diff --git a/AUTHORS.md b/AUTHORS.md
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@@ -0,0 +1,5 @@
+# Authors
+
+- [Nicolas Beltran](https://github.com/velezbeltran)
+- [Alessandro Grande](https://github.com/aagrande)
+- [Achille Nazaret](https://github.com/ANazaret)
diff --git a/AUTHORS.rst b/AUTHORS.rst
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--- a/AUTHORS.rst
+++ /dev/null
@@ -1,7 +0,0 @@
-
-Authors
-=======
-
-* Nicolas Beltran - https://github.com/velezbeltran
-* Alessandro Grande - https://github.com/aagrande
-* Achille Nazaret - https://github.com/ANazaret
diff --git a/README.md b/README.md
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+
+# Treeffuser
+
+[](https://badge.fury.io/py/treeffuser)
+[](https://opensource.org/licenses/MIT)
+[](https://github.com/blei-lab/treeffuser/stargazers)
+[](https://pypi.org/project/treeffuser/)
+[](https://blei-lab.github.io/treeffuser/)
+[](https://blei-lab.github.io/treeffuser/docs/getting-started.html)
+[](https://arxiv.org/abs/2406.07658)
+
+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.
+
+
+
+
+## Installation
+
+Install Treeffuser from PyPI:
+
+```bash
+pip install treeffuser
+```
+
+Install the development version:
+
+```bash
+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.
+
+```python
+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.
+
+```python
+# 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:
+
+```python
+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:
+
+```python
+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.
+
+---
+
+## Citing Treeffuser
+
+If you use Treeffuser in your work, please cite:
+
+```bibtex
+@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},
+}
+```
diff --git a/README.rst b/README.rst
deleted file mode 100644
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--- a/README.rst
+++ /dev/null
@@ -1,144 +0,0 @@
-====================
-Treeffuser
-====================
-
-.. raw:: html
-
-
-
- 
-
-
- 
-
-
-
- 
-
-
- 
-
-
-
- 
-
-
- 
-
-
-
- 
-
-
-
-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.
-
-.. raw:: html
-
-
-
-
-
-Installation
-============
-
-You can install Treeffuser via pip from PyPI with the following command:
-
-.. code-block:: bash
-
- pip install treeffuser
-
-You can also install the development version with:
-
-.. code-block:: bash
-
- 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 an heteroscedastic response with two sinusoidal components and heavy tails.
-
-.. code-block:: python
-
- 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.
-
-.. code-block:: python
-
- # 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")
-
-.. image:: README_example.png
- :alt: Treeffuser on heteroscedastic data with sinuisodal response and heavy tails.
- :align: center
-
-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.
-
-.. code-block:: python
-
- 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.
-
-.. code-block:: python
-
- 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 in your work, please cite the following paper:
-
-.. code-block:: bibtex
-
- @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},
- }
diff --git a/pyproject.toml b/pyproject.toml
index 2ccd5584..f90f55cd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,14 +8,14 @@ authors = [
"Achille Nazaret ",
]
license = "MIT"
-readme = "README.rst"
+readme = "README.md"
packages = [{include = "treeffuser", from= "src"}]
homepage = "https://blei-lab.github.io/treeffuser/"
repository = "https://github.com/blei-lab/treeffuser"
include = [
"pyproject.toml",
- "AUTHORS.rst",
- "README.rst",
+ "AUTHORS.md",
+ "README.md",
"LICENSE"
]