pip install giskard --upgrade

from giskard import Dataset

giskard_dataset = Dataset(
    df=raw_data,
    target=TARGET_COLUMN,
    name="Titanic dataset",
    cat_columns=CATEGORICAL_COLUMNS,
)

from giskard import Model
import numpy as np
import pandas as pd

def prediction_function(df: pd.DataFrame) -> np.ndarray:
    preprocessed_df = preprocessing_function(df)
    return classifier.predict_proba(preprocessed_df)

giskard_model = Model(
    model=prediction_function,
    model_type="classification",
    name="Titanic model",
    classification_labels=classifier.classes_,
    feature_names=FEATURE_NAMES,
)

from giskard import scan

results = scan(giskard_model, giskard_dataset)
results.to_html("scan_report.html")

test_suite = results.generate_test_suite("My first test suite")
suite_results = test_suite.run()

if not suite_results.passed:
    raise SystemExit("Giskard test suite failed; see report")

from giskard import testing

test_suite.add_test(
    testing.test_f1(
        model=giskard_model,
        dataset=giskard_dataset,
        threshold=0.7,
    )
)

# Slicing test: F1 must hold on a subset
female_slice = giskard_dataset.slice(lambda df: df[df.sex == "female"])
test_suite.add_test(
    testing.test_f1(
        model=giskard_model,
        dataset=female_slice,
        threshold=0.65,
    )
)

test_suite.run()

- name: Giskard scan
  run: |
    python ml/giskard_scan.py
    # Script raises SystemExit on failure
- name: Upload Giskard report
  uses: actions/upload-artifact@v4
  with:
    name: giskard-report
    path: scan_report.html