Add metrics_analysis_script/tests/test_main.py

metrics_analysis_script/tests/test_main.py

@@ -0,0 +1,64 @@
+import pytest
+import math
+import numpy as np
+
+from metrics_analysis_script import main
+
+
+@pytest.fixture
+def sample_data():
+    return [
+        {
+            'metric_name': 'mix_window_p95',
+            'values': [10.0, 12.0, 11.0, 9.0, 13.0, 10.5],
+            'pinned_flag': True
+        },
+        {
+            'metric_name': 'mix_window_p95',
+            'values': [20.0, 21.0, 19.0, 22.0, 23.0],
+            'pinned_flag': False
+        }
+    ]
+
+
+def test_analyze_metrics_structure(sample_data):
+    results = main.analyze_metrics(sample_data)
+    assert isinstance(results, list)
+    assert all(isinstance(r, dict) for r in results)
+    required_fields = {'metric_name', 'p50', 'p95', 'max', 'retry_free_rate', 'bootstrap_ci'}
+    for r in results:
+        assert required_fields.issubset(r.keys())
+        assert isinstance(r['bootstrap_ci'], list)
+        assert len(r['bootstrap_ci']) == 2
+
+
+def test_statistical_consistency(sample_data):
+    results = main.analyze_metrics(sample_data)
+    for r in results:
+        p50 = r['p50']
+        p95 = r['p95']
+        maximum = r['max']
+        # Ensure ordering
+        assert p50 <= p95 <= maximum
+        # Bootstrap CI bounds make sense
+        low, high = r['bootstrap_ci']
+        assert low <= p50 <= high
+
+
+def test_invalid_input_rejected():
+    invalid_data = [
+        {'metric_name': 'bad_metric', 'values': 'not_a_list', 'pinned_flag': True}
+    ]
+    with pytest.raises((AssertionError, ValueError, TypeError)):
+        main.analyze_metrics(invalid_data)
+
+
+def test_bootstrap_variability(sample_data):
+    results1 = main.analyze_metrics(sample_data)
+    results2 = main.analyze_metrics(sample_data)
+    # Same input should yield statistically consistent but not identical bootstrap for CI boundaries
+    # So we check for approximate stability
+    for r1, r2 in zip(results1, results2):
+        assert math.isclose(r1['p50'], r2['p50'], rel_tol=0.05)
+        ci_diff = np.abs(np.array(r1['bootstrap_ci']) - np.array(r2['bootstrap_ci']))
+        assert ci_diff.mean() < max(0.1 * np.mean(r1['bootstrap_ci']), 1.0)