RHOAIENG-26843: tests(trustyai): add trustyai compatibility smoke test for JupyterLab images with scikit-learn (opendatahub-io#1187)

Author: jiridanek

tests/containers/conftest.py

@@ -162,6 +162,16 @@ def jupyterlab_datascience_image(jupyterlab_image: Image) -> Image:
     return jupyterlab_image
 
 
+@pytest.fixture(scope="function")
+def jupyterlab_trustyai_image(jupyterlab_image: Image) -> Image:
+    if "-trustyai-" not in jupyterlab_image.labels["name"]:
+        pytest.skip(
+            f"Image {jupyterlab_image.name} is not trustyai image because it does not have '-trustyai-' in {jupyterlab_image.labels['name']=}'"
+        )
+
+    return jupyterlab_image
+
+
 @pytest.fixture(scope="function")
 def rstudio_image(image: str) -> Image:
     image_metadata = skip_if_not_workbench_image(image)

tests/containers/workbenches/jupyterlab/jupyterlab_trustyai_test.py

@@ -0,0 +1,124 @@
+from __future__ import annotations
+
+import pathlib
+import tempfile
+
+import allure
+
+from tests.containers import conftest, docker_utils
+from tests.containers.workbenches.workbench_image_test import WorkbenchContainer
+
+
+class TestJupyterLabDatascienceImage:
+    """Tests for JupyterLab Workbench images in this repository that have trustyai."""
+
+    APP_ROOT_HOME = "/opt/app-root/src"
+
+    @allure.issue("RHOAIENG-26843")
+    @allure.description("Check that basic trustyai+scikit-learn functionality is working.")
+    def test_trustyai_with_sklearn_smoke(self, jupyterlab_trustyai_image: conftest.Image) -> None:
+        container = WorkbenchContainer(image=jupyterlab_trustyai_image.name, user=4321, group_add=[0])
+        # language=Python
+        test_script_content = '''
+#!/usr/bin/env python3
+"""
+Standalone smoke test for TrustyAI-scikit-learn compatibility.
+Can be run directly in the trustyai notebook environment.
+"""
+
+import sys
+import traceback
+
+
+def test_sklearn_trustyai_compatibility():
+    """Test basic compatibility between TrustyAI and scikit-learn."""
+    try:
+        import numpy as np
+        import pandas as pd
+        import sklearn
+        from sklearn.ensemble import RandomForestClassifier
+        from sklearn.model_selection import train_test_split
+        import trustyai
+        from trustyai.metrics.fairness.group import statistical_parity_difference
+        from trustyai.model import output
+
+        print(f"✓ Successfully imported all required packages")
+        print(f"  - scikit-learn version: {sklearn.__version__}")
+        print(f"  - trustyai version: {trustyai.__version__}")
+
+        # Verify scikit-learn version is approximately 1.5.x
+        sklearn_version = sklearn.__version__
+        if not sklearn_version.startswith('1.5'):
+            print(f"⚠️  Warning: Expected scikit-learn ~1.5.x, got {sklearn_version}")
+
+        # Test basic sklearn functionality
+        print("✓ Testing basic scikit-learn functionality...")
+        np.random.seed(42)
+        X = np.random.randn(100, 3)
+        y = (X[:, 0] + X[:, 1] > 0).astype(int)
+
+        model = RandomForestClassifier(n_estimators=5, random_state=42)
+        model.fit(X, y)
+        predictions = model.predict(X[:10])
+        print(f"  - Model trained and made predictions: {predictions[:5]}")
+
+        # Test TrustyAI functionality
+        print("✓ Testing TrustyAI functionality...")
+        protected_attr = np.random.choice([0, 1], size=len(X), p=[0.6, 0.4])
+
+        df = pd.DataFrame(X, columns=['f1', 'f2', 'f3'])
+        df['prediction'] = model.predict(X)
+        df['protected'] = protected_attr
+        favorable = output("income", dtype="number", value=1)
+
+        # https://github.com/trustyai-explainability/trustyai-explainability-python-examples/blob/main/examples/GroupFairnessMetrics.ipynb
+        spd = statistical_parity_difference(
+            privileged=df[df.protected == 0],
+            unprivileged=df[df.protected == 1],
+            favorable=[favorable],
+        )
+        print(f"  - Statistical Parity Difference calculated: {spd:.3f}")
+
+        # Test TrustyAI output creation
+        outputs = [output(name=f"pred_{i}", dtype="number", value=float(pred))
+                   for i, pred in enumerate(predictions[:3])]
+        print(f"  - Created {len(outputs)} TrustyAI output instances")
+
+        print("🎉 All compatibility tests passed!")
+        return True
+
+    except Exception as e:
+        print(f"❌ Compatibility test failed: {e}")
+        traceback.print_exc()
+        return False
+
+
+if __name__ == "__main__":
+    success = test_sklearn_trustyai_compatibility()
+    sys.exit(0 if success else 1)
+'''
+        test_script_name = "test_trustyai.py"
+        try:
+            container.start(wait_for_readiness=False)
+            with tempfile.TemporaryDirectory() as tmpdir:
+                tmpdir_path = pathlib.Path(tmpdir)
+                script_path = tmpdir_path / test_script_name
+                script_path.write_text(test_script_content)
+                docker_utils.container_cp(
+                    container.get_wrapped_container(),
+                    src=str(script_path),
+                    dst=self.APP_ROOT_HOME,
+                )
+
+            script_container_path = f"{self.APP_ROOT_HOME}/{test_script_name}"
+            exit_code, output = container.exec(["python", script_container_path])
+            output_str = output.decode()
+
+            print(f"Script output:\n{output_str}")
+
+            assert exit_code == 0, f"Script execution failed with exit code {exit_code}. Output:\n{output_str}"
+            assert "🎉 All compatibility tests passed!" in output_str
+            assert "- Statistical Parity Difference calculated: 1.000" in output_str
+
+        finally:
+            docker_utils.NotebookContainer(container).stop(timeout=0)