Replace is_numeric class attribute with is_numerical

CHANGELOG.md

@@ -44,6 +44,7 @@ _ `_optional` subpackage for managing optional dependencies
   `SubspaceContinuous.sample_uniform`
 - `SubspaceContinuous.samples_full_factorial` has been replaced with
   `SubspaceContinuous.sample_from_full_factorial`
+- `Parameter.is_numeric` has been replaced with `Parameter.is_numerical`
 
 ## [0.9.1] - 2024-06-04
 ### Changed

baybe/campaign.py

@@ -216,7 +216,7 @@ def add_measurements(
                     f"The parameter '{param.name}' has missing values or NaNs in the "
                     f"provided dataframe. Missing parameter values are not supported."
                 )
-            if param.is_numeric and (data[param.name].dtype.kind not in "iufb"):
+            if param.is_numerical and (data[param.name].dtype.kind not in "iufb"):
                 raise TypeError(
                     f"The numerical parameter '{param.name}' has non-numeric entries in"
                     f" the provided dataframe."

baybe/parameters/base.py

@@ -1,5 +1,6 @@
 """Base classes for all parameters."""
 
+import warnings
 from abc import ABC, abstractmethod
 from functools import cached_property, partial
 from typing import Any, ClassVar
@@ -16,6 +17,7 @@
     get_base_structure_hook,
     unstructure_base,
 )
+from baybe.utils.basic import classproperty
 
 # TODO: Reactive slots in all classes once cached_property is supported:
 #   https://github.com/python-attrs/attrs/issues/164
@@ -30,13 +32,24 @@ class Parameter(ABC, SerialMixin):
     """
 
     # class variables
-    is_numeric: ClassVar[bool]
+    is_numerical: ClassVar[bool]
     """Class variable encoding whether this parameter is numeric."""
 
     # object variables
     name: str = field(validator=(instance_of(str), min_len(1)))
     """The name of the parameter"""
 
+    @classproperty
+    def is_numeric(cls) -> bool:
+        """Alias for `is_numerical`."""
+        assert "is_numerical" in cls.__dict__["__annotations__"]
+        warnings.warn(
+            "The class variable 'is_numeric' has been deprecated and will be removed "
+            "in a future version. Use 'is_numerical' instead.",
+            DeprecationWarning,
+        )
+        return cls.is_numerical
+
     @abstractmethod
     def is_in_range(self, item: Any) -> bool:
         """Return whether an item is within the parameter range.

baybe/parameters/categorical.py

@@ -19,7 +19,7 @@ class CategoricalParameter(DiscreteParameter):
     """Parameter class for categorical parameters."""
 
     # class variables
-    is_numeric: ClassVar[bool] = False
+    is_numerical: ClassVar[bool] = False
     # See base class.
 
     # object variables

baybe/parameters/custom.py

@@ -25,7 +25,7 @@ class CustomDiscreteParameter(DiscreteParameter):
     """
 
     # class variables
-    is_numeric: ClassVar[bool] = False
+    is_numerical: ClassVar[bool] = False
     # See base class.
 
     # object variables

baybe/parameters/numerical.py

@@ -21,7 +21,7 @@ class NumericalDiscreteParameter(DiscreteParameter):
     """Parameter class for discrete numerical parameters (a.k.a. setpoints)."""
 
     # class variables
-    is_numeric: ClassVar[bool] = True
+    is_numerical: ClassVar[bool] = True
     # See base class.
 
     # object variables
@@ -102,7 +102,7 @@ class NumericalContinuousParameter(ContinuousParameter):
     """Parameter class for continuous numerical parameters."""
 
     # class variables
-    is_numeric: ClassVar[bool] = True
+    is_numerical: ClassVar[bool] = True
     # See base class.
 
     # object variables

baybe/parameters/substance.py

@@ -35,7 +35,7 @@ class SubstanceParameter(DiscreteParameter):
     """
 
     # class variables
-    is_numeric: ClassVar[bool] = False
+    is_numerical: ClassVar[bool] = False
     # See base class.
 
     # object variables

baybe/utils/dataframe.py

@@ -237,7 +237,7 @@ def add_parameter_noise(
             "'absolute' or 'relative_percent'."
         )
 
-    for param in (p for p in parameters if p.is_numeric):
+    for param in (p for p in parameters if p.is_numerical):
         # Add selected noise type
         if noise_type == "relative_percent":
             data[param.name] *= np.random.uniform(
@@ -384,7 +384,7 @@ def fuzzy_row_match(
         # Check if the row represents a valid input
         valid = True
         for param in parameters:
-            if param.is_numeric:
+            if param.is_numerical:
                 if numerical_measurements_must_be_within_tolerance:
                     valid &= param.is_in_range(row[param.name])
             else:
@@ -402,8 +402,8 @@ def fuzzy_row_match(
                 )
 
         # Differentiate category-like and discrete numerical parameters
-        cat_cols = [p.name for p in parameters if not p.is_numeric]
-        num_cols = [p.name for p in parameters if (p.is_numeric and p.is_discrete)]
+        cat_cols = [p.name for p in parameters if not p.is_numerical]
+        num_cols = [p.name for p in parameters if (p.is_numerical and p.is_discrete)]
 
         # Discrete parameters must match exactly
         match = left_df[cat_cols].eq(row[cat_cols]).all(axis=1, skipna=False)

tests/test_deprecations.py

@@ -223,3 +223,9 @@ def test_deprecated_samples_full_factorial():
     with pytest.warns(DeprecationWarning):
         parameters = [NumericalContinuousParameter("x", (0, 1))]
         SubspaceContinuous(parameters).samples_full_factorial(n_points=1)
+
+
+def test_deprecated_is_numeric_class_attribute():
+    """Using the deprecated `is_numeric` class attribute raises a warning."""
+    with pytest.warns(DeprecationWarning):
+        NumericalContinuousParameter.is_numeric