ci: Update pre-commit revisions (probabl-ai#1409)

Author: thomass-dev

.pre-commit-config.yaml

@@ -11,18 +11,18 @@ repos:
       - id: trailing-whitespace
 
   - repo: https://github.com/crate-ci/typos
-    rev: v1.26.0
+    rev: v1.30.2
     hooks:
       - id: typos
 
   - repo: https://github.com/kynan/nbstripout
-    rev: 0.7.1
+    rev: 0.8.1
     hooks:
       - id: nbstripout
         args: [--keep-id]
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.6.9
+    rev: v0.9.10
     hooks:
       - id: ruff
         files: ^(skore/(src|tests))|(examples)/

skore/src/skore/sklearn/_comparison/report.py

@@ -138,8 +138,7 @@ def __init__(
         ml_tasks = {report: report._ml_task for report in reports}
         if len(set(ml_tasks.values())) > 1:
             raise ValueError(
-                f"Expected all estimators to have the same ML usecase; "
-                f"got {ml_tasks}"
+                f"Expected all estimators to have the same ML usecase; got {ml_tasks}"
             )
 
         if report_names is None:
@@ -251,9 +250,9 @@ def cache_predictions(
         if n_jobs is None:
             n_jobs = self.n_jobs
 
-        assert (
-            self._progress_info is not None
-        ), "The rich Progress class was not initialized."
+        assert self._progress_info is not None, (
+            "The rich Progress class was not initialized."
+        )
         progress = self._progress_info["current_progress"]
         main_task = self._progress_info["current_task"]
 

skore/src/skore/sklearn/_cross_validation/report.py

@@ -165,9 +165,9 @@ def _fit_estimator_reports(self) -> list[EstimatorReport]:
         estimator_reports : list of EstimatorReport
             The estimator reports.
         """
-        assert (
-            self._progress_info is not None
-        ), "The rich Progress class was not initialized."
+        assert self._progress_info is not None, (
+            "The rich Progress class was not initialized."
+        )
         progress = self._progress_info["current_progress"]
         task = self._progress_info["current_task"]
 
@@ -273,9 +273,9 @@ def cache_predictions(
         if n_jobs is None:
             n_jobs = self.n_jobs
 
-        assert (
-            self._progress_info is not None
-        ), "The rich Progress class was not initialized."
+        assert self._progress_info is not None, (
+            "The rich Progress class was not initialized."
+        )
         progress = self._progress_info["current_progress"]
         main_task = self._progress_info["current_task"]
 
@@ -337,8 +337,7 @@ def y(self, value: Any) -> None:
 
     def _get_help_panel_title(self) -> str:
         return (
-            f"[bold cyan]Tools to diagnose estimator "
-            f"{self.estimator_name_}[/bold cyan]"
+            f"[bold cyan]Tools to diagnose estimator {self.estimator_name_}[/bold cyan]"
         )
 
     def _get_help_legend(self) -> str:

skore/src/skore/sklearn/_estimator/metrics_accessor.py

@@ -1130,8 +1130,7 @@ def _log_loss(
             response_method="predict_proba",
         )
         assert isinstance(result, float), (
-            "The log loss should be a float, got "
-            f"{type(result)} with value {result}."
+            f"The log loss should be a float, got {type(result)} with value {result}."
         )
         return result
 
@@ -1248,8 +1247,7 @@ def _r2(
             )
             return result
         assert isinstance(result, float), (
-            "The R² score should be a float, got "
-            f"{type(result)} with value {result}."
+            f"The R² score should be a float, got {type(result)} with value {result}."
         )
         return result
 
@@ -1366,8 +1364,7 @@ def _rmse(
             )
             return result
         assert isinstance(result, float), (
-            "The RMSE score should be a float, got "
-            f"{type(result)} with value {result}."
+            f"The RMSE score should be a float, got {type(result)} with value {result}."
         )
         return result
 

skore/src/skore/sklearn/_estimator/report.py

@@ -258,9 +258,9 @@ def cache_predictions(
             )
         )
         # trigger the computation
-        assert (
-            self._progress_info is not None
-        ), "The rich Progress class was not initialized."
+        assert self._progress_info is not None, (
+            "The rich Progress class was not initialized."
+        )
         progress = self._progress_info["current_progress"]
         task = self._progress_info["current_task"]
         total_iterations = len(response_methods) * len(pos_labels) * len(data_sources)
@@ -333,8 +333,7 @@ def estimator_name_(self) -> str:
 
     def _get_help_panel_title(self) -> str:
         return (
-            f"[bold cyan]Tools to diagnose estimator "
-            f"{self.estimator_name_}[/bold cyan]"
+            f"[bold cyan]Tools to diagnose estimator {self.estimator_name_}[/bold cyan]"
         )
 
     def _get_help_legend(self) -> str:

skore/src/skore/sklearn/_plot/precision_recall_curve.py

@@ -198,9 +198,9 @@ def plot(
         self.lines_ = []
         default_line_kwargs: dict[str, Any] = {}
         if len(self.precision) == 1:  # binary-classification
-            assert (
-                self.pos_label is not None
-            ), "pos_label should not be None with binary classification."
+            assert self.pos_label is not None, (
+                "pos_label should not be None with binary classification."
+            )
             if len(self.precision[self.pos_label]) == 1:  # single-split
                 if pr_curve_kwargs is None:
                     pr_curve_kwargs = {}

skore/src/skore/sklearn/_plot/roc_curve.py

@@ -202,9 +202,9 @@ def plot(
         self.lines_: list[Line2D] = []
         default_line_kwargs: dict[str, Any] = {}
         if len(self.fpr) == 1:  # binary-classification
-            assert (
-                self.pos_label is not None
-            ), "pos_label should not be None with binary classification."
+            assert self.pos_label is not None, (
+                "pos_label should not be None with binary classification."
+            )
             if len(self.fpr[self.pos_label]) == 1:  # single-split
                 if roc_curve_kwargs is None:
                     roc_curve_kwargs = {}

skore/tests/unit/sklearn/estimator/test_estimator.py

@@ -785,9 +785,9 @@ def test_estimator_report_interaction_cache_metrics(regression_multioutput_data)
         if any(item == multioutput for item in cached_key):
             should_raise = False
             break
-    assert (
-        not should_raise
-    ), f"The value {multioutput} should be stored in one of the cache keys"
+    assert not should_raise, (
+        f"The value {multioutput} should be stored in one of the cache keys"
+    )
     assert result_r2_raw_values.shape == (2,)
 
     multioutput = "uniform_average"
@@ -797,9 +797,9 @@ def test_estimator_report_interaction_cache_metrics(regression_multioutput_data)
         if any(item == multioutput for item in cached_key):
             should_raise = False
             break
-    assert (
-        not should_raise
-    ), f"The value {multioutput} should be stored in one of the cache keys"
+    assert not should_raise, (
+        f"The value {multioutput} should be stored in one of the cache keys"
+    )
     assert isinstance(result_r2_uniform_average, float)
 
 
@@ -823,9 +823,9 @@ def custom_metric(y_true, y_pred, threshold=0.5):
         if any(item == threshold for item in cached_key):
             should_raise = False
             break
-    assert (
-        not should_raise
-    ), f"The value {threshold} should be stored in one of the cache keys"
+    assert not should_raise, (
+        f"The value {threshold} should be stored in one of the cache keys"
+    )
 
     assert isinstance(result, float)
     assert result == pytest.approx(
@@ -843,9 +843,9 @@ def custom_metric(y_true, y_pred, threshold=0.5):
         if any(item == threshold for item in cached_key):
             should_raise = False
             break
-    assert (
-        not should_raise
-    ), f"The value {threshold} should be stored in one of the cache keys"
+    assert not should_raise, (
+        f"The value {threshold} should be stored in one of the cache keys"
+    )
 
     assert isinstance(result, float)
     assert result == pytest.approx(
@@ -887,9 +887,9 @@ def custom_metric(y_true, y_pred, some_weights):
         if any(item == hash_weights for item in cached_key):
             should_raise = False
             break
-    assert (
-        not should_raise
-    ), "The hash of the weights should be stored in one of the cache keys"
+    assert not should_raise, (
+        "The hash of the weights should be stored in one of the cache keys"
+    )
 
     assert isinstance(result, float)
     assert result == pytest.approx(
@@ -1064,7 +1064,7 @@ def test_estimator_report_get_X_y_and_data_source_hash_error():
     report = EstimatorReport(estimator)
 
     err_msg = re.escape(
-        "Invalid data source: unknown. Possible values are: " "test, train, X_y."
+        "Invalid data source: unknown. Possible values are: test, train, X_y."
     )
     with pytest.raises(ValueError, match=err_msg):
         report.metrics.log_loss(data_source="unknown")

skore/tests/unit/sklearn/test_base.py

@@ -89,9 +89,9 @@ def test_get_cached_response_values(
 
     assert response_values.shape == y.shape
     initial_calls = getattr(estimator, f"n_call_{response_method}")
-    assert (
-        initial_calls == 1
-    ), f"Expected 1 call for {response_method}, got {initial_calls}"
+    assert initial_calls == 1, (
+        f"Expected 1 call for {response_method}, got {initial_calls}"
+    )
 
     # Reload from the cache
     response_values = _get_cached_response_values(cache=cache, **params)
@@ -109,17 +109,17 @@ def test_get_cached_response_values(
     assert response_values.shape == y.shape
     current_calls = getattr(estimator, f"n_call_{response_method}")
     expected_calls = initial_calls + (1 if pos_label_sensitive else 0)
-    assert (
-        current_calls == expected_calls
-    ), f"Unexpected number of calls for different pos_label in {response_method}"
+    assert current_calls == expected_calls, (
+        f"Unexpected number of calls for different pos_label in {response_method}"
+    )
 
     # Should reload completely from the cache
     response_values = _get_cached_response_values(cache=cache, **params)
     assert response_values.shape == y.shape
     current_calls = getattr(estimator, f"n_call_{response_method}")
-    assert (
-        current_calls == expected_calls
-    ), f"Unexpected number of calls for different pos_label in {response_method}"
+    assert current_calls == expected_calls, (
+        f"Unexpected number of calls for different pos_label in {response_method}"
+    )
 
 
 @pytest.mark.parametrize(
@@ -233,7 +233,7 @@ def test_base_accessor_get_X_y_and_data_source_hash_error():
     accessor = MockAccessor(parent=report)
 
     err_msg = re.escape(
-        "Invalid data source: unknown. Possible values are: " "test, train, X_y."
+        "Invalid data source: unknown. Possible values are: test, train, X_y."
     )
     with pytest.raises(ValueError, match=err_msg):
         accessor._get_X_y_and_data_source_hash(data_source="unknown")

skore/tests/unit/sklearn/test_cross_validation.py

@@ -403,8 +403,7 @@ def _check_metrics_names(result, expected_metrics, expected_nb_stats):
         normalized_idx = _normalize_metric_name(idx)
         matches = [metric for metric in normalized_expected if metric == normalized_idx]
         assert len(matches) == 1, (
-            f"No match found for index '{idx}' in expected metrics: "
-            f" {expected_metrics}"
+            f"No match found for index '{idx}' in expected metrics:  {expected_metrics}"
         )