Compiled scikit-learn decision trees for faster evaluation
Project description
Installation
Released under the MIT License.
pip install sklearn-compiledtrees
Rationale
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.
It has been tested to work on both OS X and Linux. We do not currently support Windows platforms for compiled evaluation, although this should not be a signficant amount of work.
Usage
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)
Benchmarks
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 Forest, D1, etc correspond to setting max-depth=1, and B10 corresponds to setting max_leaf_nodes=10.
Graphs
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
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for sklearn-compiledtrees-1.2.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | c35d02bd2963ac3c78da5a8c4d8e17fb59630f4301fbd7f3d8fd88054b5d0f5e |
|
MD5 | bb0bb670505d51dd3227c3e329196d32 |
|
BLAKE2b-256 | fa18a473b0d5bdd7d2fc015fc6a121a17a91d2b97f1ff39b308bbb128a8e6921 |
Hashes for sklearn-compiledtrees-1.2.macosx-10.9-x86_64.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | a09a478e46f616f125f8d12cd3c85524df1727bbfee1dd6a6e95b87941f4d06f |
|
MD5 | 97bf7bc5653173a18eb7ff3cdde576f8 |
|
BLAKE2b-256 | 4071777580818558cbccac4394890ee785b20011aa1915373b48e948dd81022c |