Fix some typos

baybe/constraints/conditions.py

@@ -32,7 +32,7 @@
 
 
 def _is_not_close(x: ArrayLike, y: ArrayLike, rtol: float, atol: float) -> np.ndarray:
-    """Return a boolean array indicating where ``x`` and ``y`` are not close.
+    """Return a Boolean array indicating where ``x`` and ``y`` are not close.
 
     The counterpart to ``numpy.isclose``.
 
@@ -43,15 +43,15 @@ def _is_not_close(x: ArrayLike, y: ArrayLike, rtol: float, atol: float) -> np.nd
         atol: The absolute tolerance parameter.
 
     Returns:
-        A boolean array of where ``x`` and ``y`` are not equal within the
+        A Boolean array of where ``x`` and ``y`` are not equal within the
         given tolerances.
 
     """
     return np.logical_not(_is_close(x, y, rtol=rtol, atol=atol))
 
 
 def _is_close(x: ArrayLike, y: ArrayLike, rtol: float, atol: float) -> np.ndarray:
-    """Return a boolean array indicating where ``x`` and ``y`` are close.
+    """Return a Boolean array indicating where ``x`` and ``y`` are close.
 
     The equivalent to :func:``numpy.isclose``.
     Using ``numpy.isclose`` with Polars dataframes results in this error:
@@ -64,7 +64,7 @@ def _is_close(x: ArrayLike, y: ArrayLike, rtol: float, atol: float) -> np.ndarra
         atol: The absolute tolerance parameter.
 
     Returns:
-        A boolean array of where ``x`` and ``y`` are equal within the
+        A Boolean array of where ``x`` and ``y`` are equal within the
         given tolerances.
 
     """
@@ -107,7 +107,7 @@ def evaluate(self, data: pd.Series) -> pd.Series:
             data: A series containing parameter values.
 
         Returns:
-            A boolean series indicating which elements satisfy the condition.
+            A Boolean series indicating which elements satisfy the condition.
         """
 
     @abstractmethod

baybe/constraints/discrete.py

@@ -346,7 +346,7 @@ class DiscreteCustomConstraint(DiscreteConstraint):
     # object variables
     validator: Callable[[pd.DataFrame], pd.Series] = field()
     """A user-defined function modeling the validation of the constraint. The expected
-    return is a pandas series with boolean entries True/False for search space elements
+    return is a pandas series with Boolean entries True/False for search space elements
     you want to keep/remove."""
 
     @override

baybe/surrogates/bandit.py

@@ -151,7 +151,7 @@ def _fit(self, train_x: Tensor, train_y: Tensor, _: Any = None) -> None:
         import torch
 
         # IMPROVE: The training inputs/targets can actually be represented as
-        #   integers/boolean values but the transformation pipeline currently
+        #   integers / Boolean values but the transformation pipeline currently
         #   converts them float. Potentially, this can be improved by making
         #   the type conversion configurable.
 

baybe/utils/boolean.py

@@ -1,4 +1,4 @@
-"""Functions implementing boolean checks."""
+"""Functions implementing Boolean checks."""
 
 from abc import ABC
 from typing import Any
@@ -45,7 +45,7 @@ def strtobool(val: str) -> bool:
         The ``bool`` value of the corresponding string representation.
 
     Raises:
-        ValueError: If ``val`` cannot be evaluated to a suitable boolean value.
+        ValueError: If ``val`` cannot be evaluated to a suitable Boolean value.
     """
     if val.lower() in ("y", "yes", "t", "true", "on", "1"):
         return True

docs/userguide/campaigns.md

@@ -68,7 +68,7 @@ rec = campaign.recommend(batch_size=3)
 Calling the function returns a `DataFrame` with `batch_size` many rows, each
 representing a particular parameter configuration from the campaign's search space.
 Thus, the following might be a `DataFrame` returned by `recommend` in a search space
-with the three parameters `Categorial_1`, `Categorical_2` and `Num_disc_1`:
+with the three parameters `Categorical_1`, `Categorical_2` and `Num_disc_1`:
 
 |    | Categorical_1   | Categorical_2   |   Num_disc_1 |
 |---:|:----------------|:----------------|-------------:|

docs/userguide/envvars.md

@@ -16,7 +16,7 @@ set ENVAR_NAME=my_value
 ```
 Note that variables set in this manner are interpreted as text, but converted internally
 to the needed format. See for instance the [`strtobool`](baybe.utils.boolean.strtobool) 
-converter for values that can be set so BayBE can interpret them as booleans.
+converter for values that can be set so BayBE can interpret them as Booleans.
 
 It is also possible to set environment variables in Python:
 ```python
@@ -108,7 +108,7 @@ In general, double precision is recommended because numerical stability during o
 can be bad when single precision is used. This impacts gradient-based optimization,
 i.e. search spaces with continuous parameters, more than optimization without gradients.
 
-If you still want to use single precision, you can set the following boolean variables:
+If you still want to use single precision, you can set the following Boolean variables:
 - `BAYBE_NUMPY_USE_SINGLE_PRECISION` (defaults to `False`)
 - `BAYBE_TORCH_USE_SINGLE_PRECISION` (defaults to `False`)
 

docs/userguide/serialization.md

@@ -252,7 +252,7 @@ BayBE objects typically appear as part of a larger object hierarchy.
 For instance, a
 {class}`SearchSpace <baybe.searchspace.core.SearchSpace>` can hold one or several
 {class}`Parameters <baybe.parameters.base.Parameter>`, just like an
-{class}`Objective <baybe.objectives.base.Objective>` can hold one ore several 
+{class}`Objective <baybe.objectives.base.Objective>` can hold one or several 
 {class}`Targets <baybe.targets.base.Target>`.
 This hierarchical structure can be directly replicated in the serialization string:
 

docs/userguide/surrogates.md

@@ -23,7 +23,7 @@ available within BayBE:
 In principle, the surrogate model does not need to be a persistent object during
 Bayesian optimization since each iteration performs a new fit anyway. However, for
 advanced study, such as investigating the posterior predictions, acquisition functions
-or feature importance, it can be useful to diretly extract the current surrogate model.
+or feature importance, it can be useful to directly extract the current surrogate model.
 
 For this, BayBE provides the ``get_surrogate`` method, which is available for the
 [``Campaign``](baybe.campaign.Campaign.get_surrogate) or for 

docs/userguide/targets.md

@@ -77,7 +77,7 @@ Targets are used in nearly all [examples](../../examples/examples).
 ```{important}
 At the moment, BayBE's only option for targets is the `NumericalTarget`.
 This enables many use cases due to the real-valued nature of most measurements.
-But it can also be used to model categorial targets if they are ordinal.
+But it can also be used to model categorical targets if they are ordinal.
 For example: If your experimental outcome is a categorical ranking into "bad",
 "mediocre" and "good", you could use a NumericalTarget with bounds (1, 3), where the
 categories correspond to values 1, 2 and 3 respectively.

examples/Basics/recommenders.py

@@ -79,7 +79,7 @@
     "UCB",  # upper confidence bound with beta of 1.0
 ]
 
-# Note that the qvailability of the acquisition functions might depend on the `batch_size`:
+# Note that the availability of the acquisition functions might depend on the `batch_size`:
 #   - If `batch_size` is set to 1, all available acquisition functions can be chosen
 #   - If a larger value is chosen, only those that allow batching.
 #       That is, 'q'-variants of the acquisition functions must be chosen.

pyproject.toml

@@ -21,7 +21,7 @@ classifiers = [
 ]
 keywords = [
     "Active Learning",
-    "Bayesian Optmization",
+    "Bayesian Optimization",
     "Design of Experiments",
     "DOE",
     "Optimization",

tests/simulate_telemetry.py

@@ -97,7 +97,7 @@
     add_fake_measurements(dat, campaign.targets)
     campaign.add_measurements(dat)
 
-# Fake User1a - Adds recommenations before calling recommend
+# Fake User1a - Adds recommendations before calling recommend
 print("Fake User1a")
 os.environ[VARNAME_TELEMETRY_USERNAME] = "FAKE_USER_1a"
 campaign = Campaign(**config)

tests/test_iterations.py

@@ -90,7 +90,7 @@
 valid_nonmc_acqfs = [a() for a in get_subclasses(AcquisitionFunction) if not a.is_mc]
 
 # List of all hybrid recommenders with default attributes. Is extended with other lists
-# of hybird recommenders like naive ones or recommenders not using default arguments
+# of hybrid recommenders like naive ones or recommenders not using default arguments
 # TODO the TwoPhaseMetaRecommender below can be removed if the SeqGreedy recommender
 #  allows no training data
 valid_hybrid_recommenders = [

tests/validation/test_acqf_validation.py

@@ -41,7 +41,7 @@ def test_qNIPV(sampling_n_points, sampling_fraction, sampling_method, error, mat
 
 @pytest.mark.parametrize("acqf", [qNEI, qLogNEI])
 def test_EI(acqf):
-    """Providing a non-boolean argument to `prune_baseline` raises an error."""
+    """Providing a non-Boolean argument to `prune_baseline` raises an error."""
     with pytest.raises(TypeError):
         acqf(1)