Replace pre-commit in the CI (#1666)

* Replace pre-commit in CI with explicit checks

* Ignore the remaining MyPy typing errors

.github/workflows/code-quality.yml

@@ -20,5 +20,11 @@ jobs:
           python-version: "3.13"
           build-root: false
 
-      - name: Run pre-commit on all files
-        run: poetry run pre-commit run --all-files
+      - name: MyPy type check
+        run: poetry run mypy
+
+      - name: Ruff code linting
+        run: poetry run ruff check --output-format=github river/
+
+      - name: Ruff code formatting
+        run: poetry run ruff format --check river/

river/anomaly/pad.py

@@ -130,15 +130,15 @@ def learn_one(self, x: dict | None, y: base.typing.Target | float):
             else:
                 self.predictive_model.learn_one(y=y, x=x)
         else:
-            self.predictive_model.learn_one(x=x, y=y)
+            self.predictive_model.learn_one(x=x, y=y)  # type: ignore[union-attr]
 
     def score_one(self, x: dict, y: base.typing.Target):
         # Return the predicted value of x from the predictive model, first by checking whether
         # it is a time-series forecaster.
         if isinstance(self.predictive_model, time_series.base.Forecaster):
             y_pred = self.predictive_model.forecast(self.horizon)[0]
         else:
-            y_pred = self.predictive_model.predict_one(x)
+            y_pred = self.predictive_model.predict_one(x)  # type: ignore[union-attr]
 
         # Calculate the squared error
         squared_error = (y_pred - y) ** 2

river/preprocessing/scale.py

@@ -237,7 +237,7 @@ def transform_many(self, X: pd.DataFrame):
         # Check if the dtype is integer type and convert to corresponding float type
         if np.issubdtype(dtype, np.integer):
             bytes_size = dtype.itemsize
-            dtype = np.dtype(f"float{bytes_size * 8}")
+            dtype = np.dtype(f"float{bytes_size * 8}")  # type: ignore[operator]
 
         means = np.array([self.means[c] for c in X.columns], dtype=dtype)
         Xt = X.values - means

river/stream/qa.py

@@ -140,10 +140,10 @@ def simulate_qa(
 
     mementos: list[Memento] = []
 
-    kwargs: list
+    kwargs_list: list
 
-    for i, (x, y, *kwargs) in enumerate(dataset):
-        kwargs = kwargs[0] if kwargs else None
+    for i, (x, y, *kwargs_list) in enumerate(dataset):
+        kwargs = kwargs_list[0] if kwargs_list else None
 
         t = get_moment(i, x)
         d = get_delay(x, y)  # type: ignore
@@ -168,7 +168,7 @@ def simulate_qa(
             )
             del mementos[0]
 
-        queue(mementos, Memento(i, x, y, kwargs, t + d))
+        queue(mementos, Memento(i, x, y, kwargs, t + d))  # type: ignore[operator]
         if copy:
             x = deepcopy(x)
         yield (i, x, None, kwargs) if kwargs else (i, x, None)