%global _empty_manifest_terminate_build 0 Name: python-diego Version: 0.2.7 Release: 1 Summary: Diego: Data IntElliGence Out. License: MIT URL: https://github.com/lai-bluejay/diego Source0: https://mirrors.aliyun.com/pypi/web/packages/61/b7/9f7bff11aeec39d4af912194c650107660e4a5ddbc3dee7d7fc65a799207/diego-0.2.7.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-scipy Requires: python3-scikit-learn Requires: python3-deap Requires: python3-update-checker Requires: python3-tqdm Requires: python3-stopit Requires: python3-pandas Requires: python3-xgboost Requires: python3-pyrfr Requires: python3-distributed Requires: python3-dask Requires: python3-smac Requires: python3-ConfigSpace Requires: python3-auto-sklearn Requires: python3-liac-arff Requires: python3-sklearn-contrib-lightning %description # Diego Diego: Data in, IntElliGence Out. [简体中文](README_zh_CN.md) 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 API [glossary](https://scikit-learn.org/stable/glossary.html), using Bayesian optimization and genetic algorithms for automated machine learning. Inspired by [Fast.ai](https://github.com/fastai/fastai) and [MicroSoft nni](https://github.com/Microsoft/nni). [![Build Status](https://travis-ci.org/lai-bluejay/diego.svg?branch=master)](https://travis-ci.org/lai-bluejay/diego) ![PyPI](https://img.shields.io/pypi/v/diego.svg?style=flat) ![GitHub](https://img.shields.io/github/license/lai-bluejay/diego.svg) ![GitHub code size in bytes](https://img.shields.io/github/languages/code-size/lai-bluejay/diego.svg) - [x] the classifier trained by a Study. - [x] AutoML classifier with support for scikit-learn api. Support for exporting models and use them directly. - [x] Hyperparametric optimization using Bayesian optimization and genetic algorithms - [x] Supports bucketing/binning algorithm and LUS sampling method for preprocessing - [ ] Supports scikit-learn api classifier custom classifier for parameter search and super parameter optimization ## Installation You need to install swig first, and some rely on C/C++ interface compilation. Recommended to use conda installation ```shell conda install --yes pip gcc swig libgcc=5.2.0 pip install diego ``` After installation, start with 6 lines of code to solve a machine learning classification problem. ## Usage Each task is considered to be a `Study`, and each Study consists of multiple `Trial`. It is recommended to create a Study first and then generate a Trial from the Study: ```python from diego.study import create_study import sklearn.datasets digits = 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)) ``` ## RoadMap ideas 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 output - [ ] basic Classifier - [ ] fix mac os hanged in optimize pipeline - [ ] add preprocessor - [ ] add FeatureTools for automated feature engineering ## ## Project Structure ### study, trials Study: Trial: ### 如果在OS X或者Linux多进程被 hang/crash/freeze Since 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 configure `multiprocessing` to use `forkserver` or `spawn` to start process pool management (instead of the default `fork`). For example, the `forkserver` mode is enabled globally directly in the code. ```python import multiprocessing # other imports, custom code, load data, define model... if __name__ == '__main__': multiprocessing.set_start_method('forkserver') # call scikit-learn utils with n_jobs > 1 here ``` more info :[multiprocessing document](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods) ### core #### storage For each study, the data storage and parameters, and the model is additionally stored in the `Storage` object, which ensures that Study only controls trials, and each Trial updates the results in the storage after updating, and updates the best results. #### update result When creating `Study`, you need to specify the direction of optimization `maximize` or `minimize`. Also specify the metrics for optimization when creating `Trials`. The default is `maximize accuracy`. ## auto ml 补完计划 [overview](https://hackernoon.com/a-brief-overview-of-automatic-machine-learning-solutions-automl-2826c7807a2a) ### bayes opt 1. [fmfn/bayes](https://github.com/fmfn/BayesianOptimization) 2. [auto-sklearn](https://github.com/automl/auto-sklearn) ### grid search 1. H2O.ai ### tree parzen 1. hyperopt 2. mlbox ### metaheuristics grid search 1. pybrain ### generation 1.tpot ### dl 1. ms nni ## issues ## updates ### TODO 文档更新。 %package -n python3-diego Summary: Diego: Data IntElliGence Out. Provides: python-diego BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-diego # Diego Diego: Data in, IntElliGence Out. [简体中文](README_zh_CN.md) 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 API [glossary](https://scikit-learn.org/stable/glossary.html), using Bayesian optimization and genetic algorithms for automated machine learning. Inspired by [Fast.ai](https://github.com/fastai/fastai) and [MicroSoft nni](https://github.com/Microsoft/nni). [![Build Status](https://travis-ci.org/lai-bluejay/diego.svg?branch=master)](https://travis-ci.org/lai-bluejay/diego) ![PyPI](https://img.shields.io/pypi/v/diego.svg?style=flat) ![GitHub](https://img.shields.io/github/license/lai-bluejay/diego.svg) ![GitHub code size in bytes](https://img.shields.io/github/languages/code-size/lai-bluejay/diego.svg) - [x] the classifier trained by a Study. - [x] AutoML classifier with support for scikit-learn api. Support for exporting models and use them directly. - [x] Hyperparametric optimization using Bayesian optimization and genetic algorithms - [x] Supports bucketing/binning algorithm and LUS sampling method for preprocessing - [ ] Supports scikit-learn api classifier custom classifier for parameter search and super parameter optimization ## Installation You need to install swig first, and some rely on C/C++ interface compilation. Recommended to use conda installation ```shell conda install --yes pip gcc swig libgcc=5.2.0 pip install diego ``` After installation, start with 6 lines of code to solve a machine learning classification problem. ## Usage Each task is considered to be a `Study`, and each Study consists of multiple `Trial`. It is recommended to create a Study first and then generate a Trial from the Study: ```python from diego.study import create_study import sklearn.datasets digits = 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)) ``` ## RoadMap ideas 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 output - [ ] basic Classifier - [ ] fix mac os hanged in optimize pipeline - [ ] add preprocessor - [ ] add FeatureTools for automated feature engineering ## ## Project Structure ### study, trials Study: Trial: ### 如果在OS X或者Linux多进程被 hang/crash/freeze Since 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 configure `multiprocessing` to use `forkserver` or `spawn` to start process pool management (instead of the default `fork`). For example, the `forkserver` mode is enabled globally directly in the code. ```python import multiprocessing # other imports, custom code, load data, define model... if __name__ == '__main__': multiprocessing.set_start_method('forkserver') # call scikit-learn utils with n_jobs > 1 here ``` more info :[multiprocessing document](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods) ### core #### storage For each study, the data storage and parameters, and the model is additionally stored in the `Storage` object, which ensures that Study only controls trials, and each Trial updates the results in the storage after updating, and updates the best results. #### update result When creating `Study`, you need to specify the direction of optimization `maximize` or `minimize`. Also specify the metrics for optimization when creating `Trials`. The default is `maximize accuracy`. ## auto ml 补完计划 [overview](https://hackernoon.com/a-brief-overview-of-automatic-machine-learning-solutions-automl-2826c7807a2a) ### bayes opt 1. [fmfn/bayes](https://github.com/fmfn/BayesianOptimization) 2. [auto-sklearn](https://github.com/automl/auto-sklearn) ### grid search 1. H2O.ai ### tree parzen 1. hyperopt 2. mlbox ### metaheuristics grid search 1. pybrain ### generation 1.tpot ### dl 1. ms nni ## issues ## updates ### TODO 文档更新。 %package help Summary: Development documents and examples for diego Provides: python3-diego-doc %description help # Diego Diego: Data in, IntElliGence Out. [简体中文](README_zh_CN.md) 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 API [glossary](https://scikit-learn.org/stable/glossary.html), using Bayesian optimization and genetic algorithms for automated machine learning. Inspired by [Fast.ai](https://github.com/fastai/fastai) and [MicroSoft nni](https://github.com/Microsoft/nni). [![Build Status](https://travis-ci.org/lai-bluejay/diego.svg?branch=master)](https://travis-ci.org/lai-bluejay/diego) ![PyPI](https://img.shields.io/pypi/v/diego.svg?style=flat) ![GitHub](https://img.shields.io/github/license/lai-bluejay/diego.svg) ![GitHub code size in bytes](https://img.shields.io/github/languages/code-size/lai-bluejay/diego.svg) - [x] the classifier trained by a Study. - [x] AutoML classifier with support for scikit-learn api. Support for exporting models and use them directly. - [x] Hyperparametric optimization using Bayesian optimization and genetic algorithms - [x] Supports bucketing/binning algorithm and LUS sampling method for preprocessing - [ ] Supports scikit-learn api classifier custom classifier for parameter search and super parameter optimization ## Installation You need to install swig first, and some rely on C/C++ interface compilation. Recommended to use conda installation ```shell conda install --yes pip gcc swig libgcc=5.2.0 pip install diego ``` After installation, start with 6 lines of code to solve a machine learning classification problem. ## Usage Each task is considered to be a `Study`, and each Study consists of multiple `Trial`. It is recommended to create a Study first and then generate a Trial from the Study: ```python from diego.study import create_study import sklearn.datasets digits = 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)) ``` ## RoadMap ideas 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 output - [ ] basic Classifier - [ ] fix mac os hanged in optimize pipeline - [ ] add preprocessor - [ ] add FeatureTools for automated feature engineering ## ## Project Structure ### study, trials Study: Trial: ### 如果在OS X或者Linux多进程被 hang/crash/freeze Since 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 configure `multiprocessing` to use `forkserver` or `spawn` to start process pool management (instead of the default `fork`). For example, the `forkserver` mode is enabled globally directly in the code. ```python import multiprocessing # other imports, custom code, load data, define model... if __name__ == '__main__': multiprocessing.set_start_method('forkserver') # call scikit-learn utils with n_jobs > 1 here ``` more info :[multiprocessing document](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods) ### core #### storage For each study, the data storage and parameters, and the model is additionally stored in the `Storage` object, which ensures that Study only controls trials, and each Trial updates the results in the storage after updating, and updates the best results. #### update result When creating `Study`, you need to specify the direction of optimization `maximize` or `minimize`. Also specify the metrics for optimization when creating `Trials`. The default is `maximize accuracy`. ## auto ml 补完计划 [overview](https://hackernoon.com/a-brief-overview-of-automatic-machine-learning-solutions-automl-2826c7807a2a) ### bayes opt 1. [fmfn/bayes](https://github.com/fmfn/BayesianOptimization) 2. [auto-sklearn](https://github.com/automl/auto-sklearn) ### grid search 1. H2O.ai ### tree parzen 1. hyperopt 2. mlbox ### metaheuristics grid search 1. pybrain ### generation 1.tpot ### dl 1. ms nni ## issues ## updates ### TODO 文档更新。 %prep %autosetup -n diego-0.2.7 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-diego -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Jun 20 2023 Python_Bot - 0.2.7-1 - Package Spec generated