Day5宿題

.github/workflows/test.yml

@@ -1,41 +1,56 @@
 name: ML Pipeline CI
 
 on:
-  # push:
-  #   branches: [ main, master  ]
   pull_request:
     branches: [ main, master ]
 
 jobs:
   test:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v3
-
-    - name: Set up Python
-      uses: actions/setup-python@v4
-      with:
-        python-version: '3.10'
-
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install pytest great_expectations pandas scikit-learn flake8 black mypy pytest-cov
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-
-    - name: Lint with flake8
-      run: |
-        flake8 day5/演習3 --count --select=E9,F63,F7,F82 --show-source --statistics
-        flake8 day5/演習3 --count --exit-zero --max-complexity=10 --max-line-length=88 --statistics
-
-    - name: Format check with black
-      run: |
-        black --check day5/演習3
-
-    - name: Run data tests
-      run: |
-        pytest day5/演習3/tests/test_data.py -v
-
-    - name: Run model tests
-      run: |
-        pytest day5/演習3/tests/test_model.py -v
+      - uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install pytest great_expectations pandas scikit-learn flake8 black mypy pytest-cov joblib mlflow
+         # now install all Day5-specific deps (includes kedro)
+         if [ -f day5/requirements.txt ]; then pip install -r day5/requirements.txt; fi
+
+      - name: Generate model for tests
+        run: |
+          cd day5/演習1
+          python main.py      # データ準備→学習→モデル保存
+          python pipeline.py  # （必要ならKedroパイプラインも）
+          cd ../..            # ルートに戻る
+
+      - name: Lint with flake8
+        run: |
+          flake8 day5/演習3 --count --select=E9,F63,F7,F82 --show-source --statistics
+          flake8 day5/演習3 --count --exit-zero --max-complexity=10 --max-line-length=88 --statistics
+
+      - name: Format check with black
+        run: |
+          black --check day5/演習3
+
+      - name: Run data tests
+        run: |
+          pytest day5/演習3/tests/test_data.py -v
+
+      - name: Run model tests
+        run: |
+          pytest day5/演習3/tests/test_model.py -v
+
+      - name: Run model performance accuracy test
+        run: |
+          pytest day5/演習3/tests/test_model_performance.py::test_model_inference_accuracy -v
+
+      - name: Run model performance inference-time test
+        run: |
+          pytest day5/演習3/tests/test_model_performance.py::test_model_inference_time -v
+

.gitignore

@@ -0,0 +1,2 @@
+# day5 の学習済みモデルを無視
+day5/**/models/*.pkl

day5/演習3/tests/test_model_performance.py

@@ -0,0 +1,46 @@
+import time
+import joblib
+import pandas as pd
+from sklearn.metrics import accuracy_score
+import os
+
+
+def load_test_data():
+    df = pd.read_csv(
+        os.path.join(
+            os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../..")),
+            "day5",
+            "演習1",
+            "data",
+            "titanic_test.csv",
+        )
+    )
+    X = df.drop("Survived", axis=1)
+    y = df["Survived"]
+    return X, y
+
+
+def get_model():
+    repo_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../.."))
+    model_path = os.path.join(repo_root, "day5", "演習1", "models", "titanic_model.pkl")
+    assert os.path.exists(model_path), f"Model file not found at {model_path}"
+    return joblib.load(model_path)
+
+
+def test_model_inference_accuracy():
+    model = get_model()
+    X_test, y_test = load_test_data()
+    y_pred = model.predict(X_test)
+    acc = accuracy_score(y_test, y_pred)
+    assert acc >= 0.75, f"Expected accuracy >= 0.75, got {acc:.3f}"
+
+
+def test_model_inference_time():
+    model = get_model()
+    X_test, _ = load_test_data()
+    n_runs = 100
+    start = time.time()
+    for _ in range(n_runs):
+        model.predict(X_test)
+    avg_time = (time.time() - start) / n_runs
+    assert avg_time < 0.1, f"Inference too slow: {avg_time:.3f}s per run"