Module aivis_engine_v2_ad_sdk_python.inference

Classes

class AnomalyDetectionInference (handle, key)

Class for creating and handling anomaly detection inference. This is your entry point for all inference operations.

Private constructor. To create new instances use any of:

• AnomalyDetectionInference.create_by_training()
• AnomalyDetectionInference.create_by_model()

Static methods

def create_by_incremental_learning(cls, learning: AnomalyDetectionIncrementalLearning, config_json: str) ‑> AnomalyDetectionInference

@FlavourRequirement([Flavour.FULL])

Create anomaly detection inference for the given learning handle and config.

To compute anomaly scores with this inference instance use any of:

• AnomalyDetectionInference.infer_boolean
• AnomalyDetectionInference.infer_float
• AnomalyDetectionInference.infer_string

Parameters

learning : aivis_engine_v2_ad_sdk_python.learning.AnomalyDetectionIncrementalLearning

Instance of anomaly detection incremental learning

config_json : str

DtoInferenceConfig as JSON string

Returns

AnomalyDetectionInference

Instance of anomaly detection inference

def create_by_model(cls, model_json: str, config_json: str) ‑> AnomalyDetectionInference

@FlavourRequirement([Flavour.FULL, Flavour.INFERENCE])

Create anomaly detection inference for given model and config.

Use - AnomalyDetectionInference.infer() - `AnomalyDetectionInference.infer_with_next_normal' to compute anomaly scores with this inference instance.

Parameters

model_json : str

DtoModel as JSON string

config_json : str

DtoInferenceConfig as JSON string

Returns

AnomalyDetectionInference

Instance of anomaly detection inference

def create_by_training(cls, training: AnomalyDetectionTraining, config_json: str) ‑> AnomalyDetectionInference

@FlavourRequirement([Flavour.FULL])

Create anomaly detection inference for given training handle and config.

Use - AnomalyDetectionInference.infer() - `AnomalyDetectionInference.infer_with_next_normal' to compute anomaly scores with this inference instance.

Parameters

training : AnomalyDetectionTraining

Instance of anomaly detection training

config_json : str

DtoInferenceConfig as JSON string

Returns

AnomalyDetectionInference

Instance of anomaly detection inference

Methods

def destroy(self)

@FlavourRequirement([Flavour.FULL, Flavour.INFERENCE])

Destroy this anomaly detection inference. It's always safe to destroy an inference. Internally the destruction only takes place after all references to this object have been released.

def get_data_specification(self) ‑> str

@FlavourRequirement([Flavour.FULL, Flavour.INFERENCE])

Get this inference's DtoInferenceDataSpecification

Returns

str

DtoInferenceDataSpecification as JSON string

def infer(self, data: AnomalyDetectionData, timestamps: List[int]) ‑> List[DtoFloatDataPoint]

@FlavourRequirement([Flavour.FULL, Flavour.INFERENCE])

Calculate inference for given data and timestamps. The result will be a list of DtoFloatDataPoint instances corresponding to given timestamps. Make sure to fill the AnomalyDetectionData with all data needed for calculation. Check the model's inference data specification for needed signals and time ranges.

Parameters

data :  AnomalyDetectionData

Instance of anomaly detection data

timestamps : List[int]

List of timestamps

Returns

List[DtoFloatDataPoint]

List of float data points

def infer_with_next_normal(self, data: AnomalyDetectionData, timestamps: List[int], config_json: str) ‑> List[DtoFloatDataPointWithNextNormal]

@FlavourRequirement([Flavour.FULL, Flavour.INFERENCE])

For given data, timestamps and next normal config, calculate the inference and the closest point that is normal as defined by the normal config. The result will be a list of DtoFloatDataPointWithNextNormal instances corresponding to given timestamps. Make sure to fill the AnomalyDetectionData with all data needed for calculation. Check the model's inference data specification for needed signals and time ranges.

@experimental: Might change in future releases. See the user guide for known performance issues.

Parameters

data :  AnomalyDetectionData

Instance of anomaly detection data

timestamps : List[int]

List of timestamps

config_json : str

DtoNextNormalConfig as JSON string

Returns

List[DtoFloatDataPointWithNextNormal]

List of enhanced float data points