ValidMind for validation 1 — Set up the ValidMind Library for validation

Learn how to use ValidMind for your end-to-end validation process based on common scenarios with our series of four introductory notebooks. In this first notebook, set up the ValidMind Library in preparation for validating a champion.

These notebooks use a binary classification model as an example, but the same principles shown here apply to other record (model) types.

Learn by doing

Our course tailor-made for validators new to ValidMind combines this series of notebooks with more a more in-depth introduction to the ValidMind Platform — Validator Fundamentals

Introduction

Validation aims to independently assess the compliance of champions created by developers with regulatory guidance by conducting thorough testing and analysis, potentially including the use of challengers to benchmark performance. Assessments, presented in the form of a validation report, typically include artifacts (findings) and recommendations to address those issues.

A binary classification model is a type of predictive model used in churn analysis to identify customers who are likely to leave a service or subscription by analyzing various behavioral, transactional, and demographic factors.

This model helps businesses take proactive measures to retain at-risk customers by offering personalized incentives, improving customer service, or adjusting pricing strategies.
Effective validation of a churn prediction model ensures that businesses can accurately identify potential churners, optimize retention efforts, and enhance overall customer satisfaction while minimizing revenue loss.

About ValidMind

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

You use the ValidMind Library to automate comparison and other validation tests, and then use the ValidMind Platform to submit compliance assessments of champions via comprehensive validation reports. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and developers.

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 records such as 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, you'll need access to a ValidMind account.

Register with ValidMind

Key concepts

record: A tool tracked in the ValidMind inventory, such as a model. Records include traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and other documentable items that benefit from oversight, testing, and lifecycle management.

model: SR 26-2 (which supersedes SR 11-7) defines a model as a "complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates." Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model. Within ValidMind, a model is a type of record tracked in the inventory.

validation report: A validation report is a comprehensive and structured review evaluating a record's accuracy, performance, and suitability for its intended purpose. A report follows established validation guidelines to ensure consistency and adherence to internal and regulatory standards — encompassing the process of risk assessment, identifying areas of potential error or risk within the record's components, supporting transparency, regulatory compliance, and informed decision-making by documenting the validator’s independent review and conclusions.

document template: Lays out the structure of documents, segmented into various sections and sub-sections, and functions as a test suite specifying the tests that should be run, and how the results should be displayed. Document templates help automate your development, validation, monitoring, and other risk management processes. Document templates are available for default ValidMind document types as well as custom document types.

validation report template: A default ValidMind document template that serves as a standardized framework for conducting and documenting validation, including sections designated for attaching test results, evidence, or artifacts (findings). By outlining required documentation, recommended analyses, and expected validation tests, validation report templates ensure consistency and completeness across validation reports and help guide validators through a systematic review process while promoting comparability and traceability of validation outcomes.

artifacts (findings): Observations or issues identified during validation, including any deviations from expected performance or standards. Artifacts are organized by type — default types provided by ValidMind include Validation Issue, Policy Exception, and Limitation. Custom artifact types can be created to track other categories relevant to your organization.

test: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or record. Test results are logged to the ValidMind Platform, where they are attached to documents. Tests are the building blocks of ValidMind, used to evaluate and document records and datasets, and can be run individually or as part of a suite defined by your templates.

test suite: A collection of tests designed to run together to automate and generate documentation end-to-end for specific use cases. (Learn more: test_suites)

metric: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.

custom test: Functions that you define to evaluate your record or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.

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

model: A single record that has been initialized in ValidMind with init_model(). Despite the naming convention, model objects can be any type of record you want to test, document, validate, or monitor with ValidMind.
dataset: A single dataset that has been initialized in ValidMind with init_dataset().
models: A list of ValidMind records - usually this is used when you want to compare multiple records in your custom tests.
datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: Run tests with multiple datasets)

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.

Setting up

Register a sample model

In a usual lifecycle, a champion will have been independently registered in your inventory and submitted to you for validation by your development team as part of the effective challenge process. (Learn more: Submit documents)

For this notebook, we'll have you register a dummy record (model) in the ValidMind Platform inventory and assign yourself as the validator to familiarize you with the ValidMind interface and circumvent the need for an existing model:

In a browser, log in to ValidMind.
In the left sidebar, select Inventory.
Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)
Enter the model details and click Next > to continue to assignment of inventory record stakeholders.
Select your own name under the RECORD OWNER drop-down — don’t worry, we’ll adjust these permissions next for validation.
Click Register Model to add the model to your inventory.

Assign validator credentials

In order to log tests as a validator instead of as a developer, on the details page that appears after you've successfully registered your sample model:

Remove yourself as an owner:
- Click on the OWNERS tile.
- Click the x next to your name to remove yourself from that model's role.
- Click Save to apply your changes to that role.
Remove yourself as a developer:
- Click on the DEVELOPERS tile.
- Click the x next to your name to remove yourself from that model's role.
- Click Save to apply your changes to that role.
Add yourself as a validator:
- Click on the VALIDATORS tile.
- Select your name from the drop-down menu.
- Click Save to apply your changes to that role.

Apply documentation template

Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier for developers.

We'll need this documentation template later for reference as we draft our validation report:

In the left sidebar that appears for your model, click Documents and select Documentation.
Under TEMPLATE, select Binary classification.
Click Use Template to apply the template.

Apply validation report template

Next, let's select a validation report template. A template predefines sections for your report and provides a general outline to follow, making the validation process much easier.

In the left sidebar that appears for your model, click Documents and select Validation.

If you cannot locate your Validation document, make sure Validation type documents are enabled for model records and create a new document. (Learn more: Manage documents)
Under TEMPLATE, select Generic Validation Report.
Click Use Template to apply the template.

Install the ValidMind Library

Recommended Python versions

Python 3.8 <= x <= 3.14

To install the library:

%pip install -q validmind

Initialize the ValidMind Library

Get your code snippet

Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

On the left sidebar that appears for your model, select Getting Started and select Validation from the DOCUMENT drop-down menu.
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="...",
    document="validation-report",
)

Getting to know ValidMind

Preview the validation report template

Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for your model.

You will attach evidence to this template in the form of risk assessment notes, artifacts, and test results later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library:

vm.preview_template()

View validation report in the ValidMind Platform

Next, let's head to the ValidMind Platform to see the template in action:

In a browser, log in to ValidMind.
In the left sidebar, navigate to Inventory and select the model you registered for this "ValidMind for validation" series of notebooks.
Click Validation under Documents for your model and note:
- The risk assessment compliance summary at the top of the report (screenshot below)
- How the structure of the validation report reflects the previewed template

Explore available tests

Next, let's explore the list of all available tests in the ValidMind Library with the vm.tests.list_tests() function — we'll later narrow down the tests we want to run from this list when we learn to run tests.

vm.tests.list_tests()

Upgrade ValidMind

After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements.

Retrieve the information for the currently installed version of ValidMind:

%pip show validmind

If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

%pip install --upgrade validmind

You may need to restart your kernel after running the upgrade package for changes to be applied.

In summary

In this first notebook, you learned how to:

Register a record (model) within the ValidMind Platform and assign yourself as the validator
Install and initialize the ValidMind Library
Preview the validation report template for your model
Explore the available tests offered by the ValidMind Library

Next steps

Start the validation process

Now that the ValidMind Library is connected to your model in the ValidMind Library with the correct template applied, we can go ahead and start the validation process: 2 — Start the validation process