Add exclude_detection_period_from_training flag to column/dimension anomaly tests

integration_tests/tests/test_all_columns_anomalies.py

@@ -1,4 +1,4 @@
-from datetime import datetime, timedelta
+from datetime import datetime, timedelta, timezone
 from typing import Any, Dict, List
 
 import pytest
@@ -153,3 +153,104 @@ def test_anomalyless_all_columns_anomalies_all_monitors_sanity(
         test_id, DBT_TEST_NAME, test_args, data=data, multiple_results=True
     )
     assert all([res["status"] == "pass" for res in test_results])
+
+
+# Anomalies currently not supported on ClickHouse
+@pytest.mark.skip_targets(["clickhouse"])
+@pytest.mark.parametrize(
+    "exclude_detection,expected_status",
+    [
+        (False, "pass"),
+        (True, "fail"),
+    ],
+    ids=["without_exclusion", "with_exclusion"],
+)
+def test_anomaly_in_detection_period(
+    test_id: str,
+    dbt_project: DbtProject,
+    exclude_detection: bool,
+    expected_status: str,
+):
+    """
+    Test the exclude_detection_period_from_training flag functionality for column anomalies.
+
+    Scenario:
+    - 30 days of normal data with variance in null_count pattern (8, 10, 12 nulls per day)
+    - 7 days of anomalous data (20 nulls per day) in detection period
+    - Without exclusion (exclude_detection=False): anomaly gets included in training baseline, test passes
+    - With exclusion (exclude_detection=True): anomaly excluded from training, test fails (detects anomaly)
+    """
+    utc_now = datetime.now(timezone.utc)
+
+    # Generate 30 days of normal data with variance in null_count (8, 10, 12 pattern)
+    normal_pattern = [8, 10, 12]
+    normal_data = []
+    for i in range(30):
+        date = utc_now - timedelta(days=37 - i)
+        null_count = normal_pattern[i % 3]
+        normal_data.extend(
+            [
+                {TIMESTAMP_COLUMN: date.strftime(DATE_FORMAT), "superhero": None}
+                for _ in range(null_count)
+            ]
+        )
+        normal_data.extend(
+            [
+                {
+                    TIMESTAMP_COLUMN: date.strftime(DATE_FORMAT),
+                    "superhero": "Superman" if i % 2 == 0 else "Batman",
+                }
+                for _ in range(40 - null_count)
+            ]
+        )
+
+    # Generate 7 days of anomalous data (20 nulls per day) - 100% increase from mean
+    anomalous_data = []
+    for i in range(7):
+        date = utc_now - timedelta(days=7 - i)
+        anomalous_data.extend(
+            [
+                {TIMESTAMP_COLUMN: date.strftime(DATE_FORMAT), "superhero": None}
+                for _ in range(20)
+            ]
+        )
+        anomalous_data.extend(
+            [
+                {
+                    TIMESTAMP_COLUMN: date.strftime(DATE_FORMAT),
+                    "superhero": "Superman" if i % 2 == 0 else "Batman",
+                }
+                for _ in range(20)
+            ]
+        )
+
+    all_data = normal_data + anomalous_data
+
+    test_args = {
+        "timestamp_column": TIMESTAMP_COLUMN,
+        "column_anomalies": ["null_count"],
+        "training_period": {"period": "day", "count": 30},
+        "detection_period": {"period": "day", "count": 7},
+        "time_bucket": {"period": "day", "count": 1},
+        "sensitivity": 5,
+    }
+
+    if exclude_detection:
+        test_args["exclude_detection_period_from_training"] = True
+
+    test_results = dbt_project.test(
+        test_id,
+        DBT_TEST_NAME,
+        test_args,
+        data=all_data,
+        multiple_results=True,
+    )
+
+    superhero_result = next(
+        (res for res in test_results if res["column_name"].lower() == "superhero"),
+        None,
+    )
+    assert superhero_result is not None, "superhero column result not found"
+    assert (
+        superhero_result["status"] == expected_status
+    ), f"Expected status '{expected_status}' but got '{superhero_result['status']}' (exclude_detection={exclude_detection})"