---

name: modal description: "Guides modAL-python active-learning workflows, query strategies, committees, Bayesian optimization, and optional deep integrations." disable-model-invocation: true metadata: disco-role: operating license: MIT

modAL repo skill

Use this skill when a task names modAL, modAL-python, ActiveLearner, Committee, BayesianOptimizer, active learning, uncertainty sampling, query by committee, ranked batch selection, expected error reduction, label-efficient model development, or optional modAL deep-learning integrations.

modAL is imported as modAL but installed from the package distribution modAL-python. It is a scikit-learn-oriented active-learning framework; most core workflows are CPU-only and do not require a GPU.

Start here

1. Read references/repo-provenance.md when deciding whether this skill matches a source checkout or package version.
2. Read references/package-overview.md for the public module map, dependency boundaries, and which optional integrations are intentionally not minimum installs.
3. If install/import or dependency compatibility is uncertain, read references/troubleshooting.md and run scripts/modal_environment_smoke.py.

Minimal package check for a user environment:

python -m pip install modAL-python
python - <<'PY'
from importlib.metadata import version
import modAL
from modAL.models import ActiveLearner, Committee, CommitteeRegressor, BayesianOptimizer
print("modAL-python", version("modAL-python"), "import ok")
PY

For the historical 0.4.2 code covered by this skill, modern dependency resolvers may pick versions that are too new. If imports fail with np.float, force_all_finite, or pkg_resources errors, use the compatibility guidance in references/troubleshooting.md.

Route by task

Task signal	Read
Pool-based or stream-based active learning, ActiveLearner.query, teach, fit, single-row shapes, only_new, bootstrap, on_transformed	sub-skills/learners-and-committees/SKILL.md
Query by committee, Committee, CommitteeRegressor, vote, vote_proba, predict_proba, rebag, regression standard deviation	sub-skills/learners-and-committees/SKILL.md
Choosing or composing uncertainty, disagreement, batch, density, expected-error, multilabel, selector, or custom query strategies	sub-skills/query-strategies/SKILL.md
BayesianOptimizer, PI/EI/UCB acquisition functions, bounded objective-evaluation loops, or get_max() tracking	sub-skills/bayesian-optimization/SKILL.md
DeepActiveLearner, skorch/PyTorch estimators, MC dropout, modAL.dropout, Keras/TensorFlow examples, or optional backend imports	sub-skills/deep-and-optional-integrations/SKILL.md

Operating rules

• Treat modAL strategies as callables. A learner's query method calls query_strategy(self, X_pool, **kwargs) and returns selected indices plus the selected rows; metrics are available only when the strategy returns them.
• For classical active learning, start with a fitted scikit-learn estimator or pass X_training and y_training so modAL can fit during learner initialization.
• Use probability-based strategies only with estimators that implement predict_proba; route regression uncertainty to regressors that provide predictive standard deviation or to committee-regressor disagreement.
• Preserve row shapes when teaching queried samples. For NumPy data, a single queried sample normally needs X_pool[idx].reshape(1, -1) and y_pool[idx].reshape(1,).
• Keep optional deep workflows explicit. The core modAL active-learning workflow is CPU-friendly; Keras/TensorFlow examples and CUDA-specific behavior require separate installation and smoke checks.
• Do not rely on original repository examples or tests at runtime. This skill bundles runnable smoke helpers under scripts/ and sub-skill scripts/ directories.

Bundled checks

Run these from any current working directory in an environment with modAL installed:

python path/to/modal/scripts/modal_environment_smoke.py
python path/to/modal/scripts/modal_environment_smoke.py --include-optional-deep

Sub-skills contain deeper workflow-specific smoke helpers:

• sub-skills/learners-and-committees/scripts/active_learning_smoke.py
• sub-skills/learners-and-committees/scripts/committee_smoke.py
• sub-skills/query-strategies/scripts/query_strategy_smoke.py
• sub-skills/bayesian-optimization/scripts/bayesian_optimizer_smoke.py
• sub-skills/deep-and-optional-integrations/scripts/dropout_inspection.py