Merge pull request #156 from howsunjow/bug/categorical_metric_calculations

Bug/categorical metric calculations

Author: firefly-cpp

niaarm/rule.py

@@ -237,7 +237,7 @@ def __post_init__(self, transactions):
                 contains_antecedent &= transactions[attribute.name] >= attribute.min_val
             else:
                 contains_antecedent &= (
-                    transactions[attribute.name] == attribute.categories[0]
+                    np.isin(transactions[attribute.name], attribute.categories)
                 )
 
         self.antecedent_count = contains_antecedent.sum()
@@ -255,7 +255,7 @@ def __post_init__(self, transactions):
                 contains_consequent &= transactions[attribute.name] >= attribute.min_val
             else:
                 contains_consequent &= (
-                    transactions[attribute.name] == attribute.categories[0]
+                    np.isin(transactions[attribute.name], attribute.categories)
                 )
         self.__amplitude = 1 - (1 / (len(self.antecedent) + len(self.consequent))) * acc
         self.consequent_count = contains_consequent.sum()

tests/test_metrics.py

@@ -1,4 +1,5 @@
 import os
+import pandas as pd
 from unittest import TestCase
 from niaarm import Dataset, Feature, Rule
 
@@ -74,3 +75,61 @@ def test_zhang(self):
     def test_leverage(self):
         self.assertAlmostEqual(self.rule_one.leverage, 0.102040816326)
         self.assertAlmostEqual(self.rule_two.leverage, 0.102040816326)
+
+
+class TestMetricsMultipleCategories(TestCase):
+    def setUp(self):
+        self.data = Dataset(
+            pd.DataFrame({"col1": [1.5, 2.5, 1.0], "col2": ["Green", "Blue", "Red"]})
+        )
+        self.rule = Rule(
+            [Feature("col1", dtype="float", min_val=1.0, max_val=1.5)],
+            [Feature("col2", dtype="cat", categories=["Red", "Green"])],
+            transactions=self.data.transactions,
+        )
+
+    def test_support(self):
+        self.assertEqual(self.rule.support, 2 / 3)
+
+    def test_confidence(self):
+        self.assertEqual(self.rule.confidence, 1)
+
+    def test_lift(self):
+        self.assertEqual(self.rule.lift, 1.5)
+
+    def test_coverage(self):
+        self.assertEqual(self.rule.coverage, 2 / 3)
+
+    def test_rhs_support(self):
+        self.assertEqual(self.rule.rhs_support, 2 / 3)
+
+    def test_conviction(self):
+        self.assertAlmostEqual(
+            self.rule.conviction,
+            (1 - self.rule.rhs_support)
+            / (1 - self.rule.confidence + 2.220446049250313e-16),
+        )
+
+    def test_amplitude(self):
+        self.assertEqual(self.rule.amplitude, 5 / 6)
+
+    def test_inclusion(self):
+        self.assertEqual(self.rule.inclusion, 1)
+
+    def test_interestingness(self):
+        self.assertEqual(self.rule.interestingness, 1 * 1 * (1 - (2 / 3) / 3))
+
+    def test_comprehensibility(self):
+        self.assertAlmostEqual(self.rule.comprehensibility, 0.630929753571)
+
+    def test_netconf(self):
+        self.assertAlmostEqual(self.rule.netconf, ((2/3) - (2/3 * 2/3))/(2/3 * 1/3))
+
+    def test_yulesq(self):
+        self.assertAlmostEqual(self.rule.yulesq,1)
+
+    def test_zhang(self):
+        self.assertAlmostEqual(self.rule.zhang, 1)
+
+    def test_leverage(self):
+        self.assertAlmostEqual(self.rule.leverage, 2/3 - (2/3 * 2/3))