A Python library for high performance ensemble learning
ML-Ensemble combines a Scikit-learn high-level API with a low-level computational graph framework to build memory efficient, maximally parallelized ensemble networks in as few lines of codes as possible.
ML-Ensemble is thread safe as long as base learners are and can fall back on memory mapped multiprocessing for memory-neutral process-based concurrency. For tutorials and full documentation, visit the project website.
An ensemble is built on top of a computational graph,
allowing users great design freedom. Ensembles can be built with recursion,
dynamic evaluation (e.g. if-else) and much more. A high-level API wraps common
ensemble architectures into Scikit-learn estimators.
Example computational graph of a layer in an ensemble
ML-Ensemble is optimized for speed and minimal memory consumption. No serialization of data takes place, regardless of whether multithreading or multiprocessing is used. Additionally, multithreading is pickle-free.
Ready-made ensembles are built by adding layers to an instance. No matter how
complex the ensemble, to train it call the fit method:
ensemble = Subsemble()
# First layer
ensemble.add(list_of_estimators)
# Second layer
ensemble.add(list_of_estimators)
# Final meta estimator
ensemble.add_meta(estimator)
# Train ensemble
ensemble.fit(X, y)Similarly, it's straightforward to modify an existing ensemble:
# Remove layer
ensemble.remove(2)
# Change layer parameters
ensemble.replace(0, new_list_of_estimators)And to create differentiated preprocessing pipelines for different subsets
of estimators within a given layer, simple pass a mapping to the add
method:
preprocessing = {'pipeline-1': list_of_transformers_1,
'pipeline-2': list_of_transformers_2}
estimators = {'pipeline-1': list_of_estimators_1,
'pipeline-2': list_of_estimators_2}
ensemble.add(estimators, preprocessing)ML Ensemble implements a dedicated diagnostics and model selection suite
for intuitive and speedy ensemble evaluation. The Evaluator
allows you to evaluate several preprocessing pipelines and several
estimators in one go, giving you birds-eye view of how different candidates
for the ensemble perform.
Moreover, entire ensembles can be used as a preprocessing pipeline, to
leverage model selection for higher-level layers. Simply set model_selection
to True in the ensemble (don't forget to turn it off when done).
preprocessing_dict = {'pipeline-1': list_of_transformers_1,
'pipeline-2': list_of_transformers_2}
evaluator = Evaluator(scorer=score_func)
evaluator.fit(X, y, list_of_estimators, param_dists_dict, preprocessing_dict)All ensembles and model selection instances provides summary statistics in
tabular format. You can find fit and prediction times in any ensemble by calling
data, and cv-scores if you passed a scorer. For the model selection suite,
the results attribute gives you the outcome of an evaluation::
fit_time_mean fit_time_std train_score_mean train_score_std test_score_mean test_score_std params
prep-1 est-1 0.001353 0.001316 0.957037 0.005543 0.960000 0.032660 {}
est-2 0.000447 0.000012 0.980000 0.004743 0.966667 0.033333 {'n_neighbors': 15}
prep-2 est-1 0.001000 0.000603 0.957037 0.005543 0.960000 0.032660 {}
est-2 0.000448 0.000036 0.965185 0.003395 0.960000 0.044222 {'n_neighbors': 8}
prep-3 est-1 0.000735 0.000248 0.791111 0.019821 0.780000 0.133500 {}
est-2 0.000462 0.000143 0.837037 0.014815 0.800000 0.126491 {'n_neighbors': 9}ML-Ensemble is available on PyPI. Install with
pip install mlensFork the GitHub repository:
git clone https://github.com/flennerhag/mlens.git; cd mlens;
python setup.py installFor scientific publication, ML-Ensemble can be cited as
@misc{flennerhag:2017mlens,
author = {Flennerhag, Sebastian},
title = {ML-Ensemble},
month = nov,
year = 2017,
doi = {10.5281/zenodo.1042144},
url = {https://dx.doi.org/10.5281/zenodo.1042144}
}
Please see issue tracker.
ML-Ensemble is an open-source project that welcome any contribution, small as large. Bug fixes and minor improvements can be pulled as is; larger PRs need to be unit tested. We generally follow the PEP-8 style guide.
MIT License
Copyright (c) 2017–2020 Sebastian Flennerhag
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
