Modified an existing test to check for errors in ROC-like scores caused by overly aggressive rounding of y_score.

jblackburne · jblackburne · commit 9f4aed444af8 · 2016-09-08T19:30:36.000-07:00
diff --git a/sklearn/metrics/tests/test_ranking.py b/sklearn/metrics/tests/test_ranking.py
@@ -597,18 +597,25 @@ def test_precision_recall_curve_toydata():
 def test_score_scale_invariance():
     # Test that average_precision_score and roc_auc_score are invariant by
     # the scaling or shifting of probabilities
+    # This test was expanded (added scaled_down) in response to github
+    # issue #3864 (and others), where overly aggressive rounding was causing
+    # problems for users with very small y_score values
     y_true, _, probas_pred = make_prediction(binary=True)
 
     roc_auc = roc_auc_score(y_true, probas_pred)
-    roc_auc_scaled = roc_auc_score(y_true, 100 * probas_pred)
+    roc_auc_scaled_up = roc_auc_score(y_true, 100 * probas_pred)
+    roc_auc_scaled_down = roc_auc_score(y_true, 1e-6 * probas_pred)
     roc_auc_shifted = roc_auc_score(y_true, probas_pred - 10)
-    assert_equal(roc_auc, roc_auc_scaled)
+    assert_equal(roc_auc, roc_auc_scaled_up)
+    assert_equal(roc_auc, roc_auc_scaled_down)
     assert_equal(roc_auc, roc_auc_shifted)
 
     pr_auc = average_precision_score(y_true, probas_pred)
-    pr_auc_scaled = average_precision_score(y_true, 100 * probas_pred)
+    pr_auc_scaled_up = average_precision_score(y_true, 100 * probas_pred)
+    pr_auc_scaled_down = average_precision_score(y_true, 1e-6 * probas_pred)
     pr_auc_shifted = average_precision_score(y_true, probas_pred - 10)
-    assert_equal(pr_auc, pr_auc_scaled)
+    assert_equal(pr_auc, pr_auc_scaled_up)
+    assert_equal(pr_auc, pr_auc_scaled_down)
     assert_equal(pr_auc, pr_auc_shifted)
 
 
