blei-lab / treeffuser

Treeffuser is an easy-to-use package for probabilistic prediction on tabular data with tree-based diffusion models.

Treeffuser

Treeffuser is an easy-to-use package for probabilistic prediction on tabular data with tree-based diffusion models. It estimates distributions of the form p(y|x) where x is a feature vector and y is a target vector. Treeffuser can model conditional distributions p(y|x) that are arbitrarily complex (e.g., multimodal, heteroscedastic, non-gaussian, heavy-tailed, etc.).

It is designed to adhere closely to the scikit-learn API and require minimal user tuning.

Treeffuser is detailed in the paper: Treeffuser: Probabilistic Predictions via Conditional Diffusions with Gradient-Boosted Trees.

Installation

You can install Treeffuser via pip from PyPI with the following command:

pip install treeffuser

You can also install the development version with:

pip install git+https://github.com/blei-lab/tree-diffuser.git@main

Usage Example

Here's a simple example demonstrating how to use Treeffuser.

We generate an heteroscedastic response with two sinusoidal components and heavy tails.

import matplotlib.pyplot as plt
import numpy as np
from treeffuser import Treeffuser, Samples

# Generate data
seed = 0
rng = np.random.default_rng(seed=seed)
n = 5000
x = rng.uniform(0, 2 * np.pi, size=n)
z = rng.integers(0, 2, size=n)
y = z * np.sin(x - np.pi / 2) + (1 - z) * np.cos(x) + rng.laplace(scale=x / 30, size=n)

We fit Treeffuser and generate samples. We then plot the samples against the raw data.

# Fit the model
model = Treeffuser(seed=seed)
model.fit(x, y)

# Generate and plot samples
y_samples = model.sample(x, n_samples=1, seed=seed, verbose=True)
plt.scatter(x, y, s=1, label="observed data")
plt.scatter(x, y_samples[0, :], s=1, alpha=0.7, label="Treeffuser samples")

Treeffuser accurately learns the target conditional densities and can generate samples from them.

These samples can be used to compute any downstream estimates of interest.

y_samples = model.sample(x, n_samples=100, verbose=True) # y_samples.shape[0] is 100

# Estimate downstream quantities of interest
y_mean = y_samples.mean(axis=0) # conditional mean for each x
y_std = y_samples.std(axis=0) # conditional std for each x

For convenience, we also provide a class Samples that can estimate standard quantities.

y_samples = Samples(y_samples)
y_mean = y_samples.sample_mean() # same as before
y_std = y_samples.sample_std() # same as before
y_quantiles = y_samples.sample_quantile(q=[0.05, 0.95]) # conditional quantiles for each x

Please take a look at the documentation for more information on the available methods and parameters.

Citing Treeffuser

If you use Treeffuser or this codebase in your work, please cite the following paper:

@article{beltran2024treeffuser,
  title={Treeffuser: Probabilistic Predictions via Conditional Diffusions with Gradient-Boosted Trees},
  author={Beltran-Velez, Nicolas and Grande, Alessandro Antonio and Nazaret, Achille and Kucukelbir, Alp and Blei, David},
  journal={arXiv preprint arXiv:2406.07658},
  year={2024}
}