genens
genens is an AutoML system for pipeline optimization based on developmental genetic programming.
Installation
Clone the repository.
git clone https://github.com/gabrielasuchopar/genens.git
pip install genens
Using genens
As for now, the GenensClassifier is ready to be used. It has an interface similar to other scikit-learn estimators. When fit() is called, the evolutionary optimization is run. After it finishes, predict() produces a prediction with the best of optimized pipelines. Alternatively, you can call get_best_pipelines() to get pipelines from the pareto front.
from genens import GenensClassifier
from sklearn.datasets import load_iris()
iris = load_iris()
train_X, test_X, train_y, test_y = train_test_split(iris.data, iris.target, test_size=0.25)
clf = GenensClassifier()
clf.fit(train_X, train_y)
... # process of evolution
pred = clf.predict(test_X)
Tests
Directory ./genens/tests contains scripts for running dataset tests and produce data about evolution process along with pickle files of best optimized pipelines.
Sample config files are included in ./genens/tests/config.
- Run genEns on a dataset specified in the config file.
python ./genens/tests/run_datasets.py --out OUT_DIR config CONFIG
- Runs genEns on the OpenML-CC18 benchmarking suite
python ./genens/tests/run_openml.py --out OUT_DIR --config CONFIG
More tests are to be included in later releases.
1 maintainer
Metadata
1 maintainer
Release files for genens 0.1.19
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