ValidMind for model development 3 — 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.

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 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:

• 1 — Set up the ValidMind Library
• 2 — Start the model 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 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:

1. In a browser, log in to ValidMind.

2. In the left sidebar, navigate to Inventory and select the model you registered for this "ValidMind for model development" series of notebooks.

3. 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="...",
)

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

2026-02-19 23:58:52,539 - 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 identifies pairs of features in the dataset that exhibit strong linear relationships, with the aim of detecting potential feature redundancy or multicollinearity. The results table lists the top ten feature pairs ranked by the absolute value of their Pearson correlation coefficients, along with a Pass or Fail status based on a threshold of 0.3. Only one feature pair exceeds the threshold, while the remaining pairs show lower correlation values and pass the test criteria.

Key insights:

• One feature pair exceeds correlation threshold: The pair (Age, Exited) has a Pearson correlation coefficient of 0.3524, surpassing the 0.3 threshold and receiving a Fail status.
• All other feature pairs below threshold: The remaining nine feature pairs have absolute correlation coefficients ranging from 0.0348 to 0.1923, all below the threshold and marked as Pass.
• Predominantly weak linear relationships: Most feature pairs display weak linear associations, with coefficients clustered well below the threshold.

The test results indicate that the dataset contains minimal evidence of strong linear relationships among most feature pairs, with only the (Age, Exited) pair exhibiting moderate correlation above the specified threshold. The overall correlation structure suggests low risk of widespread multicollinearity or feature redundancy, with the exception of the identified pair.

Parameters:

{
  "max_threshold": 0.3
}
            

Tables

Columns	Coefficient	Pass/Fail
(Age, Exited)	0.3524	Fail
(IsActiveMember, Exited)	-0.1923	Pass
(Balance, NumOfProducts)	-0.1766	Pass
(Balance, Exited)	0.1396	Pass
(HasCrCard, IsActiveMember)	-0.0626	Pass
(NumOfProducts, Exited)	-0.0520	Pass
(NumOfProducts, IsActiveMember)	0.0464	Pass
(CreditScore, Exited)	-0.0377	Pass
(CreditScore, IsActiveMember)	0.0376	Pass
(Tenure, HasCrCard)	0.0348	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)	0.3524	Fail
1	(IsActiveMember, Exited)	-0.1923	Pass
2	(Balance, NumOfProducts)	-0.1766	Pass
3	(Balance, Exited)	0.1396	Pass
4	(HasCrCard, IsActiveMember)	-0.0626	Pass
5	(NumOfProducts, Exited)	-0.0520	Pass
6	(NumOfProducts, IsActiveMember)	0.0464	Pass
7	(CreditScore, Exited)	-0.0377	Pass
8	(CreditScore, IsActiveMember)	0.0376	Pass
9	(Tenure, HasCrCard)	0.0348	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 the linear relationships between feature pairs to identify potential redundancy or multicollinearity. The results table presents the top ten absolute Pearson correlation coefficients among feature pairs, along with their Pass/Fail status relative to a threshold of 0.3. All reported coefficients are below the threshold, and each feature pair is marked as Pass.

Key insights:

• No feature pairs exceed correlation threshold: All absolute Pearson correlation coefficients are below the 0.3 threshold, with the highest magnitude observed at 0.1923 between IsActiveMember and Exited.
• Weak linear relationships among top pairs: The strongest correlations, both positive and negative, remain modest in magnitude, with coefficients ranging from -0.1923 to 0.0314.
• Consistent Pass status across all pairs: Every feature pair in the top ten list is marked as Pass, indicating no detected risk of linear redundancy or multicollinearity within the evaluated pairs.

The results indicate that the evaluated features do not exhibit strong linear dependencies, and no evidence of multicollinearity or feature redundancy is present among the top correlated pairs. The correlation structure supports the interpretability and stability of subsequent modeling efforts.

Parameters:

{
  "max_threshold": 0.3
}
            

Tables

Columns	Coefficient	Pass/Fail
(IsActiveMember, Exited)	-0.1923	Pass
(Balance, NumOfProducts)	-0.1766	Pass
(Balance, Exited)	0.1396	Pass
(HasCrCard, IsActiveMember)	-0.0626	Pass
(NumOfProducts, Exited)	-0.0520	Pass
(NumOfProducts, IsActiveMember)	0.0464	Pass
(CreditScore, Exited)	-0.0377	Pass
(CreditScore, IsActiveMember)	0.0376	Pass
(Tenure, HasCrCard)	0.0348	Pass
(Tenure, EstimatedSalary)	0.0314	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
5403	621	8	0.00	1	0	0	102806.60	0	False	True	False
6438	734	5	121898.58	1	1	0	61829.89	0	True	False	True
1707	511	7	0.00	2	0	1	158313.87	0	False	False	True
1736	773	2	118079.47	4	1	1	143007.49	1	True	False	True
6459	780	4	126725.25	1	1	0	195259.31	1	True	False	True

# 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/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1135: 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', and C=np.inf instead of penalty=None.

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning:

Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.

Initialize the ValidMind objects

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

# 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)

2026-02-19 23:59:00,605 - INFO(validmind.vm_models.dataset.utils): Running predict_proba()... This may take a while
2026-02-19 23:59:00,606 - INFO(validmind.vm_models.dataset.utils): Done running predict_proba()
2026-02-19 23:59:00,607 - INFO(validmind.vm_models.dataset.utils): Running predict()... This may take a while
2026-02-19 23:59:00,609 - INFO(validmind.vm_models.dataset.utils): Done running predict()
2026-02-19 23:59:00,612 - INFO(validmind.vm_models.dataset.utils): Running predict_proba()... This may take a while
2026-02-19 23:59:00,613 - INFO(validmind.vm_models.dataset.utils): Done running predict_proba()
2026-02-19 23:59:00,614 - INFO(validmind.vm_models.dataset.utils): Running predict()... This may take a while
2026-02-19 23:59:00,616 - 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 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 ConfusionMatrix:training_dataset test evaluates the classification performance of the model on the training dataset by presenting the counts of true positives, true negatives, false positives, and false negatives. The confusion matrix plot displays these values in a 2x2 grid, with the axes representing the true and predicted labels. The matrix provides a direct view of the model's ability to correctly and incorrectly classify each class, supporting further calculation of accuracy, precision, recall, and F1 score.

Key insights:

• Balanced correct classification across classes: The model correctly classified 811 negative cases (true negatives) and 827 positive cases (true positives), indicating similar performance for both classes.
• Comparable error rates for both classes: There are 478 false positives and 489 false negatives, showing that misclassification rates are of similar magnitude for both positive and negative classes.
• No evidence of class imbalance in predictions: The distribution of true and false predictions is relatively even, suggesting the model does not favor one class over the other in the training data.

The confusion matrix indicates that the model achieves a balanced classification performance on the training dataset, with similar rates of correct and incorrect predictions for both classes. The observed symmetry in true and false predictions suggests the model does not exhibit bias toward either class, and overall classification behavior appears consistent across the target categories.

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 ConfusionMatrix:test_dataset evaluates the classification model’s ability to correctly identify positive and negative cases by comparing predicted labels against true labels. The resulting confusion matrix displays the counts of true positives, true negatives, false positives, and false negatives, providing a comprehensive view of model prediction outcomes. The matrix is structured with true labels on the vertical axis and predicted labels on the horizontal axis, with each cell indicating the count for a specific prediction-actual combination.

Key insights:

• Higher true negative and true positive counts: The model correctly classified 219 negative cases (true negatives) and 191 positive cases (true positives), indicating effective identification of both classes.
• Noticeable false positive and false negative rates: There are 108 false positives and 119 false negatives, reflecting a moderate level of misclassification in both directions.
• Balanced error distribution: The counts of false positives and false negatives are of similar magnitude, suggesting that the model does not disproportionately favor one class over the other in its errors.

The confusion matrix reveals that the model demonstrates balanced performance across both classes, with true positive and true negative counts exceeding the respective false positive and false negative counts. The distribution of errors is relatively even, indicating that misclassifications are not concentrated in a single class. This pattern suggests that the model maintains a consistent approach to classification, with moderate rates of both types of errors.

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 ConfusionMatrix:test_dataset_normalized test evaluates the classification model’s ability to correctly predict binary outcomes on the test dataset, with results presented as normalized proportions. The confusion matrix displays the relative frequencies of true positives, true negatives, false positives, and false negatives, allowing for assessment of model accuracy and error distribution. Each cell in the matrix represents the proportion of total predictions falling into each category, facilitating direct comparison of model performance across classes.

Key insights:

• True negatives are the most frequent outcome: The model correctly predicts negative cases 34% of the time, representing the highest proportion among all categories.
• True positives are the second most common: Correct positive predictions account for 30% of total predictions, indicating substantial model sensitivity.
• False negatives and false positives are less prevalent: False negatives and false positives occur at rates of 20% and 17%, respectively, reflecting lower but non-negligible misclassification rates.

The confusion matrix indicates that the model achieves its highest accuracy in identifying negative cases, with true negatives comprising the largest share of predictions. True positives are also well represented, while misclassification rates for both false negatives and false positives remain moderate. The distribution of outcomes suggests balanced, though not perfect, performance across both classes, with a slight emphasis on correct negative identification.

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()

2026-02-19 23:59:12,614 - 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 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:

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/model_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"],
)

2026-02-19 23:59:13,147 - INFO(validmind.tests.decorator): Saved to /home/runner/work/documentation/documentation/site/notebooks/EXECUTED/model_development/my_tests/ConfusionMatrix.py!Be sure to add any necessary imports to the top of the file.
2026-02-19 23:59:13,147 - 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 Confusion Matrix test evaluates the classification model’s ability to correctly distinguish between positive and negative classes by comparing predicted and true labels. The results are presented as a normalized 2x2 matrix, where each cell represents the proportion of predictions for each true/predicted label combination. The matrix provides a direct view of the model’s accuracy, error types, and class-wise performance.

Key insights:

• True negatives are the most frequent outcome: The model correctly predicts the negative class in 34% of cases, representing the highest proportion among all matrix cells.
• True positives are the second most common: Correct positive predictions account for 30% of the total, indicating substantial identification of the positive class.
• False negatives exceed false positives: The proportion of false negatives (20%) is higher than that of false positives (17%), suggesting the model is more likely to miss positives than to incorrectly flag negatives.

The confusion matrix indicates that the model demonstrates higher accuracy in identifying negative cases than positive ones, with true negatives and true positives together comprising 64% of all predictions. The error distribution shows a greater tendency toward false negatives than false positives, highlighting a moderate imbalance in misclassification types. Overall, the model achieves a balanced but slightly conservative classification profile, favoring correct negative identification.

Parameters:

{
  "normalize": true
}
            

Figures

2026-02-19 23:59:17,408 - 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 (Need more help?):

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 Documentation 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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