alf-tools

Ready-to-use models, datasets, and acquisition functions for ALF (Active Learning Framework).

alf-tools builds on alf-core to give you everything needed to run active-learning experiments out of the box — example datasets (GFP, ProteinGym, FLIP, GuacaMol), models (CNN, Gaussian Process, ESM-2, Chemprop), acquisition functions, and search strategies. Use it for quick-start and prototyping; reach for alf-core alone when you want the lightweight framework with no ML-framework dependencies.

Installation

pip install alf-tools

This installs PyTorch and the core dependencies. Some models need additional dependencies — see Optional Extras below.

Optional Extras

Some models require additional dependencies. Append one or more extras to the package name. Per-model extras (esm2, esmfold, chemprop, guacamol) install exactly one model's dependencies; workflow umbrellas (protein, molecule) group the extras you are likely to use together.

pip install "alf-tools[esm2]"      # ESM-2 protein language model (ESM2Model)
pip install "alf-tools[esmfold]"   # ESMFold structure-prediction oracle (ESMFoldModel)
pip install "alf-tools[chemprop]"  # Chemprop small-molecule MPNN (ChempropModel)
pip install "alf-tools[guacamol]"  # GuacaMol RDKit-based dataset/scoring
pip install "alf-tools[protein]"   # umbrella: esm2 + esmfold
pip install "alf-tools[molecule]"  # umbrella: chemprop + guacamol

Documentation

• Full documentation: instadeepai.github.io/alf
• API reference: alf-tools API
• Installation guide: instadeepai.github.io/alf/installation.html
• Core framework: alf-core
• Tutorials: tutorials/

What's included

alf-tools bundles example datasets (GFP, ProteinGym, FLIP, GuacaMol), surrogate and oracle models (CNN, Gaussian Process, ESM-2, ESMFold, Chemprop, PyRosetta, plus an ensemble wrapper), acquisition functions (Greedy, UCB, Expected Improvement, Thompson Sampling, CoreSet, and a BoTorch wrapper), and search strategies. See the API reference for the full catalogue and configuration options.

Quick example

from alf_core import (
    BaseDatasetConfig,
    DatasetSearch,
    DesignTask,
    Optimizer,
    Oracle,
    Surrogate,
    TerminalStateLogger,
)
from alf_tools.datasets import GFP
from alf_tools.models import CNNModel
from alf_tools.optimizer.acquisition_functions import Greedy

# Configure and load the dataset
config = BaseDatasetConfig(
    name="gfp",
    modality="sequence",
    seed=42,
    train_ratio=0.1,
    validation_frac=0.5,
    test_ratio=0.2,
    split_type="random",
    problem_type="regression",
)
dataset = GFP(config)
surrogate = Surrogate(model=CNNModel())
optimizer = Optimizer(acquisition_fn=Greedy(), search_fn=DatasetSearch())
oracle = Oracle(scorer=dataset)

# Run active learning
task = DesignTask(num_acq_rounds=5, acq_batch_size=100)
state = task.setup(dataset=dataset, surrogate=surrogate)
task.run(
    state=state,
    state_loggers=[TerminalStateLogger()],
    optimizer=optimizer,
    oracle=oracle,
)

Normalisation

Models support input normalisation and output standardisation through their train configs (BaseTrainConfig). GPModel enables both by default (minmax inputs, standardised outputs); the other models default to neither. See the API reference for per-model defaults and configuration.

Creating custom components

All components subclass the alf_core base classes (BaseDataset, BaseModel, AcquisitionFunction, BaseSearch). See the how-to guides for step-by-step instructions.

License

Apache License 2.0 — see LICENSE.