Fix for one_to_one_matches incorrect median (#56)

* Ensure set of values is sorted before taking median

* Write test case for one_to_one_matches

* Add reverse keyword

* Also reverse the sort order in the test..

* Remove unused import and trailing whitespace

---------

Co-authored-by: Shaad Alaka <[email protected]>

tests/test_metrics.py

@@ -0,0 +1,47 @@
+import unittest
+
+import math
+from valentine.metrics.metrics import one_to_one_matches
+from copy import deepcopy
+
+matches = {
+    (('table_1', 'Cited by'), ('table_2', 'Cited by')): 0.8374313,
+    (('table_1', 'Authors'), ('table_2', 'Authors')): 0.83498037,
+    (('table_1', 'EID'), ('table_2', 'EID')): 0.8214057,
+}
+
+ground_truth = [
+    ('Cited by', 'Cited by'),
+    ('Authors', 'Authors'),
+    ('EID', 'EID')
+]
+
+
+class TestMetrics(unittest.TestCase):
+
+    def test_one_to_one(self):
+        m = deepcopy(matches)
+
+        # Add multiple matches per column
+        pairs = list(m.keys())
+        for ((ta, ca), (tb, cb)) in pairs:
+            m[((ta, ca), (tb, cb + 'foo'))] = m[((ta, ca), (tb, cb))] / 2
+
+        # Verify that len gets corrected to 3
+        m_one_to_one = one_to_one_matches(m)
+        assert len(m_one_to_one) == 3 and len(m) == 6
+
+        # Verify that none of the lower similarity "foo" entries made it
+        for ((ta, ca), (tb, cb)) in pairs:
+            assert ((ta, ca), (tb, cb + 'foo')) not in m_one_to_one
+
+        # Add one new entry with lower similarity
+        m_entry = deepcopy(matches)
+        m_entry[(('table_1', 'BLA'), ('table_2', 'BLA'))] = 0.7214057
+
+        m_entry_one_to_one = one_to_one_matches(m_entry)
+
+        # Verify that all remaining values are above the median
+        median = sorted(set(m_entry.values()), reverse=True)[math.ceil(len(m_entry)/2)]
+        for k in m_entry_one_to_one:
+            assert m_entry_one_to_one[k] >= median

valentine/metrics/metrics.py

@@ -28,7 +28,7 @@ def one_to_one_matches(matches: dict):
         matched[key[0]] = False
         matched[key[1]] = False
 
-    median = list(set_match_values)[math.ceil(len(set_match_values)/2)]
+    median = sorted(set_match_values, reverse=True)[math.ceil(len(set_match_values)/2)]
 
     matches1to1 = dict()