pdmlabs.experiment.batch.incremental_semi_supervised_experiment

pdmlabs.experiment.batch.incremental_semi_supervised_experiment#

Classes

IncrementalSemiSupervisedPdMExperiment([...])

Incremental semi-supervised anomaly detection with model retraining.
class pdmlabs.experiment.batch.incremental_semi_supervised_experiment.IncrementalSemiSupervisedPdMExperiment(refit_new_method_object: bool = True, refit_new_preprocessor_object: bool = True, *args, **kwargs)#

Bases: PdMExperiment

Incremental semi-supervised anomaly detection with model retraining.

This experiment flavor implements online/incremental learning scenarios: 1. Processes data incrementally over time (not all at once) 2. Optionally retrains method and preprocessor on newly observed data 3. Maintains model adaptation as new patterns emerge

Different from semi-supervised: - Processes scenarios in temporal segments (not monolithic) - Can retrain models as new data arrives - Simulates streaming/online scenarios while using batch methods

Useful for: - Detecting concept drift without full retraining - Systems where data arrives in batches - Simulating online learning behavior - Gradual performance monitoring

refit_new_method_object#

If True, creates fresh method for increments. If False, updates same method. Default True.

Type:

bool

refit_new_preprocessor_object#

If True, creates fresh preprocessor. If False, updates same preprocessor. Default True.

Type:

bool

Raises:

Examples

>>> experiment = IncrementalSemiSupervisedPdMExperiment(
...     experiment_name='incremental-demo',
...     pipeline=pipeline,
...     param_space={'method_alpha': [0.1, 1.0]},
...     refit_new_method_object=True,
...     num_iteration=20
... )
>>> results = experiment.execute()
execute() → dict#

Run incremental semi-supervised experiment with model retraining.

Processes data incrementally, optionally retraining models:

  1. For each parameter combination: a. For each target scenario’s temporal segments:

    • Fits method (optionally new instance)

    • Predicts anomaly scores

    • Applies postprocessor and thresholder

    • Evaluates current segment

    1. Aggregates across segments

  2. Returns best parameters found

Returns:

Result dictionary with best_params, best_objective, and threshold.

Return type:

dict

Raises:
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