Open In Colab View Notebook on GitHub

W&B Example - TabularΒΆ

Detecting tabular ML models vulnerabilities in W&B with GiskardΒΆ

This example demonstrates how to efficiently scan two tabular ML models for hidden vulnerabilities using Giskard, log the results and interpret them within the W&B framework in just a few lines of code. We will use the following two tabular ML models:

Model

Description

Training data

model1

A LGBMClassifier model trained only for 5 epochs.

Titanic dataset

model2

A LGBMClassifier model trained for 100 epochs.

Titanic dataset

[ ]:
import wandb

from giskard import Model, Dataset, demo, explain_with_shap, scan

model1, df = demo.titanic(model="LGBMClassifier", max_iter=5)
model2, __ = demo.titanic(model="LGBMClassifier", max_iter=100)  # Datasets are identical.
models = {"titanic-model_lgbm_max_iter=5": model1, "titanic-model_lgbm_max_iter=100": model2}

wrapped_data = Dataset(df=df,
                       target="Survived",
                       cat_columns=['Pclass', 'Sex', "SibSp", "Parch", "Embarked"])

wandb.login(key="key to retrieve from https://wandb.ai/authorize")
for model_name, model in models.items():
    wrapped_model = Model(model=model.predict_proba,
                          model_type="classification",
                          feature_names=['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked'],
                          classification_labels=model.classes_)

    run = wandb.init(project="titanic_demo", name=model_name)

    # Log results to the new W&B run.
    wrapped_data.to_wandb()

    shap_explanation_result = explain_with_shap(wrapped_model, wrapped_data)
    shap_explanation_result.to_wandb()

    scan_results = scan(wrapped_model, wrapped_data)
    scan_results.to_wandb()

    test_suite = scan_results.generate_test_suite()
    test_suite.run().to_wandb()

    # Finish a current run.
    run.finish()

After logging the results, you can visualise them on the W&B User Interface by running wandb server start via http://localhost:8080. You will be able to visualise the following:

The datasetΒΆ

52e08e34f88842449b6dd2b085647a7b

The SHAP bar plots for categorical featuresΒΆ

385d89a43d034eaa87d7996d0ee75eda

The SHAP scatter plots for numerical featuresΒΆ

ac24aadb5c334d8593db9ff93fcb2c43

The SHAP global feature importance plotΒΆ

cece46e8904d4083893e906e62249397

The Giskard scan resultsΒΆ

18155f6c6e5c4ea4bd2e2489cc2fa27c

The Giskard test-suite resultsΒΆ

3690b82176be4c1fb9d63818939bac27