linting

molpipeline/explainability/visualization/visualization.py

@@ -450,14 +450,29 @@ def structure_heatmap_shap(
 
     fig.colorbar(im, ax=ax, orientation="vertical", fraction=0.015, pad=0.0)
 
-    text = (
-        f"$P(y=1|X) = {explanation.prediction[1]:.2f}$ ="
-        "\n"
-        "\n"
-        f"  $expected \ value={explanation.expected_value[1]:.2f}$   +   "  # noqa: W605 # pylint: disable=W1401
-        f"$features_{{present}}= {sum_present_shap:.2f}$   +   "
-        f"$features_{{absent}}={sum_absent_shap:.2f}$"
-    )
+    if isinstance(explanation.prediction, float):
+        # regression case
+        raise NotImplementedError("Regression case not yet implemented.")
+    elif isinstance(explanation.prediction, np.ndarray):
+        if len(explanation.prediction) == 2:
+            # binary classification case
+            text = (
+                f"$P(y=1|X) = {explanation.prediction[1]:.2f}$ ="
+                "\n"
+                "\n"
+                f"  $expected \ value={explanation.expected_value[1]:.2f}$   +   "  # noqa: W605 # pylint: disable=W1401
+                f"$features_{{present}}= {sum_present_shap:.2f}$   +   "
+                f"$features_{{absent}}={sum_absent_shap:.2f}$"
+            )
+        else:
+            raise ValueError(
+                "Unsupported number of classes for prediction. Only binary classification is supported."
+            )
+    else:
+        raise ValueError(
+            "Unsupported type for prediction. Expected float or np.ndarray."
+        )
+
     fig.text(0.5, 0.18, text, ha="center")
 
     image = plt_to_pil(fig)