[ENH] best_on_top addition in plot_pairwise_scatter

aeon/visualisation/results/_scatter.py

@@ -41,6 +41,7 @@ def plot_pairwise_scatter(
     title=None,
     figsize=(8, 8),
     color_palette="tab10",
+    best_on_top=True,
 ):
     """Plot a scatter that compares datasets' results achieved by two methods.
 
@@ -66,6 +67,9 @@ def plot_pairwise_scatter(
         Size of the figure.
     color_palette : str, default = "tab10"
         Color palette to be used for the plot.
+    best_on_top : bool, default=True
+        If True, the estimator with better performance is placed on the y-axis (top).
+        If False, the ordering is reversed.
 
     Returns
     -------
@@ -129,7 +133,7 @@ def plot_pairwise_scatter(
     x, y = [min_value, max_value], [min_value, max_value]
     ax.plot(x, y, color="black", alpha=0.5, zorder=1)
 
-    # Choose the appropriate order for the methods. Best method is shown in the y-axis.
+    # better estimator on top (y-axis)
     if (results_a.mean() <= results_b.mean() and not lower_better) or (
         results_a.mean() >= results_b.mean() and lower_better
     ):
@@ -143,6 +147,11 @@ def plot_pairwise_scatter(
         second = results_b
         second_method = method_b
 
+    # if best_on_top is False, swap the ordering
+    if not best_on_top:
+        first, second = second, first
+        first_method, second_method = second_method, first_method
+
     differences = [
         0 if i - j == 0 else (1 if i - j > 0 else -1) for i, j in zip(first, second)
     ]

aeon/visualisation/results/tests/test_scatter.py

@@ -91,6 +91,19 @@ def test_plot_pairwise_scatter():
 
     assert isinstance(fig, plt.Figure) and isinstance(ax, plt.Axes)
 
+    # best_on_top = False (reversed ordering)
+    fig_false, ax_false = plot_pairwise_scatter(
+        res[0],
+        res[1],
+        cls[0],
+        cls[1],
+        metric="accuracy",
+        title="Test Plot best_on_top False",
+        best_on_top=False,
+    )
+    plt.gcf().canvas.draw_idle()
+    assert isinstance(fig_false, plt.Figure) and isinstance(ax_false, plt.Axes)
+
     # Test error handling for metrics
     with pytest.raises(ValueError):
         plot_pairwise_scatter(