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? See Evaluation.
• API details? Browse API Reference.
• Contributing? Check Contributing and FAQ.

Quick inference

pip install flash-ansr
flash_ansr install psaegert/flash-ansr-v23.0-120M

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

# Import flash_ansr
from flash_ansr import (
  FlashANSR,
  SoftmaxSamplingConfig,
  install_model,
  get_path,
)

# Select a model from Hugging Face
# https://huggingface.co/models?search=flash-ansr-v23.0
MODEL = "psaegert/flash-ansr-v23.0-120M"

# Download the latest snapshot of the model
# By default, the model is downloaded to the directory `./models/` in the package root
install_model(MODEL)

# Load the model
model = FlashANSR.load(
  directory=get_path('models', MODEL),
  generation_config=SoftmaxSamplingConfig(choices=32),  # or BeamSearchConfig / MCTSGenerationConfig
  n_restarts=8,
).to(device)

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

# Fit the model to the data
model.fit(X, y, verbose=True)

# Show the best expression
print(model.get_expression())

# Predict with the best expression
y_pred = model.predict(X)

Serving these docs locally

pip install mkdocs mkdocs-material mkdocs-autorefs
mkdocs serve

Visit http://127.0.0.1:8000.