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DecisionTree.jl

Build Status Coverage Status

DecisionTree DecisionTree DecisionTree

Decision Tree Classifier and Regressor in Julia

Supports the ScikitLearn.jl API

Available models: DecisionTreeClassifier, DecisionTreeRegressor, RandomForestClassifier, RandomForestRegressor, AdaBoostStumpClassifier. See each model's help (eg. ?DecisionTreeRegressor at the REPL) for more information

Classifier

Includes:

  • post pruning (pessimistic pruning)
  • parallelized bagging (random forests)
  • adaptive boosting (decision stumps)
  • cross validation (n-fold)
  • support for mixed nominal and numerical data

Adapted from MILK: Machine Learning Toolkit

Regressor

Includes:

  • parallelized bagging (random forests)
  • cross validation (n-fold)
  • support for numerical features

Note that regression is implied if labels/targets are of type float

Installation

You can install DecisionTree.jl using Julia's package manager

Pkg.add("DecisionTree")

Classification Example

Load RDatasets and DecisionTree packages

using RDatasets: dataset
using DecisionTree

Separate Fisher's Iris dataset features and labels

iris = dataset("datasets", "iris")
features = convert(Array, iris[:, 1:4]);
labels = convert(Array, iris[:, 5]);

Pruned Tree Classifier

# train full-tree classifier
model = build_tree(labels, features)
# prune tree: merge leaves having >= 90% combined purity (default: 100%)
model = prune_tree(model, 0.9)
# pretty print of the tree, to a depth of 5 nodes (optional)
print_tree(model, 5)
# apply learned model
apply_tree(model, [5.9,3.0,5.1,1.9])
# get the probability of each label
apply_tree_proba(model, [5.9,3.0,5.1,1.9], ["setosa", "versicolor", "virginica"])
# run n-fold cross validation for pruned tree,
# using 90% purity threshold pruning, and 3 CV folds
accuracy = nfoldCV_tree(labels, features, 0.9, 3)

Random Forest Classifier

# train random forest classifier
# using 2 random features, 10 trees, 0.5 portion of samples per tree (optional), and a maximum tree depth of 6 (optional)
model = build_forest(labels, features, 2, 10, 0.5, 6)
# apply learned model
apply_forest(model, [5.9,3.0,5.1,1.9])
# get the probability of each label
apply_forest_proba(model, [5.9,3.0,5.1,1.9], ["setosa", "versicolor", "virginica"])
# run n-fold cross validation for forests
# using 2 random features, 10 trees, 3 folds and 0.5 of samples per tree (optional)
accuracy = nfoldCV_forest(labels, features, 2, 10, 3, 0.5)

Adaptive-Boosted Decision Stumps Classifier

# train adaptive-boosted stumps, using 7 iterations
model, coeffs = build_adaboost_stumps(labels, features, 7);
# apply learned model
apply_adaboost_stumps(model, coeffs, [5.9,3.0,5.1,1.9])
# get the probability of each label
apply_adaboost_stumps_proba(model, coeffs, [5.9,3.0,5.1,1.9], ["setosa", "versicolor", "virginica"])
# run n-fold cross validation for boosted stumps, using 7 iterations and 3 folds
accuracy = nfoldCV_stumps(labels, features, 7, 3)

Regression Example

n, m = 10^3, 5 ;
features = randn(n, m);
weights = rand(-2:2, m);
labels = features * weights;

Regression Tree

# train regression tree, using an averaging of 5 samples per leaf (optional)
model = build_tree(labels, features, 5)
# apply learned model
apply_tree(model, [-0.9,3.0,5.1,1.9,0.0])
# run n-fold cross validation, using 3 folds, averaging of 5 samples per leaf (optional)
# returns array of coefficients of determination (R^2)
r2 = nfoldCV_tree(labels, features, 3, 5)

Regression Random Forest

# train regression forest, using 2 random features, 10 trees,
# averaging of 5 samples per leaf (optional), 0.7 of samples per tree (optional)
model = build_forest(labels,features, 2, 10, 5, 0.7)
# apply learned model
apply_forest(model, [-0.9,3.0,5.1,1.9,0.0])
# run n-fold cross validation on regression forest
# using 2 random features, 10 trees, 3 folds, averaging of 5 samples/leaf (optional),
# and 0.7 porition of samples per tree (optional)
# returns array of coefficients of determination (R^2)
r2 = nfoldCV_forest(labels, features, 2, 10, 3, 5, 0.7)

ScikitLearn.jl

DecisionTree.jl supports the ScikitLearn.jl interface and algorithms (cross-validation, hyperparameter tuning, pipelines, ...)

The classifier example above can be rewritten as:

# train full-tree classifier
model = DecisionTreeClassifier(pruning_purity_threshold=0.9, maxdepth=6)
fit!(model, features, labels)
# pretty print of the tree, to a depth of 5 nodes (optional)
print_tree(model.root, 5)
# apply learned model
predict(model, [5.9,3.0,5.1,1.9])
# get the probability of each label
predict_proba(model, [5.9,3.0,5.1,1.9])
println(get_classes(model)) # returns the ordering of the columns in predict_proba's output
# run n-fold cross validation over 3 CV folds
# See ScikitLearn.jl for installation instructions
using ScikitLearn.CrossValidation: cross_val_score
accuracy = cross_val_score(model, features, labels, cv=3)

Available models: DecisionTreeClassifier, DecisionTreeRegressor, RandomForestClassifier, RandomForestRegressor, AdaBoostStumpClassifier. See each model's help (eg. ?DecisionTreeRegressor at the REPL) for more information

Also have a look at these classification, and regression notebooks.