OpenOutLearn

A GP regression + BALD active-learning engine over text embeddings — the scoring mechanism shared by OpenOutFind's lead qualifier and OpenOutNews's article qualifier, extracted once both had converged on the same code under two different domain labels.

GPBaldQualifier fits an sklearn Pipeline (StandardScaler -> exact GaussianProcessRegressor) on whatever labelled embeddings you give it, and scores new candidates two ways: predict_probs (P(f > 0.5) from the GP posterior, for exploitation) and compute_bald (information gain via MC sampling, for exploration). acquisition_mode/acquisition_scores pick between the two by the current label balance — exploit once real negatives outnumber real positives, explore while the classes are still even.

This is a pure library: no store, no ORM, no CLI, no persistence beyond the fastembed model download. Each domain wires its own labels, its own store, and its own interactive surface on top — see openoutlearn/qualifier.py's docstring for what's deliberately not here (cold-start anchors, class balancing) and why those stay domain-side.

Install

pip install openoutlearn

Use

import numpy as np
from pathlib import Path
from openoutlearn import GPBaldQualifier, embed_texts

cache_dir = Path.home() / ".cache" / "openoutlearn" / "fastembed"
embeddings = embed_texts(["a labelled example", "another one"], cache_dir=cache_dir)

q = GPBaldQualifier(embedding_dim=384)
q.warm_start(embeddings, np.array([1, 0]))

candidates = embed_texts(["a new candidate"], cache_dir=cache_dir)
mode, scores = q.acquisition_scores(candidates)

Test

pip install -e ".[dev]"
pytest