fix: Added additional filtering to dashboard variable in data quality so that only features from the specific monitoring schedule and endpoint are loaded

Author: Sushanth Sathish Kumar

src/sagemaker/dashboard/data_quality_dashboard.py

@@ -119,7 +119,8 @@ def _generate_variables(self):
                     inputType="select",
                     variable_id="Feature",
                     label="Feature",
-                    search=AutomaticDataQualityDashboard.DATA_QUALITY_METRICS_BATCH_NAMESPACE,
+                    search=self.DATA_QUALITY_METRICS_BATCH_NAMESPACE
+                    + f'MonitoringSchedule="{self.monitoring_schedule}" ',
                     populateFrom="Feature",
                 )
             ]
@@ -131,7 +132,9 @@ def _generate_variables(self):
                 inputType="select",
                 variable_id="Feature",
                 label="Feature",
-                search=AutomaticDataQualityDashboard.DATA_QUALITY_METRICS_ENDPOINT_NAMESPACE,
+                search=self.DATA_QUALITY_METRICS_ENDPOINT_NAMESPACE
+                + f'Endpoint="{self.endpoint}" '
+                + f'MonitoringSchedule="{self.monitoring_schedule}" ',
                 populateFrom="Feature",
             )
         ]

src/sagemaker/dashboard/model_quality_dashboard.py

@@ -135,15 +135,11 @@ def _generate_widgets(self):
         list_of_widgets = []
         metrics_to_graph = None
         if self.problem_type == "Regression":
-            metrics_to_graph = AutomaticModelQualityDashboard.REGRESSION_MODEL_QUALITY_METRICS
+            metrics_to_graph = self.REGRESSION_MODEL_QUALITY_METRICS
         elif self.problem_type == "BinaryClassification":
-            metrics_to_graph = (
-                AutomaticModelQualityDashboard.BINARY_CLASSIFICATION_MODEL_QUALITY_METRICS
-            )
+            metrics_to_graph = self.BINARY_CLASSIFICATION_MODEL_QUALITY_METRICS
         elif self.problem_type == "MulticlassClassification":
-            metrics_to_graph = (
-                AutomaticModelQualityDashboard.MULTICLASS_CLASSIFICATION_MODEL_QUALITY_METRICS
-            )
+            metrics_to_graph = self.MULTICLASS_CLASSIFICATION_MODEL_QUALITY_METRICS
         else:
             raise ValueError(
                 "Parameter problem_type is invalid. Valid options are "

tests/integ/test_model_monitor.py

@@ -961,6 +961,7 @@ def test_default_monitor_create_stop_and_start_monitoring_schedule_with_customiz
 
     _wait_for_schedule_changes_to_apply(monitor=my_default_monitor)
 
+
 @pytest.mark.skipif(
     tests.integ.test_region() in tests.integ.NO_MODEL_MONITORING_REGIONS,
     reason="ModelMonitoring is not yet supported in this region.",
@@ -1082,6 +1083,7 @@ def test_default_monitor_create_and_update_schedule_config_with_customizations(
 
     assert len(predictor.list_monitors()) > 0
 
+
 @pytest.mark.skipif(
     tests.integ.test_region() in tests.integ.NO_MODEL_MONITORING_REGIONS,
     reason="ModelMonitoring is not yet supported in this region.",

tests/integ/test_model_quality_monitor.py

@@ -175,6 +175,7 @@ def scheduled_model_quality_monitor(
     )
     return model_quality_monitor
 
+
 @pytest.fixture
 def scheduled_model_quality_monitor_with_dashboard(
     sagemaker_session, model_quality_monitor, endpoint_name, ground_truth_input
@@ -260,14 +261,17 @@ def test_model_quality_monitor(
 
     # delete schedule
     monitor.delete_monitoring_schedule()
-    
+
 
 @pytest.mark.skipif(
     tests.integ.test_region() in tests.integ.NO_MODEL_MONITORING_REGIONS,
     reason="ModelMonitoring is not yet supported in this region.",
 )
 def test_model_quality_monitor_with_dashboard(
-    sagemaker_session, scheduled_model_quality_monitor_with_dashboard, endpoint_name, ground_truth_input
+    sagemaker_session,
+    scheduled_model_quality_monitor_with_dashboard,
+    endpoint_name,
+    ground_truth_input,
 ):
     monitor = scheduled_model_quality_monitor_with_dashboard
     monitor._wait_for_schedule_changes_to_apply()
@@ -297,7 +301,7 @@ def test_model_quality_monitor_with_dashboard(
 
     # update schedule
     monitor.update_monitoring_schedule(
-        max_runtime_in_seconds=UPDATED_MAX_RUNTIME_IN_SECONDS, 
+        max_runtime_in_seconds=UPDATED_MAX_RUNTIME_IN_SECONDS,
         schedule_cron_expression=UPDATED_CRON,
         enable_automatic_dashboards=True,
     )