Package: mindware
MindWare: Efficient open-source AutoML system for .Volcano-ML is a powerful AutoML system, which automates feature engineering, algorithm selection and hyperparameter tuning.
It is capable of improving its AutoML power by decomposing the entire large AutoML search space into small ones.
The system executes like the eruption of a volcano, hence the name 'Volcano-ML'.Volcano-ML is developed byDAIM Labat Peking University.
The goal of Volcano-ML is to make machine learning easier to apply both in industry and academia.
Currently, Volcano-ML is compatible with:Python >= 3.6.CharacteristicsUser friendliness.Volcano-ML needs few human assistance. To use Volcano-ML, the users can define the task by writing only a few lines of code, regardless of the techinical details of the execution of the system.High extensibility.New state-of-the-art ML algorithms or feature engineer operations can be added to the system simply. The decomposition techniques in Volcano-ML ensures the efficiency of finding the best configurations over the enlarged search space.Advanced characteristic.Volcano-ML provides special supports for large datasets. In addition, Volcano-ML enables transfer-learning, meta-learning techniques to make AutoML with more intelligent behaviors.ReleasesNew release:v1.3-released on xx-xx-2021.ExampleHere is a brief example that uses the package.fromsklearn.datasetsimportload_irisfromsklearn.model_selectionimporttrain_test_splitfrommindware.utils.data_managerimportDataManagerfrommindware.estimatorsimportClassifieriris=load_iris()X,y=iris.data,iris.targetX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.33,random_state=1,stratify=y)dm=DataManager(X_train,y_train)train_data=dm.get_data_node(X_train,y_train)test_data=dm.get_data_node(X_test,y_test)clf=Classifier(time_limit=3600)clf.fit(train_data)pred=clf.predict(test_data)For more details and characteristics, please checkexamples.VisualizationTODO.InstallationBefore installing Volcano-ML, please install the necessary libraryswig.Volcano-ML requires SWIG (>= 3.0, <4.0) as a build dependency, and we suggest you to download & installswig=3.0.12.Then, you can install Volcano-ML itself. Volcano-ML supports and is tested on Ubuntu >= 16.04, macOS >= 10.14.1, and Windows 10 >= 1809. The installation requires a python environment that haspython 64-bit >= 3.6.There are two ways to install Volcano-ML:Installation via pipVolcano-ML is available on PyPI. You can install it by tying:pipinstallmindwareManual installation from the github sourceIf you want to try latest code, please manually install Volcano-ML from source code by:gitclonehttps://github.com/thomas-young-2013/mindware.git&&cdmindware
catrequirements/main.txt|xargs-n1-L1pipinstall
pythonsetup.pyinstallTips on Installing SwigLinux:On Arch Linux (or any distribution with swig4 as default implementation), you need to confirm that the version of SWIG is in (>= 3.0, <4.0).We suggest you to installswig=3.0.12.../configure
make&makeinstallMACOSX:Before installing SWIG, you need to installpcre:cd$pcre_dir./configure
make&makeinstallThen add library path of/usr/local/libforpcre:LD_LIBRARY_PATH=/usr/local/lib:/usr/libexportLD_LIBRARY_PATHFinally, install Swig:cd$swig_dir./configure
make&makeinstallBefore installing python packagepyrfr=0.8.0, download source code frompypi:cd$pyrfr_dirpythonsetup.pyinstallWindows:You need to downloadswigwin, and then install Soln-ML.FeedbackCheckthe existing open and closed issues.File an issueon GitHub.Discuss on the Volcano-MLGitter.Related ProjectsTargeting at openness and advancing state-of-art technology, we have also released another open source project.OpenBOX: an open source system and service to efficiently solve generalized blackbox optimization problems.We encourage researchers to leverage the project to accelerate the AI development and research.Related PublicationsVolcanoML: Speeding up End-to-End AutoML via Scalable Search Space DecompositionYang Li, Yu Shen, Wentao Zhang, Jiawei Jiang, Bolin Ding, Yaliang Li, Jingren Zhou, Zhi Yang, Wentao Wu, Ce Zhang and Bin Cui
International Conference on Very Large Data Bases (VLDB 2021).Efficient Automatic CASH via Rising BanditsYang Li, Jiawei Jiang, Jinyang Gao, Yingxia Shao, Ce Zhang and Bin Cui
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2020).https://ojs.aaai.org/index.php/AAAI/article/view/5910MFES-HB: Efficient Hyperband with Multi-Fidelity Quality MeasurementsYang Li, Yu Shen, Jiawei Jiang, Jinyang Gao, Ce Zhang and Bin Cui
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2021).https://arxiv.org/abs/2012.03011OpenBox: A Generalized Black-box Optimization ServiceYang Li, Yu Shen, Wentao Zhang, Yuanwei Chen, Huaijun Jiang, Mingchao Liu, Jiawei Jiang, Jinyang Gao, Wentao Wu, Zhi Yang, Ce Zhang and Bin Cui
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD 2021).https://arxiv.org/abs/2106.00421LicenseThe entire codebase is underMIT license.