active_learning¶

Active learning module with composable pool, strategies, and iterator.

Typical workflow:

from probly.evaluation.active_learning import (
    from_dataset, MarginSampling, active_learning_steps, compute_accuracy,
)

pool = from_dataset(x_train, y_train, x_test, y_test, initial_size=100, seed=42)
for state in active_learning_steps(pool, estimator, MarginSampling(), query_size=50):
    acc = compute_accuracy(state.estimator.predict(state.pool.x_test), state.pool.y_test)

Pass numpy arrays for a numpy-backed pipeline or torch tensors for a torch-backed pipeline. Each component dispatches independently on the array type it receives.

Submodules¶

`loop`	Active learning step iterator.
`metrics`	Evaluation metrics for active learning experiments.
`pool`	Active learning pool with backend dispatch for NumPy and PyTorch.
`strategies`	Active learning query strategies with backend dispatch for NumPy and PyTorch.