Count, row count and timeliness anomaly rules refactored to use the n…

…ew version of the anomaly library function.

home/rules/percentile/anomaly_partition_row_count.py

@@ -99,30 +99,29 @@ def evaluate_rule(rule_parameters: RuleExecutionRunParameters) -> RuleExecutionR
  return RuleExecutionResult(None if rule_parameters.actual_value == filtered_median_float else False,
  filtered_median_float, filtered_median_float, filtered_median_float)
 
- degrees_of_freedom = float(rule_parameters.configuration_parameters.degrees_of_freedom)
- tail = rule_parameters.parameters.anomaly_percent / 100.0
+ tail = rule_parameters.parameters.anomaly_percent / 100.0 / 2.0
 
- upper_median_multiples_array = [(readout / filtered_median_float - 1.0) for readout in extracted
- if readout >= filtered_median_float]
- threshold_upper_multiple = detect_upper_bound_anomaly(values_above_median=upper_median_multiples_array,
- degrees_of_freedom=degrees_of_freedom, tail=tail)
+ scaled_multiples_array = [(readout / filtered_median_float - 1.0 if readout >= filtered_median_float else
+ (-1.0 / (readout / filtered_median_float)) + 1.0) for readout in extracted]
 
+ threshold_upper_multiple = detect_upper_bound_anomaly(values=scaled_multiples_array, median=0.0,
+ tail=tail, parameters=rule_parameters)
+
+ passed = True
  if threshold_upper_multiple is not None:
  threshold_upper = (threshold_upper_multiple + 1.0) * filtered_median_float
+ passed = rule_parameters.actual_value <= threshold_upper
  else:
- threshold_upper = rule_parameters.actual_value
+ threshold_upper = None
 
- lower_median_multiples_array = [(-1.0 / (readout / filtered_median_float)) for readout in extracted
- if readout <= filtered_median_float if readout != 0]
- threshold_lower_multiple = detect_lower_bound_anomaly(values_below_median=lower_median_multiples_array,
- degrees_of_freedom=degrees_of_freedom, tail=tail)
+ threshold_lower_multiple = detect_lower_bound_anomaly(values=scaled_multiples_array, median=0.0,
+ tail=tail, parameters=rule_parameters)
 
  if threshold_lower_multiple is not None:
- threshold_lower = filtered_median_float * (-1.0 / threshold_lower_multiple)
+ threshold_lower = filtered_median_float * (-1.0 / (threshold_lower_multiple - 1.0))
+ passed = passed and threshold_lower <= rule_parameters.actual_value
  else:
- threshold_lower = rule_parameters.actual_value
-
- passed = threshold_lower <= rule_parameters.actual_value <= threshold_upper
+ threshold_lower = None
 
  expected_value = filtered_median_float
  lower_bound = threshold_lower

home/rules/percentile/anomaly_stationary_count_values.py

@@ -102,29 +102,29 @@ def evaluate_rule(rule_parameters: RuleExecutionRunParameters) -> RuleExecutionR
  filtered_median_float, filtered_median_float if 0 not in all_extracted else 0,
  filtered_median_float)
 
- degrees_of_freedom = float(rule_parameters.configuration_parameters.degrees_of_freedom)
- tail = rule_parameters.parameters.anomaly_percent / 100.0
+ tail = rule_parameters.parameters.anomaly_percent / 100.0 / 2.0
 
- upper_median_multiples_array = [(readout / filtered_median_float - 1.0) for readout in extracted if readout >= filtered_median_float]
- threshold_upper_multiple = detect_upper_bound_anomaly(values_above_median=upper_median_multiples_array,
- degrees_of_freedom=degrees_of_freedom, tail=tail)
+ scaled_multiples_array = [(readout / filtered_median_float - 1.0 if readout >= filtered_median_float else
+ (-1.0 / (readout / filtered_median_float)) + 1.0) for readout in extracted]
 
+ threshold_upper_multiple = detect_upper_bound_anomaly(values=scaled_multiples_array, median=0.0,
+ tail=tail, parameters=rule_parameters)
+
+ passed = True
  if threshold_upper_multiple is not None:
  threshold_upper = (threshold_upper_multiple + 1.0) * filtered_median_float
+ passed = rule_parameters.actual_value <= threshold_upper
  else:
- threshold_upper = rule_parameters.actual_value
+ threshold_upper = None
 
- lower_median_multiples_array = [(-1.0 / (readout / filtered_median_float)) for readout in extracted
- if readout <= filtered_median_float if readout != 0]
- threshold_lower_multiple = detect_lower_bound_anomaly(values_below_median=lower_median_multiples_array,
- degrees_of_freedom=degrees_of_freedom, tail=tail)
+ threshold_lower_multiple = detect_lower_bound_anomaly(values=scaled_multiples_array, median=0.0,
+ tail=tail, parameters=rule_parameters)
 
  if threshold_lower_multiple is not None:
- threshold_lower = filtered_median_float * (-1.0 / threshold_lower_multiple)
+ threshold_lower = filtered_median_float * (-1.0 / (threshold_lower_multiple - 1.0))
+ passed = passed and threshold_lower <= rule_parameters.actual_value
  else:
- threshold_lower = rule_parameters.actual_value
-
- passed = threshold_lower <= rule_parameters.actual_value <= threshold_upper
+ threshold_lower = None
 
  expected_value = filtered_median_float
  lower_bound = threshold_lower

home/rules/percentile/anomaly_timeliness_delay.py

@@ -99,23 +99,19 @@ def evaluate_rule(rule_parameters: RuleExecutionRunParameters) -> RuleExecutionR
  return RuleExecutionResult(None if rule_parameters.actual_value == filtered_median_float else False,
  filtered_median_float, 0.0, filtered_median_float)
 
- degrees_of_freedom = float(rule_parameters.configuration_parameters.degrees_of_freedom)
  tail = rule_parameters.parameters.anomaly_percent / 100.0
 
- upper_median_multiples_array = [(readout / filtered_median_float - 1.0) for readout in extracted if readout >= filtered_median_float]
- threshold_upper_multiple = detect_upper_bound_anomaly(values_above_median=upper_median_multiples_array,
- degrees_of_freedom=degrees_of_freedom, tail=tail)
+ threshold_upper_multiple = detect_upper_bound_anomaly(values=extracted, median=filtered_median_float,
+ tail=tail, parameters=rule_parameters)
 
+ passed = True
  if threshold_upper_multiple is not None:
- threshold_upper = (threshold_upper_multiple + 1.0) * filtered_median_float
+ threshold_upper = threshold_upper_multiple
+ passed = rule_parameters.actual_value <= threshold_upper
  else:
- threshold_upper = rule_parameters.actual_value
-
- threshold_lower = 0.0 # always, our target is to have a delay of 0.0 days
-
- passed = threshold_lower <= rule_parameters.actual_value <= threshold_upper
+ threshold_upper = None
 
  expected_value = filtered_median_float
- lower_bound = threshold_lower
+ lower_bound = 0.0 # always, our target is to have a delay of 0.0 days
  upper_bound = threshold_upper
  return RuleExecutionResult(passed, expected_value, lower_bound, upper_bound)