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July 22, 2024
On top of our increased support for tests and arrays in the ValidMind Library and our first ValidMind Academy training modules, we’ve introduced customizable model workflows, new role functionality, and new report types.
You can now run comparison tests with the ValidMind Library.
The updated run_test()
function allows you to pass an input_grid
which runs a test for all combinations of inputs.
A test runs once for each of the following input groups:
from validmind.tests import run_test
input_grid = {
"model": ["XGBoost"],
"dataset": ["train_dataset", "test_dataset"],
}
result = run_test(
"validmind.model_validation.sklearn.ClassifierPerformance",
input_grid,
)
result = run_test(
"validmind.model_validation.sklearn.ConfusionMatrix",
input_grid,
)
result = run_test(
"validmind.model_validation.sklearn.ROCCurve",
input_grid,
)
You can now manage lifecycle processes within your ValidMind Platform setup using customizable model workflows.
add_extra_column
VMDataset
class, the add_extra_column()
method now supports adding 2D arrays as single columns in the dataset’s DataFrame.For example:
Users with the Create Role
permission can now add a new role under Settings.
We added a number of new report types:
We fixed a number of missing test descriptions that were caused by a scripting issue.
We’re introducing the first training modules that are part of our ValidMind Academy training program for:
To access the latest version of the ValidMind Platform,1 hard refresh your browser tab:
Ctrl
+ Shift
+ R
OR Ctrl
+ F5
⌘ Cmd
+ Shift
+ R
OR hold down ⌘ Cmd
and click the Reload
buttonTo upgrade the ValidMind Library:2
In your Jupyter Notebook:
Then within a code cell or your terminal, run:
You may need to restart your kernel after running the upgrade package for changes to be applied.