51

GitHub - levyben/DeepSuperLearner: DeepSuperLearner - Python implementation of t...

 6 years ago
source link: https://github.com/levyben/DeepSuperLearner
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.

README.md

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/pdf/1803.02323.pdf: Deep Super Learner: A Deep Ensemble for Classification Problems by Steven Young, Tamer Abdou, and Ayse Bener.

Installation and demo

  1. Clone this repository

    git clone https://github.com/levyben/DeepSuperLearner.git

  2. 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.

Alt text

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:

  • Train on some sklearn data.
  • Restore paper classification results.

report

Recommend

About Joyk


Aggregate valuable and interesting links.
Joyk means Joy of geeK