August 13, 2024

Release highlights

This release brings many user experience upgrades to ValidMind, including customization for your platform dashboard, support for more input types in comparison tests within the library, and model dependency management.

Customize your dashboard

We enhanced the ValidMind Platform Dashboard with new customization options:

  • Rearrange and resize widgets to suit your preferences.

  • Add charts from Reports to your Dashboard, for a more personalized and streamlined experience.

View reports

Customize your dashboard

A gif demonstrating dashboard widgets being moved and resized

Customizing your dashboard

Generalized support for comparison tests

To make comparison tests easier to analyze, we’ve added support to specify an input grid when a test has many datasets or models.

Run comparison tests

You’ll need to pass a list of lists for datasets or models, since the original input is a list of inputs.

For example, the following test produces a combined GINI table:

  • XGBoost on training and test dataset
  • RandomForest on training and test dataset
vm.tests.run_test(
    "validmind.model_validation.statsmodels.GINITable",

    input_grid={
        "datasets": [
            [vm_train_ds, vm_test_ds],
        ],
        "model": [vm_model_xgb, vm_model_rf],
    },
)

This test produces a combined PSI report for XGBoost and RandomForest on training and test datasets:

vm.tests.run_test(
    "validmind.model_validation.sklearn.PopulationStabilityIndex",

    input_grid={
        "datasets": [
            [vm_train_ds, vm_test_ds],
        ],
        "model": [vm_model_xgb, vm_model_rf],
    },
)

Enhancements

Reports page improvements

We added new summary widgets to the Reports tab, including:

  • Total Models
  • Total Findings
  • Open Findings
  • Past Due Findings
  • Closed Findings

View reports

These widgets summarize key information from your reports, making the data available at a glance:

An image showing the new summary widgets added to Reports on the Findings tab

New Findings Report summary widgets

Configure model interdependencies

You can now configure model dependencies in your Model Inventory.

Manage both upstream and downstream model interdependencies:

A diagram of model interdependencies

Model interdependencies diagram

Configure model interdependencies

An screenshot showcasing the Manage Model Interdependences screen

Model dependency management

Bug fixes

Exclude categorical & binary features from outlier tests

We’ve excluded categorical and binary features from the outlier tests IQROutliersTable and IQROutliersBarPlot.

This fix ensures that outlier detection is applied correctly and appropriately, improving computational efficiency and leading to more relevant, accurate, and meaningful insights from the data.

Documentation

Support for GCP Private Service Connect

We’ve introduced support for Google Cloud Private Service Connect for enhanced network security and privacy. This is in addition to the existing support for AWS PrivateLink.

  • Google Cloud Private Service Connect allows private connections between ValidMind and your company network. It ensures secure communication over the Google network without exposing traffic to the public internet.
  • By using private endpoints within your VPC, you can reduce your network’s attack surface and maintain traffic privacy.

Configure Google Cloud Private Service Connect

How to upgrade

ValidMind Platform

To access the latest version of the ValidMind Platform,1 hard refresh your browser tab:

  • Windows: Ctrl + Shift + R OR Ctrl + F5
  • MacOS: ⌘ Cmd + Shift + R OR hold down ⌘ Cmd and click the Reload button

ValidMind Library

To upgrade the ValidMind Library:2

  1. In your Jupyter Notebook:

    • Using JupyterHub:3 Hard refresh your browser tab.
    • In your own developer environment:4 Restart your notebook.

  2. Then within a code cell or your terminal, run:

    %pip install --upgrade validmind

You may need to restart your kernel after running the upgrade package for changes to be applied.