Implement tests for IQR anomaly detection

Added tests for IqrAnomalyDetection and IqrAnomalyDetectionRollingWindow using Pytest.

Signed-off-by: Mehdi-kbz <141425685+Mehdi-kbz@users.noreply.github.com>

Author: Mehdi-kbz

tests/sdk/python/rtdip_sdk/pipelines/anomaly_detection/spark/test_iqr_anomaly_detection.py

@@ -0,0 +1,123 @@
+# Copyright 2025 RTDIP
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pytest
+
+from src.sdk.python.rtdip_sdk.pipelines.anomaly_detection.spark.iqr_anomaly_detection import (
+    IqrAnomalyDetection,
+    IqrAnomalyDetectionRollingWindow,
+)
+
+
+@pytest.fixture
+def spark_dataframe_with_anomalies(spark_session):
+    data = [
+        (1, 10.0),
+        (2, 12.0),
+        (3, 10.5),
+        (4, 11.0),
+        (5, 30.0),  # Anomalous value
+        (6, 10.2),
+        (7, 9.8),
+        (8, 10.1),
+        (9, 10.3),
+        (10, 10.0),
+    ]
+    columns = ["timestamp", "value"]
+    return spark_session.createDataFrame(data, columns)
+
+
+def test_iqr_anomaly_detection(spark_dataframe_with_anomalies):
+    iqr_detector = IqrAnomalyDetection()
+    result_df = iqr_detector.detect(spark_dataframe_with_anomalies)
+
+    # direct anomaly count check
+    assert result_df.count() == 1
+
+    row = result_df.collect()[0]
+
+    assert row["value"] == 30.0
+
+
+@pytest.fixture
+def spark_dataframe_with_anomalies_big(spark_session):
+    data = [
+        (1, 5.8),
+        (2, 6.6),
+        (3, 6.2),
+        (4, 7.5),
+        (5, 7.0),
+        (6, 8.3),
+        (7, 8.1),
+        (8, 9.7),
+        (9, 9.2),
+        (10, 10.5),
+        (11, 10.7),
+        (12, 11.4),
+        (13, 12.1),
+        (14, 11.6),
+        (15, 13.0),
+        (16, 13.6),
+        (17, 14.2),
+        (18, 14.8),
+        (19, 15.3),
+        (20, 15.0),
+        (21, 16.2),
+        (22, 16.8),
+        (23, 17.4),
+        (24, 18.1),
+        (25, 17.7),
+        (26, 18.9),
+        (27, 19.5),
+        (28, 19.2),
+        (29, 20.1),
+        (30, 20.7),
+        (31, 0.0),
+        (32, 21.5),
+        (33, 22.0),
+        (34, 22.9),
+        (35, 23.4),
+        (36, 30.0),
+        (37, 23.8),
+        (38, 24.9),
+        (39, 25.1),
+        (40, 26.0),
+        (41, 40.0),
+        (42, 26.5),
+        (43, 27.4),
+        (44, 28.0),
+        (45, 28.8),
+        (46, 29.1),
+        (47, 29.8),
+        (48, 30.5),
+        (49, 31.0),
+        (50, 31.6),
+    ]
+
+    columns = ["timestamp", "value"]
+    return spark_session.createDataFrame(data, columns)
+
+
+def test_iqr_anomaly_detection_rolling_window(spark_dataframe_with_anomalies_big):
+    # Using a smaller window size to detect anomalies in the larger dataset
+    iqr_detector = IqrAnomalyDetectionRollingWindow(window_size=15)
+    result_df = iqr_detector.detect(spark_dataframe_with_anomalies_big)
+
+    # assert all 3 anomalies are detected
+    assert result_df.count() == 3
+
+    # check that the detected anomalies are the expected ones
+    assert result_df.collect()[0]["value"] == 0.0
+    assert result_df.collect()[1]["value"] == 30.0
+    assert result_df.collect()[2]["value"] == 40.0