%pip install -q validmind
Run tests with multiple datasets
To support running tests that require more than one dataset, ValidMind provides a mechanim that allows you to pass multiple datasets as inputs.
To ensure a model generalizes well to new, unseen data, it’s common to use separate datasets for training, validation, and testing, with each set serving to check the model’s performance at different stages of development. Additionally, since models often encounter data from various sources that might differ in distribution, quality, or type, using multiple datasets in testing can simulate this diversity and better prepare the model for deployment.
This interactive notebook includes the code required to load the demo dataset, preprocess the raw dataset and train a model for testing, initialize ValidMind objects, and run a test that requires multiple datasets.
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 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.
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:
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
In a browser, log in to ValidMind.
In the left sidebar, navigate to Model Inventory and click + Register Model.
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.
- Documentation template:
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}"
)
= demo_dataset.load_data()
raw_df raw_df.head()
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
, andtest_df
) usingdemo_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
).
= demo_dataset.preprocess(raw_df)
train_df, validation_df, test_df = train_df.drop(demo_dataset.target_column, axis=1)
x_train = train_df[demo_dataset.target_column]
y_train = validation_df.drop(demo_dataset.target_column, axis=1)
x_val = validation_df[demo_dataset.target_column] y_val
Train models for testing
Initialize XGBoost and Logistic Regression Classifiers
from sklearn.linear_model import LogisticRegression
import xgboost
%matplotlib inline
= xgboost.XGBClassifier(early_stopping_rounds=10)
xgb
xgb.set_params(=["error", "logloss", "auc"],
eval_metric
)
xgb.fit(
x_train,
y_train,=[(x_val, y_val)],
eval_set=False,
verbose )
Initialize ValidMind objects
Initialize the ValidMind model
= vm.init_model(
vm_model_xgb
xgb,="xgb",
input_id )
Initialize the ValidMind datasets
Before you can run 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 provide as input to testsinput_id
- a unique identifier that allows tracking what inputs are used when running each individual testtarget_column
— a required argument if tests require access to true values. This is the name of the target column in the datasetclass_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.init_dataset(
vm_train_ds ="train_dataset",
input_id=train_df,
dataset=demo_dataset.target_column,
target_column
)= vm.init_dataset(
vm_test_ds ="test_dataset", dataset=test_df, target_column=demo_dataset.target_column
input_id )
Run a test that requires multiple datasets
We are going to show the following in next two blocks:
- Assign predictions for
vm_train_ds
andvm_test_ds
- Run
RobustnessDiagnosis
which is one example test that takes two input datasets
Run predictions and link with the model
=vm_model_xgb)
vm_train_ds.assign_predictions(model=vm_model_xgb) vm_test_ds.assign_predictions(model
Run test
vm.tests.run_test("validmind.model_validation.sklearn.RobustnessDiagnosis",
={"datasets": (vm_train_ds, vm_test_ds), "model": vm_model_xgb},
inputs )
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, 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
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.