levyben / Deepsuperlearner
Licence: mit
DeepSuperLearner - Python implementation of the deep ensemble algorithm
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DeepSuperLearner (2018) in Python
This is a sklearn implementation of the machine-learning DeepSuperLearner algorithm, A Deep Ensemble method for Classification Problems.
For details about DeepSuperLearner please refer to the https://arxiv.org/abs/1803.02323: Deep Super Learner: A Deep Ensemble for Classification Problems by Steven Young, Tamer Abdou, and Ayse Bener.
Installation and demo
-
Clone this repository
git clone https://github.com/levyben/DeepSuperLearner.git
-
Install the python library
cd DeepSuperLearner python setup.py install
Example:
ERT_learner = ExtremeRandomizedTrees(n_estimators=200, max_depth=None, max_features=1)
kNN_learner = kNearestNeighbors(n_neighbors=11)
LR_learner = LogisticRegression()
RFC_learner = RandomForestClassifier(n_estimators=200, max_depth=None)
XGB_learner = XGBClassifier(n_estimators=200, max_depth=3, learning_rate=1.)
Base_learners = {'ExtremeRandomizedTrees':ERT_learner, 'kNearestNeighbors':kNN_learner, 'LogisticRegression':LR_learner,
'RandomForestClassifier':RFC_learner, 'XGBClassifier':XGB_learner}
np.random.seed(100)
X, y = datasets.make_classification(n_samples=1000, n_features=12,
n_informative=2, n_redundant=6)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
DSL_learner = DeepSuperLearner(Base_learners)
DSL_learner.fit(X_train, y_train)
DSL_learner.get_precision_recall(X_test, y_test, show_graphs=True)
See deepSuperLearner/example.py for full example.
Notes:
- For running example you need to install the XGB python lib as it is used as a base learner just as done in the paper.
- Although the algorithm is implemented for classification problems, it can be modified to perform on regression problems aswell.
TODO:
- [x] Train on some sklearn data.
- [ ] Restore paper classification results.
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