sklearn-compiledtrees 1.2

Description:

sklearncompiledtrees 1.2

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