fix: compute radar_plot axis padding from the original range

src/evaluate/visualization.py

@@ -184,12 +184,11 @@ def radar_plot(data, model_names, invert_range=[], config=None, fig=None):
     if all(x in variables for x in invert_range) is False:
         raise ValueError("All of the metrics in `invert_range` should be in the data provided.")
     min_max_per_variable = data.describe().T[["min", "max"]]
-    min_max_per_variable["min"] = min_max_per_variable["min"] - 0.1 * (
-        min_max_per_variable["max"] - min_max_per_variable["min"]
-    )
-    min_max_per_variable["max"] = min_max_per_variable["max"] + 0.1 * (
-        min_max_per_variable["max"] - min_max_per_variable["min"]
-    )
+    padding = 0.1 * (min_max_per_variable["max"] - min_max_per_variable["min"])
+    # variables that take a single value across all models would otherwise get a zero-width range
+    padding = padding.where(padding > 0, 0.1)
+    min_max_per_variable["min"] = min_max_per_variable["min"] - padding
+    min_max_per_variable["max"] = min_max_per_variable["max"] + padding
 
     ranges = list(min_max_per_variable.itertuples(index=False, name=None))
     ranges = [

tests/test_viz.py

@@ -1,6 +1,7 @@
 from unittest import TestCase
 
 import matplotlib.pyplot as plt
+import numpy as np
 
 from evaluate.visualization import radar_plot
 
@@ -22,3 +23,23 @@ def test_output_is_plot(self):
         invert_range = ["latency_in_seconds"]
         out_plt = radar_plot(data, model_names, invert_range)
         self.assertIsInstance(out_plt, plt.Figure)
+
+    def test_range_padding_is_symmetric(self):
+        data = [{"accuracy": 0.0, "precision": 1.0}, {"accuracy": 10.0, "precision": 2.0}]
+        model_names = ["model1", "model2"]
+        out_plt = radar_plot(data, model_names)
+        # the accuracy values span 10, so both ends of its axis are padded by 1
+        low, high = out_plt.axes[0].get_ylim()
+        self.assertAlmostEqual(low, -1.0)
+        self.assertAlmostEqual(high, 11.0)
+
+    def test_metric_with_identical_values(self):
+        data = [{"accuracy": 0.9, "precision": 0.8}, {"accuracy": 0.9, "precision": 0.6}]
+        model_names = ["model1", "model2"]
+        out_plt = radar_plot(data, model_names)
+        # accuracy is identical for both models, but precision is not, so the two
+        # plotted shapes must still differ from each other
+        first, second = (line.get_ydata() for line in out_plt.axes[1].lines)
+        self.assertTrue(np.isfinite(first).all())
+        self.assertTrue(np.isfinite(second).all())
+        self.assertFalse(np.array_equal(first, second))