Bump dev tools versions

.pre-commit-config.yaml

@@ -7,19 +7,19 @@ ci:
 
 repos:
 - repo: https://github.com/pycqa/flake8
-  rev: 7.1.0
+  rev: 7.1.2
   hooks:
     - id: flake8
       additional_dependencies: [pydoclint==0.5.5]  
       args: [--select=DOC, --config=pydoclint.toml]
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.5.2
+  rev: v0.9.9
   hooks:
   - id: ruff
     args: [--fix, --exit-non-zero-on-fix]
   - id: ruff-format
 - repo: https://github.com/asottile/pyupgrade
-  rev: v3.19.0
+  rev: v3.19.1
   hooks:
   - id: pyupgrade
     args: [--py310-plus]

baybe/constraints/continuous.py

@@ -232,8 +232,7 @@ def get_absolute_thresholds(self, bounds: Interval, /) -> Interval:
         """
         if not bounds.contains(0.0):
             raise ValueError(
-                f"The specified interval must contain zero. "
-                f"Given: {bounds.to_tuple()}."
+                f"The specified interval must contain zero. Given: {bounds.to_tuple()}."
             )
 
         return Interval(

baybe/insights/shap.py

@@ -364,8 +364,7 @@ def plot(
 
         if plot_type not in SHAP_PLOTS:
             raise ValueError(
-                f"Invalid plot type: '{plot_type}'. "
-                f"Available options: {SHAP_PLOTS}."
+                f"Invalid plot type: '{plot_type}'. Available options: {SHAP_PLOTS}."
             )
         plot_func = getattr(shap.plots, plot_type)
 

baybe/parameters/utils.py

@@ -63,9 +63,9 @@ def get_parameters_from_dataframe(
     # Try to find a parameter match for each dataframe column
     parameters = []
     for name, series in df.items():
-        assert isinstance(
-            name, str
-        ), "The given dataframe must only contain string-valued column names."
+        assert isinstance(name, str), (
+            "The given dataframe must only contain string-valued column names."
+        )
         unique_values = series.unique()
 
         # If a match is found, assert that the values are in range

baybe/recommenders/meta/sequential.py

@@ -235,7 +235,7 @@ def _get_recommender_at_current_step(self) -> RecommenderProtocol:
             return self.recommenders[idx]
         except IndexError as ex:
             raise NoRecommendersLeftError(
-                f"A total of {self._step+1} recommender(s) was/were requested but "
+                f"A total of {self._step + 1} recommender(s) was/were requested but "
                 f"the provided sequence contains only {self._step} element(s). "
                 f"Add more recommenders or adjust the "
                 f"'{fields(SequentialMetaRecommender).mode.name}' attribute."
@@ -292,8 +292,8 @@ def _get_recommender_at_current_step(self) -> RecommenderProtocol:
                 self._last_recommender = next(self._iterator)
             except StopIteration as ex:
                 raise NoRecommendersLeftError(
-                    f"A total of {self._step+1} recommender(s) was/were requested but "
-                    f"the provided iterator provided only {self._step} element(s). "
+                    f"A total of {self._step + 1} recommender(s) was/were requested "
+                    f"but the provided iterator provided only {self._step} element(s). "
                 ) from ex
             self._position_of_latest_recommender = self._step
 

baybe/simulation/_imputation.py

@@ -80,8 +80,7 @@ def _impute_lookup(
         match_vals = np.array(vals)
     else:
         raise IndexError(
-            f"Cannot match the recommended row {row} to any of "
-            f"the rows in the lookup."
+            f"Cannot match the recommended row {row} to any of the rows in the lookup."
         )
 
     return match_vals

examples/Constraints_Continuous/hybrid_space.py

@@ -65,7 +65,7 @@
     for k in range(0, DIMENSION // 2)
 ] + [
     NumericalContinuousParameter(
-        name=f"x_{k+1}",
+        name=f"x_{k + 1}",
         bounds=(BOUNDS[0, k], BOUNDS[1, k]),
     )
     for k in range(DIMENSION // 2, DIMENSION)

examples/Constraints_Continuous/linear_constraints.py

@@ -46,7 +46,7 @@
 
 parameters = [
     NumericalContinuousParameter(
-        name=f"x_{k+1}",
+        name=f"x_{k + 1}",
         bounds=(BOUNDS[0, k], BOUNDS[1, k]),
     )
     for k in range(DIMENSION)

examples/Constraints_Discrete/custom_constraints.py

@@ -115,7 +115,7 @@ def custom_function(df: pd.DataFrame) -> pd.Series:
 
 N_ITERATIONS = 3
 for kIter in range(N_ITERATIONS):
-    print(f"\n\n#### ITERATION {kIter+1} ####")
+    print(f"\n\n#### ITERATION {kIter + 1} ####")
 
     print("## ASSERTS ##")
     print(

examples/Constraints_Discrete/dependency_constraints.py

@@ -77,27 +77,27 @@
 
 N_ITERATIONS = 2 if SMOKE_TEST else 5
 for kIter in range(N_ITERATIONS):
-    print(f"\n#### ITERATION {kIter+1} ####")
+    print(f"\n#### ITERATION {kIter + 1} ####")
 
     print("## ASSERTS ##")
     print(
         f"Number entries with both switches on "
-        f"(expected {RESOLUTION*len(dict_solvent)*2*2}): ",
+        f"(expected {RESOLUTION * len(dict_solvent) * 2 * 2}): ",
         (
             (campaign.searchspace.discrete.exp_rep["Switch1"] == "on")
             & (campaign.searchspace.discrete.exp_rep["Switch2"] == "right")
         ).sum(),
     )
     print(
-        f"Number entries with Switch1 off " f"(expected {2*2}):       ",
+        f"Number entries with Switch1 off (expected {2 * 2}):       ",
         (
             (campaign.searchspace.discrete.exp_rep["Switch1"] == "off")
             & (campaign.searchspace.discrete.exp_rep["Switch2"] == "right")
         ).sum(),
     )
     print(
         f"Number entries with Switch2 off "
-        f"(expected {RESOLUTION*len(dict_solvent)}):"
+        f"(expected {RESOLUTION * len(dict_solvent)}):"
         f"      ",
         (
             (campaign.searchspace.discrete.exp_rep["Switch1"] == "on")

examples/Constraints_Discrete/exclusion_constraints.py

@@ -114,7 +114,7 @@
 
 N_ITERATIONS = 3
 for kIter in range(N_ITERATIONS):
-    print(f"\n\n#### ITERATION {kIter+1} ####")
+    print(f"\n\n#### ITERATION {kIter + 1} ####")
 
     print("## ASSERTS ##")
     print(

examples/Constraints_Discrete/prodsum_constraints.py

@@ -109,7 +109,7 @@
 
 N_ITERATIONS = 2 if SMOKE_TEST else 5
 for kIter in range(N_ITERATIONS):
-    print(f"\n\n#### ITERATION {kIter+1} ####")
+    print(f"\n\n#### ITERATION {kIter + 1} ####")
 
     print("## ASSERTS ##")
     print(

examples/Custom_Hooks/campaign_stopping.py

@@ -151,8 +151,8 @@ def stop_on_PI(
     n_pis_over_required = math.ceil(len(candidates_comp_rep) * PI_REQUIRED_FRACTION)
     if n_pis_over < n_pis_over_required:
         raise CampaignStoppedException(
-            f"Less than {PI_REQUIRED_FRACTION*100:.0f}% of candidates are above the PI "
-            f"threshold of {PI_THRESHOLD*100:.0f}% - Stopping the campaign."
+            f"Less than {PI_REQUIRED_FRACTION * 100:.0f}% of candidates are above the PI "
+            f"threshold of {PI_THRESHOLD * 100:.0f}% - Stopping the campaign."
         )
 
 

examples/Multi_Target/desirability.py

@@ -111,7 +111,7 @@
     campaign.add_measurements(rec)
     desirability = campaign.objective.transform(campaign.measurements, allow_extra=True)
 
-    print(f"\n\n#### ITERATION {kIter+1} ####")
+    print(f"\n\n#### ITERATION {kIter + 1} ####")
     print("\nRecommended measurements with fake measured results:\n")
     print(rec)
     print("\nInternal measurement database with desirability values:\n")

examples/Searchspaces/continuous_space.py

@@ -17,7 +17,7 @@
 BOUNDS = (-1, 1)
 
 parameters = [
-    NumericalContinuousParameter(name=f"x_{k+1}", bounds=BOUNDS)
+    NumericalContinuousParameter(name=f"x_{k + 1}", bounds=BOUNDS)
     for k in range(DIMENSION)
 ]
 

examples/Searchspaces/discrete_space.py

@@ -65,7 +65,7 @@
 
 parameters = [
     NumericalDiscreteParameter(
-        name=f"x_{k+1}",
+        name=f"x_{k + 1}",
         values=list(np.linspace(BOUNDS[0, k], BOUNDS[1, k], POINTS_PER_DIM)),
         tolerance=0.01,
     )

examples/Searchspaces/hybrid_space.py

@@ -81,7 +81,7 @@
 
 cont_parameters = [
     NumericalContinuousParameter(
-        name=f"x_{k+1}",
+        name=f"x_{k + 1}",
         bounds=(BOUNDS[0, k], BOUNDS[1, k]),
     )
     for k in CONT_INDICES
@@ -91,7 +91,7 @@
 
 disc_parameters = [
     NumericalDiscreteParameter(
-        name=f"x_{k+1}",
+        name=f"x_{k + 1}",
         values=list(np.linspace(BOUNDS[0, k], BOUNDS[1, k], POINTS_PER_DIM)),
         tolerance=0.01,
     )

examples/Transfer_Learning/basic_transfer_learning.py

@@ -137,7 +137,7 @@
     campaign = Campaign(searchspace=searchspace, objective=objective)
     initial_data = [lookup_training_task.sample(frac=p) for _ in range(N_MC_ITERATIONS)]
     result_fraction = simulate_scenarios(
-        {f"{int(100*p)}": campaign},
+        {f"{int(100 * p)}": campaign},
         lookup_test_task,
         initial_data=initial_data,
         batch_size=BATCH_SIZE,

pyproject.toml

@@ -126,11 +126,11 @@ examples = [
 ]
 
 lint = [
-    "flake8==7.1.0", # see DEV TOOLS NOTE
+    "flake8==7.1.2", # see DEV TOOLS NOTE
     "pre-commit==3.7.1", # see DEV TOOLS NOTE
     "pydoclint==0.5.5", # see DEV TOOLS NOTE
-    "pyupgrade==3.19.0", # see DEV TOOLS NOTE
-    "ruff==0.5.2", # see DEV TOOLS NOTE
+    "pyupgrade==3.19.1", # see DEV TOOLS NOTE
+    "ruff==0.9.9", # see DEV TOOLS NOTE
 ]
 
 mypy = [

tests/conftest.py

@@ -342,7 +342,7 @@ def fixture_parameters(
         valid_parameters += [
             *[
                 SubstanceParameter(
-                    name=f"Solvent_{k+1}",
+                    name=f"Solvent_{k + 1}",
                     data=mock_substances,
                 )
                 for k in range(3)
@@ -360,7 +360,7 @@ def fixture_parameters(
         valid_parameters += [
             *[
                 CategoricalParameter(
-                    name=f"Solvent_{k+1}",
+                    name=f"Solvent_{k + 1}",
                     values=tuple(mock_substances.keys()),
                 )
                 for k in range(3)

tests/constraints/test_cardinality_constraint_continuous.py

@@ -126,7 +126,7 @@ def test_polytope_sampling_with_cardinality_constraint():
     TOLERANCE = 1e-3
 
     parameters = [
-        NumericalContinuousParameter(name=f"x_{i+1}", bounds=(0, 1))
+        NumericalContinuousParameter(name=f"x_{i + 1}", bounds=(0, 1))
         for i in range(N_PARAMETERS)
     ]
     params_equality = ["x_1", "x_2", "x_3", "x_4"]
@@ -191,7 +191,9 @@ def test_min_cardinality_warning():
     upper_bound = 0.5
     stepsize = 0.05
     parameters = [
-        NumericalContinuousParameter(name=f"x_{i+1}", bounds=(lower_bound, upper_bound))
+        NumericalContinuousParameter(
+            name=f"x_{i + 1}", bounds=(lower_bound, upper_bound)
+        )
         for i in range(N_PARAMETERS)
     ]
 
@@ -245,7 +247,7 @@ def test_empty_constraints_after_cardinality_constraint():
     N_PARAMETERS = 2
 
     parameters = [
-        NumericalContinuousParameter(name=f"x_{i+1}", bounds=(0, 1))
+        NumericalContinuousParameter(name=f"x_{i + 1}", bounds=(0, 1))
         for i in range(N_PARAMETERS)
     ]
     constraints = [

tests/hypothesis_strategies/utils.py

@@ -27,9 +27,9 @@ def intervals(
     exclude_fully_unbounded: bool = False,
 ):
     """Generate :class:`baybe.utils.interval.Interval`."""
-    assert not all(
-        (exclude_bounded, exclude_half_bounded, exclude_fully_unbounded)
-    ), "At least one Interval type must be allowed."
+    assert not all((exclude_bounded, exclude_half_bounded, exclude_fully_unbounded)), (
+        "At least one Interval type must be allowed."
+    )
 
     # Draw the interval type from the allowed types
     type_gate = {

tests/test_input_output.py

@@ -78,11 +78,11 @@ def test_recommendation_is_not_ordered(n_values, n_parameters):
     """
     # Set up custom df with entries monotonically increasing
     values = list(range(n_values))
-    df = pd.DataFrame({f"p{k+1}": values for k in range(n_parameters)})
+    df = pd.DataFrame({f"p{k + 1}": values for k in range(n_parameters)})
     searchspace = SearchSpace.from_dataframe(
         df,
         parameters=[
-            NumericalDiscreteParameter(name=f"p{k+1}", values=values)
+            NumericalDiscreteParameter(name=f"p{k + 1}", values=values)
             for k in range(n_parameters)
         ],
     )

tests/test_meta_recommenders.py

@@ -97,7 +97,7 @@ def test_sequential_meta_recommender(cls, mode):
         # Selection with smaller training size raises an error
         with pytest.raises(
             RuntimeError,
-            match=f"decreased from {training_size} to {training_size-1}",
+            match=f"decreased from {training_size} to {training_size - 1}",
         ):
             select_recommender(meta_recommender, training_size - 1)
 

tests/test_substance_parameter.py

@@ -39,6 +39,6 @@ def test_degenerate_comp_df():
     }
     p = SubstanceParameter(name="p", data=dict_base, encoding="RDKITFINGERPRINT")
 
-    assert (
-        not p.comp_df.duplicated().any()
-    ), "A degenerate comp_df was not correctly treated."
+    assert not p.comp_df.duplicated().any(), (
+        "A degenerate comp_df was not correctly treated."
+    )

tests/utils/test_augmentation.py

@@ -179,9 +179,9 @@ def test_df_permutation_aug(content, col_groups, content_expected):
     # Determine equality ignoring row order
     are_equal = df_augmented.equals(df_expected)
 
-    assert (
-        are_equal
-    ), f"\norig:\n{df}\n\naugmented:\n{df_augmented}\n\nexpected:\n{df_expected}"
+    assert are_equal, (
+        f"\norig:\n{df}\n\naugmented:\n{df_augmented}\n\nexpected:\n{df_expected}"
+    )
 
 
 @pytest.mark.parametrize(
@@ -299,6 +299,6 @@ def test_df_dependency_aug(content, causing, affected, content_expected):
     # Determine equality ignoring row order
     are_equal = df_augmented.equals(df_expected)
 
-    assert (
-        are_equal
-    ), f"\norig:\n{df}\n\naugmented:\n{df_augmented}\n\nexpected:\n{df_expected}"
+    assert are_equal, (
+        f"\norig:\n{df}\n\naugmented:\n{df_augmented}\n\nexpected:\n{df_expected}"
+    )

tests/utils/test_sampling_algorithms.py

@@ -26,19 +26,19 @@ def test_discrete_sampling(fraction, method):
     n_points = math.ceil(fraction * len(df))
     sampled = sample_numerical_df(df, n_points, method=method)
 
-    assert (
-        len(sampled) == n_points
-    ), "Sampling did not return expected number of points."
+    assert len(sampled) == n_points, (
+        "Sampling did not return expected number of points."
+    )
     if fraction >= 1.0:
         # Ensure the entire dataframe is contained in the sampled points
         assert (
             pd.merge(df, sampled, how="left", indicator=True)["_merge"].eq("both").all()
         ), "Oversized sampling did not return all original points at least once."
     else:
         # Assure all points are unique
-        assert len(sampled) == len(
-            sampled.drop_duplicates()
-        ), "Undersized sampling did not return unique points."
+        assert len(sampled) == len(sampled.drop_duplicates()), (
+            "Undersized sampling did not return unique points."
+        )
 
 
 @given(