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diff --git a/python-kami.spec b/python-kami.spec new file mode 100644 index 0000000..14367a5 --- /dev/null +++ b/python-kami.spec @@ -0,0 +1,186 @@ +%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 |