Configure dataset features

When initializing a ValidMind dataset object, you can pass in a list of features to use instead of utilizing all dataset columns when running tests.

This notebook shows how to use custom feature columns with init_dataset. The default behavior of init_dataset is to utilize all dataset columns when running tests. It is also possible to pass in a list of features to use and thus restrict computations to only those features.

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.

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

  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="...",
)

Load the sample dataset

%matplotlib inline

# Import the sample dataset from the library

from validmind.datasets.classification import customer_churn as demo_dataset

# You can also try a different dataset with:
# from validmind.datasets.classification import taiwan_credit as demo_dataset

df = demo_dataset.load_data()

Initialize the training and test datasets

Before you can run a test suite, which are just a collection of tests, you must first initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module.

This function takes a number of arguments:

  • dataset — the raw dataset that you want to analyze
  • input_id - a unique identifier that allows tracking what inputs are used when running each individual test
  • target_column — the name of the target column in the dataset
  • feature_columns - the names of the feature columns in the dataset
feature_columns = [
    "CreditScore",
    "Age",
    "Tenure",
    "Balance",
    "NumOfProducts",
    "HasCrCard",
    "IsActiveMember",
    "EstimatedSalary",
]

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

Defining custom features

This section shows how we can define a subset of features to use when running dataset tests. Any feature that is not included in the feature_columns argument is omitted from the computation of the DescriptiveStatistics test in the examples below.

In the following example we use the DescriptiveStatistics test to show how the output changes when customizing features.

  1. Running a test with all the features.
vm_dataset = vm.init_dataset(
    dataset=df,
    input_id="raw_dataset_all_features",
    target_column=demo_dataset.target_column,
)

test = vm.tests.run_test(
    test_id="validmind.data_validation.DescriptiveStatistics",
    inputs={"dataset": vm_dataset},
)
  1. Running a test with a subset of features.
vm_dataset = vm.init_dataset(
    dataset=df,
    input_id="raw_dataset_subset",
    target_column=demo_dataset.target_column,
    feature_columns=["CreditScore", "Age", "Balance", "Geography"],
)

test = vm.tests.run_test(
    test_id="validmind.data_validation.DescriptiveStatistics",
    inputs={"dataset": vm_dataset},
)

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.