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This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'
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Update (Feb 1, 2021) ==================== ATTENTION! This repository will no longer be maintained, please check our new repository for Deep Forest with GREAT improvements on efficiency. Details at: * Repository: https://github.com/LAMDA-NJU/Deep-Forest * Documentation: https://deep-forest.readthedocs.io/ * Package on PyPI: https://pypi.org/project/deep-forest/ * * * You can install the newer version of gcForest via pip ``` pip install deep-forest ``` The older version (gcForest v1.1.1) in this repo will only be served as an illustration of the algorithm. * * * gcForest v1.1.1 Is Here! ======== This is the official clone for the implementation of gcForest.(The University's webserver is unstable sometimes, therefore we put the official clone here at github) Package Official Website: http://lamda.nju.edu.cn/code_gcForest.ashx This package is provided "AS IS" and free for academic usage. You can run it at your own risk. For other purposes, please contact Prof. Zhi-Hua Zhou ([email protected]). Description: A python 2.7 implementation of gcForest proposed in [1]. A demo implementation of gcForest library as well as some demo client scripts to demostrate how to use the code. The implementation is flexible enough for modifying the model or fit your own datasets. Reference: [1] Z.-H. Zhou and J. Feng. Deep Forest: Towards an Alternative to Deep Neural Networks. In IJCAI-2017. (https://arxiv.org/abs/1702.08835v2 ) Requirements: This package is developed with Python 2.7, please make sure all the dependencies are installed, which is specified in requirements.txt ATTN: This package was developed and maintained by Mr.Ji Feng(http://lamda.nju.edu.cn/fengj/) .For any problem concerning the codes, please feel free to contact Mr.Feng.（[email protected]) or open some issues here. What's NEW: ======== * Scikit-Learn style API * Some more detailed examples * GPU support if you want to use xgboost as base estimators * Support Python 3.5(v1.1.1) v1.1.1 Python 3.5 Compatibility: The package should work for Python 3.5. Haven't check everything for now but it seems OK. v1.1.1 Bug Fixed : When doing multiple predictions for the same model, the result will be consistant if you are using pooling layer. The bug only occurs for the scikit-learn APIs and now it is OK for the new api also. Quick start ===================== ### The simplest way of using the library is as follows: ``` from gcforest.gcforest import GCForest gc = GCForest(config) # should be a dict X_train_enc = gc.fit_transform(X_train, y_train) y_pred = gc.predict(X_test) ``` And that's it. Please see ```/examples/demo_mnist.py``` for a detailed useage. For order versons AND some more model configs reported in the original paper, please refer: * [v1.0](https://github.com/kingfengji/gcforest/tree/v1.0) Supported Based Classifier ===================== The based classifiers inside gcForest can be any classifiers. This library support the following ones: * RandomForestClassifier * XGBClassifier * ExtraTreesClassifier * LogisticRegression * SGDClassifier To add any classifiers, you could manually add them from ```lib/gcforest/estimators/__init__.py``` Define your own structure ===================== ### Define your model with a single json file. * IF you only need cascading forest structure. You only need to write one json file. see /examples/demo_mnist-ca.json for a reference.(here -ca is for cascading) * IF you need both fine grained and cascading forests, you will need to specifying the Finegraind structure of your model also.See /examples/demo_mnist-gc.json for a reference. * Then, use gcforest.utils.config_utils.load_json to load your json file. ``` config = load_json(your_json_file) gc = GCForest(con
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Yi-Xuan XU · Alibaba · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e6f3669749392d75, llm:Topics: deep-forest, ensemble-learning, machine-learning; description/readme: "implementation of gcForest" and "Deep Forest: Towards an Alternative to Deep Neural Networks" — Python implementation of an ensemble-based model (gcForest).
matched fp:e6f3669749392d75, llm:Topics: deep-forest, ensemble-learning, machine-learning; description/readme: "implementation of gcForest" and "Deep Forest: Towards an Alternative to Deep Neural Networks" — Python implementation of an ensemble-based model (gcForest).