feat(simple_metrics): add threshold-based alerting via send_notification

Compares each computed metric against optional per-model thresholds
from model_config.simple_metrics_thresholds. Convention:
- {metric}_max: breach when value > threshold (rmse, mse, mae)
- {metric}_min: breach when value < threshold (r2)

Fires WARNING notification on breach. Missing thresholds = no alerting.
Schema designed to be extensible for Card 2 (Drift) thresholds.

SIENTIAPDE-1986

laborious/activities/model_metrics.py

@@ -429,4 +429,34 @@ class ModelMetrics(SientiaMonitoring):
 
         self._debug_dataframe(f'Simple metrics dataframe: Size {data.shape}', data, metadata)
 
+        # Threshold alerting (optional — no crash when absent)
+        thresholds = input_data.get('thresholds')
+        if thresholds:
+            # Convention: _max thresholds breach when value > threshold,
+            #             _min thresholds breach when value < threshold.
+            for row in output_data:
+                metric_name = row['metric']
+                value = row['value']
+                max_key = f'{metric_name}_max'
+                min_key = f'{metric_name}_min'
+
+                breach_msg = None
+                if max_key in thresholds and value > thresholds[max_key]:
+                    breach_msg = f'{metric_name}={value} exceeds {max_key}={thresholds[max_key]}'
+                elif min_key in thresholds and value < thresholds[min_key]:
+                    breach_msg = f'{metric_name}={value} below {min_key}={thresholds[min_key]}'
+
+                if breach_msg:
+                    self.warning(
+                        f'Threshold breach for model {model_id}: {breach_msg}',
+                        metadata,
+                    )
+                    self.send_notification(
+                        metadata=metadata,
+                        notification_id='SIMPLE_METRICS_THRESHOLD_BREACH',
+                        message=f'Threshold breach for model {model_id}: {breach_msg}',
+                        block='model_metrics',
+                        level=NotificationLevel.WARNING,
+                    )
+
         return data.to_dict(orient='records')

tests/laborious/activities/test_model_metrics.py

@@ -1037,3 +1037,135 @@ def test_calculate_simple_metrics_no_model_type_includes_r2(model_metrics_activi
 
     assert result['metric'].values[0] == 'r2'
     mock_class.is_r2_supported.assert_not_called()
+
+
+def test_calculate_simple_metrics_threshold_breach_rmse(model_metrics_activity):
+    """When rmse exceeds rmse_max, a WARNING notification fires."""
+    target_data = DataFrame(
+        {
+            'timestamp': ['2023-05-26 11:12:27', '2023-05-26 11:12:28'],
+            'target': [1.0, 2.0],
+            'prediction': [1.1, 2.1],
+        }
+    )
+
+    input_data = {
+        **metadata,
+        'model_id': 'test_model_id',
+        'target_data': target_data.to_dict(),
+        'metrics': ['rmse'],
+        'interval_minutes': 5,
+        'thresholds': {'rmse_max': 0.05},
+    }
+
+    patcher, mock_class, mock_instance = _mock_regression_metrics_class(
+        [
+            {'metric': 'rmse', 'value': 0.1},
+        ]
+    )
+
+    with patcher:
+        result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
+
+    assert len(result) == 1
+    model_metrics_activity.send_notification.assert_called_once()
+    call_kwargs = model_metrics_activity.send_notification.call_args.kwargs
+    assert call_kwargs['notification_id'] == 'SIMPLE_METRICS_THRESHOLD_BREACH'
+    assert call_kwargs['level'] == NotificationLevel.WARNING
+    assert 'rmse' in call_kwargs['message']
+
+
+def test_calculate_simple_metrics_threshold_no_breach(model_metrics_activity):
+    """When rmse is below rmse_max, no notification fires."""
+    target_data = DataFrame(
+        {
+            'timestamp': ['2023-05-26 11:12:27', '2023-05-26 11:12:28'],
+            'target': [1.0, 2.0],
+            'prediction': [1.1, 2.1],
+        }
+    )
+
+    input_data = {
+        **metadata,
+        'model_id': 'test_model_id',
+        'target_data': target_data.to_dict(),
+        'metrics': ['rmse'],
+        'interval_minutes': 5,
+        'thresholds': {'rmse_max': 1.0},
+    }
+
+    patcher, mock_class, mock_instance = _mock_regression_metrics_class(
+        [
+            {'metric': 'rmse', 'value': 0.1},
+        ]
+    )
+
+    with patcher:
+        model_metrics_activity.calculate_simple_metrics(input_data)
+
+    model_metrics_activity.send_notification.assert_not_called()
+
+
+def test_calculate_simple_metrics_threshold_r2_below_min(model_metrics_activity):
+    """When r2 drops below r2_min, a WARNING notification fires."""
+    target_data = DataFrame(
+        {
+            'timestamp': ['2023-05-26 11:12:27', '2023-05-26 11:12:28'],
+            'target': [1.0, 2.0],
+            'prediction': [1.1, 2.1],
+        }
+    )
+
+    input_data = {
+        **metadata,
+        'model_id': 'test_model_id',
+        'target_data': target_data.to_dict(),
+        'metrics': ['r2'],
+        'interval_minutes': 5,
+        'thresholds': {'r2_min': 0.95},
+    }
+
+    patcher, mock_class, mock_instance = _mock_regression_metrics_class(
+        [
+            {'metric': 'r2', 'value': 0.8},
+        ]
+    )
+
+    with patcher:
+        model_metrics_activity.calculate_simple_metrics(input_data)
+
+    model_metrics_activity.send_notification.assert_called_once()
+    call_kwargs = model_metrics_activity.send_notification.call_args.kwargs
+    assert 'r2' in call_kwargs['message']
+
+
+def test_calculate_simple_metrics_no_thresholds_no_alert(model_metrics_activity):
+    """When thresholds is None (not configured), no alerting, no crash."""
+    target_data = DataFrame(
+        {
+            'timestamp': ['2023-05-26 11:12:27', '2023-05-26 11:12:28'],
+            'target': [1.0, 2.0],
+            'prediction': [1.1, 2.1],
+        }
+    )
+
+    input_data = {
+        **metadata,
+        'model_id': 'test_model_id',
+        'target_data': target_data.to_dict(),
+        'metrics': ['rmse'],
+        'interval_minutes': 5,
+        # no thresholds key
+    }
+
+    patcher, mock_class, mock_instance = _mock_regression_metrics_class(
+        [
+            {'metric': 'rmse', 'value': 999.0},
+        ]
+    )
+
+    with patcher:
+        result = DataFrame(model_metrics_activity.calculate_simple_metrics(input_data))
+
+    assert len(result) == 1
+    model_metrics_activity.send_notification.assert_not_called()