Package: hypergbm
中文We Are Hiring！Dear folks, we are offering challenging opportunities located in Beijing for both professionals and students who are keen on AutoML/NAS. Come be a part of DataCanvas! Please send your CV toyangjian@zetyun.com. (Application deadline: TBD.)What is HyperGBMHyperGBM is a full pipeline automated machine learning (AutoML) toolkit designed for tabular data. It covers the complete end-to-end ML processing stages, consisting of data cleaning, preprocessing, feature generation and selection, model selection and hyperparameter optimization.OverviewHyperGBM optimizes the end-to-end ML processing stages within one search space, which differs from most existing AutoML approaches that only tackle partial stages, for instance, hyperparameter optimazation. This full pipeline optimization process is very similar to a sequential decision process (SDP). Therefore, HyperGBM utilizes reinforcement learning, Monte Carlo Tree Search, evolution algorithm combined with a meta-learner to efficiently solve the pipeline optimization problem.HyperGBM, as indicated in the name, involves several gradient boosting tree models (GBM), namely, XGBoost, LightGBM and Catboost. What's more, it could access theHypernets, a general automated machine learning framework, and introduce its advanced characteristics in data cleaning, feature engineering and model ensemble. Additionally, the search space representation and search algorithm inside Hyper GBM are also supported by Hypernets.InstallationCondaInstall HyperGBM withcondafrom the channelconda-forge:condainstall-cconda-forgehypergbmPipInstall HyperGBM with differentpipoptions:Typical installation:pipinstallhypergbmTo run HyperGBM in JupyterLab/Jupyter notebook, install with command:pipinstallhypergbm[notebook]To support experiment visualization base on web, install with command:pipinstallhypergbm[board]# Temporarily unavailable in version 0.3.xTo run HyperGBM in distributed Dask cluster, install with command:pipinstallhypergbm[dask]To support dataset with simplified Chinese in feature generation,Installjiebapackage before running HyperGBM.OR install with command:pipinstallhypergbm[zhcn]Install all above with one command:pipinstallhypergbm[all]ExamplesUse HyperGBM with PythonUsers can quickly create and run an experiment withmake_experiment, which only needs one required input parametertrain_data. The example shown below is using theblooddataset astrain_datafromhypernet.tabular. If the target column of the dataset is noty, it needs to be manually set through the argumenttarget.An example codes:fromhypergbmimportmake_experimentfromhypernets.tabular.datasetsimportdsutilstrain_data=dsutils.load_blood()experiment=make_experiment(train_data,target='Class')estimator=experiment.run()print(estimator)This training experiment returns a pipeline with two default steps,data_cleanandestimator. In particular, theestimatorreturns a final model which consists of various models. The outputs：Pipeline(steps=[('data_clean',
                 DataCleanStep(...),
                ('estimator',
                 GreedyEnsemble(...)])To see more examples, please readQuick StartandExamples.Use HyperGBM with Command line toolsHypergbm also supports command line tools to perform model training, evaluation and prediction. The following codes enable the user to view command line help:hypergbm -h

usage: hypergbm [-h] [--log-level LOG_LEVEL] [-error] [-warn] [-info] [-debug]
                [--verbose VERBOSE] [-v] [--enable-gpu ENABLE_GPU] [-gpu] 
                [--enable-dask ENABLE_DASK] [-dask] [--overload OVERLOAD]
                {train,evaluate,predict} ...The example of training a model for datasetblood.csvis shown below:hypergbmtrain--train-file=blood.csv--target=Class--model-file=model.pklFor more details, please readQuick Start.GPU AccelerationHypergbm supports full pipeline GPU acceleration, including all steps from data processing to model training. In our experiments, we got a 50x performance improvement!  Most importantly, the model trained on GPU could be deployed to the environment without GPU hardware and software (e.g.,CUDA and cuML), which greatly reduces the cost of model deployment.DocumentsOverviewInstallationQuick StartExamplesHow-ToRelease NotesHyperGBM related projectsHypernets: A general automated machine learning (AutoML) framework.HyperGBM: A full pipeline AutoML tool integrated various GBM models.HyperDT/DeepTables: An AutoDL tool for tabular data.HyperTS: A full pipeline AutoML&AutoDL tool for time series datasets.HyperKeras: An AutoDL tool for Neural Architecture Search and Hyperparameter Optimization on Tensorflow and Keras.HyperBoard: A visualization tool for Hypernets.Cooka: Lightweight interactive AutoML system.CitationIf you use HyperGBM in your research, please cite us as follows:Jian Yang, Xuefeng Li, Haifeng Wu.HyperGBM: A full pipeline AutoML tool integrated with various GBM models.https://github.com/DataCanvasIO/HyperGBM. 2020. Version 0.2.x.BibTex:@misc{hypergbm,
  author={Jian Yang, Xuefeng Li, Haifeng Wu},
  title={{HyperGBM}: { A Full Pipeline AutoML Tool Integrated With Various GBM Models}},
  howpublished={https://github.com/DataCanvasIO/HyperGBM},
  note={Version 0.2.x},
  year={2020}
}DataCanvasHyperGBM is an open source project created byDataCanvas.