%pip install -q validmindConfigure 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 platform 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 AI Risk Platform UI 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 Get started with the ValidMind Library, we recommend you explore the available resources for developers at some point. There, you can learn more about documenting models, find code samples, or read our developer reference.
For access to all features available in this notebook, create a free ValidMind account.
Signing up is FREE — Register with ValidMind
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 with ValidMind to be used in the platform.
Inputs: Objects to be evaluated and documented in the ValidMind framework. 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 client library
The client library provides Python support for the ValidMind Library. To install it:
Initialize the client library
ValidMind generates a unique code snippet for each registered model to connect with your developer environment. You initialize the client 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 new model.
Enter the model details, making sure to select Binary classification as the template and Marketing/Sales - Attrition/Churn Management as the use case, and click Continue. (Need more help?)
Go to Getting Started and click Copy snippet to clipboard.
Next, replace this placeholder with your own code snippet:
# Replace with your code snippet
import validmind as vm
vm.init(
api_host="https://api.prod.validmind.ai/api/v1/tracking",
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 analyzeinput_id- a unique identifier that allows tracking what inputs are used when running each individual testtarget_column— the name of the target column in the datasetfeature_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.
- 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},
)- 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
From the Model Inventory in the ValidMind Platform UI, go to the model you registered earlier. (Need more help?)
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 validmindIf the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:
%pip install --upgrade validmindYou may need to restart your kernel after running the upgrade package for changes to be applied.