first part code review

molpipeline/experimental/explainability/explainer.py

@@ -3,15 +3,19 @@
 from __future__ import annotations
 
 import abc
-from typing import Any, TypeAlias
+from typing import Any, TypeAlias, Callable
 
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
 import shap
 from scipy.sparse import issparse, spmatrix
 from sklearn.base import BaseEstimator
-from typing_extensions import override
+
+try:
+    from typing import override  # type: ignore[attr-defined]
+except ImportError:
+    from typing_extensions import override
 
 from molpipeline import Pipeline
 from molpipeline.abstract_pipeline_elements.core import OptionalMol
@@ -51,27 +55,9 @@ def _to_dense(
     return feature_matrix
 
 
-def _convert_to_array(value: Any) -> npt.NDArray[np.float64]:
-    """Convert a value to a numpy array.
-
-    Parameters
-    ----------
-    value : Any
-        The value to convert.
-
-    Returns
-    -------
-    npt.NDArray[np.float64]
-        The value as a numpy array.
-    """
-    if isinstance(value, np.ndarray):
-        return value
-    if np.isscalar(value):
-        return np.array([value])
-    raise ValueError("Value is not a scalar or numpy array.")
-
-
-def _get_prediction_function(pipeline: Pipeline | BaseEstimator) -> Any:
+def _get_prediction_function(
+    pipeline: Pipeline | BaseEstimator,
+) -> Callable[[npt.Arraylike], npt.Arraylike]:
     """Get the prediction function of a model.
 
     Parameters
@@ -359,7 +345,7 @@ def explain(
             if issubclass(self.return_element_type_, BondExplanationMixin):
                 explanation_data["bond_weights"] = bond_weights
             if issubclass(self.return_element_type_, SHAPExplanationMixin):
-                explanation_data["expected_value"] = _convert_to_array(
+                explanation_data["expected_value"] = np.atleast_1d(
                     self.explainer.expected_value
                 )
 

molpipeline/experimental/explainability/visualization/heatmaps.py

@@ -50,17 +50,19 @@ def __init__(
         self.y_lim = y_lim
         self.x_res = x_res
         self.y_res = y_res
+        self._dx = (max(self.x_lim) - min(self.x_lim)) / self.x_res
+        self._dy = (max(self.y_lim) - min(self.y_lim)) / self.y_res
         self.values = np.zeros((self.x_res, self.y_res))
 
     @property
     def dx(self) -> float:
         """Length of cell in x-direction."""
-        return (max(self.x_lim) - min(self.x_lim)) / self.x_res
+        return self._dx
 
     @property
     def dy(self) -> float:
         """Length of cell in y-direction."""
-        return (max(self.y_lim) - min(self.y_lim)) / self.y_res
+        return self._dy
 
     def grid_field_center(self, x_idx: int, y_idx: int) -> tuple[float, float]:
         """Center of cell specified by index along x and y.
@@ -149,6 +151,9 @@ def __init__(
         y_lim: Sequence[float],
         x_res: int,
         y_res: int,
+        function_list: (
+            list[Callable[[npt.NDArray[np.float64]], npt.NDArray[np.float64]]] | None
+        ) = None,
     ):
         """Initialize the ValueGrid with limits and resolution of the axes.
 
@@ -162,11 +167,18 @@ def __init__(
             Resolution (number of cells) along x-axis.
         y_res: int
             Resolution (number of cells) along y-axis.
+        function_list: list[Callable[[npt.NDArray[np.float64]], npt.NDArray[np.float64]]], optional
+            List of functions to be evaluated for each cell, by default None.
         """
         super().__init__(x_lim, y_lim, x_res, y_res)
-        self.function_list: list[
-            Callable[[npt.NDArray[np.float64]], npt.NDArray[np.float64]]
-        ] = []
+        if function_list is not None:
+            self.function_list: list[
+                Callable[[npt.NDArray[np.float64]], npt.NDArray[np.float64]]
+            ] = function_list
+        else:
+            self.function_list: list[
+                Callable[[npt.NDArray[np.float64]], npt.NDArray[np.float64]]
+            ] = []
         self.values = np.zeros((self.x_res, self.y_res))
 
     def add_function(