%global _empty_manifest_terminate_build 0
Name: python-FLAML
Version: 1.2.0
Release: 1
Summary: A fast library for automated machine learning and tuning
License: MIT License
URL: https://github.com/microsoft/FLAML
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/fb/f7/38298ae67a633f668e68bf08cc13d7c401852b036ddfb95098a86315f028/FLAML-1.2.0.tar.gz
BuildArch: noarch
Requires: python3-NumPy
Requires: python3-lightgbm
Requires: python3-xgboost
Requires: python3-scipy
Requires: python3-pandas
Requires: python3-scikit-learn
Requires: python3-azureml-mlflow
Requires: python3-catboost
Requires: python3-psutil
Requires: python3-xgboost
Requires: python3-optuna
Requires: python3-catboost
Requires: python3-holidays
Requires: python3-prophet
Requires: python3-statsmodels
Requires: python3-hcrystalball
Requires: python3-pytorch-forecasting
Requires: python3-transformers[torch]
Requires: python3-datasets
Requires: python3-nltk
Requires: python3-rouge-score
Requires: python3-seqeval
Requires: python3-transformers[torch]
Requires: python3-datasets
Requires: python3-nltk
Requires: python3-rouge-score
Requires: python3-seqeval
Requires: python3-nni
Requires: python3-jupyter
Requires: python3-matplotlib
Requires: python3-openml
Requires: python3-openai
Requires: python3-diskcache
Requires: python3-optuna
Requires: python3-ray[tune]
Requires: python3-pyspark
Requires: python3-joblibspark
Requires: python3-joblibspark
Requires: python3-optuna
Requires: python3-pyspark
Requires: python3-flake8
Requires: python3-thop
Requires: python3-pytest
Requires: python3-coverage
Requires: python3-pre-commit
Requires: python3-torch
Requires: python3-torchvision
Requires: python3-catboost
Requires: python3-rgf-python
Requires: python3-optuna
Requires: python3-openml
Requires: python3-statsmodels
Requires: python3-psutil
Requires: python3-dataclasses
Requires: python3-transformers[torch]
Requires: python3-datasets
Requires: python3-nltk
Requires: python3-rouge-score
Requires: python3-hcrystalball
Requires: python3-seqeval
Requires: python3-pytorch-forecasting
Requires: python3-mlflow
Requires: python3-pyspark
Requires: python3-joblibspark
Requires: python3-nbconvert
Requires: python3-nbformat
Requires: python3-ipykernel
Requires: python3-pytorch-lightning
Requires: python3-holidays
Requires: python3-prophet
Requires: python3-statsmodels
Requires: python3-hcrystalball
Requires: python3-vowpalwabbit
%description
[](https://badge.fury.io/py/FLAML)
[](https://github.com/microsoft/FLAML/actions/workflows/python-package.yml)
[](https://pepy.tech/project/flaml)
[](https://gitter.im/FLAMLer/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[](https://discord.gg/Cppx2vSPVP)
# A Fast Library for Automated Machine Learning & Tuning
:fire: OpenAI GPT-3 models support in v1.1.3. ChatGPT and GPT-4 support will be added in v1.2.0.
:fire: A [lab forum](https://github.com/microsoft/FLAML/tree/tutorial-aaai23/tutorial) on FLAML at AAAI 2023.
:fire: A [hands-on tutorial](https://github.com/microsoft/FLAML/tree/tutorial/tutorial) on FLAML presented at KDD 2022
## What is FLAML
FLAML is a lightweight Python library that finds accurate machine
learning models automatically, efficiently and economically. It frees users from selecting
models and hyperparameters for each model. It can also be used to tune generic hyperparameters for foundation models, MLOps/LMOps workflows, pipelines, mathematical/statistical models, algorithms, computing experiments, software configurations and so on.
1. For common machine learning or AI tasks like classification, regression, and generation, it quickly finds quality models for user-provided data with low computational resources. It supports both classical machine learning models and deep neural networks, including foundation models such as the GPT series.
1. It is easy to customize or extend. Users can find their desired customizability from a smooth range: minimal customization (computational resource budget), medium customization (e.g., scikit-style learner, search space and metric), or full customization (arbitrary training and evaluation code).
1. It supports fast automatic tuning, capable of handling complex constraints/guidance/early stopping. FLAML is powered by a new, [cost-effective
hyperparameter optimization](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function/#hyperparameter-optimization-algorithm)
and model selection method invented by Microsoft Research, and many followup [research studies](https://microsoft.github.io/FLAML/docs/Research).
FLAML has a .NET implementation in [ML.NET](http://dot.net/ml), an open-source, cross-platform machine learning framework for .NET. In ML.NET, you can use FLAML via low-code solutions like [Model Builder](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-builder) Visual Studio extension and the cross-platform [ML.NET CLI](https://docs.microsoft.com/dotnet/machine-learning/automate-training-with-cli). Alternatively, you can use the [ML.NET AutoML API](https://www.nuget.org/packages/Microsoft.ML.AutoML/#versions-body-tab) for a code-first experience.
## Installation
### Python
FLAML requires **Python version >= 3.7**. It can be installed from pip:
```bash
pip install flaml
```
To run the [`notebook examples`](https://github.com/microsoft/FLAML/tree/main/notebook),
install flaml with the [notebook] option:
```bash
pip install flaml[notebook]
```
### .NET
Use the following guides to get started with FLAML in .NET:
- [Install Model Builder](https://docs.microsoft.com/dotnet/machine-learning/how-to-guides/install-model-builder?tabs=visual-studio-2022)
- [Install ML.NET CLI](https://docs.microsoft.com/dotnet/machine-learning/how-to-guides/install-ml-net-cli?tabs=windows)
- [Microsoft.AutoML](https://www.nuget.org/packages/Microsoft.ML.AutoML/0.20.0)
## Quickstart
* With three lines of code, you can start using this economical and fast
AutoML engine as a [scikit-learn style estimator](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML).
```python
from flaml import AutoML
automl = AutoML()
automl.fit(X_train, y_train, task="classification")
```
* You can restrict the learners and use FLAML as a fast hyperparameter tuning
tool for XGBoost, LightGBM, Random Forest etc. or a [customized learner](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#estimator-and-search-space).
```python
automl.fit(X_train, y_train, task="classification", estimator_list=["lgbm"])
```
* You can also run generic hyperparameter tuning for a [custom function](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function).
```python
from flaml import tune
tune.run(evaluation_function, config={…}, low_cost_partial_config={…}, time_budget_s=3600)
```
* [Zero-shot AutoML](https://microsoft.github.io/FLAML/docs/Use-Cases/Zero-Shot-AutoML) allows using the existing training API from lightgbm, xgboost etc. while getting the benefit of AutoML in choosing high-performance hyperparameter configurations per task.
```python
from flaml.default import LGBMRegressor
# Use LGBMRegressor in the same way as you use lightgbm.LGBMRegressor.
estimator = LGBMRegressor()
# The hyperparameters are automatically set according to the training data.
estimator.fit(X_train, y_train)
```
* (New) You can optimize [generations](https://microsoft.github.io/FLAML/docs/Use-Cases/Auto-Generation) by ChatGPT or GPT-4 etc. with your own tuning data, success metrics and budgets.
```python
from flaml import oai
config, analysis = oai.Completion.tune(
data=tune_data,
metric="success",
mode="max",
eval_func=eval_func,
inference_budget=0.05,
optimization_budget=3,
num_samples=-1,
)
```
## Documentation
You can find a detailed documentation about FLAML [here](https://microsoft.github.io/FLAML/) where you can find the API documentation, use cases and examples.
In addition, you can find:
- [Talks](https://www.youtube.com/channel/UCfU0zfFXHXdAd5x-WvFBk5A) and [tutorials](https://github.com/microsoft/FLAML/tree/tutorial/tutorial) about FLAML.
- Research around FLAML [here](https://microsoft.github.io/FLAML/docs/Research).
- FAQ [here](https://microsoft.github.io/FLAML/docs/FAQ).
- Contributing guide [here](https://microsoft.github.io/FLAML/docs/Contribute).
- ML.NET documentation and tutorials for [Model Builder](https://learn.microsoft.com/dotnet/machine-learning/tutorials/predict-prices-with-model-builder), [ML.NET CLI](https://learn.microsoft.com/dotnet/machine-learning/tutorials/sentiment-analysis-cli), and [AutoML API](https://learn.microsoft.com/dotnet/machine-learning/how-to-guides/how-to-use-the-automl-api).
## Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit .
If you are new to GitHub [here](https://help.github.com/categories/collaborating-with-issues-and-pull-requests/) is a detailed help source on getting involved with development on GitHub.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
%package -n python3-FLAML
Summary: A fast library for automated machine learning and tuning
Provides: python-FLAML
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-FLAML
[](https://badge.fury.io/py/FLAML)
[](https://github.com/microsoft/FLAML/actions/workflows/python-package.yml)
[](https://pepy.tech/project/flaml)
[](https://gitter.im/FLAMLer/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[](https://discord.gg/Cppx2vSPVP)
# A Fast Library for Automated Machine Learning & Tuning
:fire: OpenAI GPT-3 models support in v1.1.3. ChatGPT and GPT-4 support will be added in v1.2.0.
:fire: A [lab forum](https://github.com/microsoft/FLAML/tree/tutorial-aaai23/tutorial) on FLAML at AAAI 2023.
:fire: A [hands-on tutorial](https://github.com/microsoft/FLAML/tree/tutorial/tutorial) on FLAML presented at KDD 2022
## What is FLAML
FLAML is a lightweight Python library that finds accurate machine
learning models automatically, efficiently and economically. It frees users from selecting
models and hyperparameters for each model. It can also be used to tune generic hyperparameters for foundation models, MLOps/LMOps workflows, pipelines, mathematical/statistical models, algorithms, computing experiments, software configurations and so on.
1. For common machine learning or AI tasks like classification, regression, and generation, it quickly finds quality models for user-provided data with low computational resources. It supports both classical machine learning models and deep neural networks, including foundation models such as the GPT series.
1. It is easy to customize or extend. Users can find their desired customizability from a smooth range: minimal customization (computational resource budget), medium customization (e.g., scikit-style learner, search space and metric), or full customization (arbitrary training and evaluation code).
1. It supports fast automatic tuning, capable of handling complex constraints/guidance/early stopping. FLAML is powered by a new, [cost-effective
hyperparameter optimization](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function/#hyperparameter-optimization-algorithm)
and model selection method invented by Microsoft Research, and many followup [research studies](https://microsoft.github.io/FLAML/docs/Research).
FLAML has a .NET implementation in [ML.NET](http://dot.net/ml), an open-source, cross-platform machine learning framework for .NET. In ML.NET, you can use FLAML via low-code solutions like [Model Builder](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-builder) Visual Studio extension and the cross-platform [ML.NET CLI](https://docs.microsoft.com/dotnet/machine-learning/automate-training-with-cli). Alternatively, you can use the [ML.NET AutoML API](https://www.nuget.org/packages/Microsoft.ML.AutoML/#versions-body-tab) for a code-first experience.
## Installation
### Python
FLAML requires **Python version >= 3.7**. It can be installed from pip:
```bash
pip install flaml
```
To run the [`notebook examples`](https://github.com/microsoft/FLAML/tree/main/notebook),
install flaml with the [notebook] option:
```bash
pip install flaml[notebook]
```
### .NET
Use the following guides to get started with FLAML in .NET:
- [Install Model Builder](https://docs.microsoft.com/dotnet/machine-learning/how-to-guides/install-model-builder?tabs=visual-studio-2022)
- [Install ML.NET CLI](https://docs.microsoft.com/dotnet/machine-learning/how-to-guides/install-ml-net-cli?tabs=windows)
- [Microsoft.AutoML](https://www.nuget.org/packages/Microsoft.ML.AutoML/0.20.0)
## Quickstart
* With three lines of code, you can start using this economical and fast
AutoML engine as a [scikit-learn style estimator](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML).
```python
from flaml import AutoML
automl = AutoML()
automl.fit(X_train, y_train, task="classification")
```
* You can restrict the learners and use FLAML as a fast hyperparameter tuning
tool for XGBoost, LightGBM, Random Forest etc. or a [customized learner](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#estimator-and-search-space).
```python
automl.fit(X_train, y_train, task="classification", estimator_list=["lgbm"])
```
* You can also run generic hyperparameter tuning for a [custom function](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function).
```python
from flaml import tune
tune.run(evaluation_function, config={…}, low_cost_partial_config={…}, time_budget_s=3600)
```
* [Zero-shot AutoML](https://microsoft.github.io/FLAML/docs/Use-Cases/Zero-Shot-AutoML) allows using the existing training API from lightgbm, xgboost etc. while getting the benefit of AutoML in choosing high-performance hyperparameter configurations per task.
```python
from flaml.default import LGBMRegressor
# Use LGBMRegressor in the same way as you use lightgbm.LGBMRegressor.
estimator = LGBMRegressor()
# The hyperparameters are automatically set according to the training data.
estimator.fit(X_train, y_train)
```
* (New) You can optimize [generations](https://microsoft.github.io/FLAML/docs/Use-Cases/Auto-Generation) by ChatGPT or GPT-4 etc. with your own tuning data, success metrics and budgets.
```python
from flaml import oai
config, analysis = oai.Completion.tune(
data=tune_data,
metric="success",
mode="max",
eval_func=eval_func,
inference_budget=0.05,
optimization_budget=3,
num_samples=-1,
)
```
## Documentation
You can find a detailed documentation about FLAML [here](https://microsoft.github.io/FLAML/) where you can find the API documentation, use cases and examples.
In addition, you can find:
- [Talks](https://www.youtube.com/channel/UCfU0zfFXHXdAd5x-WvFBk5A) and [tutorials](https://github.com/microsoft/FLAML/tree/tutorial/tutorial) about FLAML.
- Research around FLAML [here](https://microsoft.github.io/FLAML/docs/Research).
- FAQ [here](https://microsoft.github.io/FLAML/docs/FAQ).
- Contributing guide [here](https://microsoft.github.io/FLAML/docs/Contribute).
- ML.NET documentation and tutorials for [Model Builder](https://learn.microsoft.com/dotnet/machine-learning/tutorials/predict-prices-with-model-builder), [ML.NET CLI](https://learn.microsoft.com/dotnet/machine-learning/tutorials/sentiment-analysis-cli), and [AutoML API](https://learn.microsoft.com/dotnet/machine-learning/how-to-guides/how-to-use-the-automl-api).
## Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit .
If you are new to GitHub [here](https://help.github.com/categories/collaborating-with-issues-and-pull-requests/) is a detailed help source on getting involved with development on GitHub.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
%package help
Summary: Development documents and examples for FLAML
Provides: python3-FLAML-doc
%description help
[](https://badge.fury.io/py/FLAML)
[](https://github.com/microsoft/FLAML/actions/workflows/python-package.yml)
[](https://pepy.tech/project/flaml)
[](https://gitter.im/FLAMLer/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[](https://discord.gg/Cppx2vSPVP)
# A Fast Library for Automated Machine Learning & Tuning
:fire: OpenAI GPT-3 models support in v1.1.3. ChatGPT and GPT-4 support will be added in v1.2.0.
:fire: A [lab forum](https://github.com/microsoft/FLAML/tree/tutorial-aaai23/tutorial) on FLAML at AAAI 2023.
:fire: A [hands-on tutorial](https://github.com/microsoft/FLAML/tree/tutorial/tutorial) on FLAML presented at KDD 2022
## What is FLAML
FLAML is a lightweight Python library that finds accurate machine
learning models automatically, efficiently and economically. It frees users from selecting
models and hyperparameters for each model. It can also be used to tune generic hyperparameters for foundation models, MLOps/LMOps workflows, pipelines, mathematical/statistical models, algorithms, computing experiments, software configurations and so on.
1. For common machine learning or AI tasks like classification, regression, and generation, it quickly finds quality models for user-provided data with low computational resources. It supports both classical machine learning models and deep neural networks, including foundation models such as the GPT series.
1. It is easy to customize or extend. Users can find their desired customizability from a smooth range: minimal customization (computational resource budget), medium customization (e.g., scikit-style learner, search space and metric), or full customization (arbitrary training and evaluation code).
1. It supports fast automatic tuning, capable of handling complex constraints/guidance/early stopping. FLAML is powered by a new, [cost-effective
hyperparameter optimization](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function/#hyperparameter-optimization-algorithm)
and model selection method invented by Microsoft Research, and many followup [research studies](https://microsoft.github.io/FLAML/docs/Research).
FLAML has a .NET implementation in [ML.NET](http://dot.net/ml), an open-source, cross-platform machine learning framework for .NET. In ML.NET, you can use FLAML via low-code solutions like [Model Builder](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-builder) Visual Studio extension and the cross-platform [ML.NET CLI](https://docs.microsoft.com/dotnet/machine-learning/automate-training-with-cli). Alternatively, you can use the [ML.NET AutoML API](https://www.nuget.org/packages/Microsoft.ML.AutoML/#versions-body-tab) for a code-first experience.
## Installation
### Python
FLAML requires **Python version >= 3.7**. It can be installed from pip:
```bash
pip install flaml
```
To run the [`notebook examples`](https://github.com/microsoft/FLAML/tree/main/notebook),
install flaml with the [notebook] option:
```bash
pip install flaml[notebook]
```
### .NET
Use the following guides to get started with FLAML in .NET:
- [Install Model Builder](https://docs.microsoft.com/dotnet/machine-learning/how-to-guides/install-model-builder?tabs=visual-studio-2022)
- [Install ML.NET CLI](https://docs.microsoft.com/dotnet/machine-learning/how-to-guides/install-ml-net-cli?tabs=windows)
- [Microsoft.AutoML](https://www.nuget.org/packages/Microsoft.ML.AutoML/0.20.0)
## Quickstart
* With three lines of code, you can start using this economical and fast
AutoML engine as a [scikit-learn style estimator](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML).
```python
from flaml import AutoML
automl = AutoML()
automl.fit(X_train, y_train, task="classification")
```
* You can restrict the learners and use FLAML as a fast hyperparameter tuning
tool for XGBoost, LightGBM, Random Forest etc. or a [customized learner](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#estimator-and-search-space).
```python
automl.fit(X_train, y_train, task="classification", estimator_list=["lgbm"])
```
* You can also run generic hyperparameter tuning for a [custom function](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function).
```python
from flaml import tune
tune.run(evaluation_function, config={…}, low_cost_partial_config={…}, time_budget_s=3600)
```
* [Zero-shot AutoML](https://microsoft.github.io/FLAML/docs/Use-Cases/Zero-Shot-AutoML) allows using the existing training API from lightgbm, xgboost etc. while getting the benefit of AutoML in choosing high-performance hyperparameter configurations per task.
```python
from flaml.default import LGBMRegressor
# Use LGBMRegressor in the same way as you use lightgbm.LGBMRegressor.
estimator = LGBMRegressor()
# The hyperparameters are automatically set according to the training data.
estimator.fit(X_train, y_train)
```
* (New) You can optimize [generations](https://microsoft.github.io/FLAML/docs/Use-Cases/Auto-Generation) by ChatGPT or GPT-4 etc. with your own tuning data, success metrics and budgets.
```python
from flaml import oai
config, analysis = oai.Completion.tune(
data=tune_data,
metric="success",
mode="max",
eval_func=eval_func,
inference_budget=0.05,
optimization_budget=3,
num_samples=-1,
)
```
## Documentation
You can find a detailed documentation about FLAML [here](https://microsoft.github.io/FLAML/) where you can find the API documentation, use cases and examples.
In addition, you can find:
- [Talks](https://www.youtube.com/channel/UCfU0zfFXHXdAd5x-WvFBk5A) and [tutorials](https://github.com/microsoft/FLAML/tree/tutorial/tutorial) about FLAML.
- Research around FLAML [here](https://microsoft.github.io/FLAML/docs/Research).
- FAQ [here](https://microsoft.github.io/FLAML/docs/FAQ).
- Contributing guide [here](https://microsoft.github.io/FLAML/docs/Contribute).
- ML.NET documentation and tutorials for [Model Builder](https://learn.microsoft.com/dotnet/machine-learning/tutorials/predict-prices-with-model-builder), [ML.NET CLI](https://learn.microsoft.com/dotnet/machine-learning/tutorials/sentiment-analysis-cli), and [AutoML API](https://learn.microsoft.com/dotnet/machine-learning/how-to-guides/how-to-use-the-automl-api).
## Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit .
If you are new to GitHub [here](https://help.github.com/categories/collaborating-with-issues-and-pull-requests/) is a detailed help source on getting involved with development on GitHub.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
%prep
%autosetup -n FLAML-1.2.0
%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-FLAML -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 11 2023 Python_Bot - 1.2.0-1
- Package Spec generated