Run individual documentation sections

For targeted testing, you can run tests on individual sections or specific groups of sections in your model documentation.

As a model developer, running individual documentation sections is useful in various development scenarios. For instance, when updates are made to a model, often only certain parts of the documentation require revision. The run_documentation_tests() function allows you to directly test only these affected sections, thus saving you time and ensuring that the documentation accurately reflects the latest changes.

This interactive notebook includes the code required to load the demo dataset, preprocess the raw dataset, train a model for testing, initialize ValidMind objects, and run the data preparation, model development, and multiple documentation sections.

Contents

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.

Metrics: 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 metrics: Custom metrics 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 metric.
  • datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom metric. 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 metric, customize its behavior, or provide additional context.

Outputs: Custom metrics 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

  1. In a browser, log in to ValidMind.

  2. In the left sidebar, navigate to Model Inventory and click + Register Model.

  3. Enter the model details and click Continue. (Need more help?)

    For example, to register a model for use with this notebook, select:

    • Documentation template: Binary classification
    • Use case: Marketing/Sales - Attrition/Churn Management

    You can fill in other options according to your preference.

  4. 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="...",
)
%matplotlib inline

import xgboost as xgb

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()

Load the Demo Dataset

# You can also import taiwan_credit like this:
# from validmind.datasets.classification import taiwan_credit as demo_dataset
from validmind.datasets.classification import customer_churn as demo_dataset

df = demo_dataset.load_data()

Prepocess the raw dataset

train_df, validation_df, test_df = demo_dataset.preprocess(df)

Train a model for testing

We train a simple customer churn model for our test.

x_train = train_df.drop(demo_dataset.target_column, axis=1)
y_train = train_df[demo_dataset.target_column]
x_val = validation_df.drop(demo_dataset.target_column, axis=1)
y_val = validation_df[demo_dataset.target_column]

model = xgb.XGBClassifier(early_stopping_rounds=10)
model.set_params(
    eval_metric=["error", "logloss", "auc"],
)
model.fit(
    x_train,
    y_train,
    eval_set=[(x_val, y_val)],
    verbose=False,
)

Initialize ValidMind objects

We initize the objects required to run test suites using the ValidMind Library.

vm_dataset = vm.init_dataset(
    input_id="raw_dataset",
    dataset=df,
    target_column=demo_dataset.target_column,
    class_labels=demo_dataset.class_labels,
)

vm_train_ds = vm.init_dataset(
    input_id="train_dataset",
    dataset=train_df,
    type="generic",
    target_column=demo_dataset.target_column,
)

vm_test_ds = vm.init_dataset(
    input_id="test_dataset",
    dataset=test_df,
    type="generic",
    target_column=demo_dataset.target_column,
)

vm_model = vm.init_model(model, input_id="model")

Assign predictions to the datasets

We can now use the assign_predictions() method from the Dataset object to link existing predictions to any model. If no prediction values are passed, the method will compute predictions automatically:

vm_train_ds.assign_predictions(
    model=vm_model,
)
vm_test_ds.assign_predictions(
    model=vm_model,
)

Run the data preparation section

In this section, we focus on running the tests within the data preparation section of the model documentation. After running this function, only the tests associated with this section will be executed, and the corresponding section in the model documentation will be updated.

results = vm.run_documentation_tests(
    section="data_preparation",
    inputs={
        "dataset": vm_dataset,
    },
)

Run the model development section

In this section, we focus on running the tests within the model development section of the model documentation. After running this function, only the tests associated with this section will be executed, and the corresponding section in the model documentation will be updated.

results = vm.run_documentation_tests(
    section="model_development",
    inputs={
        "dataset": vm_train_ds,
        "model": vm_model,
        "datasets": (vm_train_ds, vm_test_ds),
    },
)

Run multiple model documentation sections

This section demonstrates how you can execute both the data preparation and model development sections using run_documentation_tests(). After running this function, the tests associated with both sections will be executed, and their corresponding model documentation sections updated.

results = vm.run_documentation_tests(
    section=["model_development", "model_diagnosis"],
    inputs={
        "dataset": vm_test_ds,
        "model": vm_model,
        "datasets": (vm_train_ds, vm_test_ds),
    },
)

Next steps

You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation.

Work with your model documentation

  1. From the Model Inventory in the ValidMind Platform, go to the model you registered earlier. (Need more help?)

  2. Click and expand the Model Development section.

What you see is the full draft of your model documentation in a more easily consumable version. From here, you can make qualitative edits to model documentation, view guidelines, collaborate with validators, and submit your model documentation for approval when it's ready. Learn more ...

Discover more learning resources

We offer many interactive notebooks to help you document models:

Or, visit our documentation to learn more about ValidMind.

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.