Fix categorial regression

holoviews/plotting/bokeh/element.py

@@ -1713,9 +1713,7 @@ def _find_axes(self, plot, element):
         Looks up the axes and plot ranges given the plot and an element.
         """
         axis_dims = self._get_axis_dims(element)[:2]
-        if self.invert_axes:
-            axis_dims[0], axis_dims[1] = axis_dims[::-1]
-        x, y = axis_dims
+        x, y = axis_dims[::-1] if self.invert_axes else axis_dims
         if isinstance(x, Dimension) and x.name in plot.extra_x_ranges:
             x_range = plot.extra_x_ranges[x.name]
             xaxes = [xaxis for xaxis in plot.xaxis if xaxis.x_range_name == x.name]

holoviews/plotting/mixins.py

@@ -161,7 +161,7 @@ def get_extents(self, element, ranges, range_type='combined', **kwargs):
         s1 = max(s1, 0) if util.isfinite(s1) else 0
         ranges[vdim]['soft'] = (s0, s1)
         if range_type not in ('combined', 'data'):
-            return super().get_extents(element, ranges, range_type)
+            return super().get_extents(element, ranges, range_type, ydim=element.vdims[0])
 
         # Compute stack heights
         xdim = element.kdims[0]

holoviews/tests/plotting/bokeh/test_barplot.py

@@ -1,10 +1,10 @@
 import numpy as np
 
-from holoviews.core.overlay import NdOverlay
+from holoviews.core.overlay import NdOverlay, Overlay
 from holoviews.element import Bars
 from holoviews.plotting.bokeh.util import property_to_dict
 
-from bokeh.models import CategoricalColorMapper, LinearColorMapper
+from bokeh.models import CategoricalColorMapper, LinearColorMapper, LinearAxis
 
 from ..utils import ParamLogStream
 from .test_plot import TestBokehPlot, bokeh_renderer
@@ -65,6 +65,15 @@ def test_box_whisker_multi_level_sorted_alphanumerically(self):
         self.assertEqual(x_range.factors, [
             ('1', 'A'), ('1', 'B'), ('3', 'A'), ('3', 'B'), ('10', 'A'), ('10', 'B')])
 
+    def test_bars_multi_level_two_factors_in_overlay(self):
+        # See: https://github.com/holoviz/holoviews/pull/5850
+        box= Bars((["1", "2", "3"]*10, ['A', 'B']*15, np.random.randn(30)),
+                  ['Group', 'Category'], 'Value').aggregate(function=np.mean)
+        overlay = Overlay([box])
+        plot = bokeh_renderer.get_plot(overlay)
+        left_axis = plot.handles["plot"].left[0]
+        assert isinstance(left_axis, LinearAxis)
+
     def test_bars_positive_negative_mixed(self):
         bars = Bars([('A', 0, 1), ('A', 1, -1), ('B', 0, 2)],
                     kdims=['Index', 'Category'], vdims=['Value'])