Lift Curve: Add tests

Orange/widgets/evaluate/tests/test_owliftcurve.py

@@ -1,4 +1,6 @@
 import copy
+import unittest
+from unittest.mock import Mock
 
 import numpy as np
 
@@ -9,7 +11,8 @@
 from Orange.widgets.evaluate.tests.base import EvaluateTest
 from Orange.widgets.tests.base import WidgetTest
 from Orange.widgets.tests.utils import simulate
-from Orange.widgets.evaluate.owliftcurve import OWLiftCurve
+from Orange.widgets.evaluate.owliftcurve import OWLiftCurve, cumulative_gains, \
+    cumulative_gains_from_results
 from Orange.tests import test_filename
 
 
@@ -53,3 +56,64 @@ def test_nan_input(self):
         self.assertTrue(self.widget.Error.invalid_results.is_shown())
         self.send_signal(self.widget.Inputs.evaluation_results, None)
         self.assertFalse(self.widget.Error.invalid_results.is_shown())
+
+
+class UtilsTest(unittest.TestCase):
+    def test_cumulative_gains(self):
+        shuffle = [1, 2, 0, 3, 5, 4]
+        y_true = np.array([1, 1, 0, 0, 1, 0])[shuffle]
+        y_scores = np.array([0.9, 0.6, 0.5, 0.4, 0.4, 0.2])[shuffle]
+
+        assert_almost_equal = np.testing.assert_almost_equal
+
+        contacted, respondents, thresholds = cumulative_gains(y_true, y_scores)
+        assert_almost_equal(contacted, np.array([1, 2, 3, 5, 6]) / 6)
+        assert_almost_equal(thresholds, [0.9, 0.6, 0.5, 0.4, 0.2])
+        assert_almost_equal(respondents, np.array([1, 2, 2, 3, 3]) / 3)
+
+        contacted, respondents, thresholds = cumulative_gains(y_true, 1 - y_scores, target=0)
+        assert_almost_equal(contacted, np.array([1, 3, 4, 5, 6]) / 6)
+        assert_almost_equal(thresholds, [0.8, 0.6, 0.5, 0.4, 0.1])
+        assert_almost_equal(respondents, np.array([1, 2, 3, 3, 3]) / 3)
+
+        contacted, respondents, thresholds = \
+            cumulative_gains(np.array([], dtype=int), np.array([]))
+        assert_almost_equal(contacted, [])
+        assert_almost_equal(respondents, [])
+        assert_almost_equal(thresholds, [])
+
+    def test_cumulative_gains_from_results(self):
+        shuffle = [1, 2, 0, 3, 5, 4]
+        y_true = np.array([1, 1, 0, 0, 1, 0])[shuffle]
+        y_scores = np.array([0.9, 0.6, 0.5, 0.4, 0.4, 0.2])[shuffle]
+
+        results = Mock()
+        results.actual = y_true
+        results.probabilities = \
+            [Mock(), Mock(), np.vstack((1 - y_scores, y_scores)).T]
+
+        assert_almost_equal = np.testing.assert_almost_equal
+
+        contacted, respondents, thresholds = \
+            cumulative_gains_from_results(results, 1, 2)
+        assert_almost_equal(thresholds, [0.9, 0.6, 0.5, 0.4, 0.2])
+        assert_almost_equal(contacted, np.array([1, 2, 3, 5, 6]) / 6)
+        assert_almost_equal(respondents, np.array([1, 2, 2, 3, 3]) / 3)
+
+        contacted, respondents, thresholds = \
+            cumulative_gains_from_results(results, 0, 2)
+        assert_almost_equal(contacted, np.array([1, 3, 4, 5, 6]) / 6)
+        assert_almost_equal(thresholds, [0.8, 0.6, 0.5, 0.4, 0.1])
+        assert_almost_equal(respondents, np.array([1, 2, 3, 3, 3]) / 3)
+
+        results.actual = np.array([], dtype=int)
+        results.probabilities = np.empty((3, 0, 2))
+        contacted, respondents, thresholds = \
+            cumulative_gains(np.array([], dtype=int), np.array([]))
+        assert_almost_equal(contacted, [])
+        assert_almost_equal(respondents, [])
+        assert_almost_equal(thresholds, [])
+
+
+if __name__ == "__main__":
+    unittest.main()