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| author | CoprDistGit <infra@openeuler.org> | 2023-04-10 10:27:35 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-04-10 10:27:35 +0000 |
| commit | ea41d25bd9a55e395357f0558caf07a6aa6c58f4 (patch) | |
| tree | 0cab40138c5cd3ef80dac98cda5466c66566d82f | |
| parent | 22d4745d7874ecac34d7ef3da09b788ef5e29952 (diff) | |
automatic import of python-pmdarima
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-pmdarima.spec | 524 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 526 insertions, 0 deletions
@@ -0,0 +1 @@ +/pmdarima-2.0.3.tar.gz diff --git a/python-pmdarima.spec b/python-pmdarima.spec new file mode 100644 index 0000000..4edf663 --- /dev/null +++ b/python-pmdarima.spec @@ -0,0 +1,524 @@ +%global _empty_manifest_terminate_build 0 +Name: python-pmdarima +Version: 2.0.3 +Release: 1 +Summary: Python's forecast::auto.arima equivalent +License: MIT +URL: http://alkaline-ml.com/pmdarima +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/61/05/0690150bb6784db4d30fcde6062e9410597f767f024a0d1a5fd850804ec9/pmdarima-2.0.3.tar.gz + +Requires: python3-joblib +Requires: python3-Cython +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-scikit-learn +Requires: python3-scipy +Requires: python3-statsmodels +Requires: python3-urllib3 +Requires: python3-setuptools + +%description +# pmdarima + +[](https://badge.fury.io/py/pmdarima) +[](https://circleci.com/gh/alkaline-ml/pmdarima) +[](https://github.com/alkaline-ml/pmdarima/actions?query=workflow%3A%22Mac+and+Windows+Builds%22+branch%3Amaster) +[](https://codecov.io/gh/alkaline-ml/pmdarima) + + + + +Pmdarima (originally `pyramid-arima`, for the anagram of 'py' + 'arima') is a statistical +library designed to fill the void in Python's time series analysis capabilities. This includes: + + * The equivalent of R's [`auto.arima`](https://www.rdocumentation.org/packages/forecast/versions/7.3/topics/auto.arima) functionality + * A collection of statistical tests of stationarity and seasonality + * Time series utilities, such as differencing and inverse differencing + * Numerous endogenous and exogenous transformers and featurizers, including Box-Cox and Fourier transformations + * Seasonal time series decompositions + * Cross-validation utilities + * A rich collection of built-in time series datasets for prototyping and examples + * Scikit-learn-esque pipelines to consolidate your estimators and promote productionization + +Pmdarima wraps [statsmodels](https://github.com/statsmodels/statsmodels/blob/master/statsmodels) +under the hood, but is designed with an interface that's familiar to users coming +from a scikit-learn background. + +## Installation + +### pip + +Pmdarima has binary and source distributions for Windows, Mac and Linux (`manylinux`) on pypi +under the package name `pmdarima` and can be downloaded via `pip`: + +```bash +pip install pmdarima +``` + +### conda + +Pmdarima also has Mac and Linux builds available via `conda` and can be installed like so: + +```bash +conda config --add channels conda-forge +conda config --set channel_priority strict +conda install pmdarima +``` + +**Note:** We do not maintain our own Conda binaries, they are maintained at https://github.com/conda-forge/pmdarima-feedstock. +See that repo for further documentation on working with Pmdarima on Conda. + +## Quickstart Examples + +Fitting a simple auto-ARIMA on the [`wineind`](https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.datasets.load_wineind.html#pmdarima.datasets.load_wineind) dataset: + +```python +import pmdarima as pm +from pmdarima.model_selection import train_test_split +import numpy as np +import matplotlib.pyplot as plt + +# Load/split your data +y = pm.datasets.load_wineind() +train, test = train_test_split(y, train_size=150) + +# Fit your model +model = pm.auto_arima(train, seasonal=True, m=12) + +# make your forecasts +forecasts = model.predict(test.shape[0]) # predict N steps into the future + +# Visualize the forecasts (blue=train, green=forecasts) +x = np.arange(y.shape[0]) +plt.plot(x[:150], train, c='blue') +plt.plot(x[150:], forecasts, c='green') +plt.show() +``` + +<img src="http://alkaline-ml.com/img/static/pmdarima_readme_example1.png" alt="Wineind example"/> + + +Fitting a more complex pipeline on the [`sunspots`](https://www.rdocumentation.org/packages/datasets/versions/3.6.1/topics/sunspots) dataset, +serializing it, and then loading it from disk to make predictions: + +```python +import pmdarima as pm +from pmdarima.model_selection import train_test_split +from pmdarima.pipeline import Pipeline +from pmdarima.preprocessing import BoxCoxEndogTransformer +import pickle + +# Load/split your data +y = pm.datasets.load_sunspots() +train, test = train_test_split(y, train_size=2700) + +# Define and fit your pipeline +pipeline = Pipeline([ + ('boxcox', BoxCoxEndogTransformer(lmbda2=1e-6)), # lmbda2 avoids negative values + ('arima', pm.AutoARIMA(seasonal=True, m=12, + suppress_warnings=True, + trace=True)) +]) + +pipeline.fit(train) + +# Serialize your model just like you would in scikit: +with open('model.pkl', 'wb') as pkl: + pickle.dump(pipeline, pkl) + +# Load it and make predictions seamlessly: +with open('model.pkl', 'rb') as pkl: + mod = pickle.load(pkl) + print(mod.predict(15)) +# [25.20580375 25.05573898 24.4263037 23.56766793 22.67463049 21.82231043 +# 21.04061069 20.33693017 19.70906027 19.1509862 18.6555793 18.21577243 +# 17.8250318 17.47750614 17.16803394] +``` + + +### Availability + +`pmdarima` is available on PyPi in pre-built Wheel files for Python 3.7+ for the following platforms: + +* Mac (64-bit) +* Linux (64-bit manylinux) +* Windows (64-bit) + * 32-bit wheels are available for pmdarima versions below 2.0.0 and Python versions below 3.10 + +If a wheel doesn't exist for your platform, you can still `pip install` and it +will build from the source distribution tarball, however you'll need `cython>=0.29` +and `gcc` (Mac/Linux) or `MinGW` (Windows) in order to build the package from source. + +Note that legacy versions (<1.0.0) are available under the name +"`pyramid-arima`" and can be pip installed via: + +```bash +# Legacy warning: +$ pip install pyramid-arima +# python -c 'import pyramid;' +``` + +However, this is not recommended. + +## Documentation + +All of your questions and more (including examples and guides) can be answered by +the [`pmdarima` documentation](https://www.alkaline-ml.com/pmdarima). If not, always +feel free to file an issue. + + +%package -n python3-pmdarima +Summary: Python's forecast::auto.arima equivalent +Provides: python-pmdarima +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +BuildRequires: python3-cffi +BuildRequires: gcc +BuildRequires: gdb +%description -n python3-pmdarima +# pmdarima + +[](https://badge.fury.io/py/pmdarima) +[](https://circleci.com/gh/alkaline-ml/pmdarima) +[](https://github.com/alkaline-ml/pmdarima/actions?query=workflow%3A%22Mac+and+Windows+Builds%22+branch%3Amaster) +[](https://codecov.io/gh/alkaline-ml/pmdarima) + + + + +Pmdarima (originally `pyramid-arima`, for the anagram of 'py' + 'arima') is a statistical +library designed to fill the void in Python's time series analysis capabilities. This includes: + + * The equivalent of R's [`auto.arima`](https://www.rdocumentation.org/packages/forecast/versions/7.3/topics/auto.arima) functionality + * A collection of statistical tests of stationarity and seasonality + * Time series utilities, such as differencing and inverse differencing + * Numerous endogenous and exogenous transformers and featurizers, including Box-Cox and Fourier transformations + * Seasonal time series decompositions + * Cross-validation utilities + * A rich collection of built-in time series datasets for prototyping and examples + * Scikit-learn-esque pipelines to consolidate your estimators and promote productionization + +Pmdarima wraps [statsmodels](https://github.com/statsmodels/statsmodels/blob/master/statsmodels) +under the hood, but is designed with an interface that's familiar to users coming +from a scikit-learn background. + +## Installation + +### pip + +Pmdarima has binary and source distributions for Windows, Mac and Linux (`manylinux`) on pypi +under the package name `pmdarima` and can be downloaded via `pip`: + +```bash +pip install pmdarima +``` + +### conda + +Pmdarima also has Mac and Linux builds available via `conda` and can be installed like so: + +```bash +conda config --add channels conda-forge +conda config --set channel_priority strict +conda install pmdarima +``` + +**Note:** We do not maintain our own Conda binaries, they are maintained at https://github.com/conda-forge/pmdarima-feedstock. +See that repo for further documentation on working with Pmdarima on Conda. + +## Quickstart Examples + +Fitting a simple auto-ARIMA on the [`wineind`](https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.datasets.load_wineind.html#pmdarima.datasets.load_wineind) dataset: + +```python +import pmdarima as pm +from pmdarima.model_selection import train_test_split +import numpy as np +import matplotlib.pyplot as plt + +# Load/split your data +y = pm.datasets.load_wineind() +train, test = train_test_split(y, train_size=150) + +# Fit your model +model = pm.auto_arima(train, seasonal=True, m=12) + +# make your forecasts +forecasts = model.predict(test.shape[0]) # predict N steps into the future + +# Visualize the forecasts (blue=train, green=forecasts) +x = np.arange(y.shape[0]) +plt.plot(x[:150], train, c='blue') +plt.plot(x[150:], forecasts, c='green') +plt.show() +``` + +<img src="http://alkaline-ml.com/img/static/pmdarima_readme_example1.png" alt="Wineind example"/> + + +Fitting a more complex pipeline on the [`sunspots`](https://www.rdocumentation.org/packages/datasets/versions/3.6.1/topics/sunspots) dataset, +serializing it, and then loading it from disk to make predictions: + +```python +import pmdarima as pm +from pmdarima.model_selection import train_test_split +from pmdarima.pipeline import Pipeline +from pmdarima.preprocessing import BoxCoxEndogTransformer +import pickle + +# Load/split your data +y = pm.datasets.load_sunspots() +train, test = train_test_split(y, train_size=2700) + +# Define and fit your pipeline +pipeline = Pipeline([ + ('boxcox', BoxCoxEndogTransformer(lmbda2=1e-6)), # lmbda2 avoids negative values + ('arima', pm.AutoARIMA(seasonal=True, m=12, + suppress_warnings=True, + trace=True)) +]) + +pipeline.fit(train) + +# Serialize your model just like you would in scikit: +with open('model.pkl', 'wb') as pkl: + pickle.dump(pipeline, pkl) + +# Load it and make predictions seamlessly: +with open('model.pkl', 'rb') as pkl: + mod = pickle.load(pkl) + print(mod.predict(15)) +# [25.20580375 25.05573898 24.4263037 23.56766793 22.67463049 21.82231043 +# 21.04061069 20.33693017 19.70906027 19.1509862 18.6555793 18.21577243 +# 17.8250318 17.47750614 17.16803394] +``` + + +### Availability + +`pmdarima` is available on PyPi in pre-built Wheel files for Python 3.7+ for the following platforms: + +* Mac (64-bit) +* Linux (64-bit manylinux) +* Windows (64-bit) + * 32-bit wheels are available for pmdarima versions below 2.0.0 and Python versions below 3.10 + +If a wheel doesn't exist for your platform, you can still `pip install` and it +will build from the source distribution tarball, however you'll need `cython>=0.29` +and `gcc` (Mac/Linux) or `MinGW` (Windows) in order to build the package from source. + +Note that legacy versions (<1.0.0) are available under the name +"`pyramid-arima`" and can be pip installed via: + +```bash +# Legacy warning: +$ pip install pyramid-arima +# python -c 'import pyramid;' +``` + +However, this is not recommended. + +## Documentation + +All of your questions and more (including examples and guides) can be answered by +the [`pmdarima` documentation](https://www.alkaline-ml.com/pmdarima). If not, always +feel free to file an issue. + + +%package help +Summary: Development documents and examples for pmdarima +Provides: python3-pmdarima-doc +%description help +# pmdarima + +[](https://badge.fury.io/py/pmdarima) +[](https://circleci.com/gh/alkaline-ml/pmdarima) +[](https://github.com/alkaline-ml/pmdarima/actions?query=workflow%3A%22Mac+and+Windows+Builds%22+branch%3Amaster) +[](https://codecov.io/gh/alkaline-ml/pmdarima) + + + + +Pmdarima (originally `pyramid-arima`, for the anagram of 'py' + 'arima') is a statistical +library designed to fill the void in Python's time series analysis capabilities. This includes: + + * The equivalent of R's [`auto.arima`](https://www.rdocumentation.org/packages/forecast/versions/7.3/topics/auto.arima) functionality + * A collection of statistical tests of stationarity and seasonality + * Time series utilities, such as differencing and inverse differencing + * Numerous endogenous and exogenous transformers and featurizers, including Box-Cox and Fourier transformations + * Seasonal time series decompositions + * Cross-validation utilities + * A rich collection of built-in time series datasets for prototyping and examples + * Scikit-learn-esque pipelines to consolidate your estimators and promote productionization + +Pmdarima wraps [statsmodels](https://github.com/statsmodels/statsmodels/blob/master/statsmodels) +under the hood, but is designed with an interface that's familiar to users coming +from a scikit-learn background. + +## Installation + +### pip + +Pmdarima has binary and source distributions for Windows, Mac and Linux (`manylinux`) on pypi +under the package name `pmdarima` and can be downloaded via `pip`: + +```bash +pip install pmdarima +``` + +### conda + +Pmdarima also has Mac and Linux builds available via `conda` and can be installed like so: + +```bash +conda config --add channels conda-forge +conda config --set channel_priority strict +conda install pmdarima +``` + +**Note:** We do not maintain our own Conda binaries, they are maintained at https://github.com/conda-forge/pmdarima-feedstock. +See that repo for further documentation on working with Pmdarima on Conda. + +## Quickstart Examples + +Fitting a simple auto-ARIMA on the [`wineind`](https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.datasets.load_wineind.html#pmdarima.datasets.load_wineind) dataset: + +```python +import pmdarima as pm +from pmdarima.model_selection import train_test_split +import numpy as np +import matplotlib.pyplot as plt + +# Load/split your data +y = pm.datasets.load_wineind() +train, test = train_test_split(y, train_size=150) + +# Fit your model +model = pm.auto_arima(train, seasonal=True, m=12) + +# make your forecasts +forecasts = model.predict(test.shape[0]) # predict N steps into the future + +# Visualize the forecasts (blue=train, green=forecasts) +x = np.arange(y.shape[0]) +plt.plot(x[:150], train, c='blue') +plt.plot(x[150:], forecasts, c='green') +plt.show() +``` + +<img src="http://alkaline-ml.com/img/static/pmdarima_readme_example1.png" alt="Wineind example"/> + + +Fitting a more complex pipeline on the [`sunspots`](https://www.rdocumentation.org/packages/datasets/versions/3.6.1/topics/sunspots) dataset, +serializing it, and then loading it from disk to make predictions: + +```python +import pmdarima as pm +from pmdarima.model_selection import train_test_split +from pmdarima.pipeline import Pipeline +from pmdarima.preprocessing import BoxCoxEndogTransformer +import pickle + +# Load/split your data +y = pm.datasets.load_sunspots() +train, test = train_test_split(y, train_size=2700) + +# Define and fit your pipeline +pipeline = Pipeline([ + ('boxcox', BoxCoxEndogTransformer(lmbda2=1e-6)), # lmbda2 avoids negative values + ('arima', pm.AutoARIMA(seasonal=True, m=12, + suppress_warnings=True, + trace=True)) +]) + +pipeline.fit(train) + +# Serialize your model just like you would in scikit: +with open('model.pkl', 'wb') as pkl: + pickle.dump(pipeline, pkl) + +# Load it and make predictions seamlessly: +with open('model.pkl', 'rb') as pkl: + mod = pickle.load(pkl) + print(mod.predict(15)) +# [25.20580375 25.05573898 24.4263037 23.56766793 22.67463049 21.82231043 +# 21.04061069 20.33693017 19.70906027 19.1509862 18.6555793 18.21577243 +# 17.8250318 17.47750614 17.16803394] +``` + + +### Availability + +`pmdarima` is available on PyPi in pre-built Wheel files for Python 3.7+ for the following platforms: + +* Mac (64-bit) +* Linux (64-bit manylinux) +* Windows (64-bit) + * 32-bit wheels are available for pmdarima versions below 2.0.0 and Python versions below 3.10 + +If a wheel doesn't exist for your platform, you can still `pip install` and it +will build from the source distribution tarball, however you'll need `cython>=0.29` +and `gcc` (Mac/Linux) or `MinGW` (Windows) in order to build the package from source. + +Note that legacy versions (<1.0.0) are available under the name +"`pyramid-arima`" and can be pip installed via: + +```bash +# Legacy warning: +$ pip install pyramid-arima +# python -c 'import pyramid;' +``` + +However, this is not recommended. + +## Documentation + +All of your questions and more (including examples and guides) can be answered by +the [`pmdarima` documentation](https://www.alkaline-ml.com/pmdarima). If not, always +feel free to file an issue. + + +%prep +%autosetup -n pmdarima-2.0.3 + +%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-pmdarima -f filelist.lst +%dir %{python3_sitearch}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 2.0.3-1 +- Package Spec generated @@ -0,0 +1 @@ +257ebfb92e3abc2fab71b7d3f1140efb pmdarima-2.0.3.tar.gz |