Rename decorator utility

CHANGELOG.md

@@ -16,8 +16,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - `benchmarks` subpackage for defining and running performance tests
 – `Campaign.toggle_discrete_candidates` to dynamically in-/exclude discrete candidates
 - `filter_df` utility for filtering dataframe content
-- `label_input_and_output_columns` decorator to create lookups from array-based
-  callables
+- `arrays_to_dataframes` decorator to create lookups from array-based callables
 - `DiscreteConstraint.get_valid` to conveniently access valid candidates
 - Functionality for persisting benchmarking results on S3 from a manual pipeline run
 

baybe/utils/dataframe.py

@@ -683,13 +683,17 @@ def filter_df(
     return out
 
 
-def label_input_and_output_columns(
-    input_labels: Sequence[str], output_labels: Sequence[str], use_torch: bool = False
+def arrays_to_dataframes(
+    input_labels: Sequence[str],
+    output_labels: Sequence[str],
+    /,
+    use_torch: bool = False,
 ) -> Callable[
     [Callable[[_ArrayLike], _ArrayLike]], Callable[[pd.DataFrame], pd.DataFrame]
 ]:
-    """Create a decorator for labeling the inputs and outputs of array-based callables.
+    """Make a decorator for labeling the input/output columns of array-based callables.
 
+    Useful for creating parameter-to-target lookups from array-based logic.
     The decorator transforms a callable designed to work with unlabelled arrays such
     that it can operate with dataframes instead. The original callable is expected to
     accept and return two-dimensional arrays. When decorated, the callable accepts and

examples/Constraints_Continuous/hybrid_space.py

@@ -25,7 +25,7 @@
 from baybe.parameters import NumericalContinuousParameter, NumericalDiscreteParameter
 from baybe.searchspace import SearchSpace
 from baybe.targets import NumericalTarget
-from baybe.utils.dataframe import label_input_and_output_columns
+from baybe.utils.dataframe import arrays_to_dataframes
 
 ### Defining the test function
 
@@ -93,7 +93,7 @@
 
 ### Wrap the test function as a dataframe-based lookup callable
 
-lookup = label_input_and_output_columns(
+lookup = arrays_to_dataframes(
     [p.name for p in parameters], [target.name], use_torch=True
 )(TestFunction)
 

examples/Constraints_Continuous/linear_constraints.py

@@ -22,7 +22,7 @@
 from baybe.parameters import NumericalContinuousParameter
 from baybe.searchspace import SearchSpace
 from baybe.targets import NumericalTarget
-from baybe.utils.dataframe import label_input_and_output_columns
+from baybe.utils.dataframe import arrays_to_dataframes
 
 ### Defining the test function
 
@@ -79,7 +79,7 @@
 
 ### Wrap the test function as a dataframe-based lookup callable
 
-lookup = label_input_and_output_columns(
+lookup = arrays_to_dataframes(
     [p.name for p in parameters], [target.name], use_torch=True
 )(TestFunction)
 

examples/Custom_Hooks/probability_of_improvement.py

@@ -37,7 +37,7 @@
 from baybe.surrogates import GaussianProcessSurrogate
 from baybe.targets import NumericalTarget
 from baybe.utils.basic import register_hooks
-from baybe.utils.dataframe import label_input_and_output_columns, to_tensor
+from baybe.utils.dataframe import arrays_to_dataframes, to_tensor
 from baybe.utils.plotting import create_example_plots
 from baybe.utils.random import set_random_seed
 
@@ -141,7 +141,7 @@ def extract_pi(
 
 # Now, we perform a couple of experimental iterations with the active hook:
 
-lookup = label_input_and_output_columns(
+lookup = arrays_to_dataframes(
     [p.name for p in discrete_params], [target.name], use_torch=True
 )(test_function)
 

examples/Searchspaces/discrete_space.py

@@ -13,7 +13,7 @@
 from baybe.parameters import NumericalDiscreteParameter
 from baybe.searchspace import SearchSpace
 from baybe.targets import NumericalTarget
-from baybe.utils.dataframe import label_input_and_output_columns
+from baybe.utils.dataframe import arrays_to_dataframes
 
 ### Defining the test function
 
@@ -89,7 +89,7 @@
 
 # Evaluate the test function.
 
-lookup = label_input_and_output_columns(
+lookup = arrays_to_dataframes(
     [p.name for p in parameters], [target.name], use_torch=True
 )(TestFunction)
 

examples/Searchspaces/hybrid_space.py

@@ -18,7 +18,7 @@
 from baybe.recommenders import NaiveHybridSpaceRecommender, TwoPhaseMetaRecommender
 from baybe.searchspace import SearchSpace
 from baybe.targets import NumericalTarget
-from baybe.utils.dataframe import label_input_and_output_columns
+from baybe.utils.dataframe import arrays_to_dataframes
 
 ### Defining the test function and the hybrid dimensions
 
@@ -104,7 +104,7 @@
 
 ### Wrap the test function as a dataframe-based lookup callable
 
-lookup = label_input_and_output_columns(
+lookup = arrays_to_dataframes(
     searchspace.parameter_names, [target.name], use_torch=True
 )(TestFunction)
 

examples/Transfer_Learning/backtesting.py

@@ -23,7 +23,7 @@
 from baybe.searchspace import SearchSpace
 from baybe.simulation import simulate_scenarios, simulate_transfer_learning
 from baybe.targets import NumericalTarget
-from baybe.utils.dataframe import label_input_and_output_columns
+from baybe.utils.dataframe import arrays_to_dataframes
 from baybe.utils.plotting import create_example_plots
 
 ### Settings
@@ -92,7 +92,7 @@
 # and vice versa. The used model is of course not aware of this relationship but
 # needs to infer it from the data gathered during the optimization process.
 
-wrapper = label_input_and_output_columns(
+wrapper = arrays_to_dataframes(
     [p.name for p in discrete_params], [target.name], use_torch=True
 )
 

examples/Transfer_Learning/basic_transfer_learning.py

@@ -21,7 +21,7 @@
 from baybe.searchspace import SearchSpace
 from baybe.simulation import simulate_scenarios
 from baybe.targets import NumericalTarget
-from baybe.utils.dataframe import label_input_and_output_columns
+from baybe.utils.dataframe import arrays_to_dataframes
 from baybe.utils.plotting import create_example_plots
 
 ### Settings
@@ -96,7 +96,7 @@
 # noise. The used model is of course not aware of this relationship but needs to infer
 # it from the data gathered during the optimization process.
 
-wrapper = label_input_and_output_columns(
+wrapper = arrays_to_dataframes(
     [p.name for p in discrete_params], [target.name], use_torch=True
 )