Flash-ANSR

Flash Amortized Neural Symbolic Regression combines a SetTransformer encoder, Transformer decoder, and constant refiner to map tabular data to symbolic expressions. These docs are structured for first-time users and contributors.

• New here? Start with Getting Started.
• How does it work? Read Concepts & Architecture.
• Train or finetune? See Training.
• Benchmarks/baselines? Evaluation, baselines, and benchmarking live in the standalone srbf package (pip install srbf); see https://github.com/psaegert/srbf.
• API details? Browse API Reference.
• Contributing? Check Contributing and FAQ.

Quick inference

Requires Python >= 3.12.

pip install flash-ansr
flash_ansr install psaegert/flash-ansr-v25.0-T8-20M

import torch
from flash_ansr import FlashANSR, SoftmaxSamplingConfig, get_path

device = "cuda" if torch.cuda.is_available() else "cpu"

# The estimator's policy: the sampler (with its draw budget), the ranking, the compute
model = FlashANSR.load(
  directory=get_path("models", "psaegert/flash-ansr-v25.0-T8-20M"),
  generation_config=SoftmaxSamplingConfig(draws=1024),
  ranking="mdl",
  compute={"device": device},
)

# Define data
X = ...
y = ...

# One call: draw candidates, fit their constants, rank them
result = model.fit(X, y, verbose=True)

# The answer, and its evaluation on new data
print(result.best.expression_infix)
y_pred = model.predict(X)

fit returns a FitResult (the score-sorted refined candidates, the full classified ledger, the ranking, the timings) and keeps it as model.result_; predict, get_expression and results read it. See Getting Started.

Serving these docs locally

pip install -r docs/requirements.txt
mkdocs serve

Visit http://127.0.0.1:8000.