Package: diego
DiegoDiego: Data in,  IntElliGence Out.简体中文A fast framework that supports the rapid construction of automated learning tasks. Simply create an automated learning study (Study) and generate correlated trials (Trial). Then run the code and get a machine learning model. Implemented using Scikit-learn APIglossary, using Bayesian optimization and genetic algorithms for automated machine learning.Inspired byFast.aiandMicroSoft nni.the classifier trained by a Study.AutoML classifier with support for scikit-learn api. Support for exporting models and use them directly.Hyperparametric optimization using Bayesian optimization and genetic algorithmsSupports bucketing/binning algorithm and LUS sampling method for preprocessingSupports scikit-learn api classifier custom classifier for parameter search and super parameter optimizationInstallationYou need to install swig first, and some rely on C/C++ interface compilation. Recommended to use conda installationcondainstall--yespipgccswiglibgcc=5.2.0
pipinstalldiegoAfter installation, start with 6 lines of code to solve a machine learning classification problem.UsageEach task is considered to be aStudy, and each Study consists of multipleTrial.
It is recommended to create a Study first and then generate a Trial from the Study:fromdiego.studyimportcreate_studyimportsklearn.datasetsdigits=sklearn.datasets.load_digits()X_train,X_test,y_train,y_test=sklearn.model_selection.train_test_split(digits.data,digits.target,train_size=0.75,test_size=0.25)s=create_study(X_train,y_train)# can use default trials in Study# or generate one# s.generate_trials(mode='fast')s.optimize(X_test,y_test)# all_trials = s.get_all_trials()# for t in all_trials:#     print(t.__dict__)#     print(t.clf.score(X_test, y_test))RoadMapideas for releases in the future回归。add documents.不同类型的Trial。TPE， BayesOpt， RandomSearch自定义的Trial。Trials by custom Classifier (like sklearn, xgboost)模型保存。model persistence模型输出。model outputbasic Classifierfix mac os hanged in optimize pipelineadd preprocessoradd FeatureTools for automated feature engineeringProject Structurestudy, trialsStudy:Trial:如果在OS X或者Linux多进程被 hang/crash/freezeSince n_jobs>1 may get stuck during parallelization. Similar problems may occur in [scikit-learn] (https://scikit-learn.org/stable/faq.html#why-do-i-sometime-get-a-crash-freeze-with-n-jobs-1-under-osx-or-linux)In Python 3.4+, one solution is to directly configuremultiprocessingto useforkserverorspawnto start process pool management (instead of the defaultfork). For example, theforkservermode is enabled globally directly in the code.importmultiprocessing# other imports, custom code, load data, define model...if__name__=='__main__':multiprocessing.set_start_method('forkserver')# call scikit-learn utils with n_jobs > 1 heremore info :multiprocessing documentcorestorageFor each study, the data storage and parameters, and the model is additionally stored in theStorageobject, which ensures that Study only controls trials, and each Trial updates the results in the storage after updating, and updates the best results.update resultWhen creatingStudy, you need to specify the direction of optimizationmaximizeorminimize. Also specify the metrics for optimization when creatingTrials. The default ismaximize accuracy.auto ml 补完计划overviewbayes optfmfn/bayesauto-sklearngrid searchH2O.aitree parzenhyperoptmlboxmetaheuristics grid searchpybraingeneration1.tpotdlms nniissuesupdatesTODO 文档更新。