Deliverable1 red green

.github/workflows/ci.yml

@@ -8,16 +8,14 @@ on:
 
 jobs:
   test:
-    runs-on: ubuntu-latest
+    runs-on: self-hosted
 
     steps:
       - name: Checkout
         uses: actions/checkout@v4
 
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
+      - name: Show Python
+        run: python3 --version
 
       - name: Install dependencies
         run: |
@@ -28,7 +26,8 @@ jobs:
         env:
           PYTHONPATH: ${{ github.workspace }}
         run: |
-          python -m pytest -q --cov=prediction_pipeline_demo --cov-report=term-missing --cov-fail-under=50
+          python -m pytest -q --cov=prediction_pipeline_demo --cov-report=term-missing --cov-fail-under=70
 
-      # TODO: Add a step here to run the demo pipeline end-to-end
-      # The goal is to execute prediction_pipeline_demo.py so CI logs show the model’s R^2 score.
+      - name: Run demo ML pipeline
+        run: |
+          python prediction_pipeline_demo.py

tests/test_prediction_pipeline_demo.py

@@ -56,20 +56,22 @@ def feature_target_sample(housing_data_sample):
 
 def test_data_split_returns_four_parts(feature_target_sample):
     # TODO(1): Uncomment the line below to get the split
-    # parts = data_split(*feature_target_sample)
+    parts = data_split(*feature_target_sample)
     # TODO(2): Add assertions to check:
     #   - parts is a tuple
     #   - tuple has exactly 4 elements
-    pass
+    assert isinstance(parts, tuple)
+    assert len(parts) == 4
 
 
 def test_end_to_end_train_and_eval(feature_target_sample):
     # TODO(3): Uncomment these lines to train and evaluate the model
-    # X_train, X_test, y_train, y_test = data_split(*feature_target_sample)
-    # model = train_model(X_train, y_train)
-    # score = eval_model(X_test, y_test, model)
+    X_train, X_test, y_train, y_test = data_split(*feature_target_sample)
+    model = train_model(X_train, y_train)
+    score = eval_model(X_test, y_test, model)
     # TODO(4): Add assertions to check:
     #   - score is a float
     #   - score is finite (not NaN or inf)
-    pass
+    assert isinstance(score, float)
+    assert np.isfinite(score)