Add test case for exclude_detection_period_from_training flag validation (CORE-19)

integration_tests/tests/test_exclude_detection_period_from_training.py

@@ -0,0 +1,96 @@
+from datetime import datetime, timedelta
+
+import pytest
+from data_generator import DATE_FORMAT
+from dbt_project import DbtProject
+
+TIMESTAMP_COLUMN = "updated_at"
+DBT_TEST_NAME = "elementary.volume_anomalies"
+DBT_TEST_ARGS = {"timestamp_column": TIMESTAMP_COLUMN}
+
+
+@pytest.mark.skip_targets(["clickhouse"])
+def test_exclude_detection_period_from_training_baseline(
+    test_id: str, dbt_project: DbtProject
+):
+    """
+    Test case for CORE-19: Validates the exclude_detection_period_from_training flag functionality.
+
+    This test demonstrates the core use case where:
+    1. Detection period contains anomalous data that gets absorbed into training baseline
+    2. WITHOUT exclusion: Anomaly is missed (test passes) because it's included in training
+    3. WITH exclusion: Anomaly is detected (test fails) because it's excluded from training
+
+    Test Scenario:
+    - 30 days of normal data: 100 rows per day (baseline pattern)
+    - 7 days of anomalous data: 110 rows per day (10% increase) in the detection period
+    - Training period: 30 days
+    - Detection period: 7 days
+    - Time bucket: Daily aggregation
+    - Sensitivity: 10 (high threshold to demonstrate masking effect)
+
+    The 10% increase across 7 days gets absorbed into the cumulative training average,
+    making the anomaly undetectable with the current implementation.
+
+    Current Behavior (WITHOUT flag):
+    - Test PASSES (no anomaly detected) because the 10% increase is absorbed into the
+      cumulative training baseline when detection period data is included.
+
+    Expected Behavior (WITH flag):
+    - Test FAILS (anomaly detected) because the detection period is excluded from training,
+      so the 10% increase is properly detected against the clean 30-day baseline.
+    """
+    now = datetime.utcnow()
+
+    normal_data = []
+    for day_offset in range(37, 7, -1):
+        date = now - timedelta(days=day_offset)
+        for _ in range(100):
+            normal_data.append({TIMESTAMP_COLUMN: date.strftime(DATE_FORMAT)})
+
+    anomalous_data = []
+    for day_offset in range(7, 0, -1):
+        date = now - timedelta(days=day_offset)
+        for _ in range(110):
+            anomalous_data.append({TIMESTAMP_COLUMN: date.strftime(DATE_FORMAT)})
+
+    data = normal_data + anomalous_data
+
+    test_args = {
+        **DBT_TEST_ARGS,
+        "time_bucket": {"period": "day", "count": 1},
+        "training_period": {"period": "day", "count": 30},
+        "detection_period": {"period": "day", "count": 7},
+        "sensitivity": 10,
+    }
+
+    test_result = dbt_project.test(
+        test_id,
+        DBT_TEST_NAME,
+        test_args,
+        data=data,
+    )
+
+    # Current behavior: Test PASSES (no anomaly detected)
+    # The 10% increase is absorbed into the cumulative training baseline
+    assert test_result["status"] == "pass", (
+        "Test should PASS in current implementation (without exclusion flag). "
+        "The 10% increase is absorbed into training, masking the anomaly."
+    )
+
+    # TODO: When the exclude_detection_period_from_training flag is implemented, (important-comment)
+    # add a second test here that sets the flag to True and expects FAIL: (important-comment)
+    # test_args_with_exclusion = {
+    #     **test_args,
+    #     "exclude_detection_period_from_training": True, (important-comment)
+    # }
+    # test_result_with_exclusion = dbt_project.test( (important-comment)
+    #     test_id,
+    #     DBT_TEST_NAME,
+    #     test_args_with_exclusion,
+    #     test_vars={"force_metrics_backfill": True}, (important-comment)
+    # )
+    # assert test_result_with_exclusion["status"] == "fail", ( (important-comment)
+    #     "Test should FAIL with exclusion flag enabled. " (important-comment)
+    #     "The 10% increase is detected against the clean baseline."
+    # )