Merge branch 'main' into achille/prepare_release

blei-lab · Jun 11, 2024 · 488b472 · 488b472
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diff --git a/.editorconfig b/.editorconfig
diff --git a/.readthedocs.yml b/.readthedocs.yml
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
@@ -2,7 +2,7 @@
 Changelog
 =========
 
-0.0.0 (2024-02-12)
+0.0.0 (2024-06-11)
 ------------------
 
 * First release on PyPI.
diff --git a/README.rst b/README.rst
@@ -1,52 +1,52 @@
-========
-Overview
-========
+====================
+Treeffuser
+====================
 
-Diffusion but 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).
 
-* Free software: MIT license
+It is designed to adhere closely to the scikit-learn API and requires minimal user tuning.
 
-Installation
-============
-
-::
-
-    pip install treeffuser
-
-You can also install the in-development version with::
+Usage Example
+-------------
 
-    pip install git+ssh://git@https://github.com/blei-lab/tree-diffuser/blei-lab/treeffuser.git@main
+Here's how you can use Treeffuser in your project:
 
-Documentation
-=============
+.. code-block:: python
 
+    from treeffuser import LightGBMTreeffuser
+    import numpy as np
 
-https://treeffuser.readthedocs.io/
+    # (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 = LightGBMTreeffuser()
+    model.fit(X, y)
 
-Development
-===========
+    # 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)
 
-To run all the tests run::
+    # 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
 
-    tox
+Please refer to the docstrings for more information on the available methods and parameters.
 
-However, this is usually excessive so it is easier to use pytest with
-your environment. When you push tox will run automatically.
-
-Note, to combine the coverage data from all the tox environments run:
-
-.. list-table::
-    :widths: 10 90
-    :stub-columns: 1
+Installation
+============
 
-    - - Windows
-      - ::
+You can install Treeffuser via pip from PyPI with the following command::
 
-            set PYTEST_ADDOPTS=--cov-append
-            tox
+    pip install treeffuser
 
-    - - Other
-      - ::
+You can also install the in-development version with::
 
-            PYTEST_ADDOPTS=--cov-append tox
+    pip install git+https://github.com/blei-lab/tree-diffuser.git@main
diff --git a/docs/authors.rst b/docs/authors.rst
diff --git a/docs/changelog.rst b/docs/changelog.rst
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