treeffuser 0.1.3

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

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

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

Usage Example

Here’s how you can use Treeffuser in your project:

from treeffuser import Treeffuser
import numpy as np

# (n_training, n_features), (n_training, n_targets)
X, y = ...  # load your data
# (n_test, n_features)
X_test = ...  # load your test data

# Estimate p(y|x) with a tree-based diffusion model
model = Treeffuser()
model.fit(X, y)

# Draw samples y ~ p(y|x) for each test point
# (n_samples, n_test, n_targets)
y_samples = model.sample(X_test, n_samples=1000)

# Compute downstream metrics
mean = np.mean(y_samples, axis=0)
std = np.std(y_samples, axis=0)
median = np.median(y_samples, axis=0)
quantile = np.quantile(y_samples, q=0 axis=0)
... # other metrics

Please refer to the docstrings for more information on the available methods and parameters.

Installation

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

pip install treeffuser

You can also install the in-development version with:

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