UnivariateEntireDetectionResult Class
The response of entire anomaly detection.
All required parameters must be populated in order to send to Azure.
- Inheritance
-
azure.ai.anomalydetector._model_base.ModelUnivariateEntireDetectionResult
Constructor
UnivariateEntireDetectionResult(*args: Any, **kwargs: Any)
Variables
Name | Description |
---|---|
period
|
Frequency extracted from the series, zero means no recurrent pattern has been found. Required. |
expected_values
|
ExpectedValues contain expected value for each input point. The index of the array is consistent with the input series. Required. |
upper_margins
|
UpperMargins contain upper margin of each input point. UpperMargin is used to calculate upperBoundary, which equals to expectedValue + (100 - marginScale)*upperMargin. Anomalies in response can be filtered by upperBoundary and lowerBoundary. By adjusting marginScale value, less significant anomalies can be filtered in client side. The index of the array is consistent with the input series. Required. |
lower_margins
|
LowerMargins contain lower margin of each input point. LowerMargin is used to calculate lowerBoundary, which equals to expectedValue - (100 - marginScale)*lowerMargin. Points between the boundary can be marked as normal ones in client side. The index of the array is consistent with the input series. Required. |
is_anomaly
|
IsAnomaly contains anomaly properties for each input point. True means an anomaly either negative or positive has been detected. The index of the array is consistent with the input series. Required. |
is_negative_anomaly
|
IsNegativeAnomaly contains anomaly status in negative direction for each input point. True means a negative anomaly has been detected. A negative anomaly means the point is detected as an anomaly and its real value is smaller than the expected one. The index of the array is consistent with the input series. Required. |
is_positive_anomaly
|
IsPositiveAnomaly contain anomaly status in positive direction for each input point. True means a positive anomaly has been detected. A positive anomaly means the point is detected as an anomaly and its real value is larger than the expected one. The index of the array is consistent with the input series. Required. |
severity
|
The severity score for each input point. The larger the value is, the more sever the anomaly is. For normal points, the "severity" is always 0. |
Methods
clear | |
copy | |
get | |
items | |
keys | |
pop | |
popitem | |
setdefault | |
update | |
values |
clear
clear() -> None
copy
copy()
get
get(key: str, default: Any = None) -> Any
Parameters
Name | Description |
---|---|
key
Required
|
|
default
|
Default value: None
|
items
items() -> ItemsView
keys
keys() -> KeysView
pop
pop(key: ~typing.Any, default: ~typing.Any = <object object>) -> Any
Parameters
Name | Description |
---|---|
key
Required
|
|
default
|
|
popitem
popitem() -> Tuple[str, Any]
setdefault
setdefault(key: ~typing.Any, default: ~typing.Any = <object object>) -> Any
Parameters
Name | Description |
---|---|
key
Required
|
|
default
|
|
update
update(*args: Any, **kwargs: Any) -> None
values
values() -> ValuesView
Attributes
expected_values
ExpectedValues contain expected value for each input point. The index of the array is consistent with the input series. Required.
expected_values: List[float]
is_anomaly
IsAnomaly contains anomaly properties for each input point. True means an anomaly either negative or positive has been detected. The index of the array is consistent with the input series. Required.
is_anomaly: List[bool]
is_negative_anomaly
IsNegativeAnomaly contains anomaly status in negative direction for each input point. True means a negative anomaly has been detected. A negative anomaly means the point is detected as an anomaly and its real value is smaller than the expected one. The index of the array is consistent with the input series. Required.
is_negative_anomaly: List[bool]
is_positive_anomaly
IsPositiveAnomaly contain anomaly status in positive direction for each input point. True means a positive anomaly has been detected. A positive anomaly means the point is detected as an anomaly and its real value is larger than the expected one. The index of the array is consistent with the input series. Required.
is_positive_anomaly: List[bool]
lower_margins
LowerMargins contain lower margin of each input point. LowerMargin is used to calculate lowerBoundary, which equals to expectedValue - (100 - marginScale)*lowerMargin. Points between the boundary can be marked as normal ones in client side. The index of the array is consistent with the input series. Required.
lower_margins: List[float]
period
Frequency extracted from the series, zero means no recurrent pattern has been found. Required.
period: int
severity
The severity score for each input point. The larger the value is, the more sever the anomaly is. For normal points, the "severity" is always 0.
severity: List[float] | None
upper_margins
UpperMargins contain upper margin of each input point. UpperMargin is used to calculate upperBoundary, which equals to expectedValue + (100 - marginScale)*upperMargin. Anomalies in response can be filtered by upperBoundary and lowerBoundary. By adjusting marginScale value, less significant anomalies can be filtered in client side. The index of the array is consistent with the input series. Required.
upper_margins: List[float]
Azure SDK for Python