%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