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@@ -0,0 +1 @@ +/hcrystalball-0.1.12.tar.gz diff --git a/python-hcrystalball.spec b/python-hcrystalball.spec new file mode 100644 index 0000000..c5dcd30 --- /dev/null +++ b/python-hcrystalball.spec @@ -0,0 +1,506 @@ +%global _empty_manifest_terminate_build 0 +Name: python-hcrystalball +Version: 0.1.12 +Release: 1 +Summary: A library that unifies the API for most commonly used libraries and modelling techniques for time-series forecasting in the Python ecosystem. +License: mit +URL: https://hcrystalball.readthedocs.io/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c4/d8/d4ac5b406a2ce1d40bc605d3b6d7d86d3571bc721c50ea1eb4fad5d6a68a/hcrystalball-0.1.12.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-scipy +Requires: python3-workalendar +Requires: python3-scikit-learn +Requires: python3-matplotlib +Requires: python3-pytest +Requires: python3-pytest-cov + +%description +[](https://github.com/heidelbergcement/hcrystalball/actions/?query=workflow%3ACI) +[](https://github.com/heidelbergcement/hcrystalball/actions?query=workflow%3ACD) +[](https://hcrystalball.readthedocs.io/en/latest/?badge=latest) +[](https://pypi.org/project/hcrystalball/) +[](https://anaconda.org/conda-forge/hcrystalball) +[](https://codecov.io/gh/heidelbergcement/hcrystalball) +[](https://mybinder.org/v2/gh/heidelbergcement/hcrystalball/master?filepath=docs/examples/) +[](https://raw.githubusercontent.com/heidelbergcement/hcrystalball/master/LICENSE.txt) +[](https://github.com/heidelbergcement/hcrystalball/graphs/contributors) +[](https://github.com/psf/black) + +# HCrystal Ball + +<div> + <a href="https://hcrystalball.readthedocs.io/en/latest/"> + <img src="https://raw.githubusercontent.com/heidelbergcement/hcrystalball/master/docs/_static/hcrystal_ball_logo_green.png" width="150px" align="left" /></a> + <i><br>A library that unifies the API for most commonly <br> + used libraries and modelling techniques for time-series <br> + forecasting in the Python ecosystem.</i> +</div> +<br><br><br> + +**HCrystal Ball** consists of two main parts: + +* **Wrappers** - which bring different 3rd party + libraries to time series compatible sklearn API +* **Model Selection** - to enable gridsearch over wrappers, general or custom made transformers + and add convenient layer over whole process (access to results, plots, storage, ...) + +## Documentation +See examples, tutorials, contribution, API and more on the documentation [site](https://hcrystalball.readthedocs.io/en/latest) try notebooks on [binder](https://mybinder.org/v2/gh/heidelbergcement/hcrystalball/master) or browse example notebooks in [docs/examples](https://github.com/heidelbergcement/hcrystalball/tree/master/docs/examples) directly. + +## Core Installation + +If you want really minimal installation, you can install from pip or from conda-forge + +```bash +pip install hcrystalball +``` + +```bash +conda install -c conda-forge hcrystalball +``` + +## Typical Installation + +Very often you will want to use more wrappers, than just Sklearn, run examples in jupyterlab, or execute model selection in parallel. Getting such dependencies to play together nicely might be cumbersome, so checking `envrionment.yml` might give you faster start. + +```bash +# get dependencies file, e.g. using curl +curl -O https://raw.githubusercontent.com/heidelbergcement/hcrystalball/master/environment.yml +# check comments in environment.yml, keep or remove as requested, than create environment using +conda env create -f environment.yml +# activate the environment +conda activate hcrystalball +# if you want to see progress bar in jupyterlab, execute also +jupyter labextension install @jupyter-widgets/jupyterlab-manager +# install the library from pip +pip install hcrystalball +# or from conda +conda install -c conda-forge hcrystalball +``` + +## Development Installation: + +To have everything in place including docs build or executing tests, execute following code + +```bash +git clone https://github.com/heidelbergcement/hcrystalball +cd hcrystalball +conda env create -f environment.yml +conda activate hcrystalball +# ensures interactive progress bar will work in example notebooks +jupyter labextension install @jupyter-widgets/jupyterlab-manager +python setup.py develop +``` + +## Example Usage +### Wrappers +```python +from hcrystalball.utils import generate_tsdata +from hcrystalball.wrappers import ProphetWrapper + +X, y = generate_tsdata(n_dates=365*2) +X_train, y_train, X_test, y_test = X[:-10], y[:-10], X[-10:], y[-10:] + +model = ProphetWrapper() +y_pred = model.fit(X_train, y_train).predict(X_test) +y_pred + prophet +2018-12-22 6.066999 +2018-12-23 6.050076 +2018-12-24 6.105620 +2018-12-25 6.141953 +2018-12-26 6.150229 +2018-12-27 6.163615 +2018-12-28 6.147420 +2018-12-29 6.048633 +2018-12-30 6.031711 +2018-12-31 6.087255 +``` + +### Model Selection + +```python +import pandas as pd +import matplotlib.pyplot as plt +plt.style.use('seaborn') +plt.rcParams['figure.figsize'] = [12, 6] + +from hcrystalball.utils import get_sales_data +from hcrystalball.model_selection import ModelSelector + +df = get_sales_data(n_dates=200, + n_assortments=1, + n_states=2, + n_stores=2) + +ms = ModelSelector(horizon=10, + frequency="D", + country_code_column="HolidayCode", + ) + +ms.create_gridsearch(n_splits=2, + sklearn_models=True, + prophet_models=False, + exog_cols=["Open","Promo","SchoolHoliday","Promo2"], + ) + +ms.select_model(df=df, + target_col_name="Sales", + partition_columns=["Assortment", "State","Store"], + ) + +ms.plot_results(plot_from="2015-06-01", + partitions=[{"Assortment":"a","State":"NW","Store":335}] + ) +``` + +<img src="https://raw.githubusercontent.com/heidelbergcement/hcrystalball/master/docs/_static/forecast.png" width="100%" align="left"/> + + + + +%package -n python3-hcrystalball +Summary: A library that unifies the API for most commonly used libraries and modelling techniques for time-series forecasting in the Python ecosystem. +Provides: python-hcrystalball +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-hcrystalball +[](https://github.com/heidelbergcement/hcrystalball/actions/?query=workflow%3ACI) +[](https://github.com/heidelbergcement/hcrystalball/actions?query=workflow%3ACD) +[](https://hcrystalball.readthedocs.io/en/latest/?badge=latest) +[](https://pypi.org/project/hcrystalball/) +[](https://anaconda.org/conda-forge/hcrystalball) +[](https://codecov.io/gh/heidelbergcement/hcrystalball) +[](https://mybinder.org/v2/gh/heidelbergcement/hcrystalball/master?filepath=docs/examples/) +[](https://raw.githubusercontent.com/heidelbergcement/hcrystalball/master/LICENSE.txt) +[](https://github.com/heidelbergcement/hcrystalball/graphs/contributors) +[](https://github.com/psf/black) + +# HCrystal Ball + +<div> + <a href="https://hcrystalball.readthedocs.io/en/latest/"> + <img src="https://raw.githubusercontent.com/heidelbergcement/hcrystalball/master/docs/_static/hcrystal_ball_logo_green.png" width="150px" align="left" /></a> + <i><br>A library that unifies the API for most commonly <br> + used libraries and modelling techniques for time-series <br> + forecasting in the Python ecosystem.</i> +</div> +<br><br><br> + +**HCrystal Ball** consists of two main parts: + +* **Wrappers** - which bring different 3rd party + libraries to time series compatible sklearn API +* **Model Selection** - to enable gridsearch over wrappers, general or custom made transformers + and add convenient layer over whole process (access to results, plots, storage, ...) + +## Documentation +See examples, tutorials, contribution, API and more on the documentation [site](https://hcrystalball.readthedocs.io/en/latest) try notebooks on [binder](https://mybinder.org/v2/gh/heidelbergcement/hcrystalball/master) or browse example notebooks in [docs/examples](https://github.com/heidelbergcement/hcrystalball/tree/master/docs/examples) directly. + +## Core Installation + +If you want really minimal installation, you can install from pip or from conda-forge + +```bash +pip install hcrystalball +``` + +```bash +conda install -c conda-forge hcrystalball +``` + +## Typical Installation + +Very often you will want to use more wrappers, than just Sklearn, run examples in jupyterlab, or execute model selection in parallel. Getting such dependencies to play together nicely might be cumbersome, so checking `envrionment.yml` might give you faster start. + +```bash +# get dependencies file, e.g. using curl +curl -O https://raw.githubusercontent.com/heidelbergcement/hcrystalball/master/environment.yml +# check comments in environment.yml, keep or remove as requested, than create environment using +conda env create -f environment.yml +# activate the environment +conda activate hcrystalball +# if you want to see progress bar in jupyterlab, execute also +jupyter labextension install @jupyter-widgets/jupyterlab-manager +# install the library from pip +pip install hcrystalball +# or from conda +conda install -c conda-forge hcrystalball +``` + +## Development Installation: + +To have everything in place including docs build or executing tests, execute following code + +```bash +git clone https://github.com/heidelbergcement/hcrystalball +cd hcrystalball +conda env create -f environment.yml +conda activate hcrystalball +# ensures interactive progress bar will work in example notebooks +jupyter labextension install @jupyter-widgets/jupyterlab-manager +python setup.py develop +``` + +## Example Usage +### Wrappers +```python +from hcrystalball.utils import generate_tsdata +from hcrystalball.wrappers import ProphetWrapper + +X, y = generate_tsdata(n_dates=365*2) +X_train, y_train, X_test, y_test = X[:-10], y[:-10], X[-10:], y[-10:] + +model = ProphetWrapper() +y_pred = model.fit(X_train, y_train).predict(X_test) +y_pred + prophet +2018-12-22 6.066999 +2018-12-23 6.050076 +2018-12-24 6.105620 +2018-12-25 6.141953 +2018-12-26 6.150229 +2018-12-27 6.163615 +2018-12-28 6.147420 +2018-12-29 6.048633 +2018-12-30 6.031711 +2018-12-31 6.087255 +``` + +### Model Selection + +```python +import pandas as pd +import matplotlib.pyplot as plt +plt.style.use('seaborn') +plt.rcParams['figure.figsize'] = [12, 6] + +from hcrystalball.utils import get_sales_data +from hcrystalball.model_selection import ModelSelector + +df = get_sales_data(n_dates=200, + n_assortments=1, + n_states=2, + n_stores=2) + +ms = ModelSelector(horizon=10, + frequency="D", + country_code_column="HolidayCode", + ) + +ms.create_gridsearch(n_splits=2, + sklearn_models=True, + prophet_models=False, + exog_cols=["Open","Promo","SchoolHoliday","Promo2"], + ) + +ms.select_model(df=df, + target_col_name="Sales", + partition_columns=["Assortment", "State","Store"], + ) + +ms.plot_results(plot_from="2015-06-01", + partitions=[{"Assortment":"a","State":"NW","Store":335}] + ) +``` + +<img src="https://raw.githubusercontent.com/heidelbergcement/hcrystalball/master/docs/_static/forecast.png" width="100%" align="left"/> + + + + +%package help +Summary: Development documents and examples for hcrystalball +Provides: python3-hcrystalball-doc +%description help +[](https://github.com/heidelbergcement/hcrystalball/actions/?query=workflow%3ACI) +[](https://github.com/heidelbergcement/hcrystalball/actions?query=workflow%3ACD) +[](https://hcrystalball.readthedocs.io/en/latest/?badge=latest) +[](https://pypi.org/project/hcrystalball/) +[](https://anaconda.org/conda-forge/hcrystalball) +[](https://codecov.io/gh/heidelbergcement/hcrystalball) +[](https://mybinder.org/v2/gh/heidelbergcement/hcrystalball/master?filepath=docs/examples/) +[](https://raw.githubusercontent.com/heidelbergcement/hcrystalball/master/LICENSE.txt) +[](https://github.com/heidelbergcement/hcrystalball/graphs/contributors) +[](https://github.com/psf/black) + +# HCrystal Ball + +<div> + <a href="https://hcrystalball.readthedocs.io/en/latest/"> + <img src="https://raw.githubusercontent.com/heidelbergcement/hcrystalball/master/docs/_static/hcrystal_ball_logo_green.png" width="150px" align="left" /></a> + <i><br>A library that unifies the API for most commonly <br> + used libraries and modelling techniques for time-series <br> + forecasting in the Python ecosystem.</i> +</div> +<br><br><br> + +**HCrystal Ball** consists of two main parts: + +* **Wrappers** - which bring different 3rd party + libraries to time series compatible sklearn API +* **Model Selection** - to enable gridsearch over wrappers, general or custom made transformers + and add convenient layer over whole process (access to results, plots, storage, ...) + +## Documentation +See examples, tutorials, contribution, API and more on the documentation [site](https://hcrystalball.readthedocs.io/en/latest) try notebooks on [binder](https://mybinder.org/v2/gh/heidelbergcement/hcrystalball/master) or browse example notebooks in [docs/examples](https://github.com/heidelbergcement/hcrystalball/tree/master/docs/examples) directly. + +## Core Installation + +If you want really minimal installation, you can install from pip or from conda-forge + +```bash +pip install hcrystalball +``` + +```bash +conda install -c conda-forge hcrystalball +``` + +## Typical Installation + +Very often you will want to use more wrappers, than just Sklearn, run examples in jupyterlab, or execute model selection in parallel. Getting such dependencies to play together nicely might be cumbersome, so checking `envrionment.yml` might give you faster start. + +```bash +# get dependencies file, e.g. using curl +curl -O https://raw.githubusercontent.com/heidelbergcement/hcrystalball/master/environment.yml +# check comments in environment.yml, keep or remove as requested, than create environment using +conda env create -f environment.yml +# activate the environment +conda activate hcrystalball +# if you want to see progress bar in jupyterlab, execute also +jupyter labextension install @jupyter-widgets/jupyterlab-manager +# install the library from pip +pip install hcrystalball +# or from conda +conda install -c conda-forge hcrystalball +``` + +## Development Installation: + +To have everything in place including docs build or executing tests, execute following code + +```bash +git clone https://github.com/heidelbergcement/hcrystalball +cd hcrystalball +conda env create -f environment.yml +conda activate hcrystalball +# ensures interactive progress bar will work in example notebooks +jupyter labextension install @jupyter-widgets/jupyterlab-manager +python setup.py develop +``` + +## Example Usage +### Wrappers +```python +from hcrystalball.utils import generate_tsdata +from hcrystalball.wrappers import ProphetWrapper + +X, y = generate_tsdata(n_dates=365*2) +X_train, y_train, X_test, y_test = X[:-10], y[:-10], X[-10:], y[-10:] + +model = ProphetWrapper() +y_pred = model.fit(X_train, y_train).predict(X_test) +y_pred + prophet +2018-12-22 6.066999 +2018-12-23 6.050076 +2018-12-24 6.105620 +2018-12-25 6.141953 +2018-12-26 6.150229 +2018-12-27 6.163615 +2018-12-28 6.147420 +2018-12-29 6.048633 +2018-12-30 6.031711 +2018-12-31 6.087255 +``` + +### Model Selection + +```python +import pandas as pd +import matplotlib.pyplot as plt +plt.style.use('seaborn') +plt.rcParams['figure.figsize'] = [12, 6] + +from hcrystalball.utils import get_sales_data +from hcrystalball.model_selection import ModelSelector + +df = get_sales_data(n_dates=200, + n_assortments=1, + n_states=2, + n_stores=2) + +ms = ModelSelector(horizon=10, + frequency="D", + country_code_column="HolidayCode", + ) + +ms.create_gridsearch(n_splits=2, + sklearn_models=True, + prophet_models=False, + exog_cols=["Open","Promo","SchoolHoliday","Promo2"], + ) + +ms.select_model(df=df, + target_col_name="Sales", + partition_columns=["Assortment", "State","Store"], + ) + +ms.plot_results(plot_from="2015-06-01", + partitions=[{"Assortment":"a","State":"NW","Store":335}] + ) +``` + +<img src="https://raw.githubusercontent.com/heidelbergcement/hcrystalball/master/docs/_static/forecast.png" width="100%" align="left"/> + + + + +%prep +%autosetup -n hcrystalball-0.1.12 + +%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-hcrystalball -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.12-1 +- Package Spec generated @@ -0,0 +1 @@ +9ab2fcf9dec7b89c8a86f824e00d5afc hcrystalball-0.1.12.tar.gz |