fix: ruff rule F821 (undefined name) (#54)

* chore: remove F821 from ruff ignore

* fix: missing mpl import

* fix: undefined name errors in beeswarm

And also temporarily remove the SHAP interaction beeswarm plot code
(it's not runnable at the moment, because we ensured that the ndim of
SHAP values was 2 above, so it can never be 3 to trigger the interaction
beeswarm plot here).

The removal of code here is mostly just to appease ruff.
Will create a separate issue for this to re-introduce SHAP interaction
values plotting again later on. Maybe it should be implemented as a
separate function.

pyproject.toml

@@ -13,6 +13,5 @@ select = ["F"]
 ignore = [
   "F401", # unused imports
   "F811", # Redefinition of unused variable
-  "F821", # Undefined name
   "F841", # Local variables assigned but unused
 ]

shap/plots/_beeswarm.py

@@ -66,7 +66,6 @@ def beeswarm(shap_values, max_display=10, order=Explanation.abs.mean(0),
     #     out_names = shap_exp.output_names
 
     order = convert_ordering(order, values)
-
 
     # # deprecation warnings
     # if auto_size_plot is not None:
@@ -134,88 +133,91 @@ def beeswarm(shap_values, max_display=10, order=Explanation.abs.mean(0),
         partition_tree = None
     else:
         partition_tree = clustering
-    
+
     if partition_tree is not None:
         assert partition_tree.shape[1] == 4, "The clustering provided by the Explanation object does not seem to be a partition tree (which is all shap.plots.bar supports)!"
 
-    # plotting SHAP interaction values
-    if len(values.shape) == 3:
-
-        if plot_type == "compact_dot":
-            new_values = values.reshape(values.shape[0], -1)
-            new_features = np.tile(features, (1, 1, features.shape[1])).reshape(features.shape[0], -1)
-
-            new_feature_names = []
-            for c1 in feature_names:
-                for c2 in feature_names:
-                    if c1 == c2:
-                        new_feature_names.append(c1)
-                    else:
-                        new_feature_names.append(c1 + "* - " + c2)
-
-            return beeswarm(
-                new_values, new_features, new_feature_names,
-                max_display=max_display, plot_type="dot", color=color, axis_color=axis_color,
-                title=title, alpha=alpha, show=show, sort=sort,
-                color_bar=color_bar, plot_size=plot_size, class_names=class_names,
-                color_bar_label="*" + color_bar_label
-            )
-
-        if max_display is None:
-            max_display = 7
-        else:
-            max_display = min(len(feature_names), max_display)
-
-        interaction_sort_inds = order#np.argsort(-np.abs(values.sum(1)).sum(0))
-
-        # get plotting limits
-        delta = 1.0 / (values.shape[1] ** 2)
-        slow = np.nanpercentile(values, delta)
-        shigh = np.nanpercentile(values, 100 - delta)
-        v = max(abs(slow), abs(shigh))
-        slow = -v
-        shigh = v
-
-        pl.figure(figsize=(1.5 * max_display + 1, 0.8 * max_display + 1))
-        pl.subplot(1, max_display, 1)
-        proj_values = values[:, interaction_sort_inds[0], interaction_sort_inds]
-        proj_values[:, 1:] *= 2  # because off diag effects are split in half
-        beeswarm(
-            proj_values, features[:, interaction_sort_inds] if features is not None else None,
-            feature_names=feature_names[interaction_sort_inds],
-            sort=False, show=False, color_bar=False,
-            plot_size=None,
-            max_display=max_display
-        )
-        pl.xlim((slow, shigh))
-        pl.xlabel("")
-        title_length_limit = 11
-        pl.title(shorten_text(feature_names[interaction_sort_inds[0]], title_length_limit))
-        for i in range(1, min(len(interaction_sort_inds), max_display)):
-            ind = interaction_sort_inds[i]
-            pl.subplot(1, max_display, i + 1)
-            proj_values = values[:, ind, interaction_sort_inds]
-            proj_values *= 2
-            proj_values[:, i] /= 2  # because only off diag effects are split in half
-            summary(
-                proj_values, features[:, interaction_sort_inds] if features is not None else None,
-                sort=False,
-                feature_names=["" for i in range(len(feature_names))],
-                show=False,
-                color_bar=False,
-                plot_size=None,
-                max_display=max_display
-            )
-            pl.xlim((slow, shigh))
-            pl.xlabel("")
-            if i == min(len(interaction_sort_inds), max_display) // 2:
-                pl.xlabel(labels['INTERACTION_VALUE'])
-            pl.title(shorten_text(feature_names[ind], title_length_limit))
-        pl.tight_layout(pad=0, w_pad=0, h_pad=0.0)
-        pl.subplots_adjust(hspace=0, wspace=0.1)
-        if show:
-            pl.show()
-        return
+    # FIXME: introduce beeswarm interaction values as a separate function `beeswarm_interaction()` (?)
+    #   In the meantime, users can use the `shap.summary_plot()` function.
+    #
+    # # plotting SHAP interaction values
+    # if len(values.shape) == 3:
+    #
+    #     if plot_type == "compact_dot":
+    #         new_values = values.reshape(values.shape[0], -1)
+    #         new_features = np.tile(features, (1, 1, features.shape[1])).reshape(features.shape[0], -1)
+    #
+    #         new_feature_names = []
+    #         for c1 in feature_names:
+    #             for c2 in feature_names:
+    #                 if c1 == c2:
+    #                     new_feature_names.append(c1)
+    #                 else:
+    #                     new_feature_names.append(c1 + "* - " + c2)
+    #
+    #         return beeswarm(
+    #             new_values, new_features, new_feature_names,
+    #             max_display=max_display, plot_type="dot", color=color, axis_color=axis_color,
+    #             title=title, alpha=alpha, show=show, sort=sort,
+    #             color_bar=color_bar, plot_size=plot_size, class_names=class_names,
+    #             color_bar_label="*" + color_bar_label
+    #         )
+    #
+    #     if max_display is None:
+    #         max_display = 7
+    #     else:
+    #         max_display = min(len(feature_names), max_display)
+    #
+    #     interaction_sort_inds = order#np.argsort(-np.abs(values.sum(1)).sum(0))
+    #
+    #     # get plotting limits
+    #     delta = 1.0 / (values.shape[1] ** 2)
+    #     slow = np.nanpercentile(values, delta)
+    #     shigh = np.nanpercentile(values, 100 - delta)
+    #     v = max(abs(slow), abs(shigh))
+    #     slow = -v
+    #     shigh = v
+    #
+    #     pl.figure(figsize=(1.5 * max_display + 1, 0.8 * max_display + 1))
+    #     pl.subplot(1, max_display, 1)
+    #     proj_values = values[:, interaction_sort_inds[0], interaction_sort_inds]
+    #     proj_values[:, 1:] *= 2  # because off diag effects are split in half
+    #     beeswarm(
+    #         proj_values, features[:, interaction_sort_inds] if features is not None else None,
+    #         feature_names=feature_names[interaction_sort_inds],
+    #         sort=False, show=False, color_bar=False,
+    #         plot_size=None,
+    #         max_display=max_display
+    #     )
+    #     pl.xlim((slow, shigh))
+    #     pl.xlabel("")
+    #     title_length_limit = 11
+    #     pl.title(shorten_text(feature_names[interaction_sort_inds[0]], title_length_limit))
+    #     for i in range(1, min(len(interaction_sort_inds), max_display)):
+    #         ind = interaction_sort_inds[i]
+    #         pl.subplot(1, max_display, i + 1)
+    #         proj_values = values[:, ind, interaction_sort_inds]
+    #         proj_values *= 2
+    #         proj_values[:, i] /= 2  # because only off diag effects are split in half
+    #         summary(
+    #             proj_values, features[:, interaction_sort_inds] if features is not None else None,
+    #             sort=False,
+    #             feature_names=["" for i in range(len(feature_names))],
+    #             show=False,
+    #             color_bar=False,
+    #             plot_size=None,
+    #             max_display=max_display
+    #         )
+    #         pl.xlim((slow, shigh))
+    #         pl.xlabel("")
+    #         if i == min(len(interaction_sort_inds), max_display) // 2:
+    #             pl.xlabel(labels['INTERACTION_VALUE'])
+    #         pl.title(shorten_text(feature_names[ind], title_length_limit))
+    #     pl.tight_layout(pad=0, w_pad=0, h_pad=0.0)
+    #     pl.subplots_adjust(hspace=0, wspace=0.1)
+    #     if show:
+    #         pl.show()
+    #     return
 
     # determine how many top features we will plot
     if max_display is None:
@@ -234,9 +236,9 @@ def beeswarm(shap_values, max_display=10, order=Explanation.abs.mean(0),
             clust_order = sort_inds(partition_tree, np.abs(values))
 
             # now relax the requirement to match the parition tree ordering for connections above cluster_threshold
-            dist = scipy.spatial.distance.squareform(scipy.cluster.hierarchy.cophenet(partition_tree))
+            dist = sp.spatial.distance.squareform(sp.cluster.hierarchy.cophenet(partition_tree))
             feature_order = get_sort_order(dist, clust_order, cluster_threshold, feature_order)
-        
+
             # if the last feature we can display is connected in a tree the next feature then we can't just cut
             # off the feature ordering, so we need to merge some tree nodes and then try again.
             if max_display < len(feature_order) and dist[feature_order[max_display-1],feature_order[max_display-2]] <= cluster_threshold:

shap/plots/_utils.py

@@ -2,19 +2,20 @@
 from ..utils import OpChain
 from . import colors
 import numpy as np
+import matplotlib.pyplot as pl
 
 
 def convert_color(color):
     try:
         color = pl.get_cmap(color)
-    except:
+    except Exception:
         pass
-    
+
     if color == "shap_red":
         color = colors.red_rgb
     elif color == "shap_blue":
         color = colors.blue_rgb
-    
+
     return color
 
 def convert_ordering(ordering, shap_values):