From 50f787b6f1b356b0169e794aa86967b5232b33dc Mon Sep 17 00:00:00 2001
From: Mohit Singh Chauhan <mohitsingh.chauhan@cvshealth.com>
Date: Mon, 16 Dec 2024 20:29:25 +0000
Subject: [PATCH 1/2] classification unit tests

---
 tests/test_classification.py | 59 ++++++++++++++++++++++++++++++++++++
 1 file changed, 59 insertions(+)
 create mode 100644 tests/test_classification.py

diff --git a/tests/test_classification.py b/tests/test_classification.py
new file mode 100644
index 0000000..a160f26
--- /dev/null
+++ b/tests/test_classification.py
@@ -0,0 +1,59 @@
+import numpy as np
+
+from langfair.metrics.classification import ClassificationMetrics
+from langfair.metrics.classification.metrics import (
+    FalseDiscoveryRateParity,
+    FalseNegativeRateParity,
+    FalseOmissionRateParity,
+    FalsePositiveRateParity,
+    PredictedPrevalenceRateParity,
+)
+
+groups = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1])
+y_pred = np.array([0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0])
+y_true = np.array([0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1])
+
+
+def test_falsediscoveryrateparity():
+    fdrp = FalseDiscoveryRateParity()
+    x1 = fdrp.evaluate(groups=groups, y_pred=y_pred, y_true=y_true)
+    x2 = fdrp.evaluate(groups=groups, y_pred=y_pred, y_true=y_true, ratio=True)
+    assert np.round(x1, 5) == 0.25 and np.round(x2, 5) == 2.0
+    
+def test_falsenegativerateparity():
+    fnrp = FalseNegativeRateParity()
+    x1 = fnrp.evaluate(groups=groups, y_pred=y_pred, y_true=y_true)
+    x2 = fnrp.evaluate(groups=groups, y_pred=y_pred, y_true=y_true, ratio=True)
+    assert np.round(x1, 5) == 0.0 and np.round(x2, 5) == 1.0
+    
+def test_falseomissionrateparity():
+    forp = FalseOmissionRateParity()
+    x1 = forp.evaluate(groups=groups, y_pred=y_pred, y_true=y_true)
+    x2 = forp.evaluate(groups=groups, y_pred=y_pred, y_true=y_true, ratio=True)
+    assert np.round(x1, 5) == 0.1 and np.round(x2, 5) == 0.83333
+    
+def test_falsepositiverateparity():
+    fprp = FalsePositiveRateParity()
+    x1 = fprp.evaluate(groups=groups, y_pred=y_pred, y_true=y_true)
+    x2 = fprp.evaluate(groups=groups, y_pred=y_pred, y_true=y_true, ratio=True)
+    assert np.round(x1, 5) == 0.16667 and np.round(x2, 5) == 1.5
+    
+def test_predictedprevalencerateparity():
+    pprp = PredictedPrevalenceRateParity()
+    x1 = pprp.evaluate(groups=groups, y_pred=y_pred)
+    x2 = pprp.evaluate(groups=groups, y_pred=y_pred, ratio=True)
+    assert np.round(x1, 5) == 0.05556 and np.round(x2, 5) == 1.125
+    
+def test_classificationmetrics1():
+    cm = ClassificationMetrics(metric_type='all')
+    x = cm.evaluate(groups=groups, y_pred=y_pred, y_true=y_true)
+    assert x = {'FalseNegativeRateParity': 0.0, 'FalseOmissionRateParity': 0.09999999999999998, 
+                'FalsePositiveRateParity': 0.16666666666666669, 'FalseDiscoveryRateParity': 0.25, 
+                'PredictedPrevalenceRateParity': 0.05555555555555558}
+    
+def test_classificationmetrics2():
+    cm = ClassificationMetrics(metric_type='all')
+    x = cm.evaluate(groups=groups, y_pred=y_pred, y_true=y_true, ratio=True)
+    assert x = {'FalseNegativeRateParity': 1.0, 'FalseOmissionRateParity': 0.8333333333333334, 
+                'FalsePositiveRateParity': 1.5, 'FalseDiscoveryRateParity': 2.0, 
+                'PredictedPrevalenceRateParity': 1.125}

From cabc37ad4044ee3db1664f13cf11957451368a94 Mon Sep 17 00:00:00 2001
From: 2613037 <mohitsingh.chauhan@cvshealth.com>
Date: Mon, 16 Dec 2024 15:46:16 -0500
Subject: [PATCH 2/2] minor updated

---
 tests/test_classification.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tests/test_classification.py b/tests/test_classification.py
index a160f26..1ead128 100644
--- a/tests/test_classification.py
+++ b/tests/test_classification.py
@@ -47,13 +47,13 @@ def test_predictedprevalencerateparity():
 def test_classificationmetrics1():
     cm = ClassificationMetrics(metric_type='all')
     x = cm.evaluate(groups=groups, y_pred=y_pred, y_true=y_true)
-    assert x = {'FalseNegativeRateParity': 0.0, 'FalseOmissionRateParity': 0.09999999999999998, 
-                'FalsePositiveRateParity': 0.16666666666666669, 'FalseDiscoveryRateParity': 0.25, 
-                'PredictedPrevalenceRateParity': 0.05555555555555558}
+    assert x == {'FalseNegativeRateParity': 0.0, 'FalseOmissionRateParity': 0.09999999999999998, 
+                 'FalsePositiveRateParity': 0.16666666666666669, 'FalseDiscoveryRateParity': 0.25, 
+                 'PredictedPrevalenceRateParity': 0.05555555555555558}
     
 def test_classificationmetrics2():
     cm = ClassificationMetrics(metric_type='all')
     x = cm.evaluate(groups=groups, y_pred=y_pred, y_true=y_true, ratio=True)
-    assert x = {'FalseNegativeRateParity': 1.0, 'FalseOmissionRateParity': 0.8333333333333334, 
-                'FalsePositiveRateParity': 1.5, 'FalseDiscoveryRateParity': 2.0, 
-                'PredictedPrevalenceRateParity': 1.125}
+    assert x == {'FalseNegativeRateParity': 1.0, 'FalseOmissionRateParity': 0.8333333333333334, 
+                 'FalsePositiveRateParity': 1.5, 'FalseDiscoveryRateParity': 2.0, 
+                 'PredictedPrevalenceRateParity': 1.125}