Develop

day5/演習2/black_check.py

@@ -1,7 +1,18 @@
+def say_hello(name):
+    print("Hello," + name + "!")  # greet
+
+
+def say_hello(name):
+    print("Hello," + name + "!")  # greet
+
+
+def add(a, b):
+    return a + b
+
+
+def add(a, b):
+    return a + b
+
 
-def say_hello(name):print("Hello,"+name+"!")   # greet
-def say_hello(name):print("Hello,"     +    name   +"!")   # greet
-def add( a,b):return a+b
-def add( a ,     b    ):return a+b
 def add(a, b):
-    return a+b
+    return a + b

day5/演習2/main.py

@@ -11,6 +11,7 @@
 import time
 import great_expectations as gx
 
+
 class DataLoader:
     """データロードを行うクラス"""
 

day5/演習3/tests/test_model.py

@@ -12,6 +12,8 @@
 from sklearn.compose import ColumnTransformer
 from sklearn.pipeline import Pipeline
 
+BASELINE_PATH = pathlib.Path(__file__).resolve().parents[1] / "baseline.json"
+
 # テスト用データとモデルパスを定義
 DATA_PATH = os.path.join(os.path.dirname(__file__), "../data/Titanic.csv")
 MODEL_DIR = os.path.join(os.path.dirname(__file__), "../models")
@@ -101,6 +103,19 @@ def train_model(sample_data, preprocessor):
 
     return model, X_test, y_test
 
+def _load_baseline() -> float | None:
+    """baseline.json から accuracy を読む（無ければ None）"""
+    if BASELINE_PATH.exists():
+        data = json.loads(BASELINE_PATH.read_text())
+        return data.get("accuracy")
+    return None
+
+def _save_baseline(new_acc: float):
+    """精度が向上したとき baseline.json を更新"""
+    BASELINE_PATH.write_text(json.dumps(
+        {"accuracy": new_acc, "updated": datetime.utcnow().isoformat()},
+        indent=2
+    ))
 
 def test_model_exists():
     """モデルファイルが存在するか確認"""