Planner

Observes the situation for possible new predictions and based on them chooses whether the drift is actually valid. If it’s a real drift, it should be sent to the Executor. A custom Predictor can be done for that too, like a voting one or a more time-consuming algorithm from Scikit-Learn, TensorFlow, PyTorch, or others.

Planner Predictor

class driftage.planner.predictor.PlannerPredictor(connection: driftage.db.connection.Connection)

Predictor base class for Concept Drift detection.

Parameters

connection (Connection) – Knowledge Based connection

abstract async predict() → List[driftage.planner.predictor.PredictResult]

Using data stored on KB predicts if this data is a Concept Drift of not, to then send or not to the Executor.

Raises

NotImplementedError – Needs to be implemented when overridden

Returns

Results predicted that should or shouldn’t be sent to the Executor

Return type

List[PredictResult]

abstract property predict_period

Predict time period (in seconds). This property defines how long PlannerPredictor will wait until next predict call.

Raises

NotImplementedError – Needs to be implemented when overridden

Returns

Time it takes to wait for predict in seconds

Return type

Union[float, int]

class driftage.planner.predictor.PredictResult(identifier: str, predicted: Union[bool, str, int, float], should_send: bool)

Dataclass to store each prediction, result and if this prediction should be sent to Executor.

Parameters

• identifier (str) – Data identifier that comes from Monitor or any identifier you want.

• predicted (Union[bool, str, int, float]) – Value predicted from Drift detection algorithm. This can even inform type of drift to Executor.

• should_send (bool) – If this prediction should be sent to Executor. Sometimes your Planner can decide to not send it because of time or other business rules.

identifier: str = None

predicted: Union[bool, str, int, float] = None

should_send: bool = None