Featune

Auditable feature search for tabular data. Featune proposes features through a constrained DSL, evaluates them with cross-validation, records each trial, and exports a fitted scikit-learn pipeline. Random and evolutionary search work without an LLM; Anthropic Messages and OpenAI Chat Completions are optional proposal sources.

Install

pip install featune

Python 3.10 or newer is required. The default model uses pinned TabPFN-2 weights downloaded on first use; the weights are not included in this package. Review NOTICE and the model licenses in LICENSES/ before using TabPFN. TabPFN-3.5 is an opt-in model with separate access and use restrictions.

Minimal example

import pandas as pd
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import featune

data, labels = make_classification(
    n_samples=120, n_features=2, n_informative=2, n_redundant=0, random_state=42
)
X = pd.DataFrame(data, columns=["signal_a", "signal_b"])
y = pd.Series(labels)
schema = featune.DatasetSchema(
    fields=[
        featune.FieldSchema(name="signal_a", description="First synthetic signal"),
        featune.FieldSchema(name="signal_b", description="Second synthetic signal"),
    ],
    objective="Predict a synthetic binary label",
)
train, test = train_test_split(range(len(X)), stratify=y, random_state=42)
study = featune.create_study(metric="auc", sampler=featune.RandomSampler(seed=42))
study.optimize(X.iloc[train], y.iloc[train], schema, evaluator=featune.CVEvaluator(cv=2), n_trials=1)
print(study.trials_dataframe())

Use a larger dataset and reserve an untouched test set for real evaluation. See the English notebook, Chinese notebook, and API guide. LLM credentials are read from FEATUNE_API_KEY; keep them outside source files and notebooks.

Featune source code is MIT licensed. TabPFN code and model weights have separate licenses; see the included notices. Generated pipelines may retain training data and should be handled accordingly.