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|
%global _empty_manifest_terminate_build 0
Name: python-NitroFE
Version: 0.0.10
Release: 1
Summary: NitroFE is a Python feature engineering engine which provides a variety of feature engineering modules designed to handle continous calcualtion.
License: Apache License 2.0
URL: https://github.com/NITRO-AI/NitroFE
Source0: https://mirrors.aliyun.com/pypi/web/packages/8a/80/ab9d3ced563b6c7a722ced87e52c20efd305183add071e592d02abd69de2/NitroFE-0.0.10.tar.gz
BuildArch: noarch
%description
# NitroFE ( Nitro Feature Engineering )
[](https://sonarcloud.io/dashboard?id=NITRO-AI_NitroFE)
[](https://sonarcloud.io/dashboard?id=NITRO-AI_NitroFE)
[](https://sonarcloud.io/dashboard?id=NITRO-AI_NitroFE)
``NitroFE`` is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.
## Documentation
https://nitro-ai.github.io/NitroFE/
## Installation
[](https://pypi.com/project/NitroFE)
### Using pip
Use the package manager to install NitroFE.
```bash
pip install NitroFE
```
# Available feature domains
# [Time based Features](https://nitro-ai.github.io/NitroFE/Time%20based%20features/)
Indicator / windows / moving averages features are dependent on past values for calculation, e.g. a rolling window of size 4 is dependent on past 4 values.
While creating such features during training is quite straighforward , taking it to production becomes challenging as it would requires one to externally save past values and implement logic. Creating indicators becomes even more complex as they are dependent on several other differently sized window components.
NitroFE internally handles saving past dependant values, and makes feature creation hassle free. Just use **first_fit=True** for your initial fit
* Jump right in for a handson [](https://colab.research.google.com/drive/1LDM9er9x7NJogRlHkRcLB4bhU1mK0l-9?usp=sharing)
The Time based domain is divided into 'Moving average features', 'Weighted window features' and 'indicator based features'
## [Indicators based Features](https://nitro-ai.github.io/NitroFE/indicators%20features/)
NitroFe provides a rich variety of features which are inspired and translated from market indicators.
* [Absolute Price Oscillator](https://nitro-ai.github.io/NitroFE/Absolute%20Price%20Oscillator/)
* [Percentage price oscillator](https://nitro-ai.github.io/NitroFE/Percentage%20Value%20Oscillator/)
* [Moving average convergence divergence](https://nitro-ai.github.io/NitroFE/Moving%20Average%20Convergence%20Divergence/)
* [Average true range](https://nitro-ai.github.io/NitroFE/Average%20True%20Range/)
* [Average directional index](https://nitro-ai.github.io/NitroFE/Average%20Directional%20Movement%20Index/)
* [Aroon Oscillator](https://nitro-ai.github.io/NitroFE/Aroon%20Oscillator/)
* [Bollinger Bands](https://nitro-ai.github.io/NitroFE/Bollinger%20Bands/)
* [Kaufman Efficiency](https://nitro-ai.github.io/NitroFE/Kaufman%20Efficiency/)
* [Triple Exponential Moving Average Oscillator](https://nitro-ai.github.io/NitroFE/Kaufman%20Efficiency/)
* [Zero lag exponential moving average](https://nitro-ai.github.io/NitroFE/Zero%20Lag%20Exponential%20Moving%20Feature/)
* [Relative Strength Index](https://nitro-ai.github.io/NitroFE/Relative%20Strength%20Index/)
* [Inverse Fisher Relative Strength Index](https://nitro-ai.github.io/NitroFE/Inverse%20Fisher%20Relative%20Strength%20Index/)
* [Series Weighted Average](https://nitro-ai.github.io/NitroFE/Series%20Weighted%20Average/)
* [Series Weighted Moving Feature](https://nitro-ai.github.io/NitroFE/Series%20Weighted%20Moving%20Feature/)
* [Keltner channel](https://nitro-ai.github.io/NitroFE/Keltner%20Channel/)
## [Moving average features](https://nitro-ai.github.io/NitroFE/moving%20average%20features/)
In statistics, a moving average (rolling average or running average) is a calculation to analyze data points by creating a series of averages of different subsets of the full data set. NitroFE provides an array of variety of moving averages type for you to utilize.
* [Exponential moving average](https://nitro-ai.github.io/NitroFE/exponential%20moving%20average/)
* [Hull Moving Average](https://nitro-ai.github.io/NitroFE/hull%20moving%20average/)
* [Kaufman's Adaptive Moving Average](https://nitro-ai.github.io/NitroFE/kaufman%20adaptive%20moving%20average/)
* [Fractal Adaptive Moving Average](https://nitro-ai.github.io/NitroFE/fractal%20adaptive%20moving%20average/)
* [Triple exponential moving average](https://nitro-ai.github.io/NitroFE/triple%20exponential%20moving%20average/)
* [Smoothed Moving Average](https://nitro-ai.github.io/NitroFE/smoothed%20moving%20average/)
## [Weighted window Features](https://nitro-ai.github.io/NitroFE/weighted%20window%20features/)
NitroFe provides easy to use functions to create specified weighted window featuresm and send custom operations as per your need
%package -n python3-NitroFE
Summary: NitroFE is a Python feature engineering engine which provides a variety of feature engineering modules designed to handle continous calcualtion.
Provides: python-NitroFE
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-NitroFE
# NitroFE ( Nitro Feature Engineering )
[](https://sonarcloud.io/dashboard?id=NITRO-AI_NitroFE)
[](https://sonarcloud.io/dashboard?id=NITRO-AI_NitroFE)
[](https://sonarcloud.io/dashboard?id=NITRO-AI_NitroFE)
``NitroFE`` is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.
## Documentation
https://nitro-ai.github.io/NitroFE/
## Installation
[](https://pypi.com/project/NitroFE)
### Using pip
Use the package manager to install NitroFE.
```bash
pip install NitroFE
```
# Available feature domains
# [Time based Features](https://nitro-ai.github.io/NitroFE/Time%20based%20features/)
Indicator / windows / moving averages features are dependent on past values for calculation, e.g. a rolling window of size 4 is dependent on past 4 values.
While creating such features during training is quite straighforward , taking it to production becomes challenging as it would requires one to externally save past values and implement logic. Creating indicators becomes even more complex as they are dependent on several other differently sized window components.
NitroFE internally handles saving past dependant values, and makes feature creation hassle free. Just use **first_fit=True** for your initial fit
* Jump right in for a handson [](https://colab.research.google.com/drive/1LDM9er9x7NJogRlHkRcLB4bhU1mK0l-9?usp=sharing)
The Time based domain is divided into 'Moving average features', 'Weighted window features' and 'indicator based features'
## [Indicators based Features](https://nitro-ai.github.io/NitroFE/indicators%20features/)
NitroFe provides a rich variety of features which are inspired and translated from market indicators.
* [Absolute Price Oscillator](https://nitro-ai.github.io/NitroFE/Absolute%20Price%20Oscillator/)
* [Percentage price oscillator](https://nitro-ai.github.io/NitroFE/Percentage%20Value%20Oscillator/)
* [Moving average convergence divergence](https://nitro-ai.github.io/NitroFE/Moving%20Average%20Convergence%20Divergence/)
* [Average true range](https://nitro-ai.github.io/NitroFE/Average%20True%20Range/)
* [Average directional index](https://nitro-ai.github.io/NitroFE/Average%20Directional%20Movement%20Index/)
* [Aroon Oscillator](https://nitro-ai.github.io/NitroFE/Aroon%20Oscillator/)
* [Bollinger Bands](https://nitro-ai.github.io/NitroFE/Bollinger%20Bands/)
* [Kaufman Efficiency](https://nitro-ai.github.io/NitroFE/Kaufman%20Efficiency/)
* [Triple Exponential Moving Average Oscillator](https://nitro-ai.github.io/NitroFE/Kaufman%20Efficiency/)
* [Zero lag exponential moving average](https://nitro-ai.github.io/NitroFE/Zero%20Lag%20Exponential%20Moving%20Feature/)
* [Relative Strength Index](https://nitro-ai.github.io/NitroFE/Relative%20Strength%20Index/)
* [Inverse Fisher Relative Strength Index](https://nitro-ai.github.io/NitroFE/Inverse%20Fisher%20Relative%20Strength%20Index/)
* [Series Weighted Average](https://nitro-ai.github.io/NitroFE/Series%20Weighted%20Average/)
* [Series Weighted Moving Feature](https://nitro-ai.github.io/NitroFE/Series%20Weighted%20Moving%20Feature/)
* [Keltner channel](https://nitro-ai.github.io/NitroFE/Keltner%20Channel/)
## [Moving average features](https://nitro-ai.github.io/NitroFE/moving%20average%20features/)
In statistics, a moving average (rolling average or running average) is a calculation to analyze data points by creating a series of averages of different subsets of the full data set. NitroFE provides an array of variety of moving averages type for you to utilize.
* [Exponential moving average](https://nitro-ai.github.io/NitroFE/exponential%20moving%20average/)
* [Hull Moving Average](https://nitro-ai.github.io/NitroFE/hull%20moving%20average/)
* [Kaufman's Adaptive Moving Average](https://nitro-ai.github.io/NitroFE/kaufman%20adaptive%20moving%20average/)
* [Fractal Adaptive Moving Average](https://nitro-ai.github.io/NitroFE/fractal%20adaptive%20moving%20average/)
* [Triple exponential moving average](https://nitro-ai.github.io/NitroFE/triple%20exponential%20moving%20average/)
* [Smoothed Moving Average](https://nitro-ai.github.io/NitroFE/smoothed%20moving%20average/)
## [Weighted window Features](https://nitro-ai.github.io/NitroFE/weighted%20window%20features/)
NitroFe provides easy to use functions to create specified weighted window featuresm and send custom operations as per your need
%package help
Summary: Development documents and examples for NitroFE
Provides: python3-NitroFE-doc
%description help
# NitroFE ( Nitro Feature Engineering )
[](https://sonarcloud.io/dashboard?id=NITRO-AI_NitroFE)
[](https://sonarcloud.io/dashboard?id=NITRO-AI_NitroFE)
[](https://sonarcloud.io/dashboard?id=NITRO-AI_NitroFE)
``NitroFE`` is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.
## Documentation
https://nitro-ai.github.io/NitroFE/
## Installation
[](https://pypi.com/project/NitroFE)
### Using pip
Use the package manager to install NitroFE.
```bash
pip install NitroFE
```
# Available feature domains
# [Time based Features](https://nitro-ai.github.io/NitroFE/Time%20based%20features/)
Indicator / windows / moving averages features are dependent on past values for calculation, e.g. a rolling window of size 4 is dependent on past 4 values.
While creating such features during training is quite straighforward , taking it to production becomes challenging as it would requires one to externally save past values and implement logic. Creating indicators becomes even more complex as they are dependent on several other differently sized window components.
NitroFE internally handles saving past dependant values, and makes feature creation hassle free. Just use **first_fit=True** for your initial fit
* Jump right in for a handson [](https://colab.research.google.com/drive/1LDM9er9x7NJogRlHkRcLB4bhU1mK0l-9?usp=sharing)
The Time based domain is divided into 'Moving average features', 'Weighted window features' and 'indicator based features'
## [Indicators based Features](https://nitro-ai.github.io/NitroFE/indicators%20features/)
NitroFe provides a rich variety of features which are inspired and translated from market indicators.
* [Absolute Price Oscillator](https://nitro-ai.github.io/NitroFE/Absolute%20Price%20Oscillator/)
* [Percentage price oscillator](https://nitro-ai.github.io/NitroFE/Percentage%20Value%20Oscillator/)
* [Moving average convergence divergence](https://nitro-ai.github.io/NitroFE/Moving%20Average%20Convergence%20Divergence/)
* [Average true range](https://nitro-ai.github.io/NitroFE/Average%20True%20Range/)
* [Average directional index](https://nitro-ai.github.io/NitroFE/Average%20Directional%20Movement%20Index/)
* [Aroon Oscillator](https://nitro-ai.github.io/NitroFE/Aroon%20Oscillator/)
* [Bollinger Bands](https://nitro-ai.github.io/NitroFE/Bollinger%20Bands/)
* [Kaufman Efficiency](https://nitro-ai.github.io/NitroFE/Kaufman%20Efficiency/)
* [Triple Exponential Moving Average Oscillator](https://nitro-ai.github.io/NitroFE/Kaufman%20Efficiency/)
* [Zero lag exponential moving average](https://nitro-ai.github.io/NitroFE/Zero%20Lag%20Exponential%20Moving%20Feature/)
* [Relative Strength Index](https://nitro-ai.github.io/NitroFE/Relative%20Strength%20Index/)
* [Inverse Fisher Relative Strength Index](https://nitro-ai.github.io/NitroFE/Inverse%20Fisher%20Relative%20Strength%20Index/)
* [Series Weighted Average](https://nitro-ai.github.io/NitroFE/Series%20Weighted%20Average/)
* [Series Weighted Moving Feature](https://nitro-ai.github.io/NitroFE/Series%20Weighted%20Moving%20Feature/)
* [Keltner channel](https://nitro-ai.github.io/NitroFE/Keltner%20Channel/)
## [Moving average features](https://nitro-ai.github.io/NitroFE/moving%20average%20features/)
In statistics, a moving average (rolling average or running average) is a calculation to analyze data points by creating a series of averages of different subsets of the full data set. NitroFE provides an array of variety of moving averages type for you to utilize.
* [Exponential moving average](https://nitro-ai.github.io/NitroFE/exponential%20moving%20average/)
* [Hull Moving Average](https://nitro-ai.github.io/NitroFE/hull%20moving%20average/)
* [Kaufman's Adaptive Moving Average](https://nitro-ai.github.io/NitroFE/kaufman%20adaptive%20moving%20average/)
* [Fractal Adaptive Moving Average](https://nitro-ai.github.io/NitroFE/fractal%20adaptive%20moving%20average/)
* [Triple exponential moving average](https://nitro-ai.github.io/NitroFE/triple%20exponential%20moving%20average/)
* [Smoothed Moving Average](https://nitro-ai.github.io/NitroFE/smoothed%20moving%20average/)
## [Weighted window Features](https://nitro-ai.github.io/NitroFE/weighted%20window%20features/)
NitroFe provides easy to use functions to create specified weighted window featuresm and send custom operations as per your need
%prep
%autosetup -n NitroFE-0.0.10
%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-NitroFE -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.10-1
- Package Spec generated
|
