DeepChecks Tutorial: Automating Machine Learning Testing

This tutorial explores DeepChecks for data validation and machine learning model testing, and leverages GitHub Actions for automated testing and artifact creation. We'll cover machine learning testing principles, DeepChecks functionality, and a complete automated workflow.

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Understanding Machine Learning Testing

Effective machine learning requires rigorous testing beyond simple accuracy metrics. We must assess fairness, robustness, and ethical considerations, including bias detection, false positives/negatives, performance metrics, throughput, and alignment with AI ethics. This involves techniques like data validation, cross-validation, F1-score calculation, confusion matrix analysis, and drift detection (data and prediction). Data splitting (train/test/validation) is crucial for reliable model evaluation. Automating this process is key to building dependable AI systems.

For beginners, the Machine Learning Fundamentals with Python skill track provides a solid foundation.

DeepChecks, an open-source Python library, simplifies comprehensive machine learning testing. It offers built-in checks for model performance, data integrity, and distribution, supporting continuous validation for reliable model deployment.

Getting Started with DeepChecks

Install DeepChecks using pip:

pip install deepchecks --upgrade -q

Data Loading and Preparation (Loan Dataset)

We'll use the Loan Data dataset from DataCamp.

import pandas as pd
loan_data = pd.read_csv("loan_data.csv")
loan_data.head()

Create a DeepChecks dataset:

from sklearn.model_selection import train_test_split
from deepchecks.tabular import Dataset

label_col = 'not.fully.paid'
deep_loan_data = Dataset(loan_data, label=label_col, cat_features=["purpose"])

Data Integrity Testing

DeepChecks' data integrity suite performs automated checks.

from deepchecks.tabular.suites import data_integrity
integ_suite = data_integrity()
suite_result = integ_suite.run(deep_loan_data)
suite_result.show_in_iframe() # Use show_in_iframe for DataLab compatibility

This generates a report covering: Feature-Label Correlation, Feature-Feature Correlation, Single Value Checks, Special Character Detection, Null Value Analysis, Data Type Consistency, String Mismatches, Duplicate Detection, String Length Validation, Conflicting Labels, and Outlier Detection.

Save the report:

suite_result.save_as_html()

Individual Test Execution

For efficiency, run individual tests:

from deepchecks.tabular.checks import IsSingleValue, DataDuplicates
result = IsSingleValue().run(deep_loan_data)
print(result.value) # Unique value counts per column

result = DataDuplicates().run(deep_loan_data)
print(result.value) # Duplicate sample count

Model Evaluation with DeepChecks

We'll train an ensemble model (Logistic Regression, Random Forest, Gaussian Naive Bayes) and evaluate it using DeepChecks.

pip install deepchecks --upgrade -q

The model evaluation report includes: ROC curves, weak segment performance, unused feature detection, train-test performance comparison, prediction drift analysis, simple model comparisons, model inference time, confusion matrices, and more.

JSON output:

import pandas as pd
loan_data = pd.read_csv("loan_data.csv")
loan_data.head()

Individual test example (Label Drift):

from sklearn.model_selection import train_test_split
from deepchecks.tabular import Dataset

label_col = 'not.fully.paid'
deep_loan_data = Dataset(loan_data, label=label_col, cat_features=["purpose"])

Automating with GitHub Actions

This section details setting up a GitHub Actions workflow to automate data validation and model testing. The process involves creating a repository, adding data and Python scripts (data_validation.py, train_validation.py), and configuring a GitHub Actions workflow (main.yml) to execute these scripts and save the results as artifacts. Detailed steps and code snippets are provided in the original input. Refer to the kingabzpro/Automating-Machine-Learning-Testing repository for a complete example. The workflow utilizes the actions/checkout, actions/setup-python, and actions/upload-artifact actions.

Conclusion

Automating machine learning testing using DeepChecks and GitHub Actions significantly improves efficiency and reliability. Early detection of issues enhances model accuracy and fairness. This tutorial provides a practical guide to implementing this workflow, enabling developers to build more robust and trustworthy AI systems. Consider the Machine Learning Scientist with Python career track for further development in this field.

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