Undo unnecessary lazy imports

emdgroup · Apr 3, 2024 · abdfe38 · abdfe38
1 parent 7ce820f
commit abdfe38
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Showing 3 changed files with 3 additions and 6 deletions.
diff --git a/baybe/objective.py b/baybe/objective.py
@@ -12,6 +12,7 @@
 
 from baybe.serialization import SerialMixin
 from baybe.targets.base import Target
+from baybe.utils.numerical import geom_mean
 
 if TYPE_CHECKING:
     from baybe.targets.numerical import NumericalTarget
@@ -134,8 +135,6 @@ def transform(self, data: pd.DataFrame) -> pd.DataFrame:
         Raises:
             ValueError: If the specified averaging function is unknown.
         """
-        from baybe.utils.numerical import geom_mean
-
         # Perform transformations that are required independent of the mode
         transformed = data[[t.name for t in self.targets]].copy()
         for target in self.targets:

diff --git a/baybe/parameters/numerical.py b/baybe/parameters/numerical.py
@@ -13,6 +13,7 @@
 from baybe.parameters.base import ContinuousParameter, DiscreteParameter
 from baybe.parameters.validation import validate_is_finite, validate_unique_values
 from baybe.utils.interval import InfiniteIntervalError, Interval, convert_bounds
+from baybe.utils.numerical import DTypeFloatNumpy
 
 
 @define(frozen=True, slots=False)
@@ -56,8 +57,6 @@ def _validate_tolerance(  # noqa: DOC101, DOC103
         Raises:
             ValueError: If the tolerance is not safe.
         """
-        from baybe.utils.numerical import DTypeFloatNumpy
-
         # For zero tolerance, the only left requirement is that all parameter values
         # are distinct, which is already ensured by the corresponding validator.
         if tolerance == 0.0:

diff --git a/baybe/utils/interval.py b/baybe/utils/interval.py
@@ -11,6 +11,7 @@
 from packaging import version
 
 from baybe.serialization import SerialMixin, converter
+from baybe.utils.numerical import DTypeFloatNumpy
 
 if TYPE_CHECKING:
     from torch import Tensor
@@ -125,8 +126,6 @@ def to_tuple(self) -> tuple[float, float]:
 
     def to_ndarray(self) -> np.ndarray:
         """Transform the interval to a :class:`numpy.ndarray`."""
-        from baybe.utils.numerical import DTypeFloatNumpy
-
         return np.array([self.lower, self.upper], dtype=DTypeFloatNumpy)
 
     def to_tensor(self) -> "Tensor":