Merge pull request #199 from JuliaAI/dev

For a 0.12.0 release

Project.toml

@@ -2,7 +2,7 @@ name = "DecisionTree"
 uuid = "7806a523-6efd-50cb-b5f6-3fa6f1930dbb"
 license = "MIT"
 desc = "Julia implementation of Decision Tree (CART) and Random Forest algorithms"
-version = "0.11.3"
+version = "0.12.0"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"

README.md

@@ -3,6 +3,7 @@
 [![CI](https://github.com/JuliaAI/DecisionTree.jl/workflows/CI/badge.svg)](https://github.com/JuliaAI/DecisionTree.jl/actions?query=workflow%3ACI)
 [![Codecov](https://codecov.io/gh/JuliaAI/DecisionTree.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/JuliaAI/DecisionTree.jl)
 [![Docs Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliahub.com/docs/DecisionTree/pEDeB/0.10.11/)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7359268.svg)](https://doi.org/10.5281/zenodo.7359268)
 
 Julia implementation of Decision Tree (CART) and Random Forest algorithms
 
@@ -44,11 +45,15 @@ Available models: `DecisionTreeClassifier, DecisionTreeRegressor, RandomForestCl
 See each model's help (eg. `?DecisionTreeRegressor` at the REPL) for more information
 
 ### Classification Example
+
 Load DecisionTree package
+
 ```julia
 using DecisionTree
 ```
+
 Separate Fisher's Iris dataset features and labels
+
 ```julia
 features, labels = load_data("iris")    # also see "adult" and "digits" datasets
 
@@ -57,7 +62,9 @@ features, labels = load_data("iris")    # also see "adult" and "digits" datasets
 features = float.(features)
 labels   = string.(labels)
 ```
+
 Pruned Tree Classifier
+
 ```julia
 # train depth-truncated classifier
 model = DecisionTreeClassifier(max_depth=2)
@@ -78,8 +85,11 @@ accuracy = cross_val_score(model, features, labels, cv=3)
 Also, have a look at these [classification](https://github.com/cstjean/ScikitLearn.jl/blob/master/examples/Classifier_Comparison_Julia.ipynb) and [regression](https://github.com/cstjean/ScikitLearn.jl/blob/master/examples/Decision_Tree_Regression_Julia.ipynb) notebooks.
 
 ## Native API
+
 ### Classification Example
+
 Decision Tree Classifier
+
 ```julia
 # train full-tree classifier
 model = build_tree(labels, features)
@@ -129,6 +139,7 @@ accuracy = nfoldCV_tree(labels, features,
                         rng = seed)
 ```
 Random Forest Classifier
+
 ```julia
 # train random forest classifier
 # using 2 random features, 10 trees, 0.5 portion of samples per tree, and a maximum tree depth of 6
@@ -176,7 +187,9 @@ accuracy = nfoldCV_forest(labels, features,
                           verbose = true,
                           rng = seed)
 ```
+
 Adaptive-Boosted Decision Stumps Classifier
+
 ```julia
 # train adaptive-boosted stumps, using 7 iterations
 model, coeffs = build_adaboost_stumps(labels, features, 7);
@@ -193,13 +206,15 @@ accuracy = nfoldCV_stumps(labels, features,
 ```
 
 ### Regression Example
+
 ```julia
 n, m = 10^3, 5
 features = randn(n, m)
 weights = rand(-2:2, m)
 labels = features * weights
 ```
 Regression Tree
+
 ```julia
 # train regression tree
 model = build_tree(labels, features)
@@ -238,7 +253,9 @@ r2 =  nfoldCV_tree(labels, features,
                    verbose = true,
                    rng = seed)
 ```
+
 Regression Random Forest
+
 ```julia
 # train regression forest, using 2 random features, 10 trees,
 # averaging of 5 samples per leaf, and 0.7 portion of samples per tree
@@ -285,6 +302,14 @@ r2 =  nfoldCV_forest(labels, features,
                      rng = seed)
 ```
 
+## Saving Models
+Models can be saved to disk and loaded back with the use of the [JLD2.jl](https://github.com/JuliaIO/JLD2.jl) package.
+```julia
+using JLD2
+@save "model_file.jld2" model
+```
+Note that even though features and labels of type `Array{Any}` are supported, it is highly recommended that data be cast to explicit types (ie with `float.(), string.()`, etc). This significantly improves model training and prediction execution times, and also drastically reduces the size of saved models.
+
 ## MLJ.jl API
 
 To use DecsionTree.jl models in
@@ -318,15 +343,6 @@ The following methods provide measures of feature importance for all models:
 `impurity_importance`, `split_importance`, `permutation_importance`. Query the document
 strings for details.
 
-
-## Saving Models
-Models can be saved to disk and loaded back with the use of the [JLD2.jl](https://github.com/JuliaIO/JLD2.jl) package.
-```julia
-using JLD2
-@save "model_file.jld2" model
-```
-Note that even though features and labels of type `Array{Any}` are supported, it is highly recommended that data be cast to explicit types (ie with `float.(), string.()`, etc). This significantly improves model training and prediction execution times, and also drastically reduces the size of saved models.
-
 ## Visualization
 A `DecisionTree` model can be visualized using the `print_tree`-function of its native interface
 (for an example see above in section 'Classification Example').
@@ -335,3 +351,34 @@ In addition, an abstraction layer using `AbstractTrees.jl` has been implemented
 
 Apart from this, `AbstractTrees.jl` brings its own implementation of `print_tree`. 
 
+
+## Citing the package in publications
+
+DOI: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7359268.svg)](https://doi.org/10.5281/zenodo.7359268).
+
+BibTeX entry:
+
+```
+@software{ben_sadeghi_2022_7359268,
+  author       = {Ben Sadeghi and
+                  Poom Chiarawongse and
+                  Kevin Squire and
+                  Daniel C. Jones and
+                  Andreas Noack and
+                  Cédric St-Jean and
+                  Rik Huijzer and
+                  Roland Schätzle and
+                  Ian Butterworth and
+                  Yu-Fong Peng and
+                  Anthony Blaom},
+  title        = {{DecisionTree.jl - A Julia implementation of the 
+                   CART Decision Tree and Random Forest algorithms}},
+  month        = nov,
+  year         = 2022,
+  publisher    = {Zenodo},
+  version      = {0.11.3},
+  doi          = {10.5281/zenodo.7359268},
+  url          = {https://doi.org/10.5281/zenodo.7359268}
+}
+```
+> 

src/classification/main.jl

@@ -370,12 +370,10 @@ function build_forest(
     loss = (ns, n) -> util.entropy(ns, n, entropy_terms)
 
     if rng isa Random.AbstractRNG
+        shared_seed = rand(rng, UInt)
         Threads.@threads for i in 1:n_trees
             # The Mersenne Twister (Julia's default) is not thread-safe.
-            _rng = copy(rng)
-            # Take some elements from the ring to have different states for each tree.  This
-            # is the only way given that only a `copy` can be expected to exist for RNGs.
-            rand(_rng, i)
+            _rng = Random.seed!(copy(rng), shared_seed + i)
             inds = rand(_rng, 1:t_samples, n_samples)
             forest[i] = build_tree(
                 labels[inds],

src/regression/main.jl

@@ -95,12 +95,10 @@ function build_forest(
     forest = impurity_importance ? Vector{Root{S, T}}(undef, n_trees) : Vector{LeafOrNode{S, T}}(undef, n_trees)
 
     if rng isa Random.AbstractRNG
+        shared_seed = rand(rng, UInt)
         Threads.@threads for i in 1:n_trees
             # The Mersenne Twister (Julia's default) is not thread-safe.
-            _rng = copy(rng)
-            # Take some elements from the ring to have different states for each tree.
-            # This is the only way given that only a `copy` can be expected to exist for RNGs.
-            rand(_rng, i)
+            _rng = Random.seed!(copy(rng), shared_seed + i)
             inds = rand(_rng, 1:t_samples, n_samples)
             forest[i] = build_tree(
                 labels[inds],