ValidMind for model development — 103 Integrate custom tests

Learn how to use ValidMind for your end-to-end model documentation process with our series of four introductory notebooks. In this third notebook, supplement ValidMind tests with your own and include them as additional evidence in your documentation.

This notebook assumes that you already have a repository of custom made tests considered critical to include in your documentation. A custom test is any function that takes a set of inputs and parameters as arguments and returns one or more outputs:

The function can be as simple or as complex as you need it to be — it can use external libraries, make API calls, or do anything else that you can do in Python.
The only requirement is that the function signature and return values can be “understood” and handled by the ValidMind Library. As such, custom tests offer added flexibility by extending the default tests provided by ValidMind, enabling you to document any type of model or use case.

For a more in-depth introduction to custom tests, refer to our Implement custom tests notebook.

Prerequisites

In order to log test results or evidence to your model documentation with this notebook, you’ll need to first have:

Registered a model within the ValidMind Platform with a predefined documentation template
Installed the ValidMind Library in your local environment, allowing you to access all its features
Learned how to import and initialize datasets for use with ValidMind
Understood the basics of how to run and log tests with ValidMind
Inserted a test-driven block for the results of your HighPearsonCorrelation:balanced_raw_dataset test into your model’s documentation

Need help with the above steps?

Refer to the first two notebooks in this series:

Setting up

This section should be quite familiar to you — as we performed the same actions in the previous notebook, 102 Start the model development process.

Initialize the ValidMind Library

As usual, let’s first connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:

In a browser, log in to ValidMind.
In the left sidebar, navigate to Inventory and select the model you registered for this “ValidMind for model development” series of notebooks.
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:

# Make sure the ValidMind Library is installed

%pip install -q validmind

# 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="...",
)

Import sample dataset

Next, we’ll import the same public Bank Customer Churn Prediction dataset from Kaggle we used in the last notebook so that we have something to work with:

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()

We’ll apply a simple rebalancing technique to the dataset before continuing:

import pandas as pd

raw_copy_df = raw_df.sample(frac=1)  # Create a copy of the raw dataset

# Create a balanced dataset with the same number of exited and not exited customers
exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 1]
not_exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 0].sample(n=exited_df.shape[0])

balanced_raw_df = pd.concat([exited_df, not_exited_df])
balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)

Remove highly correlated features

Let’s also quickly remove highly correlated features from the dataset using the output from a ValidMind test.

As you learned previously, before we can run tests you’ll need to initialize a ValidMind dataset object:

# Register new data and now 'balanced_raw_dataset' is the new dataset object of interest
vm_balanced_raw_dataset = vm.init_dataset(
    dataset=balanced_raw_df,
    input_id="balanced_raw_dataset",
    target_column="Exited",
)

With our balanced dataset initialized, we can then run our test and utilize the output to help us identify the features we want to remove:

# Run HighPearsonCorrelation test with our balanced dataset as input and return a result object
corr_result = vm.tests.run_test(
    test_id="validmind.data_validation.HighPearsonCorrelation",
    params={"max_threshold": 0.3},
    inputs={"dataset": vm_balanced_raw_dataset},
)

# From result object, extract table from `corr_result.tables`
features_df = corr_result.tables[0].data
features_df

# Extract list of features that failed the test
high_correlation_features = features_df[features_df["Pass/Fail"] == "Fail"]["Columns"].tolist()
high_correlation_features

# Extract feature names from the list of strings
high_correlation_features = [feature.split(",")[0].strip("()") for feature in high_correlation_features]
high_correlation_features

We can then re-initialize the dataset with a different input_id and the highly correlated features removed and re-run the test for confirmation:

# Remove the highly correlated features from the dataset
balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)

# Re-initialize the dataset object
vm_raw_dataset_preprocessed = vm.init_dataset(
    dataset=balanced_raw_no_age_df,
    input_id="raw_dataset_preprocessed",
    target_column="Exited",
)

# Re-run the test with the reduced feature set
corr_result = vm.tests.run_test(
    test_id="validmind.data_validation.HighPearsonCorrelation",
    params={"max_threshold": 0.3},
    inputs={"dataset": vm_raw_dataset_preprocessed},
)

Train the model

We’ll then use ValidMind tests to train a simple logistic regression model on our prepared dataset:

# First encode the categorical features in our dataset with the highly correlated features removed
balanced_raw_no_age_df = pd.get_dummies(
    balanced_raw_no_age_df, columns=["Geography", "Gender"], drop_first=True
)
balanced_raw_no_age_df.head()

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

# Split the input and target variables
X = balanced_raw_no_age_df.drop("Exited", axis=1)
y = balanced_raw_no_age_df["Exited"]
X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    test_size=0.2,
    random_state=42,
)

# Logistic Regression grid params
log_reg_params = {
    "penalty": ["l1", "l2"],
    "C": [0.001, 0.01, 0.1, 1, 10, 100, 1000],
    "solver": ["liblinear"],
}

# Grid search for Logistic Regression
from sklearn.model_selection import GridSearchCV

grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)
grid_log_reg.fit(X_train, y_train)

# Logistic Regression best estimator
log_reg = grid_log_reg.best_estimator_

Initialize the ValidMind objects

Let’s initialize the ValidMind Dataset and Model objects in preparation for assigning model predictions to each dataset:

train_df = X_train
train_df["Exited"] = y_train
test_df = X_test
test_df["Exited"] = y_test

# Initialize the datasets into their own dataset objects
vm_train_ds = vm.init_dataset(
    input_id="train_dataset_final",
    dataset=train_df,
    target_column="Exited",
)

vm_test_ds = vm.init_dataset(
    input_id="test_dataset_final",
    dataset=test_df,
    target_column="Exited",
)

# Initialize a model object
vm_model = vm.init_model(log_reg, input_id="log_reg_model_v1")

Assign predictions

Once the model is registered, we’ll assign predictions to the training and test datasets:

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

Implementing a custom inline test

With the set up out of the way, let’s implement a custom inline test that calculates the confusion matrix for a binary classification model.

An inline test refers to a test written and executed within the same environment as the code being tested — in this case, right in this Jupyter Notebook — without requiring a separate test file or framework.
You’ll note that the custom test function is just a regular Python function that can include and require any Python library as you see fit.

Create a confusion matrix plot

Let’s first create a confusion matrix plot using the confusion_matrix function from the sklearn.metrics module:

import matplotlib.pyplot as plt
from sklearn import metrics

# Get the predicted classes
y_pred = log_reg.predict(vm_test_ds.x)

confusion_matrix = metrics.confusion_matrix(y_test, y_pred)

cm_display = metrics.ConfusionMatrixDisplay(
    confusion_matrix=confusion_matrix, display_labels=[False, True]
)
cm_display.plot()

Next, create a @vm.test wrapper that will allow you to create a reusable test. Note the following changes in the code below:

The function confusion_matrix takes two arguments dataset and model. This is a VMDataset and VMModel object respectively.
- VMDataset objects allow you to access the dataset’s true (target) values by accessing the .y attribute.
- VMDataset objects allow you to access the predictions for a given model by accessing the .y_pred() method.
The function docstring provides a description of what the test does. This will be displayed along with the result in this notebook as well as in the ValidMind Platform.
The function body calculates the confusion matrix using the sklearn.metrics.confusion_matrix function as we just did above.
The function then returns the ConfusionMatrixDisplay.figure_ object — this is important as the ValidMind Library expects the output of the custom test to be a plot or a table.
The @vm.test decorator is doing the work of creating a wrapper around the function that will allow it to be run by the ValidMind Library. It also registers the test so it can be found by the ID my_custom_tests.ConfusionMatrix.

@vm.test("my_custom_tests.ConfusionMatrix")
def confusion_matrix(dataset, model):
    """The confusion matrix is a table that is often used to describe the performance of a classification model on a set of data for which the true values are known.

    The confusion matrix is a 2x2 table that contains 4 values:

    - True Positive (TP): the number of correct positive predictions
    - True Negative (TN): the number of correct negative predictions
    - False Positive (FP): the number of incorrect positive predictions
    - False Negative (FN): the number of incorrect negative predictions

    The confusion matrix can be used to assess the holistic performance of a classification model by showing the accuracy, precision, recall, and F1 score of the model on a single figure.
    """
    y_true = dataset.y
    y_pred = dataset.y_pred(model=model)

    confusion_matrix = metrics.confusion_matrix(y_true, y_pred)

    cm_display = metrics.ConfusionMatrixDisplay(
        confusion_matrix=confusion_matrix, display_labels=[False, True]
    )
    cm_display.plot()

    plt.close()  # close the plot to avoid displaying it

    return cm_display.figure_  # return the figure object itself

You can now run the newly created custom test on both the training and test datasets using the run_test() function:

# Training dataset
result = vm.tests.run_test(
    "my_custom_tests.ConfusionMatrix:training_dataset",
    inputs={"model": vm_model, "dataset": vm_train_ds},
)

# Test dataset
result = vm.tests.run_test(
    "my_custom_tests.ConfusionMatrix:test_dataset",
    inputs={"model": vm_model, "dataset": vm_test_ds},
)

Add parameters to custom tests

Custom tests can take parameters just like any other function. To demonstrate, let’s modify the confusion_matrix function to take an additional parameter normalize that will allow you to normalize the confusion matrix:

@vm.test("my_custom_tests.ConfusionMatrix")
def confusion_matrix(dataset, model, normalize=False):
    """The confusion matrix is a table that is often used to describe the performance of a classification model on a set of data for which the true values are known.

    The confusion matrix is a 2x2 table that contains 4 values:

    - True Positive (TP): the number of correct positive predictions
    - True Negative (TN): the number of correct negative predictions
    - False Positive (FP): the number of incorrect positive predictions
    - False Negative (FN): the number of incorrect negative predictions

    The confusion matrix can be used to assess the holistic performance of a classification model by showing the accuracy, precision, recall, and F1 score of the model on a single figure.
    """
    y_true = dataset.y
    y_pred = dataset.y_pred(model=model)

    if normalize:
        confusion_matrix = metrics.confusion_matrix(y_true, y_pred, normalize="all")
    else:
        confusion_matrix = metrics.confusion_matrix(y_true, y_pred)

    cm_display = metrics.ConfusionMatrixDisplay(
        confusion_matrix=confusion_matrix, display_labels=[False, True]
    )
    cm_display.plot()

    plt.close()  # close the plot to avoid displaying it

    return cm_display.figure_  # return the figure object itself

Pass parameters to custom tests

You can pass parameters to custom tests by providing a dictionary of parameters to the run_test() function.

The parameters will override any default parameters set in the custom test definition. Note that dataset and model are still passed as inputs.
Since these are VMDataset or VMModel inputs, they have a special meaning.
When declaring a dataset, model, datasets or models argument in a custom test function, the ValidMind Library will expect these get passed as inputs to run_test() or run_documentation_tests().

Re-running the confusion matrix with normalize=True looks like this:

# Test dataset with normalize=True
result = vm.tests.run_test(
    "my_custom_tests.ConfusionMatrix:test_dataset_normalized",
    inputs={"model": vm_model, "dataset": vm_test_ds},
    params={"normalize": True},
)

Log the confusion matrix results

As we learned in 102 Start the model development process under Documenting results > Run and log an individual tests, you can log any result to the ValidMind Platform with the .log() method of the result object, allowing you to then add the result to the documentation.

You can now do the same for the confusion matrix results:

result.log()

Note the output returned indicating that a test-driven block doesn’t currently exist in your model’s documentation for this particular test ID.

That’s expected, as when we run individual tests the results logged need to be manually added to your documentation within the ValidMind Platform.

Using external test providers

Creating inline custom tests with a function is a great way to customize your model documentation. However, sometimes you may want to reuse the same set of tests across multiple models and share them with others in your organization. In this case, you can create an external custom test provider that will allow you to load custom tests from a local folder or a Git repository.

In this section you will learn how to declare a local filesystem test provider that allows loading tests from a local folder following these high level steps:

Create a folder of custom tests from existing inline tests (tests that exist in your active Jupyter Notebook)
Save an inline test to a file
Define and register a LocalTestProvider that points to that folder
Run test provider tests
Add the test results to your documentation

Create custom tests folder

Let’s start by creating a new folder that will contain reusable custom tests from your existing inline tests.

The following code snippet will create a new my_tests directory in the current working directory if it doesn’t exist:

tests_folder = "my_tests"

import os

# create tests folder
os.makedirs(tests_folder, exist_ok=True)

# remove existing tests
for f in os.listdir(tests_folder):
    # remove files and pycache
    if f.endswith(".py") or f == "__pycache__":
        os.system(f"rm -rf {tests_folder}/{f}")

After running the command above, confirm that the new my_tests directory was created successfully:

Screenshot showing the new `my_tests` directory

Save an inline test

The @vm.test decorator we used in Implementing a custom inline test above to register one-off custom tests also includes a convenience method on the function object that allows you to simply call <func_name>.save() to save the test to a Python file at a specified path.

While save() will get you started by creating the file and saving the function code with the correct name, it won’t automatically include any imports, or other functions or variables, outside of the functions that are needed for the test to run. To solve this, pass in an optional imports argument ensuring necessary imports are added to the file.

The confusion_matrix test requires the following additional imports:

import matplotlib.pyplot as plt
from sklearn import metrics

Let’s pass these imports to the save() method to ensure they are included in the file with the following command:

confusion_matrix.save(
    # Save it to the custom tests folder we created
    tests_folder,
    imports=["import matplotlib.pyplot as plt", "from sklearn import metrics"],
)

Confirm that the save() method saved the confusion_matrix function to a file named ConfusionMatrix.py in the my_tests folder.

Note that the new file provides some context on the origin of the test, which is useful for traceability:

# Saved from __main__.confusion_matrix
# Original Test ID: my_custom_tests.ConfusionMatrix
# New Test ID: <test_provider_namespace>.ConfusionMatrix

Additionally, the new test function has been stripped off its decorator, as it now resides in a file that will be loaded by the test provider:
```
def ConfusionMatrix(dataset, model, normalize=False):
```

Register a local test provider

Now that your my_tests folder has a sample custom test, let’s initialize a test provider that will tell the ValidMind Library where to find your custom tests:

ValidMind offers out-of-the-box test providers for local tests (tests in a folder) or a Github provider for tests in a Github repository.
You can also create your own test provider by creating a class that has a load_test method that takes a test ID and returns the test function matching that ID.

Want to learn more about test providers?

An extended introduction to test providers can be found in: Integrate external test providers

Initialize a local test provider

For most use cases, using a LocalTestProvider that allows you to load custom tests from a designated directory should be sufficient.

The most important attribute for a test provider is its namespace. This is a string that will be used to prefix test IDs in model documentation. This allows you to have multiple test providers with tests that can even share the same ID, but are distinguished by their namespace.

Let’s go ahead and load the custom tests from our my_tests directory:

from validmind.tests import LocalTestProvider

# initialize the test provider with the tests folder we created earlier
my_test_provider = LocalTestProvider(tests_folder)

vm.tests.register_test_provider(
    namespace="my_test_provider",
    test_provider=my_test_provider,
)
# `my_test_provider.load_test()` will be called for any test ID that starts with `my_test_provider`
# e.g. `my_test_provider.ConfusionMatrix` will look for a function named `ConfusionMatrix` in `my_tests/ConfusionMatrix.py` file

Run test provider tests

Now that we’ve set up the test provider, we can run any test that’s located in the tests folder by using the run_test() method as with any other test:

For tests that reside in a test provider directory, the test ID will be the namespace specified when registering the provider, followed by the path to the test file relative to the tests folder.
For example, the Confusion Matrix test we created earlier will have the test ID my_test_provider.ConfusionMatrix. You could organize the tests in subfolders, say classification and regression, and the test ID for the Confusion Matrix test would then be my_test_provider.classification.ConfusionMatrix.

Let’s go ahead and re-run the confusion matrix test by using the test ID my_test_provider.ConfusionMatrix. This should load the test from the test provider and run it as before.

result = vm.tests.run_test(
    "my_test_provider.ConfusionMatrix",
    inputs={"model": vm_model, "dataset": vm_test_ds},
    params={"normalize": True},
)

result.log()

Again, note the output returned indicating that a test-driven block doesn’t currently exist in your model’s documentation for this particular test ID.

That’s expected, as when we run individual tests the results logged need to be manually added to your documentation within the ValidMind Platform.

Add test results to documentation

With our custom tests run and results logged to the ValidMind Platform, let’s head to the model we connected to at the beginning of this notebook and insert our test results into the documentation (Need more help?):

From the Inventory in the ValidMind Platform, go to the model you connected to earlier.
In the left sidebar that appears for your model, click Documentation.
Locate the Data Preparation section and click on 3.2 Model Evaluation to expand that section.
Hover under the Pearson Correlation Matrix content block until a horizontal dashed line with a + button appears, indicating that you can insert a new block.
Click + and then select Test-Driven Block:
- In the search bar, type in ConfusionMatrix.
- Select the custom ConfusionMatrix tests you logged above:
Finally, click Insert 2 Test Results to Document to add the test results to the documentation.

Confirm that the two individual results for the confusion matrix tests have been correctly inserted into section 3.2 Model Evaluation of the documentation.

In summary

In this third notebook, you learned how to:

Implement a custom inline test
Run and log your custom inline tests
Use external custom test providers
Run and log tests from your custom test providers

Next steps

Finalize testing and documentation

Now that you’re proficient at using the ValidMind Library to run and log tests, let’s put the last pieces in place to prepare our fully documented sample model for review: 104 Finalize testing and documentation