[ENH] MI and CMI simulation module and relevant estimators from the forest

.spin/cmds.py

@@ -80,7 +80,7 @@ def setup_submodule(forcesubmodule=False):
                 "submodule",
                 "update",
                 "--init",
-                "--force",
+                # "--force",
             ]
         )
 
@@ -111,6 +111,7 @@ def setup_submodule(forcesubmodule=False):
                 commit_fpath,
             ],
         )
+        print(commit_fpath)
         with open(commit_fpath, "w") as f:
             f.write(current_hash)
 

README.md

@@ -101,6 +101,10 @@ You can also do the same thing using Meson/Ninja itself. Run the following to bu
     python -c "from sktree import tree"
     python -c "import sklearn; print(sklearn.__version__);"
 
+Alternatively, you can use editable installs
+
+    pip install --no-build-isolation --editable .
+
 References
 ==========
 [1]: [`Li, Adam, et al. "Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks." arXiv preprint arXiv:1909.11799 (2019)`](https://arxiv.org/abs/1909.11799)

docs/api.rst

@@ -65,3 +65,40 @@ The trees that comprise those forests are also available as standalone classes.
 
    tree.UnsupervisedDecisionTree
    tree.UnsupervisedObliqueDecisionTree
+
+
+Distance Metrics
+----------------
+Trees inherently produce a "distance-like" metric. We provide an API for
+extracting pairwise distances from the trees that include a correction
+that turns the "tree-distance" into a proper distance metric.
+
+.. currentmodule:: sktree.ensemble
+.. autosummary::
+   :toctree: generated/
+
+   pairwise_forest_distance
+
+Experimental Functionality
+--------------------------
+We also include experimental functionality that is works in progress.
+
+.. currentmodule:: sktree.experimental
+.. autosummary::
+   :toctree: generated/
+
+   mutual_info_ksg
+
+We also include functions that help simulate and evaluate mutual information (MI)
+and conditional mutual information (CMI) estimators. Specifically, functions that
+help simulate multivariate gaussian data and compute the analytical solutions
+for the entropy, MI and CMI of the Gaussian distributions.
+
+.. currentmodule:: sktree.experimental
+.. autosummary::
+   :toctree: generated/
+
+   mi_gaussian
+   cmi_gaussian
+   entropy_gaussian
+   simulate_multivariate_gaussian

docs/whats_new/v0.1.rst

@@ -36,6 +36,8 @@ Changelog
 - |Feature| A general-kernel MORF is now implemented where users can pass in a kernel library, by `Adam Li`_ (:pr:`70`)
 - |Feature| Implementation of ObliqueDecisionTreeRegressor, PatchObliqueDecisionTreeRegressor, ObliqueRandomForestRegressor, PatchObliqueRandomForestRegressor, by `SUKI-O`_ (:pr:`72`)
 - |Feature| Implementation of HonestTreeClassifier, HonestForestClassifier, by `Sambit Panda`_, `Adam Li`_, `Ronan Perry`_ and `Haoyin Xu`_ (:pr:`57`)
+- |Feature| Implementation of (conditional) mutual information estimation via unsupervised tree models, by `Adam Li`_ (:pr:`47`)
+
 
 Code and Documentation Contributors
 -----------------------------------