_code(
    user_instructions="""
    Please provide a summary of the following bullet points only.
      - Describe Git usage, branching, tagging, and commit standards.
      - Include model artifact versioning practices (e.g., DVC, MLflow).
      - Reference any automation in CI/CD.
    Please remove the following sections: 
      - Potential Risks or Failure Points
      - Assumptions or Limitations
      - Breakdown of Key Functions or Components
    Please don't add any other sections.
    """,
    content_id="version_tracking_description"
)

12. Security and Access Control

Let's analyze the security and access control measures implemented in the codebase. We'll examine how sensitive data and code are protected through access controls, encryption, and compliance measures. Additionally, we'll review secure deployment practices and any specific handling of PII data.

result = explain_code(
    user_instructions="""
    Please provide a summary of the following bullet points only.
      - Document access controls for source code and data.
      - Include any encryption, PII handling, or compliance measures.
      - Mention secure deployment practices.
    Please remove the following sections: 
      - Potential Risks or Failure Points
      - Assumptions or Limitations
      - Breakdown of Key Functions or Components
    Please don't add any other sections.
    """,
    content_id="security_policies_notes"
)

13. Example Runs and Scripts

Let's explore example runs and scripts that demonstrate how to use this codebase in practice. We'll look at working examples, command-line usage, and sample notebooks that showcase the core functionality. This section will also point to demo datasets and test scenarios that can help new users get started quickly.

result = explain_code(
    user_instructions="""
    Please provide a summary of the following bullet points only.
      - Provide working script examples.
      - Include CLI usage instructions or sample notebooks.
      - Link to demo datasets or test scenarios.
    Please remove the following sections: 
      - Potential Risks or Failure Points
      - Assumptions or Limitations
      - Breakdown of Key Functions or Components
    Please don't add any other sections.
    """,
    content_id="runnable_examples"
)

14. Known Issues and Future Improvements

Let's examine the current limitations and areas for improvement in the codebase. This section will document known technical debt, bugs, and feature gaps that need to be addressed. We'll also outline proposed enhancements and reference any existing tickets or GitHub issues tracking these improvements.

result = explain_code(
    user_instructions="""
    Please provide a summary of the following bullet points only.
      - List current limitations or technical debt.
      - Outline proposed enhancements or refactors.
      - Reference relevant tickets, GitHub issues, or roadmap items.
    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
    """,
    content_id="issues_and_improvements_log"
)