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diff --git a/python-automl-alex.spec b/python-automl-alex.spec new file mode 100644 index 0000000..7ddf7dc --- /dev/null +++ b/python-automl-alex.spec @@ -0,0 +1,398 @@ +%global _empty_manifest_terminate_build 0 +Name: python-automl-alex +Version: 2023.3.11 +Release: 1 +Summary: State-of-the art Automated Machine Learning python library for Tabular Data +License: MIT +URL: https://pypi.org/project/automl-alex/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/4e/be/345d3d0a44e7ee8e9432c049a1b7663ecf84923f4babc4465b37d5a0f24c/automl-alex-2023.3.11.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-scikit-learn +Requires: python3-seaborn +Requires: python3-lightgbm +Requires: python3-catboost +Requires: python3-xgboost +Requires: python3-tqdm +Requires: python3-optuna +Requires: python3-category-encoders +Requires: python3-optuna-dashboard +Requires: python3-loguru +Requires: python3-psutil +Requires: python3-nbformat + +%description +<p align="center"> State-of-the art Automated Machine Learning python library for Tabular Data</p> +## Works with Tasks: +- [x] Binary Classification +- [x] Regression +- [ ] Multiclass Classification (in progress...) +### Benchmark Results +<img width=800 src="https://github.com/Alex-Lekov/AutoML-Benchmark/blob/master/img/Total_SUM.png" alt="bench"> +The bigger, the better +From [AutoML-Benchmark](https://github.com/Alex-Lekov/AutoML-Benchmark/) +### Scheme +<img width=800 src="https://github.com/Alex-Lekov/AutoML_Alex/blob/develop/examples/img/shema.png" alt="scheme"> +# Features +- Automated Data Clean (Auto Clean) +- Automated **Feature Engineering** (Auto FE) +- Smart Hyperparameter Optimization (HPO) +- Feature Generation +- Feature Selection +- Models Selection +- Cross Validation +- Optimization Timelimit and EarlyStoping +- Save and Load (Predict new data) +# Installation +```python +pip install automl-alex +``` +# Docs +[DocPage](https://alex-lekov.github.io/AutoML_Alex/) +# 🚀 Examples +Classifier: +```python +from automl_alex import AutoMLClassifier +model = AutoMLClassifier() +model.fit(X_train, y_train, timeout=600) +predicts = model.predict(X_test) +``` +Regression: +```python +from automl_alex import AutoMLRegressor +model = AutoMLRegressor() +model.fit(X_train, y_train, timeout=600) +predicts = model.predict(X_test) +``` +DataPrepare: +```python +from automl_alex import DataPrepare +de = DataPrepare() +X_train = de.fit_transform(X_train) +X_test = de.transform(X_test) +``` +Simple Models Wrapper: +```python +from automl_alex import LightGBMClassifier +model = LightGBMClassifier() +model.fit(X_train, y_train) +predicts = model.predict_proba(X_test) +model.opt(X_train, y_train, + timeout=600, # optimization time in seconds, + ) +predicts = model.predict_proba(X_test) +``` +More examples in the folder ./examples: +- [01_Quick_Start.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/01_Quick_Start.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/01_Quick_Start.ipynb) +- [02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb) +- [03_Models.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/03_Models.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/03_Models.ipynb) +- [04_ModelsReview.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/04_ModelsReview.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/04_ModelsReview.ipynb) +- [05_BestSingleModel.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/05_BestSingleModel.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/05_BestSingleModel.ipynb) +- [Production Docker template](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/prod_sample) +# What's inside +It integrates many popular frameworks: +- scikit-learn +- XGBoost +- LightGBM +- CatBoost +- Optuna +- ... +# Works with Features +- [x] Categorical Features +- [x] Numerical Features +- [x] Binary Features +- [ ] Text +- [ ] Datetime +- [ ] Timeseries +- [ ] Image +# Note +- **With a large dataset, a lot of memory is required!** +Library creates many new features. If you have a large dataset with a large number of features (more than 100), you may need a lot of memory. +# Realtime Dashboard +Works with [optuna-dashboard](https://github.com/optuna/optuna-dashboard) +<img width=800 src="https://github.com/Alex-Lekov/AutoML_Alex/blob/develop/examples/img/dashboard.gif" alt="Dashboard"> +Run +```console +$ optuna-dashboard sqlite:///db.sqlite3 +``` +# Road Map +- [x] Feature Generation +- [x] Save/Load and Predict on New Samples +- [x] Advanced Logging +- [x] Add opt Pruners +- [ ] Docs Site +- [ ] DL Encoders +- [ ] Add More libs (NNs) +- [ ] Multiclass Classification +- [ ] Build pipelines +# Contact +[Telegram Group](https://t.me/automlalex) + +%package -n python3-automl-alex +Summary: State-of-the art Automated Machine Learning python library for Tabular Data +Provides: python-automl-alex +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-automl-alex +<p align="center"> State-of-the art Automated Machine Learning python library for Tabular Data</p> +## Works with Tasks: +- [x] Binary Classification +- [x] Regression +- [ ] Multiclass Classification (in progress...) +### Benchmark Results +<img width=800 src="https://github.com/Alex-Lekov/AutoML-Benchmark/blob/master/img/Total_SUM.png" alt="bench"> +The bigger, the better +From [AutoML-Benchmark](https://github.com/Alex-Lekov/AutoML-Benchmark/) +### Scheme +<img width=800 src="https://github.com/Alex-Lekov/AutoML_Alex/blob/develop/examples/img/shema.png" alt="scheme"> +# Features +- Automated Data Clean (Auto Clean) +- Automated **Feature Engineering** (Auto FE) +- Smart Hyperparameter Optimization (HPO) +- Feature Generation +- Feature Selection +- Models Selection +- Cross Validation +- Optimization Timelimit and EarlyStoping +- Save and Load (Predict new data) +# Installation +```python +pip install automl-alex +``` +# Docs +[DocPage](https://alex-lekov.github.io/AutoML_Alex/) +# 🚀 Examples +Classifier: +```python +from automl_alex import AutoMLClassifier +model = AutoMLClassifier() +model.fit(X_train, y_train, timeout=600) +predicts = model.predict(X_test) +``` +Regression: +```python +from automl_alex import AutoMLRegressor +model = AutoMLRegressor() +model.fit(X_train, y_train, timeout=600) +predicts = model.predict(X_test) +``` +DataPrepare: +```python +from automl_alex import DataPrepare +de = DataPrepare() +X_train = de.fit_transform(X_train) +X_test = de.transform(X_test) +``` +Simple Models Wrapper: +```python +from automl_alex import LightGBMClassifier +model = LightGBMClassifier() +model.fit(X_train, y_train) +predicts = model.predict_proba(X_test) +model.opt(X_train, y_train, + timeout=600, # optimization time in seconds, + ) +predicts = model.predict_proba(X_test) +``` +More examples in the folder ./examples: +- [01_Quick_Start.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/01_Quick_Start.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/01_Quick_Start.ipynb) +- [02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb) +- [03_Models.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/03_Models.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/03_Models.ipynb) +- [04_ModelsReview.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/04_ModelsReview.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/04_ModelsReview.ipynb) +- [05_BestSingleModel.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/05_BestSingleModel.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/05_BestSingleModel.ipynb) +- [Production Docker template](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/prod_sample) +# What's inside +It integrates many popular frameworks: +- scikit-learn +- XGBoost +- LightGBM +- CatBoost +- Optuna +- ... +# Works with Features +- [x] Categorical Features +- [x] Numerical Features +- [x] Binary Features +- [ ] Text +- [ ] Datetime +- [ ] Timeseries +- [ ] Image +# Note +- **With a large dataset, a lot of memory is required!** +Library creates many new features. If you have a large dataset with a large number of features (more than 100), you may need a lot of memory. +# Realtime Dashboard +Works with [optuna-dashboard](https://github.com/optuna/optuna-dashboard) +<img width=800 src="https://github.com/Alex-Lekov/AutoML_Alex/blob/develop/examples/img/dashboard.gif" alt="Dashboard"> +Run +```console +$ optuna-dashboard sqlite:///db.sqlite3 +``` +# Road Map +- [x] Feature Generation +- [x] Save/Load and Predict on New Samples +- [x] Advanced Logging +- [x] Add opt Pruners +- [ ] Docs Site +- [ ] DL Encoders +- [ ] Add More libs (NNs) +- [ ] Multiclass Classification +- [ ] Build pipelines +# Contact +[Telegram Group](https://t.me/automlalex) + +%package help +Summary: Development documents and examples for automl-alex +Provides: python3-automl-alex-doc +%description help +<p align="center"> State-of-the art Automated Machine Learning python library for Tabular Data</p> +## Works with Tasks: +- [x] Binary Classification +- [x] Regression +- [ ] Multiclass Classification (in progress...) +### Benchmark Results +<img width=800 src="https://github.com/Alex-Lekov/AutoML-Benchmark/blob/master/img/Total_SUM.png" alt="bench"> +The bigger, the better +From [AutoML-Benchmark](https://github.com/Alex-Lekov/AutoML-Benchmark/) +### Scheme +<img width=800 src="https://github.com/Alex-Lekov/AutoML_Alex/blob/develop/examples/img/shema.png" alt="scheme"> +# Features +- Automated Data Clean (Auto Clean) +- Automated **Feature Engineering** (Auto FE) +- Smart Hyperparameter Optimization (HPO) +- Feature Generation +- Feature Selection +- Models Selection +- Cross Validation +- Optimization Timelimit and EarlyStoping +- Save and Load (Predict new data) +# Installation +```python +pip install automl-alex +``` +# Docs +[DocPage](https://alex-lekov.github.io/AutoML_Alex/) +# 🚀 Examples +Classifier: +```python +from automl_alex import AutoMLClassifier +model = AutoMLClassifier() +model.fit(X_train, y_train, timeout=600) +predicts = model.predict(X_test) +``` +Regression: +```python +from automl_alex import AutoMLRegressor +model = AutoMLRegressor() +model.fit(X_train, y_train, timeout=600) +predicts = model.predict(X_test) +``` +DataPrepare: +```python +from automl_alex import DataPrepare +de = DataPrepare() +X_train = de.fit_transform(X_train) +X_test = de.transform(X_test) +``` +Simple Models Wrapper: +```python +from automl_alex import LightGBMClassifier +model = LightGBMClassifier() +model.fit(X_train, y_train) +predicts = model.predict_proba(X_test) +model.opt(X_train, y_train, + timeout=600, # optimization time in seconds, + ) +predicts = model.predict_proba(X_test) +``` +More examples in the folder ./examples: +- [01_Quick_Start.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/01_Quick_Start.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/01_Quick_Start.ipynb) +- [02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb) +- [03_Models.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/03_Models.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/03_Models.ipynb) +- [04_ModelsReview.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/04_ModelsReview.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/04_ModelsReview.ipynb) +- [05_BestSingleModel.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/05_BestSingleModel.ipynb) [](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/05_BestSingleModel.ipynb) +- [Production Docker template](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/prod_sample) +# What's inside +It integrates many popular frameworks: +- scikit-learn +- XGBoost +- LightGBM +- CatBoost +- Optuna +- ... +# Works with Features +- [x] Categorical Features +- [x] Numerical Features +- [x] Binary Features +- [ ] Text +- [ ] Datetime +- [ ] Timeseries +- [ ] Image +# Note +- **With a large dataset, a lot of memory is required!** +Library creates many new features. If you have a large dataset with a large number of features (more than 100), you may need a lot of memory. +# Realtime Dashboard +Works with [optuna-dashboard](https://github.com/optuna/optuna-dashboard) +<img width=800 src="https://github.com/Alex-Lekov/AutoML_Alex/blob/develop/examples/img/dashboard.gif" alt="Dashboard"> +Run +```console +$ optuna-dashboard sqlite:///db.sqlite3 +``` +# Road Map +- [x] Feature Generation +- [x] Save/Load and Predict on New Samples +- [x] Advanced Logging +- [x] Add opt Pruners +- [ ] Docs Site +- [ ] DL Encoders +- [ ] Add More libs (NNs) +- [ ] Multiclass Classification +- [ ] Build pipelines +# Contact +[Telegram Group](https://t.me/automlalex) + +%prep +%autosetup -n automl-alex-2023.3.11 + +%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-automl-alex -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 2023.3.11-1 +- Package Spec generated |