https://github.com/levyben/DeepSuperLearner
DeepSuperLearner - Python implementation of the deep ensemble algorithm
https://github.com/levyben/DeepSuperLearner
classification-algorithm ensemble-learning machine-learning
Last synced: 3 months ago
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DeepSuperLearner - Python implementation of the deep ensemble algorithm
- Host: GitHub
- URL: https://github.com/levyben/DeepSuperLearner
- Owner: levyben
- License: mit
- Created: 2018-04-14T17:19:19.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-06-22T06:23:45.000Z (over 7 years ago)
- Last Synced: 2024-11-27T04:30:40.927Z (11 months ago)
- Topics: classification-algorithm, ensemble-learning, machine-learning
- Language: Python
- Size: 185 KB
- Stars: 136
- Watchers: 11
- Forks: 23
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DeepSuperLearner (2018) in Python
This is a [sklearn](http://scikit-learn.org/stable/)
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](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
1. Clone this repository
```bash
git clone https://github.com/levyben/DeepSuperLearner.git
```
2. Install the python library
```bash
cd DeepSuperLearner
python setup.py install
```
### Example:
```python
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:
1. 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.
2. 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.