treeffuser 0.2.0

Probabilistic predictions for tabular data, using diffusion models and decision trees.

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.

Website | GitHub | Documentation | Paper (NeurIPS 2024)

Installation

Install Treeffuser from PyPI:

pip install treeffuser

Install the development version:

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

The GitHub repository is located at: https://github.com/blei-lab/treeffuser

Usage Example

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

We generate a 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
y_std = y_samples.std(axis=0)    # conditional std

You can also use the Samples helper class:

y_samples = Samples(y_samples)
y_mean = y_samples.sample_mean()
y_std = y_samples.sample_std()
y_quantiles = y_samples.sample_quantile(q=[0.05, 0.95])

See the documentation for more information on available methods and parameters.

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Citing Treeffuser

If you use Treeffuser in your work, please cite:

@article{beltranvelez2024treeffuser,
  title={Treeffuser: Probabilistic Predictions via Conditional Diffusions with Gradient-Boosted Trees},
  author={Nicolas Beltran-Velez and Alessandro Antonio Grande and Achille Nazaret and Alp Kucukelbir and David Blei},
  year={2024},
  eprint={2406.07658},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2406.07658},
}