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%global _empty_manifest_terminate_build 0
Name: python-Kami
Version: 0.4.3
Release: 1
Summary: Forecast sales with Entity Embedding LSTM
License: MIT
URL: https://github.com/MacarielAerial
Source0: https://mirrors.aliyun.com/pypi/web/packages/9d/3b/be3a4187ada38578703bd07e6f2cd69f42598a4885a89d44d56f6d902804/Kami-0.4.3.tar.gz
BuildArch: noarch
%description
# AM18_SPR20_LondonLAB
## Package Description
The package contains one object **Kami** with four methods in the order of execution:
1. **Preprocess()**
2. **Analyse(n_sample)**
3. **Vis()**
4. **Forecast(store_list, product_list, start, end)**
To initiate the object **Kami**, the user is required to supply at least these three arguments:
1. Path to the grouped product sales input data (***input_f_path***)
2. Path to an intermediary folder to store intermediary data (***cache_dir_path***)
3. Path to an output folder to store final predictions (***output_dir_path***)
While **Preprocess** and **Vis** methods are executed without any argument, **Analyse** method can be supplied with an optional argument *n_sample* which is the number of random samples drawn from the predefined training data.
**Forecast** method is required to be supplied with four arguments including:
1. A list of stores whose sales are predicted (***store_list***)
2. A list of products whose sales are predicted (***product_list***)
3. The start date of the forecast (***start***)
4. The end date of the forecast (***end***)
## Typical Use Case
***
from Kami import Kami
obj = Kami(input_f_path = 'PATH_TO_SALES_DATA/SALES_DATA.csv',
output_dir_path = 'OUTPUT_FOLDER/',
cache_dir_path = 'CACHE_FOLDER/')
obj.Preprocess()
obj.Analyse()
obj.Vis()
obj.Forecast(store_list = ['STORE_A', 'STORE_B'],
product_list = ['PRODUCT_A', 'PRODUCT_B'],
start = 'MM/DD/YYYY',
end = 'MM/DD/YYYY')
***
%package -n python3-Kami
Summary: Forecast sales with Entity Embedding LSTM
Provides: python-Kami
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-Kami
# AM18_SPR20_LondonLAB
## Package Description
The package contains one object **Kami** with four methods in the order of execution:
1. **Preprocess()**
2. **Analyse(n_sample)**
3. **Vis()**
4. **Forecast(store_list, product_list, start, end)**
To initiate the object **Kami**, the user is required to supply at least these three arguments:
1. Path to the grouped product sales input data (***input_f_path***)
2. Path to an intermediary folder to store intermediary data (***cache_dir_path***)
3. Path to an output folder to store final predictions (***output_dir_path***)
While **Preprocess** and **Vis** methods are executed without any argument, **Analyse** method can be supplied with an optional argument *n_sample* which is the number of random samples drawn from the predefined training data.
**Forecast** method is required to be supplied with four arguments including:
1. A list of stores whose sales are predicted (***store_list***)
2. A list of products whose sales are predicted (***product_list***)
3. The start date of the forecast (***start***)
4. The end date of the forecast (***end***)
## Typical Use Case
***
from Kami import Kami
obj = Kami(input_f_path = 'PATH_TO_SALES_DATA/SALES_DATA.csv',
output_dir_path = 'OUTPUT_FOLDER/',
cache_dir_path = 'CACHE_FOLDER/')
obj.Preprocess()
obj.Analyse()
obj.Vis()
obj.Forecast(store_list = ['STORE_A', 'STORE_B'],
product_list = ['PRODUCT_A', 'PRODUCT_B'],
start = 'MM/DD/YYYY',
end = 'MM/DD/YYYY')
***
%package help
Summary: Development documents and examples for Kami
Provides: python3-Kami-doc
%description help
# AM18_SPR20_LondonLAB
## Package Description
The package contains one object **Kami** with four methods in the order of execution:
1. **Preprocess()**
2. **Analyse(n_sample)**
3. **Vis()**
4. **Forecast(store_list, product_list, start, end)**
To initiate the object **Kami**, the user is required to supply at least these three arguments:
1. Path to the grouped product sales input data (***input_f_path***)
2. Path to an intermediary folder to store intermediary data (***cache_dir_path***)
3. Path to an output folder to store final predictions (***output_dir_path***)
While **Preprocess** and **Vis** methods are executed without any argument, **Analyse** method can be supplied with an optional argument *n_sample* which is the number of random samples drawn from the predefined training data.
**Forecast** method is required to be supplied with four arguments including:
1. A list of stores whose sales are predicted (***store_list***)
2. A list of products whose sales are predicted (***product_list***)
3. The start date of the forecast (***start***)
4. The end date of the forecast (***end***)
## Typical Use Case
***
from Kami import Kami
obj = Kami(input_f_path = 'PATH_TO_SALES_DATA/SALES_DATA.csv',
output_dir_path = 'OUTPUT_FOLDER/',
cache_dir_path = 'CACHE_FOLDER/')
obj.Preprocess()
obj.Analyse()
obj.Vis()
obj.Forecast(store_list = ['STORE_A', 'STORE_B'],
product_list = ['PRODUCT_A', 'PRODUCT_B'],
start = 'MM/DD/YYYY',
end = 'MM/DD/YYYY')
***
%prep
%autosetup -n Kami-0.4.3
%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-Kami -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.3-1
- Package Spec generated
|
