Add integration test for exclude_detection_period_from_training in freshness anomalies

Co-Authored-By: Yosef Arbiv <yosef.arbiv@gmail.com>

Author: devin-ai-integration[bot]

integration_tests/tests/test_freshness_anomalies.py

@@ -233,3 +233,77 @@ def test_first_metric_null(test_id, dbt_project: DbtProject):
             materialization="incremental",
         )
         assert result["status"] == "pass"
+
+
+# Test for exclude_detection_period_from_training functionality
+# This test demonstrates the use case where:
+# 1. Detection period contains anomalous freshness data that would normally be included in training
+# 2. With exclude_detection_period_from_training=False: anomaly is missed (test passes) because training includes the anomaly
+# 3. With exclude_detection_period_from_training=True: anomaly is detected (test fails) because training excludes the anomaly
+@pytest.mark.skip_targets(["clickhouse"])
+def test_exclude_detection_from_training(test_id: str, dbt_project: DbtProject):
+    """
+    Test the exclude_detection_period_from_training flag functionality for freshness anomalies.
+
+    Scenario:
+    - 30 days of normal data with consistent freshness (data arrives every 2 hours)
+    - 3 days of anomalous data (data arrives every 8 hours - slower/stale) in detection period
+    - Without exclusion: anomaly gets included in training baseline, test passes (misses anomaly)
+    - With exclusion: anomaly excluded from training, test fails (detects anomaly)
+    """
+    utc_now = datetime.utcnow()
+
+    # Generate 30 days of normal data with consistent freshness (every 2 hours)
+    normal_data = [
+        {TIMESTAMP_COLUMN: date.strftime(DATE_FORMAT)}
+        for date in generate_dates(
+            utc_now - timedelta(days=33), step=timedelta(hours=2), days_back=30
+        )
+    ]
+
+    anomalous_data = [
+        {TIMESTAMP_COLUMN: date.strftime(DATE_FORMAT)}
+        for date in generate_dates(utc_now, step=timedelta(hours=8), days_back=3)
+    ]
+
+    all_data = normal_data + anomalous_data
+
+    # Test 1: WITHOUT exclusion (should pass - misses the anomaly because it's included in training)
+    test_args_without_exclusion = {
+        "timestamp_column": TIMESTAMP_COLUMN,
+        "training_period": {"period": "day", "count": 30},
+        "detection_period": {"period": "day", "count": 3},
+        "time_bucket": {"period": "day", "count": 1},
+        "sensitivity": 5,  # Higher sensitivity to allow anomaly to be absorbed
+        # exclude_detection_period_from_training is not set (defaults to False/None)
+    }
+
+    test_result_without_exclusion = dbt_project.test(
+        test_id + "_without_exclusion",
+        TEST_NAME,
+        test_args_without_exclusion,
+        data=all_data,
+    )
+
+    # This should PASS because the anomaly is included in training, making it part of the baseline
+    assert (
+        test_result_without_exclusion["status"] == "pass"
+    ), "Test should pass when anomaly is included in training"
+
+    # Test 2: WITH exclusion (should fail - detects the anomaly because it's excluded from training)
+    test_args_with_exclusion = {
+        **test_args_without_exclusion,
+        "exclude_detection_period_from_training": True,
+    }
+
+    test_result_with_exclusion = dbt_project.test(
+        test_id + "_with_exclusion",
+        TEST_NAME,
+        test_args_with_exclusion,
+        data=all_data,
+    )
+
+    # This should FAIL because the anomaly is excluded from training, so it's detected as anomalous
+    assert (
+        test_result_with_exclusion["status"] == "fail"
+    ), "Test should fail when anomaly is excluded from training"