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%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
|
