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@@ -0,0 +1 @@ +/ARAMBHML-0.2.4.tar.gz diff --git a/python-arambhml.spec b/python-arambhml.spec new file mode 100644 index 0000000..6e25531 --- /dev/null +++ b/python-arambhml.spec @@ -0,0 +1,225 @@ +%global _empty_manifest_terminate_build 0 +Name: python-ARAMBHML +Version: 0.2.4 +Release: 1 +Summary: An Auto ML framework that solves Classification Tasks +License: MIT License +URL: https://pypi.org/project/ARAMBHML/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b7/66/6f645cb49554775688745ae35782f37279b79d2e157f22d94bf248a4e1c9/ARAMBHML-0.2.4.tar.gz +BuildArch: noarch + +Requires: python3-pandas +Requires: python3-numpy +Requires: python3-matplotlib +Requires: python3-seaborn +Requires: python3-scikit-learn +Requires: python3-sklearn-pandas +Requires: python3-xgboost +Requires: python3-plotly +Requires: python3-plotly-express + +%description + +# Auto ML for Solving Classification Problems for Tabular Data + +[](https://www.python.org/) +[](https://www.python.org/downloads/release/python-360/) + + +## Functionality of the Auto ML Framework + +- Detects the type of problem from the given target feature +- Does EDA on it's own +- Finds out if there are null values present or not +- If null values are present they are treated specifically with the class they are present in +- Forms train-test split on it's own +- Applies 6 ML models to show the use which model performs the best + +## Usage + +- Make sure you have Python installed in your system. +- Run Following command in the CMD. + ``` + pip install ARAMBHML + ``` +## Example + + +# test.py + ``` + from ARAMBHML import arambhNet + ``` + ``` + new = arambhNet(path,target) #target is the dependent feature and path is the path of csv file + ``` + ``` + new.get_model_details(path,target) + ``` + +## Run the Above Commands to get the results. + + +**NOTE** There are more than functionalities available, you can press "tab" after "new." to get the options + +## Here you can perform EDA and also see how do models perform on the dataset. + +## Note +- I have tried to implement all the functionality, it might have some bugs also. Moreover a number of more functionalities would be added later. + + + + +%package -n python3-ARAMBHML +Summary: An Auto ML framework that solves Classification Tasks +Provides: python-ARAMBHML +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-ARAMBHML + +# Auto ML for Solving Classification Problems for Tabular Data + +[](https://www.python.org/) +[](https://www.python.org/downloads/release/python-360/) + + +## Functionality of the Auto ML Framework + +- Detects the type of problem from the given target feature +- Does EDA on it's own +- Finds out if there are null values present or not +- If null values are present they are treated specifically with the class they are present in +- Forms train-test split on it's own +- Applies 6 ML models to show the use which model performs the best + +## Usage + +- Make sure you have Python installed in your system. +- Run Following command in the CMD. + ``` + pip install ARAMBHML + ``` +## Example + + +# test.py + ``` + from ARAMBHML import arambhNet + ``` + ``` + new = arambhNet(path,target) #target is the dependent feature and path is the path of csv file + ``` + ``` + new.get_model_details(path,target) + ``` + +## Run the Above Commands to get the results. + + +**NOTE** There are more than functionalities available, you can press "tab" after "new." to get the options + +## Here you can perform EDA and also see how do models perform on the dataset. + +## Note +- I have tried to implement all the functionality, it might have some bugs also. Moreover a number of more functionalities would be added later. + + + + +%package help +Summary: Development documents and examples for ARAMBHML +Provides: python3-ARAMBHML-doc +%description help + +# Auto ML for Solving Classification Problems for Tabular Data + +[](https://www.python.org/) +[](https://www.python.org/downloads/release/python-360/) + + +## Functionality of the Auto ML Framework + +- Detects the type of problem from the given target feature +- Does EDA on it's own +- Finds out if there are null values present or not +- If null values are present they are treated specifically with the class they are present in +- Forms train-test split on it's own +- Applies 6 ML models to show the use which model performs the best + +## Usage + +- Make sure you have Python installed in your system. +- Run Following command in the CMD. + ``` + pip install ARAMBHML + ``` +## Example + + +# test.py + ``` + from ARAMBHML import arambhNet + ``` + ``` + new = arambhNet(path,target) #target is the dependent feature and path is the path of csv file + ``` + ``` + new.get_model_details(path,target) + ``` + +## Run the Above Commands to get the results. + + +**NOTE** There are more than functionalities available, you can press "tab" after "new." to get the options + +## Here you can perform EDA and also see how do models perform on the dataset. + +## Note +- I have tried to implement all the functionality, it might have some bugs also. Moreover a number of more functionalities would be added later. + + + + +%prep +%autosetup -n ARAMBHML-0.2.4 + +%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-ARAMBHML -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.4-1 +- Package Spec generated @@ -0,0 +1 @@ +db0453990b92b30f5dfb7776cdb35ed3 ARAMBHML-0.2.4.tar.gz |