Scikit-Learn Compiled Trees
Released under the MIT License.
pip install sklearn-compiledtrees
Or to get the latest development version:
pip install git+https://github.com/ajtulloch/sklearn-compiledtrees.git
sklearn-compiledtrees has been tested to work on OS X, Linux and Windows.
Installing on Windows requires GCC compiler and dlfcn-win32, setting CXX environment variable (set "CXX=gcc -pthread" for CMD), and manual installation from source directory. Using msys2 distribution in conda is strongly recommended.
In some use cases, predicting given a model is in the hot-path, so speeding up decision tree evaluation is very useful.
An effective way of speeding up evaluation of decision trees can be to generate code representing the evaluation of the tree, compile that to optimized object code, and dynamically load that file via dlopen/dlsym or equivalent.
See https://courses.cs.washington.edu/courses/cse501/10au/compile-machlearn.pdf for a detailed discussion, and http://tullo.ch/articles/decision-tree-evaluation/ for a more pedagogical explanation and more benchmarks in C++.
This package implements compiled decision tree evaluation for the simple case of a single-output regression tree or ensemble.
import compiledtrees import sklearn.ensemble X_train, y_train, X_test, y_test = ... clf = ensemble.GradientBoostingRegressor() clf.fit(X_train, y_train) compiled_predictor = compiledtrees.CompiledRegressionPredictor(clf) predictions = compiled_predictor.predict(X_test)
For random forests, we see 5x to 8x speedup in evaluation. For gradient boosted ensembles, it's between a 1.5x and 3x speedup in evaluation. This is due to the fact that gradient boosted trees already have an optimized prediction implementation.
There is a benchmark script attached that allows us to examine the performance of evaluation across a range of ensemble configurations and datasets.
In the graphs attached,
GB is Gradient Boosted,
RF is Random
D1, etc correspond to setting
corresponds to setting
for dataset in friedman1 friedman2 friedman3 uniform hastie; do python ../benchmarks/bench_compiled_tree.py \ --iterations=10 \ --num_examples=1000 \ --num_features=50 \ --dataset=$dataset \ --max_estimators=300 \ --num_estimator_values=6 done