ValidMind for development 3 — Integrate custom tests

Learn how to use ValidMind for your end-to-end 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 record (model) or use case.

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

Learn by doing

Our course tailor-made for developers new to ValidMind combines this series of notebooks with more a more in-depth introduction to the ValidMind Platform — Developer Fundamentals

Prerequisites

In order to integrate custom tests with your 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:

• 1 — Set up the ValidMind Library
• 2 — Start the development process

Setting up

This section should be quite familiar to you — as we performed the same actions in the previous notebook, 2 — Start the 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:

1. On the left sidebar that appears for your model, select Getting Started and select Development from the Document drop-down menu.

2. Click Copy snippet to clipboard.

3. 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="...",
    document="documentation",
)

Note: you may need to restart the kernel to use updated packages.

2026-07-31 16:42:30,566 - INFO(validmind.api_client): 🎉 Connected to ValidMind!
📊 Model: [ValidMind Academy] Model development (ID: cmalgf3qi02ce199qm3rdkl46)
📁 Document Type: model_documentation

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()

Loaded demo dataset with: 

    • Target column: 'Exited' 
    • Class labels: {'0': 'Did not exit', '1': 'Exited'}

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},
)

❌ High Pearson Correlation

The High Pearson Correlation test evaluates pairwise linear relationships between features to identify potentially redundant variables or evidence of multicollinearity. The results table reports the top correlation pairs, their Pearson coefficients, and a Pass/Fail assessment against the configured threshold of 0.3. Among the ten reported pairs, coefficient values range from -0.1851 to 0.3705, with one pair exceeding the threshold and the remaining nine pairs classified as passing.

Key insights:

• One pair exceeds threshold: The pair (Age, Exited) records the largest absolute correlation at 0.3705 and is the only result classified as Fail under the 0.3 threshold.
• Remaining correlations are modest: All other reported pairs have absolute correlation values of 0.1851 or lower, remaining below the configured threshold and receiving Pass classifications.
• Negative relationships are limited in magnitude: The strongest negative correlation shown is (Balance, NumOfProducts) = -0.1851, followed by (IsActiveMember, Exited) = -0.1746, indicating relatively weak inverse linear associations within the reported set.
• Most reported relationships are near zero: Several pairs, including (Age, Balance) = 0.0565, (NumOfProducts, IsActiveMember) = 0.0511, and (CreditScore, EstimatedSalary) = -0.0319, show very small linear relationships.

The reported correlation structure is dominated by low-magnitude pairwise relationships, with only one observed coefficient above the 0.3 threshold. The largest relationship is between Age and Exited, while the rest of the reported pairs remain materially lower in absolute value. Overall, the results indicate a largely weak linear dependence structure across the top reported feature pairs, with a single flagged exception in the output.

Parameters:

{
  "max_threshold": 0.3
}
            

Tables

Columns	Coefficient	Pass/Fail
(Age, Exited)	0.3705	Fail
(Balance, NumOfProducts)	-0.1851	Pass
(IsActiveMember, Exited)	-0.1746	Pass
(Balance, Exited)	0.1485	Pass
(Age, Balance)	0.0565	Pass
(NumOfProducts, Exited)	-0.0531	Pass
(NumOfProducts, IsActiveMember)	0.0511	Pass
(Age, NumOfProducts)	-0.0468	Pass
(HasCrCard, IsActiveMember)	-0.0444	Pass
(CreditScore, EstimatedSalary)	-0.0319	Pass

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

	Columns	Coefficient	Pass/Fail
0	(Age, Exited)	0.3705	Fail
1	(Balance, NumOfProducts)	-0.1851	Pass
2	(IsActiveMember, Exited)	-0.1746	Pass
3	(Balance, Exited)	0.1485	Pass
4	(Age, Balance)	0.0565	Pass
5	(NumOfProducts, Exited)	-0.0531	Pass
6	(NumOfProducts, IsActiveMember)	0.0511	Pass
7	(Age, NumOfProducts)	-0.0468	Pass
8	(HasCrCard, IsActiveMember)	-0.0444	Pass
9	(CreditScore, EstimatedSalary)	-0.0319	Pass

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

['(Age, Exited)']

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

['Age']

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},
)

✅ High Pearson Correlation

The High Pearson Correlation test evaluates pairwise linear relationships between features to identify potentially redundant variables or signs of multicollinearity. The result table lists the top feature pairs by absolute Pearson correlation coefficient, together with Pass/Fail status based on the configured threshold of 0.3. In this run, the reported coefficients range from -0.1851 to 0.1485 across the ten strongest observed relationships, and all listed pairs are marked as Pass.

Key insights:

• No correlations exceed threshold: All reported feature-pair coefficients remain below the configured absolute threshold of 0.3. Every listed relationship is therefore classified as Pass in the test output.
• Observed relationships are weak: The largest absolute correlation is 0.1851 for (Balance, NumOfProducts), followed by 0.1746 for (IsActiveMember, Exited) and 0.1485 for (Balance, Exited). These values indicate limited linear association among the strongest reported pairs.
• Top correlations include both signs: The reported relationships include both negative and positive coefficients. The strongest negative coefficients are for (Balance, NumOfProducts) and (IsActiveMember, Exited), while the strongest positive coefficient is for (Balance, Exited).
• Strongest pairs are concentrated in a narrow range: The ten reported coefficients are all relatively close to zero, with the smallest absolute values in the list at 0.0257 for (Tenure, EstimatedSalary) and 0.0297 for (Balance, IsActiveMember). This indicates that even the highest-ranked pairwise linear relationships are limited in magnitude.

The test output shows that none of the top reported feature pairs exhibit high Pearson correlation under the configured 0.3 threshold. The strongest observed associations are weak in magnitude and include both positive and negative directions, with all listed pairs passing the test. Collectively, the reported results indicate a low level of pairwise linear dependence among the highest-ranked feature relationships in this dataset.

Parameters:

{
  "max_threshold": 0.3
}
            

Tables

Columns	Coefficient	Pass/Fail
(Balance, NumOfProducts)	-0.1851	Pass
(IsActiveMember, Exited)	-0.1746	Pass
(Balance, Exited)	0.1485	Pass
(NumOfProducts, Exited)	-0.0531	Pass
(NumOfProducts, IsActiveMember)	0.0511	Pass
(HasCrCard, IsActiveMember)	-0.0444	Pass
(CreditScore, EstimatedSalary)	-0.0319	Pass
(Tenure, IsActiveMember)	-0.0301	Pass
(Balance, IsActiveMember)	-0.0297	Pass
(Tenure, EstimatedSalary)	0.0257	Pass

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()

	CreditScore	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited	Geography_Germany	Geography_Spain	Gender_Male
3478	501	2	0.00	2	0	0	141946.92	0	False	False	False
7142	516	8	113143.12	1	0	0	3363.36	0	False	False	False
5274	678	1	123699.28	2	0	1	105221.76	0	True	False	False
1424	818	2	0.00	1	0	1	192298.84	1	False	False	False
1601	668	7	173962.32	1	0	0	106457.11	1	False	True	False

# Split the processed dataset into train and test
from sklearn.model_selection import train_test_split

train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)

X_train = train_df.drop("Exited", axis=1)
y_train = train_df["Exited"]
X_test = test_df.drop("Exited", axis=1)
y_test = test_df["Exited"]

from sklearn.linear_model import LogisticRegression

# 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_

/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1403: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.
  warnings.warn(
/opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1429: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.
  warnings.warn(

Initialize the ValidMind objects

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

# Initialize the datasets into their own ValidMind 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 the ValidMind 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)

2026-07-31 16:42:43,714 - INFO(validmind.vm_models.dataset.utils): Running predict_proba()... This may take a while
2026-07-31 16:42:43,717 - INFO(validmind.vm_models.dataset.utils): Done running predict_proba()
2026-07-31 16:42:43,717 - INFO(validmind.vm_models.dataset.utils): Running predict()... This may take a while
2026-07-31 16:42:43,720 - INFO(validmind.vm_models.dataset.utils): Done running predict()
2026-07-31 16:42:43,723 - INFO(validmind.vm_models.dataset.utils): Running predict_proba()... This may take a while
2026-07-31 16:42:43,724 - INFO(validmind.vm_models.dataset.utils): Done running predict_proba()
2026-07-31 16:42:43,726 - INFO(validmind.vm_models.dataset.utils): Running predict()... This may take a while
2026-07-31 16:42:43,727 - INFO(validmind.vm_models.dataset.utils): Done running predict()

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 record (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},
)

Confusion Matrix Training Dataset

The Confusion Matrix test evaluates classification performance by comparing predicted labels against true labels on the training dataset. The result is presented as a 2x2 matrix with true labels on the y-axis and predicted labels on the x-axis, showing counts for correct and incorrect classifications across the two classes. The matrix contains 800 cases with true label False predicted as False, 510 cases with true label False predicted as True, 490 cases with true label True predicted as False, and 785 cases with true label True predicted as True.

Key insights:

• Correct classifications exceed misclassifications: The diagonal cells contain 800 true negatives and 785 true positives, for 1,585 correct classifications in total, compared with 1,000 off-diagonal errors.
• Errors are distributed across both classes: Misclassifications appear in both directions, with 510 false positives and 490 false negatives. The difference between the two error types is 20 cases.
• Class support is relatively balanced: The true-label totals are 1,310 for the False class and 1,275 for the True class, indicating similar representation of both classes in the training data.
• Prediction volumes are also balanced: The model predicts 1,290 False labels and 1,295 True labels, showing nearly equal prediction frequency across the two classes.

The training confusion matrix shows that correct predictions are more frequent than incorrect predictions, with similar counts for true negatives and true positives. Misclassification counts are also closely matched between false positives and false negatives, indicating no strong asymmetry in the observed error distribution. Both the observed class counts and predicted class counts are nearly balanced, which supports a consistent view of model behavior across the two classes on the training sample.

Figures

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

Confusion Matrix Test Dataset

The Confusion Matrix test evaluates classification outcomes by comparing predicted labels against true labels on the test dataset. The result is presented as a 2×2 matrix with counts for true negatives, false positives, false negatives, and true positives. In this matrix, the counts are 196 for true negatives, 110 for false positives, 104 for false negatives, and 237 for true positives, showing the distribution of correct and incorrect classifications across both classes.

Key insights:

• True positives are the largest cell: The highest count in the matrix is 237 in the true positive cell, indicating that correctly predicted positive cases occur more frequently than any other outcome category.
• Correct classifications exceed errors: The two diagonal cells total 433 correct predictions (196 true negatives and 237 true positives), compared with 214 misclassifications across the off-diagonal cells.
• Error counts are similar across classes: False positives total 110 and false negatives total 104, indicating that the two types of classification error are of comparable magnitude in the test results.
• Positive predictions are more frequent: Predicted positive cases total 347 (110 false positives plus 237 true positives), while predicted negative cases total 300 (196 true negatives plus 104 false negatives).

The confusion matrix shows that correct classifications are concentrated on the diagonal, with 433 correct predictions versus 214 errors. The largest single outcome is the true positive count, while false positives and false negatives are close in magnitude, indicating a relatively balanced distribution of error types. Overall, the result reflects stronger classification counts in the positive class and a higher number of predicted positives than predicted negatives on the test dataset.

Figures

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 and our testing dataset 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}
)

Confusion Matrix Test Dataset Normalized

The Confusion Matrix test evaluates classification outcomes by comparing predicted labels with true labels, and this result shows the normalized share of observations in each outcome category on the test dataset. The matrix is presented as a 2x2 heatmap with rows for true labels and columns for predicted labels. The observed cell values are 0.30 for true negatives, 0.17 for false positives, 0.16 for false negatives, and 0.37 for true positives.

Key insights:

• Correct classifications account for 0.67: The diagonal cells sum to 0.67, combining 0.30 true negatives and 0.37 true positives. This indicates that normalized correct predictions represent the majority of observations in the matrix.
• True positives are the largest cell: The highest individual cell value is 0.37 for observations with true label True and predicted label True. This exceeds the 0.30 true negative share and both misclassification cells.
• Error shares are relatively balanced: The off-diagonal values are 0.17 for false positives and 0.16 for false negatives. The difference between the two error categories is 0.01, indicating similar normalized proportions of the two misclassification types.
• Predicted positives exceed predicted negatives: Summing by prediction column gives 0.54 predicted True (0.17 + 0.37) and 0.46 predicted False (0.30 + 0.16). The model therefore assigns the positive class more frequently than the negative class in this test result.

The normalized confusion matrix shows that correct classifications comprise the larger share of outcomes, led by the true positive cell at 0.37 and followed by the true negative cell at 0.30. Misclassifications are split almost evenly between false positives and false negatives at 0.17 and 0.16, respectively. Overall, the result reflects a modest tilt toward positive predictions, with a larger proportion of correctly identified positive cases than correctly identified negative cases.

Parameters:

{
  "normalize": true
}
            

Figures

Log the confusion matrix results

As we learned in 2 — 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()

2026-07-31 16:43:03,323 - INFO(validmind.vm_models.result.result): Test driven block with result_id my_custom_tests.ConfusionMatrix:test_dataset_normalized does not exist in model's document

Note the output returned indicating that a test-driven block doesn't currently exist in your 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 documentation. However, sometimes you may want to reuse the same set of tests across multiple records (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:

1. Create a folder of custom tests from existing inline tests (tests that exist in your active Jupyter Notebook)
2. Save an inline test to a file
3. Define and register a LocalTestProvider that points to that folder
4. Run test provider tests
5. 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 a new my_tests directory was created successfully. For example:

~/notebooks/tutorials/development/my_tests/

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"],
)

2026-07-31 16:43:04,178 - INFO(validmind.tests.decorator): Saved to /home/runner/work/documentation/documentation/site/notebooks/EXECUTED/development/my_tests/ConfusionMatrix.py!Be sure to add any necessary imports to the top of the file.
2026-07-31 16:43:04,178 - INFO(validmind.tests.decorator): This metric can be run with the ID: <test_provider_namespace>.ConfusionMatrix

• 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 with our testing dataset 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()

Confusion Matrix

The ConfusionMatrix test evaluates classification performance by comparing predicted labels with true labels in a normalized 2x2 matrix. The displayed matrix reports the relative frequency of observations in each outcome category, with rows corresponding to true labels and columns corresponding to predicted labels. The four cells show values of 0.30 for true negatives, 0.17 for false positives, 0.16 for false negatives, and 0.37 for true positives. Because normalization is enabled, these values represent proportions rather than raw counts.

Key insights:

• Correct classifications dominate overall: The diagonal cells sum to 0.67, comprising 0.30 true negatives and 0.37 true positives, while the off-diagonal error cells sum to 0.33.
• True positives are the largest cell: The highest single proportion in the matrix is 0.37 for cases where the true label is True and the predicted label is also True.
• Error types are similar in magnitude: False positives account for 0.17 of observations and false negatives account for 0.16, indicating nearly balanced misclassification across the two error categories.
• Positive predictions occur more frequently: The predicted True column totals 0.54 (0.17 + 0.37), compared with 0.46 for the predicted False column (0.30 + 0.16).

The normalized confusion matrix shows that correct predictions represent the majority of observations, with true positives contributing the largest share of the matrix. Misclassifications are distributed relatively evenly between false positives and false negatives, with only a 0.01 difference between the two error proportions. Overall, the result indicates a modestly stronger concentration in predicted positive outcomes than in predicted negative outcomes.

Parameters:

{
  "normalize": true
}
            

Figures

2026-07-31 16:43:09,510 - INFO(validmind.vm_models.result.result): Test driven block with result_id my_test_provider.ConfusionMatrix does not exist in model's document

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 (Learn more: Work with test results):

1. From the Inventory in the ValidMind Platform, go to the model you connected to earlier.

2. In the left sidebar that appears for your model, click Development under Documents.

3. Locate the Data Preparation section and click on 3.2. Model Evaluation to expand that section.

4. 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.

   
   
   

5. Click + and then select Test-Driven Block under FROM LIBRARY:

   • Click on Custom under TEST-DRIVEN in the left sidebar.
   • Select the two custom ConfusionMatrix tests you logged above:

   
   
   

6. 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: 4 — Finalize testing and documentation

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