Package rename.

Author: piotrczarnas

home/lib/anomalies/anomaly_detection.py

@@ -18,7 +18,7 @@
 from .data_types import RuleExecutionRunParameters, HistoricData
 
 try:
-    import rules.dqopspaid.anomaly_detection
+    import rules.dqopspaid.ai.anomaly_detection
 except ModuleNotFoundError:
     ai_module_present = False
 else:
@@ -85,7 +85,7 @@ def test_significance(values: list[float], parameters: RuleExecutionRunParameter
 def detect_upper_bound_anomaly(historic_data: HistoricData, median: float, tail: float,
                                parameters: RuleExecutionRunParameters):
     if hasattr(parameters.parameters, 'use_ai') and parameters.parameters.use_ai and ai_module_present:
-        return rules.dqopspaid.anomaly_detection.detect_upper_bound_anomaly(historic_data, median, tail, parameters)
+        return rules.dqopspaid.ai.anomaly_detection.detect_upper_bound_anomaly(historic_data, median, tail, parameters)
 
     values = historic_data['converted_values']
     values_above_median = [value for value in values if value is not None and value >= median]
@@ -107,7 +107,7 @@ def detect_upper_bound_anomaly(historic_data: HistoricData, median: float, tail:
 def detect_lower_bound_anomaly(historic_data: HistoricData, median: float, tail: float,
                                parameters: RuleExecutionRunParameters):
     if hasattr(parameters.parameters, 'use_ai') and parameters.parameters.use_ai and ai_module_present:
-        return rules.dqopspaid.anomaly_detection.detect_lower_bound_anomaly(historic_data, median, tail, parameters)
+        return rules.dqopspaid.ai.anomaly_detection.detect_lower_bound_anomaly(historic_data, median, tail, parameters)
 
     values = historic_data['converted_values']
     values_below_median = [value for value in values if value is not None and value <= median]