Remove no longer needed suppression of D102 linting rule

CONTRIBUTING.md

@@ -160,10 +160,6 @@ Apart from that, we generally recommend adhering to the following guideline:
     * an optional extended summary or description below and
     * all relevant sections (`Args`, `Raises`, ...).
 
-  Potential exceptions are functions whose docstring is to be fully inherited from a 
-  parent class.
-  In this case, use `# noqa: D102` to disable the automatic checks locally.
-
 - Use type hints (for variables/constants, attributes, function/method signatures, ...).
   Avoid repeating type hints in docstrings.
 

baybe/constraints/conditions.py

@@ -172,7 +172,7 @@ def _make_operator_function(self):
         return func
 
     @override
-    def evaluate(self, data: pd.Series) -> pd.Series:  # noqa: D102
+    def evaluate(self, data: pd.Series) -> pd.Series:
         if data.dtype.kind not in "iufb":
             raise ValueError(
                 "You tried to apply a threshold condition to non-numeric data. "
@@ -183,7 +183,7 @@ def evaluate(self, data: pd.Series) -> pd.Series:  # noqa: D102
         return data.apply(func)
 
     @override
-    def to_polars(self, expr: pl.Expr, /) -> pl.Expr:  # noqa: D102
+    def to_polars(self, expr: pl.Expr, /) -> pl.Expr:
         op = self._make_operator_function()
         return op(expr)
 
@@ -204,19 +204,19 @@ class SubSelectionCondition(Condition):
     """The internal list of items which are considered valid."""
 
     @property
-    def selection(self) -> tuple:  # noqa: D102
+    def selection(self) -> tuple:
         """The list of items which are considered valid."""
         return tuple(
             DTypeFloatNumpy(itm) if isinstance(itm, (float, int, bool)) else itm
             for itm in self._selection
         )
 
     @override
-    def evaluate(self, data: pd.Series) -> pd.Series:  # noqa: D102
+    def evaluate(self, data: pd.Series) -> pd.Series:
         return data.isin(self.selection)
 
     @override
-    def to_polars(self, expr: pl.Expr, /) -> pl.Expr:  # noqa: D102
+    def to_polars(self, expr: pl.Expr, /) -> pl.Expr:
         return expr.is_in(self.selection)
 
 

baybe/constraints/discrete.py

@@ -42,7 +42,7 @@ class DiscreteExcludeConstraint(DiscreteConstraint):
     """Operator encoding how to combine the individual conditions."""
 
     @override
-    def get_invalid(self, data: pd.DataFrame) -> pd.Index:  # noqa: D102
+    def get_invalid(self, data: pd.DataFrame) -> pd.Index:
         satisfied = [
             cond.evaluate(data[self.parameters[k]])
             for k, cond in enumerate(self.conditions)
@@ -51,7 +51,7 @@ def get_invalid(self, data: pd.DataFrame) -> pd.Index:  # noqa: D102
         return data.index[res]
 
     @override
-    def get_invalid_polars(self) -> pl.Expr:  # noqa: D102
+    def get_invalid_polars(self) -> pl.Expr:
         from baybe._optional.polars import polars as pl
 
         satisfied = []
@@ -78,14 +78,14 @@ class DiscreteSumConstraint(DiscreteConstraint):
     """The condition modeled by this constraint."""
 
     @override
-    def get_invalid(self, data: pd.DataFrame) -> pd.Index:  # noqa: D102
+    def get_invalid(self, data: pd.DataFrame) -> pd.Index:
         evaluate_data = data[self.parameters].sum(axis=1)
         mask_bad = ~self.condition.evaluate(evaluate_data)
 
         return data.index[mask_bad]
 
     @override
-    def get_invalid_polars(self) -> pl.Expr:  # noqa: D102
+    def get_invalid_polars(self) -> pl.Expr:
         from baybe._optional.polars import polars as pl
 
         return self.condition.to_polars(pl.sum_horizontal(self.parameters)).not_()
@@ -106,14 +106,14 @@ class DiscreteProductConstraint(DiscreteConstraint):
     """The condition that is used for this constraint."""
 
     @override
-    def get_invalid(self, data: pd.DataFrame) -> pd.Index:  # noqa: D102
+    def get_invalid(self, data: pd.DataFrame) -> pd.Index:
         evaluate_data = data[self.parameters].prod(axis=1)
         mask_bad = ~self.condition.evaluate(evaluate_data)
 
         return data.index[mask_bad]
 
     @override
-    def get_invalid_polars(self) -> pl.Expr:  # noqa: D102
+    def get_invalid_polars(self) -> pl.Expr:
         from baybe._optional.polars import polars as pl
 
         op = _threshold_operators[self.condition.operator]
@@ -140,13 +140,13 @@ class DiscreteNoLabelDuplicatesConstraint(DiscreteConstraint):
     """
 
     @override
-    def get_invalid(self, data: pd.DataFrame) -> pd.Index:  # noqa: D102
+    def get_invalid(self, data: pd.DataFrame) -> pd.Index:
         mask_bad = data[self.parameters].nunique(axis=1) != len(self.parameters)
 
         return data.index[mask_bad]
 
     @override
-    def get_invalid_polars(self) -> pl.Expr:  # noqa: D102
+    def get_invalid_polars(self) -> pl.Expr:
         from baybe._optional.polars import polars as pl
 
         expr = (
@@ -168,13 +168,13 @@ class DiscreteLinkedParametersConstraint(DiscreteConstraint):
     """
 
     @override
-    def get_invalid(self, data: pd.DataFrame) -> pd.Index:  # noqa: D102
+    def get_invalid(self, data: pd.DataFrame) -> pd.Index:
         mask_bad = data[self.parameters].nunique(axis=1) != 1
 
         return data.index[mask_bad]
 
     @override
-    def get_invalid_polars(self) -> pl.Expr:  # noqa: D102
+    def get_invalid_polars(self) -> pl.Expr:
         from baybe._optional.polars import polars as pl
 
         expr = (
@@ -229,7 +229,7 @@ def _validate_affected_parameters(  # noqa: DOC101, DOC103
             )
 
     @override
-    def get_invalid(self, data: pd.DataFrame) -> pd.Index:  # noqa: D102
+    def get_invalid(self, data: pd.DataFrame) -> pd.Index:
         # Create data copy and mark entries where the dependency conditions are negative
         # with a dummy value to cause degeneracy.
         censored_data = data.copy()
@@ -295,7 +295,7 @@ class DiscretePermutationInvarianceConstraint(DiscreteConstraint):
     """Dependencies connected with the invariant parameters."""
 
     @override
-    def get_invalid(self, data: pd.DataFrame) -> pd.Index:  # noqa: D102
+    def get_invalid(self, data: pd.DataFrame) -> pd.Index:
         # Get indices of entries with duplicate label entries. These will also be
         # dropped by this constraint.
         mask_duplicate_labels = pd.Series(False, index=data.index)
@@ -350,7 +350,7 @@ class DiscreteCustomConstraint(DiscreteConstraint):
     you want to keep/remove."""
 
     @override
-    def get_invalid(self, data: pd.DataFrame) -> pd.Index:  # noqa: D102
+    def get_invalid(self, data: pd.DataFrame) -> pd.Index:
         mask_bad = ~self.validator(data[self.parameters])
 
         return data.index[mask_bad]
@@ -365,7 +365,7 @@ class DiscreteCardinalityConstraint(CardinalityConstraint, DiscreteConstraint):
     # See base class.
 
     @override
-    def get_invalid(self, data: pd.DataFrame) -> pd.Index:  # noqa: D102
+    def get_invalid(self, data: pd.DataFrame) -> pd.Index:
         non_zeros = (data[self.parameters] != 0.0).sum(axis=1)
         mask_bad = non_zeros > self.max_cardinality
         mask_bad |= non_zeros < self.min_cardinality

baybe/kernels/basic.py

@@ -31,7 +31,7 @@ class LinearKernel(BasicKernel):
     """An optional initial value for the kernel variance parameter."""
 
     @override
-    def to_gpytorch(self, *args, **kwargs):  # noqa: D102
+    def to_gpytorch(self, *args, **kwargs):
         import torch
 
         from baybe.utils.torch import DTypeFloatTorch
@@ -98,7 +98,7 @@ class PeriodicKernel(BasicKernel):
     """An optional initial value for the kernel period length."""
 
     @override
-    def to_gpytorch(self, *args, **kwargs):  # noqa: D102
+    def to_gpytorch(self, *args, **kwargs):
         import torch
 
         from baybe.utils.torch import DTypeFloatTorch
@@ -153,7 +153,7 @@ class PolynomialKernel(BasicKernel):
     """An optional initial value for the kernel offset."""
 
     @override
-    def to_gpytorch(self, *args, **kwargs):  # noqa: D102
+    def to_gpytorch(self, *args, **kwargs):
         import torch
 
         from baybe.utils.torch import DTypeFloatTorch

baybe/kernels/composite.py

@@ -35,7 +35,7 @@ class ScaleKernel(CompositeKernel):
     """An optional initial value for the output scale."""
 
     @override
-    def to_gpytorch(self, *args, **kwargs):  # noqa: D102
+    def to_gpytorch(self, *args, **kwargs):
         import torch
 
         from baybe.utils.torch import DTypeFloatTorch
@@ -61,7 +61,7 @@ class AdditiveKernel(CompositeKernel):
     """The individual kernels to be summed."""
 
     @override
-    def to_gpytorch(self, *args, **kwargs):  # noqa: D102
+    def to_gpytorch(self, *args, **kwargs):
         return reduce(add, (k.to_gpytorch(*args, **kwargs) for k in self.base_kernels))
 
 
@@ -78,7 +78,7 @@ class ProductKernel(CompositeKernel):
     """The individual kernels to be multiplied."""
 
     @override
-    def to_gpytorch(self, *args, **kwargs):  # noqa: D102
+    def to_gpytorch(self, *args, **kwargs):
         return reduce(mul, (k.to_gpytorch(*args, **kwargs) for k in self.base_kernels))
 
 

baybe/objectives/desirability.py

@@ -117,7 +117,7 @@ def _validate_weights(self, _, weights) -> None:  # noqa: DOC101, DOC103
 
     @override
     @property
-    def targets(self) -> tuple[Target, ...]:  # noqa: D102
+    def targets(self) -> tuple[Target, ...]:
         return self._targets
 
     @cached_property
@@ -140,7 +140,7 @@ def __str__(self) -> str:
         return to_string("Objective", *fields)
 
     @override
-    def transform(self, data: pd.DataFrame) -> pd.DataFrame:  # noqa: D102
+    def transform(self, data: pd.DataFrame) -> pd.DataFrame:
         # Transform all targets individually
         transformed = data[[t.name for t in self.targets]].copy()
         for target in self.targets:

baybe/objectives/single.py

@@ -34,11 +34,11 @@ def __str__(self) -> str:
 
     @override
     @property
-    def targets(self) -> tuple[Target, ...]:  # noqa: D102
+    def targets(self) -> tuple[Target, ...]:
         return (self._target,)
 
     @override
-    def transform(self, data: pd.DataFrame) -> pd.DataFrame:  # noqa: D102
+    def transform(self, data: pd.DataFrame) -> pd.DataFrame:
         target_data = data[[self._target.name]].copy()
         return self._target.transform(target_data)
 

baybe/parameters/base.py

@@ -110,7 +110,7 @@ def comp_df(self) -> pd.DataFrame:
 
     @override
     @property
-    def comp_rep_columns(self) -> tuple[str, ...]:  # noqa: D102
+    def comp_rep_columns(self) -> tuple[str, ...]:
         return tuple(self.comp_df.columns)
 
     def to_subspace(self) -> SubspaceDiscrete:
@@ -120,7 +120,7 @@ def to_subspace(self) -> SubspaceDiscrete:
         return SubspaceDiscrete.from_parameter(self)
 
     @override
-    def is_in_range(self, item: Any) -> bool:  # noqa: D102
+    def is_in_range(self, item: Any) -> bool:
         return item in self.values
 
     def transform(self, series: pd.Series, /) -> pd.DataFrame:
@@ -147,7 +147,7 @@ def transform(self, series: pd.Series, /) -> pd.DataFrame:
         return transformed
 
     @override
-    def summary(self) -> dict:  # noqa: D102
+    def summary(self) -> dict:
         param_dict = dict(
             Name=self.name,
             Type=self.__class__.__name__,

baybe/parameters/categorical.py

@@ -56,7 +56,7 @@ def values(self) -> tuple:
 
     @override
     @cached_property
-    def comp_df(self) -> pd.DataFrame:  # noqa: D102
+    def comp_df(self) -> pd.DataFrame:
         if self.encoding is CategoricalEncoding.OHE:
             cols = [f"{self.name}_{val}" for val in self.values]
             comp_df = pd.DataFrame(

baybe/parameters/custom.py

@@ -98,7 +98,7 @@ def values(self) -> tuple:
 
     @override
     @cached_property
-    def comp_df(self) -> pd.DataFrame:  # noqa: D102
+    def comp_df(self) -> pd.DataFrame:
         # The encoding is directly provided by the user
         # We prepend the parameter name to the columns names to avoid potential
         # conflicts with other parameters

baybe/parameters/numerical.py

@@ -81,19 +81,19 @@ def _validate_tolerance(  # noqa: DOC101, DOC103
 
     @override
     @property
-    def values(self) -> tuple:  # noqa: D102
+    def values(self) -> tuple:
         return tuple(DTypeFloatNumpy(itm) for itm in self._values)
 
     @override
     @cached_property
-    def comp_df(self) -> pd.DataFrame:  # noqa: D102
+    def comp_df(self) -> pd.DataFrame:
         comp_df = pd.DataFrame(
             {self.name: self.values}, index=self.values, dtype=DTypeFloatNumpy
         )
         return comp_df
 
     @override
-    def is_in_range(self, item: float) -> bool:  # noqa: D102
+    def is_in_range(self, item: float) -> bool:
         differences_acceptable = [
             np.abs(val - item) <= self.tolerance for val in self.values
         ]
@@ -131,16 +131,16 @@ def _validate_bounds(self, _: Any, value: Interval) -> None:  # noqa: DOC101, DO
             )
 
     @override
-    def is_in_range(self, item: float) -> bool:  # noqa: D102
+    def is_in_range(self, item: float) -> bool:
         return self.bounds.contains(item)
 
     @override
     @property
-    def comp_rep_columns(self) -> tuple[str, ...]:  # noqa: D102
+    def comp_rep_columns(self) -> tuple[str, ...]:
         return (self.name,)
 
     @override
-    def summary(self) -> dict:  # noqa: D102
+    def summary(self) -> dict:
         param_dict = dict(
             Name=self.name,
             Type=self.__class__.__name__,

baybe/parameters/substance.py

@@ -114,7 +114,7 @@ def values(self) -> tuple:
 
     @override
     @cached_property
-    def comp_df(self) -> pd.DataFrame:  # noqa: D102
+    def comp_df(self) -> pd.DataFrame:
         from baybe.utils import chemistry
 
         vals = list(self.data.values())

baybe/priors/basic.py

@@ -102,7 +102,7 @@ class BetaPrior(Prior):
     """Beta concentration parameter. Controls mass accumulated toward one."""
 
     @override
-    def to_gpytorch(self, *args, **kwargs):  # noqa: D102
+    def to_gpytorch(self, *args, **kwargs):
         raise NotImplementedError(
             f"'{self.__class__.__name__}' does not have a gpytorch analog."
         )

baybe/recommenders/meta/sequential.py

@@ -53,7 +53,7 @@ class TwoPhaseMetaRecommender(MetaRecommender):
     requested batch."""
 
     @override
-    def select_recommender(  # noqa: D102
+    def select_recommender(
         self,
         batch_size: int,
         searchspace: SearchSpace | None = None,
@@ -133,7 +133,7 @@ class SequentialMetaRecommender(MetaRecommender):
     """The number of measurements that were available at the last call."""
 
     @override
-    def select_recommender(  # noqa: D102
+    def select_recommender(
         self,
         batch_size: int,
         searchspace: SearchSpace | None = None,
@@ -223,7 +223,7 @@ def default_iterator(self):
         return iter(self.recommenders)
 
     @override
-    def select_recommender(  # noqa: D102
+    def select_recommender(
         self,
         batch_size: int,
         searchspace: SearchSpace | None = None,

baybe/recommenders/naive.py

@@ -79,7 +79,7 @@ def __attrs_post_init__(self):
             )
 
     @override
-    def recommend(  # noqa: D102
+    def recommend(
         self,
         batch_size: int,
         searchspace: SearchSpace,