align test with change of interface

tests/unittests/test_ag.py

@@ -91,19 +91,20 @@ def test_recall_diff(use_fast=True):
 
     fpredictor = fair.FairPredictor(predictor, test_data, "sex", use_fast=use_fast)
 
-    fpredictor.fit(gm.accuracy, gm.recall.diff, 0.025)
+    limit =0.01
+    fpredictor.fit(gm.accuracy, gm.recall.diff, limit)
 
     # Evaluate the change in fairness (recall difference corresponds to EO)
     measures = fpredictor.evaluate_fairness(verbose=False)
 
-    assert measures["updated"]["recall.diff"] < 0.025
+    assert measures["updated"]["recall.diff"] < limit
     measures = fpredictor.evaluate()
     acc = measures["updated"]["Accuracy"]
-    fpredictor.fit(gm.accuracy, gm.recall.diff, 0.025, greater_is_better_const=True)
+    fpredictor.fit(gm.accuracy, gm.recall.diff, limit, greater_is_better_const=True)
     measures = fpredictor.evaluate_fairness(verbose=False)
-    assert measures["original"]["recall.diff"] > 0.025
+    assert measures["original"]["recall.diff"] > limit
 
-    fpredictor.fit(gm.accuracy, gm.recall.diff, 0.01, greater_is_better_obj=False)
+    fpredictor.fit(gm.accuracy, gm.recall.diff, limit/2, greater_is_better_obj=False)
     assert acc >= fpredictor.evaluate()["updated"]["Accuracy"]
 
 
@@ -117,11 +118,11 @@ def test_subset(use_fast=True):
 
     # Check that metrics computed over a subset of the data is consistent with metrics over all data
     for group in (" White", " Black", " Amer-Indian-Eskimo"):
-        assert all(full_group_metrics.loc[group] == partial_group_metrics.loc[group])
+        assert all(full_group_metrics.loc[('original', group)] == partial_group_metrics.loc[('original', group)])
 
     assert all(
-        full_group_metrics.loc["Maximum difference"]
-        >= partial_group_metrics.loc["Maximum difference"]
+        full_group_metrics.loc[('original', "Maximum difference")]
+        >= partial_group_metrics.loc[('original',"Maximum difference")]
     )
 
 

tests/unittests/test_scipy.py

@@ -102,9 +102,11 @@ def test_conflict_groups():
 def test_fit_creates_updated(use_fast=True):
     """eval should return 'updated' iff fit has been called"""
     fpredictor = FairPredictor(predictor, val_dict, use_fast=use_fast)
-    assert isinstance(fpredictor.evaluate(), pd.Series)
+    assert not isinstance(fpredictor.evaluate(), pd.Series)
+    assert 'original' in fpredictor.evaluate().columns
     fpredictor.fit(gm.accuracy, gm.recall, 0)  # constraint is intentionally slack
     assert not isinstance(fpredictor.evaluate(), pd.Series)
+    assert 'original' in fpredictor.evaluate().columns
     assert 'updated' in fpredictor.evaluate().columns