DOC update document references & include summary for papers at home page

README.md

@@ -60,18 +60,18 @@ We use the ``spin`` CLI to abstract away build details:
 
     # run the build using Meson/Ninja
     ./spin build
-    
+
     # you can run the following command to see what other options there are
     ./spin --help
     ./spin build --help
-    
+
     # For example, you might want to start from a clean build
     ./spin build --clean
-    
+
     # or build in parallel for faster builds
     ./spin build -j 2
 
-    # you will need to double check the build-install has the proper path 
+    # you will need to double check the build-install has the proper path
     # this might be different from machine to machine
     export PYTHONPATH=${PWD}/build-install/usr/lib/python3.9/site-packages
 
@@ -105,4 +105,4 @@ Alternatively, you can use editable installs
 
 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)
+[1]: [`Li, Adam, et al. "Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks" SIAM Journal on Mathematics of Data Science, 5(1), 77-96, 2023`](https://doi.org/10.1137/21M1449117)

docs/api.rst

@@ -54,15 +54,15 @@ on unsupervised criterion such as variance and BIC.
 .. currentmodule:: sktree
 .. autosummary::
    :toctree: generated/
-   
+
    UnsupervisedRandomForest
    UnsupervisedObliqueRandomForest
 
 The trees that comprise those forests are also available as standalone classes.
 
 .. autosummary::
    :toctree: generated/
-   
+
    tree.UnsupervisedDecisionTree
    tree.UnsupervisedObliqueDecisionTree
 
@@ -84,7 +84,7 @@ provide a natural way to compute neighbors based on the splits. We provide
 an API for extracting the nearest neighbors from a tree-model. This provides
 an API-like interface similar to :class:`~sklearn.neighbors.NearestNeighbors`.
 
-.. currentmodule:: sktree
+.. currentmodule:: sktree.neighbors
 .. autosummary::
    :toctree: generated/
 
@@ -120,4 +120,4 @@ for the entropy, MI and CMI of the Gaussian distributions.
 
    mi_gaussian
    cmi_gaussian
-   entropy_gaussian
+   entropy_gaussian

docs/index.rst

@@ -4,12 +4,38 @@ Scikit-tree is a package for modern tree-based algorithms for supervised and uns
 learning problems. It extends the robust API of `scikit-learn <https://github.com/scikit-learn/scikit-learn>`_
 for tree algorithms that achieve strong performance in benchmark tasks.
 
+Our package has implemented unsupervised forests (Geodesic Forests
+[Madhyastha2020]_), oblique random forests (SPORF [Tomita2020]_ and
+MORF [Li2023]_), and honest forests [Perry2021]_.
+In the near future, we also plan to include extended isolation forests
+and stream decision forests [Xu2022]_.
+
 We encourage you to use the package for your research and also build on top
 with relevant Pull Requests. See our examples for walk-throughs of how to use the package.
 Also, see our `contributing guide <https://github.com/neurodata/scikit-tree/blob/main/CONTRIBUTING.md>`_.
 
 We are licensed under BSD-3 (see `License <https://github.com/neurodata/scikit-tree/blob/main/LICENSE>`_).
 
+.. topic:: References
+
+ .. [Madhyastha2020] Madhyastha, Meghana, et al. :doi:`"Geodesic Forests"
+    <10.1145/3394486.3403094>`, KDD 2020, 513-523, 2020.
+
+ .. [Tomita2020] Tomita, Tyler M., et al. "Sparse Projection Oblique
+    Randomer Forests", The Journal of Machine Learning Research, 21(104),
+    1-39, 2020.
+
+ .. [Li2023] Li, Adam, et al. :doi:`"Manifold Oblique Random Forests: Towards
+    Closing the Gap on Convolutional Deep Networks" <10.1137/21M1449117>`,
+    SIAM Journal on Mathematics of Data Science, 5(1), 77-96, 2023.
+
+ .. [Perry2021] Perry, Ronan, et al. :arxiv:`"Random Forests for Adaptive
+    Nearest Neighbor Estimation of Information-Theoretic Quantities"
+    <1907.00325>`, arXiv preprint arXiv:1907.00325, 2021.
+
+ .. [Xu2022] Xu, Haoyin, et al. :arxiv:`"Simplest Streaming Trees"
+    <2110.08483>`, arXiv preprint arXiv:2110.08483, 2022.
+
 Contents
 --------
 

docs/install.rst

@@ -21,7 +21,7 @@ Installing with ``pip``
 
 .. code-block:: bash
 
-    pip install scikit-tree
+    pip install sktree
 
 Installing from source with Meson
 ---------------------------------
@@ -43,12 +43,12 @@ Then run installation of build packages
 
     pip install -r build_requirements.txt
     pip install spin
-    
+
     # use spin CLI to run Meson build locally
     ./spin build -j 2
 
     # you can now run tests
-    ./spin test 
+    ./spin test
 
 via pip, you will be able to install in editable mode (pending Meson-Python support).
 
@@ -70,7 +70,7 @@ First, create a virtual environment using Conda.
     conda create -n sklearn-dev python=3.9
 
 # activate the virtual environment and install necessary packages to build from source
-    
+
     conda activate sklearn-dev
     conda install -c conda-forge numpy scipy cython joblib threadpoolctl pytest compilers llvm-openmp poetry
 

docs/modules/ensemble.rst

@@ -27,9 +27,9 @@ more information and intuition, see
 
 .. topic:: References
 
- .. [B2001] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
+ .. [B2001] Breiman, L. "Random Forests", Machine Learning, 45(1), 5-32, 2001.
 
- * P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized
+ .. [G2006] Geurts, P. and Ernst., D. and Wehenkel, L. "Extremely randomized
    trees", Machine Learning, 63(1), 3-42, 2006.
 
 .. _oblique_forest_feature_importance:
@@ -52,8 +52,8 @@ By **averaging** the estimates of predictive ability over several randomized
 trees one can **reduce the variance** of such an estimate and use it
 for feature selection. This is known as the mean decrease in impurity, or MDI.
 Refer to [L2014]_ for more information on MDI and feature importance
-evaluation with Random Forests. We implement the approach taken in [Li2019]_
-and [Tomita2015]_.
+evaluation with Random Forests. We implement the approach taken in [Li2023]_
+and [Tomita2020]_.
 
 .. warning::
 
@@ -76,12 +76,14 @@ to the prediction function.
 
 .. topic:: References
 
- .. [L2014] G. Louppe, :arxiv:`"Understanding Random Forests: From Theory to
+ .. [L2014] Louppe, G. :arxiv:`"Understanding Random Forests: From Theory to
     Practice" <1407.7502>`,
     PhD Thesis, U. of Liege, 2014.
 
- .. [Li2019] Li, Adam, et al. :arxiv:`"Manifold Oblique Random Forests: Towards
-    Closing the Gap on Convolutional Deep Networks."` arXiv preprint arXiv:1909.11799 (2019).
+ .. [Li2023] Li, Adam, et al. :doi:`"Manifold Oblique Random Forests: Towards
+    Closing the Gap on Convolutional Deep Networks" <10.1137/21M1449117>`,
+    SIAM Journal on Mathematics of Data Science, 5(1), 77-96, 2023.
 
- .. [Tomita2015] Tomita, Tyler M., et al. :arxiv:`"Sparse Projection Oblique Randomer Forests."`
-    arXiv preprint arXiv:1506.03410 (2015).
+ .. [Tomita2020] Tomita, Tyler M., et al. "Sparse Projection Oblique
+    Randomer Forests", The Journal of Machine Learning Research, 21(104),
+    1-39, 2020.

docs/modules/unsupervised_tree.rst

@@ -6,9 +6,9 @@ Unsupervised Decision Trees
 
 .. currentmodule:: sklearn.tree
 
-In unsupervised learning, the goal is to identify patterns 
-or structure in data without using labeled examples. Clustering is a common 
-unsupervised learning technique that groups similar examples together 
+In unsupervised learning, the goal is to identify patterns
+or structure in data without using labeled examples. Clustering is a common
+unsupervised learning technique that groups similar examples together
 based on their features. Unsupervised tree models are an adaptive way of generating
 clusters of samples. For information on supervised tree models, see :ref:`supervised_tree`
 
@@ -29,7 +29,7 @@ The two means split finds the cutpoint that minimizes the one-dimensional
 2-means objective, which is finding the cutoff point where the total variance
 from cluster 1 and cluster 2 are minimal.
 
-.. math:: 
+.. math::
   \min_s \sum_{i=1}^s (x_i - \hat{\mu}_1)^2 + \sum_{i=s+1}^N (x_i - \hat{\mu}_2)^2
 
 where x is a N-dimensional feature vector, N is the number of sample_indices and
@@ -38,14 +38,15 @@ the \mu terms are the estimated means of each cluster 1 and 2.
 Fast-BIC
 ~~~~~~~~
 
-The Bayesian Information Criterion (BIC) is a popular model seleciton 
+The Bayesian Information Criterion (BIC) is a popular model seleciton
 criteria that is based on the log likelihood of the model given data.
-Fast-BIC is a method that combines the speed of the :class:`sklearn.cluster.KMeans` clustering 
-method with the model flexibility of Mclust-BIC. It sorts data for each 
-feature and tries all possible splits to assign data points to one of 
-two Gaussian distributions based on their position relative to the split.
-The parameters for each cluster are estimated using maximum likelihood 
-estimation (MLE).The method performs hard clustering rather than soft 
+Fast-BIC [Madhyastha2020]_ is a method that combines the speed of the
+:class:`sklearn.cluster.KMeans` clustering method with the model flexibility
+of Mclust-BIC. It sorts data for each feature and tries all possible splits to
+assign data points to one of two Gaussian distributions based on their position
+relative to the split.
+The parameters for each cluster are estimated using maximum likelihood
+estimation (MLE).The method performs hard clustering rather than soft
 clustering like in GMM, resulting in a simpler calculation of the likelihood.
 
 .. math::
@@ -63,9 +64,14 @@ where the prior, mean, and variance are defined as follows, respectively:
 
 .. _unsup_evaluation:
 
+.. topic:: References
+
+ .. [Madhyastha2020] Madhyastha, Meghana, et al. :doi:`"Geodesic Forests"
+    <10.1145/3394486.3403094>`, KDD 2020, 513-523, 2020.
+
 Evaluating Unsupervised Trees
 -----------------------------
 
-In clustering settings, there may be no natural 
-notion of “true” class-labels, thus the efficacy of the clustering scheme is 
+In clustering settings, there may be no natural
+notion of “true” class-labels, thus the efficacy of the clustering scheme is
 often measured based on distance based metrics such as :func:`sklearn.metrics.adjusted_rand_score`.

docs/references.bib

@@ -11,15 +11,19 @@ @article{breiman2001random
   publisher = {Springer}
 }
 
-@article{Li2019manifold,
-  title   = {Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks},
-  author  = {Li, Adam and Perry, Ronan and Huynh, Chester and Tomita, Tyler M and Mehta, Ronak and Arroyo, Jesus and Patsolic, Jesse and Falk, Benjamin and Vogelstein, Joshua T},
-  journal = {arXiv preprint arXiv:1909.11799},
-  year    = {2019}
+@article{Li2023manifold,
+  author = {Li, Adam and Perry, Ronan and Huynh, Chester and Tomita, Tyler M. and Mehta, Ronak and Arroyo, Jesus and Patsolic, Jesse and Falk, Ben and Sarma, Sridevi and Vogelstein, Joshua},
+  title = {Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks},
+  journal = {SIAM Journal on Mathematics of Data Science},
+  volume = {5},
+  number = {1},
+  pages = {77-96},
+  year = {2023},
+  doi = {10.1137/21M1449117},
 }
 
 @article{perry2021random,
-    title={Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities}, 
+    title={Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities},
     author={Ronan Perry and Ronak Mehta and Richard Guo and Eva Yezerets and Jesús Arroyo and Mike Powell and Hayden Helm and Cencheng Shen and Joshua T. Vogelstein},
     year={2021},
     eprint={1907.00325},
@@ -79,4 +83,4 @@ @article{Kraskov_2004
   note     = {Publisher: American Physical Society},
   pages    = {066138},
   file     = {APS Snapshot:/Users/adam2392/Zotero/storage/GRW23BYU/PhysRevE.69.html:text/html;Full Text PDF:/Users/adam2392/Zotero/storage/NJT9QCVA/Kraskov et al. - 2004 - Estimating mutual information.pdf:application/pdf}
-}
+}

sktree/ensemble/_supervised_forest.py

@@ -653,7 +653,7 @@ class PatchObliqueRandomForestClassifier(SimMatrixMixin, ForestClassifier):
     forest that fits a number of patch oblique decision tree classifiers
     on various sub-samples of the dataset and uses averaging to
     improve the predictive accuracy and control over-fitting. For more
-    details, see :footcite:`Li2019manifold`.
+    details, see :footcite:`Li2023manifold`.
 
     Parameters
     ----------
@@ -996,7 +996,7 @@ class PatchObliqueRandomForestRegressor(SimMatrixMixin, ForestRegressor):
     forest that fits a number of patch oblique decision tree regressors
     on various sub-samples of the dataset and uses averaging to
     improve the predictive accuracy and control over-fitting. For more
-    details, see :footcite:`Li2019manifold`.
+    details, see :footcite:`Li2023manifold`.
 
     Parameters
     ----------

sktree/tree/_classes.py

@@ -1301,7 +1301,7 @@ class PatchObliqueDecisionTreeClassifier(SimMatrixMixin, DecisionTreeClassifier)
     """A oblique decision tree classifier that operates over patches of data.
 
     A patch oblique decision tree is also known as a manifold oblique decision tree
-    (called MORF in :footcite:`Li2019manifold`), where the splitter is aware of
+    (called MORF in :footcite:`Li2023manifold`), where the splitter is aware of
     the structure in the data. For example, in an image, a patch would be contiguous
     along the rows and columns of the image. In a multivariate time-series, a patch
     would be contiguous over time, but possibly discontiguous over the sensors.
@@ -1791,7 +1791,7 @@ class PatchObliqueDecisionTreeRegressor(SimMatrixMixin, DecisionTreeRegressor):
     """A oblique decision tree regressor that operates over patches of data.
 
     A patch oblique decision tree is also known as a manifold oblique decision tree
-    (called MORF in :footcite:`Li2019manifold`), where the splitter is aware of
+    (called MORF in :footcite:`Li2023manifold`), where the splitter is aware of
     the structure in the data. For example, in an image, a patch would be contiguous
     along the rows and columns of the image. In a multivariate time-series, a patch
     would be contiguous over time, but possibly discontiguous over the sensors.