Run documentation tests with custom configurations

When running documentation tests, you can configure inputs and parameters for individual tests by passing a config as a parameter.

As a model developer, configuring individual tests is useful in various models development scenarios. For instance, based on a use case, a model might require changing inputs and/or parameters for certain tests. The run_documentation_tests() function allows you to directly configure tests through config, thus giving you flexibility to run tests according to your use case.

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 documentation tests with custom configurations.

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="...",
)

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 sample dataset

The sample dataset used here is provided by the ValidMind library. To be able to use it, you need to import the dataset and load it into a pandas DataFrame, a two-dimensional tabular data structure that makes use of rows and columns:

# Import the sample dataset from the library

from validmind.datasets.classification import customer_churn as demo_dataset

print(
    f"Loaded demo dataset with: \n\n\t• Target column: '{demo_dataset.target_column}' \n\t• Class labels: {demo_dataset.class_labels}"
)

raw_df = demo_dataset.load_data()
raw_df.head()

Document the model

As part of documenting the model with the ValidMind Library, you need to preprocess the raw dataset, initialize some training and test datasets, initialize a model object you can use for testing, and then run the full suite of tests.

Prepocess the raw dataset

Preprocessing performs a number of operations to get ready for the subsequent steps:

  • Preprocess the data: Splits the DataFrame (df) into multiple datasets (train_df, validation_df, and test_df) using demo_dataset.preprocess to simplify preprocessing.
  • Separate features and targets: Drops the target column to create feature sets (x_train, x_val) and target sets (y_train, y_val).
  • Initialize XGBoost classifier: Creates an XGBClassifier object with early stopping rounds set to 10.
  • Set evaluation metrics: Specifies metrics for model evaluation as "error," "logloss," and "auc."
  • Fit the model: Trains the model on x_train and y_train using the validation set (x_val, y_val). Verbose output is disabled.
train_df, validation_df, test_df = demo_dataset.preprocess(raw_df)

Train a model for testing

We train a simple customer churn model for our test.

import xgboost
%matplotlib inline

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]

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

Initialize ValidMind objects

Initialize ValidMind model object

Before you can run tests, you must first initialize a ValidMind model object using the init_model function from the ValidMind (vm) module.

This function takes a number of arguments:

  • model — the model that you want to provide as input to tests
  • input_id - a unique identifier that allows tracking what inputs are used when running each individual test
vm_model_xgb = vm.init_model(
    xgb,
    input_id="xgb",
)

Initialize the ValidMind datasets

Similarly, 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 provide as input to tests
  • input_id - a unique identifier that allows tracking what inputs are used when running each individual test
  • target_column — a required argument if tests require access to true values. This is the name of the target column in the dataset
  • class_labels — an optional value to map predicted classes to class labels

With all datasets ready, you can now initialize the raw, training and test datasets (raw_df, train_df and test_df) created earlier into their own dataset objects using vm.init_dataset():

vm_raw_ds = vm.init_dataset(
    input_id="raw_dataset",
    dataset=raw_df,
    target_column=demo_dataset.target_column,
)

feature_columns = [
    "CreditScore",
    "Gender",
    "Age",
    "Tenure",
    "Balance",
    "NumOfProducts",
    "HasCrCard",
    "IsActiveMember",
    "EstimatedSalary",
    "Geography_France",
    "Geography_Germany",
    "Geography_Spain",
]

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

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

Run predictions through assign_predictions interface

We can use assign_predictions() to run and assign model predictions to our training and test datasets:

vm_train_ds.assign_predictions(model=vm_model_xgb)
vm_test_ds.assign_predictions(model=vm_model_xgb)

Run documentation tests

Preview config

You can preview the default config for the documentation template using the vm.get_test_suite().get_default_config() interface.

import json

model_test_suite = vm.get_test_suite()
config = model_test_suite.get_default_config()
print("Suite Config: \n", json.dumps(config, indent=2))

Updating config

The test configuration can be updated to fit with your use case and requirements

config = {
    "validmind.data_validation.DatasetSplit": {
        "inputs": {"datasets": (vm_train_ds, vm_test_ds)},
    },
    "validmind.model_validation.sklearn.PopulationStabilityIndex": {
        "inputs": {"model": vm_model_xgb, "datasets": (vm_train_ds, vm_test_ds)},
    },
    "validmind.model_validation.sklearn.ConfusionMatrix": {
        "inputs": {"model": vm_model_xgb, "dataset": vm_test_ds},
    },
    "validmind.model_validation.sklearn.ClassifierPerformance:in_sample": {
        "inputs": {"model": vm_model_xgb, "dataset": vm_train_ds},
    },
    "validmind.model_validation.sklearn.ClassifierPerformance:out_of_sample": {
        "inputs": {"model": vm_model_xgb, "dataset": vm_test_ds},
    },
    "validmind.model_validation.sklearn.PrecisionRecallCurve": {
        "inputs": {"model": vm_model_xgb, "dataset": vm_test_ds},
    },
    "validmind.model_validation.sklearn.ROCCurve": {
        "inputs": {"model": vm_model_xgb, "dataset": vm_test_ds},
    },
    "validmind.model_validation.sklearn.TrainingTestDegradation": {
        "inputs": {"model": vm_model_xgb, "datasets": (vm_train_ds, vm_test_ds)},
    },
    "validmind.model_validation.sklearn.MinimumAccuracy": {
        "inputs": {"model": vm_model_xgb, "dataset": vm_test_ds},
    },
    "validmind.model_validation.sklearn.MinimumF1Score": {
        "inputs": {"model": vm_model_xgb, "dataset": vm_test_ds},
    },
    "validmind.model_validation.sklearn.MinimumROCAUCScore": {
        "inputs": {"model": vm_model_xgb, "dataset": vm_test_ds},
    },
    "validmind.model_validation.sklearn.PermutationFeatureImportance": {
        "inputs": {"model": vm_model_xgb, "dataset": vm_test_ds},
    },
    "validmind.model_validation.sklearn.SHAPGlobalImportance": {
        "inputs": {"model": vm_model_xgb, "dataset": vm_test_ds},
    },
    "validmind.model_validation.sklearn.WeakspotsDiagnosis": {
        "inputs": {"model": vm_model_xgb, "datasets": (vm_train_ds, vm_test_ds)},
    },
    "validmind.model_validation.sklearn.OverfitDiagnosis": {
        "inputs": {"model": vm_model_xgb, "datasets": (vm_train_ds, vm_test_ds)},
    },
    "validmind.model_validation.sklearn.RobustnessDiagnosis": {
        "inputs": {"model": vm_model_xgb, "datasets": (vm_train_ds, vm_test_ds)},
    },
}

Run documentation tests

You can now run all documentation tests and pass an extra config parameter that overrides input and parameter configuration for the tests specified in the object.

full_suite = vm.run_documentation_tests(
    inputs={
        "dataset": vm_raw_ds,
        "model": vm_model_xgb,
    },
    config=config,
)

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.