pdmlabs.experiment.streaming.semi_supervised_experiment

Classes

StreamingSemiSupervisedPdMExperiment(...[, ...])

Streaming (online) semi-supervised anomaly detection.

class pdmlabs.experiment.streaming.semi_supervised_experiment.StreamingSemiSupervisedPdMExperiment(experiment_name: str, pipeline: PdMPipeline, param_space: dict, constraint_function: Callable = None, target_data: list[DataFrame] = None, target_sources: list[str] = None, historic_data: list[DataFrame] = [], historic_sources: list[str] = [], optimization_param: str = 'AD1_AUC', initial_random: int = 2, num_iteration: int = 20, batch_size: int = 1, n_jobs: int = 1, random_state: int = 42, random_n_tries: int = 3, constraint_max_retries: int = 10, historic_data_header: str = 'infer', target_data_header: str = 'infer', artifacts: str = 'artifacts', debug: bool = False, delay: float = None, log_best_scores: bool = False, maximize: bool = True, custom_evaluators: list = None)

Bases: PdMExperiment

Streaming (online) semi-supervised anomaly detection.

Status: Experimental/Stub Implementation

This experiment flavor is designed for streaming data scenarios: - Processes data continuously as it arrives (row-by-row or in small batches) - Adapts models online without batch retraining - Produces predictions in real-time

Current Implementation: This is an early-stage stub that iterates over target data but does not yet implement full streaming evaluation logic. Use batch experiments for production.

Future Work: - Streaming parameter tuning - Online model adaptation - Concept drift detection - Memory-efficient processing

Raises:

NotImplementedError – Full streaming functionality not yet implemented.

Examples

>>> experiment = StreamingSemiSupervisedPdMExperiment(...)
>>> # Note: streaming experiments are currently stubs
>>> # Use batch experiments instead for now

execute() → None

Execute placeholder streaming experiment (not fully implemented).

Returns:

Streaming experiments are currently stubs.

Return type:

None