automatic import of python-pyglmnetopeneuler20.03

3 files changed, 404 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..f55074e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/pyglmnet-1.1.tar.gz
diff --git a/python-pyglmnet.spec b/python-pyglmnet.spec
new file mode 100644
index 0000000..9c1fe62
--- /dev/null
+++ b/python-pyglmnet.spec
@@ -0,0 +1,402 @@
+%global _empty_manifest_terminate_build 0
+Name:		python-pyglmnet
+Version:	1.1
+Release:	1
+Summary:	Elastic-net regularized generalized linear models.
+License:	MIT
+URL:		http://glm-tools.github.io/pyglmnet/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/b3/b4/862550f7a6289752abd9c5ceb534259530c57a930371485c0704944ec1d4/pyglmnet-1.1.tar.gz
+BuildArch:	noarch
+
+
+%description
+A python implementation of elastic-net regularized generalized linear models
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+|License| |Travis| |Codecov| |Circle| |Gitter| |DOI|
+`[Documentation (stable version)]`_ `[Documentation (development version)]`_
+`Generalized linear
+models <https://en.wikipedia.org/wiki/Generalized_linear_model>`__ are
+well-established tools for regression and classification and are widely
+applied across the sciences, economics, business, and finance. They are
+uniquely identifiable due to their convex loss and easy to interpret due
+to their point-wise non-linearities and well-defined noise models.
+In the era of exploratory data analyses with a large number of predictor
+variables, it is important to regularize. Regularization prevents
+overfitting by penalizing the negative log likelihood and can be used to
+articulate prior knowledge about the parameters in a structured form.
+Despite the attractiveness of regularized GLMs, the available tools in
+the Python data science eco-system are highly fragmented. More
+specifically,
+-  `statsmodels <http://statsmodels.sourceforge.net/devel/glm.html>`__
+   provides a wide range of link functions but no regularization.
+-  `scikit-learn <http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html>`__
+   provides elastic net regularization but only for linear models.
+-  `lightning <https://github.com/scikit-learn-contrib/lightning>`__
+   provides elastic net and group lasso regularization, but only for
+   linear and logistic regression.
+**Pyglmnet** is a response to this fragmentation. It runs on Python 3.5+,
+and here are some of the highlights.
+-  Pyglmnet provides a wide range of noise models (and paired canonical
+   link functions): ``'gaussian'``, ``'binomial'``, ``'probit'``,
+   ``'gamma'``, '``poisson``', and ``'softplus'``.
+-  It supports a wide range of regularizers: ridge, lasso, elastic net,
+   `group
+   lasso <https://en.wikipedia.org/wiki/Proximal_gradient_methods_for_learning#Group_lasso>`__,
+   and `Tikhonov
+   regularization <https://en.wikipedia.org/wiki/Tikhonov_regularization>`__.
+-  Pyglmnet's API is designed to be compatible with scikit-learn, so you
+   can deploy ``Pipeline`` tools such as ``GridSearchCV()`` and
+   ``cross_val_score()``.
+-  We follow the same approach and notations as in `Friedman, J.,
+   Hastie, T., & Tibshirani, R.
+   (2010) <https://core.ac.uk/download/files/153/6287975.pdf>`__ and the
+   accompanying widely popular `R
+   package <https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html>`__.
+-  We have implemented a cyclical coordinate descent optimizer with
+   Newton update, active sets, update caching, and warm restarts. This
+   optimization approach is identical to the one used in R package.
+-  A number of Python wrappers exist for the R glmnet package (e.g.
+   `here <https://github.com/civisanalytics/python-glmnet>`__ and
+   `here <https://github.com/dwf/glmnet-python>`__) but in contrast to
+   these, Pyglmnet is a pure python implementation. Therefore, it is
+   easy to modify and introduce additional noise models and regularizers
+   in the future.
+Installation
+~~~~~~~~~~~~
+Install the stable PyPI version with ``pip``
+    $ pip install pyglmnet
+For the bleeding edge development version:
+Clone the repository.
+    $ pip install https://api.github.com/repos/glm-tools/pyglmnet/zipball/master
+Getting Started
+~~~~~~~~~~~~~~~
+Here is an example on how to use the ``GLM`` estimator.
+   import numpy as np
+   import scipy.sparse as sps
+   from pyglmnet import GLM, simulate_glm
+   n_samples, n_features = 1000, 100
+   distr = 'poisson'
+   # sample a sparse model
+   beta0 = np.random.rand()
+   beta = np.random.random(n_features)
+   beta[beta < 0.9] = 0
+   # simulate data
+   Xtrain = np.random.normal(0.0, 1.0, [n_samples, n_features])
+   ytrain = simulate_glm('poisson', beta0, beta, Xtrain)
+   Xtest = np.random.normal(0.0, 1.0, [n_samples, n_features])
+   ytest = simulate_glm('poisson', beta0, beta, Xtest)
+   # create an instance of the GLM class
+   glm = GLM(distr='poisson', score_metric='deviance')
+   # fit the model on the training data
+   glm.fit(Xtrain, ytrain)
+   # predict using fitted model on the test data
+   yhat = glm.predict(Xtest)
+   # score the model on test data
+   deviance = glm.score(Xtest, ytest)
+`More pyglmnet examples and use
+cases <http://glm-tools.github.io/pyglmnet/auto_examples/index.html>`__.
+Tutorial
+~~~~~~~~
+Here is an `extensive
+tutorial <http://glm-tools.github.io/pyglmnet/tutorial.html>`__ on GLMs,
+optimization and pseudo-code.
+Here are
+`slides <https://pavanramkumar.github.io/pydata-chicago-2016>`__ from a
+talk at `PyData Chicago
+2016 <http://pydata.org/chicago2016/schedule/presentation/15/>`__,
+corresponding `tutorial
+notebooks <http://github.com/pavanramkumar/pydata-chicago-2016>`__ and a
+`video <https://www.youtube.com/watch?v=zXec96KD1uA>`__.
+How to contribute?
+~~~~~~~~~~~~~~~~~~
+We welcome pull requests. Please see our `developer documentation
+page <http://glm-tools.github.io/pyglmnet/developers.html>`__ for more
+details.
+Acknowledgments
+~~~~~~~~~~~~~~~
+-  `Konrad Kording <http://kordinglab.com>`__ for funding and support
+-  `Sara
+   Solla <http://www.physics.northwestern.edu/people/joint-faculty/sara-solla.html>`__
+   for masterful GLM lectures
+License
+~~~~~~~
+MIT License Copyright (c) 2016-2019 Pavan Ramkumar
+
+%package -n python3-pyglmnet
+Summary:	Elastic-net regularized generalized linear models.
+Provides:	python-pyglmnet
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pyglmnet
+A python implementation of elastic-net regularized generalized linear models
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+|License| |Travis| |Codecov| |Circle| |Gitter| |DOI|
+`[Documentation (stable version)]`_ `[Documentation (development version)]`_
+`Generalized linear
+models <https://en.wikipedia.org/wiki/Generalized_linear_model>`__ are
+well-established tools for regression and classification and are widely
+applied across the sciences, economics, business, and finance. They are
+uniquely identifiable due to their convex loss and easy to interpret due
+to their point-wise non-linearities and well-defined noise models.
+In the era of exploratory data analyses with a large number of predictor
+variables, it is important to regularize. Regularization prevents
+overfitting by penalizing the negative log likelihood and can be used to
+articulate prior knowledge about the parameters in a structured form.
+Despite the attractiveness of regularized GLMs, the available tools in
+the Python data science eco-system are highly fragmented. More
+specifically,
+-  `statsmodels <http://statsmodels.sourceforge.net/devel/glm.html>`__
+   provides a wide range of link functions but no regularization.
+-  `scikit-learn <http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html>`__
+   provides elastic net regularization but only for linear models.
+-  `lightning <https://github.com/scikit-learn-contrib/lightning>`__
+   provides elastic net and group lasso regularization, but only for
+   linear and logistic regression.
+**Pyglmnet** is a response to this fragmentation. It runs on Python 3.5+,
+and here are some of the highlights.
+-  Pyglmnet provides a wide range of noise models (and paired canonical
+   link functions): ``'gaussian'``, ``'binomial'``, ``'probit'``,
+   ``'gamma'``, '``poisson``', and ``'softplus'``.
+-  It supports a wide range of regularizers: ridge, lasso, elastic net,
+   `group
+   lasso <https://en.wikipedia.org/wiki/Proximal_gradient_methods_for_learning#Group_lasso>`__,
+   and `Tikhonov
+   regularization <https://en.wikipedia.org/wiki/Tikhonov_regularization>`__.
+-  Pyglmnet's API is designed to be compatible with scikit-learn, so you
+   can deploy ``Pipeline`` tools such as ``GridSearchCV()`` and
+   ``cross_val_score()``.
+-  We follow the same approach and notations as in `Friedman, J.,
+   Hastie, T., & Tibshirani, R.
+   (2010) <https://core.ac.uk/download/files/153/6287975.pdf>`__ and the
+   accompanying widely popular `R
+   package <https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html>`__.
+-  We have implemented a cyclical coordinate descent optimizer with
+   Newton update, active sets, update caching, and warm restarts. This
+   optimization approach is identical to the one used in R package.
+-  A number of Python wrappers exist for the R glmnet package (e.g.
+   `here <https://github.com/civisanalytics/python-glmnet>`__ and
+   `here <https://github.com/dwf/glmnet-python>`__) but in contrast to
+   these, Pyglmnet is a pure python implementation. Therefore, it is
+   easy to modify and introduce additional noise models and regularizers
+   in the future.
+Installation
+~~~~~~~~~~~~
+Install the stable PyPI version with ``pip``
+    $ pip install pyglmnet
+For the bleeding edge development version:
+Clone the repository.
+    $ pip install https://api.github.com/repos/glm-tools/pyglmnet/zipball/master
+Getting Started
+~~~~~~~~~~~~~~~
+Here is an example on how to use the ``GLM`` estimator.
+   import numpy as np
+   import scipy.sparse as sps
+   from pyglmnet import GLM, simulate_glm
+   n_samples, n_features = 1000, 100
+   distr = 'poisson'
+   # sample a sparse model
+   beta0 = np.random.rand()
+   beta = np.random.random(n_features)
+   beta[beta < 0.9] = 0
+   # simulate data
+   Xtrain = np.random.normal(0.0, 1.0, [n_samples, n_features])
+   ytrain = simulate_glm('poisson', beta0, beta, Xtrain)
+   Xtest = np.random.normal(0.0, 1.0, [n_samples, n_features])
+   ytest = simulate_glm('poisson', beta0, beta, Xtest)
+   # create an instance of the GLM class
+   glm = GLM(distr='poisson', score_metric='deviance')
+   # fit the model on the training data
+   glm.fit(Xtrain, ytrain)
+   # predict using fitted model on the test data
+   yhat = glm.predict(Xtest)
+   # score the model on test data
+   deviance = glm.score(Xtest, ytest)
+`More pyglmnet examples and use
+cases <http://glm-tools.github.io/pyglmnet/auto_examples/index.html>`__.
+Tutorial
+~~~~~~~~
+Here is an `extensive
+tutorial <http://glm-tools.github.io/pyglmnet/tutorial.html>`__ on GLMs,
+optimization and pseudo-code.
+Here are
+`slides <https://pavanramkumar.github.io/pydata-chicago-2016>`__ from a
+talk at `PyData Chicago
+2016 <http://pydata.org/chicago2016/schedule/presentation/15/>`__,
+corresponding `tutorial
+notebooks <http://github.com/pavanramkumar/pydata-chicago-2016>`__ and a
+`video <https://www.youtube.com/watch?v=zXec96KD1uA>`__.
+How to contribute?
+~~~~~~~~~~~~~~~~~~
+We welcome pull requests. Please see our `developer documentation
+page <http://glm-tools.github.io/pyglmnet/developers.html>`__ for more
+details.
+Acknowledgments
+~~~~~~~~~~~~~~~
+-  `Konrad Kording <http://kordinglab.com>`__ for funding and support
+-  `Sara
+   Solla <http://www.physics.northwestern.edu/people/joint-faculty/sara-solla.html>`__
+   for masterful GLM lectures
+License
+~~~~~~~
+MIT License Copyright (c) 2016-2019 Pavan Ramkumar
+
+%package help
+Summary:	Development documents and examples for pyglmnet
+Provides:	python3-pyglmnet-doc
+%description help
+A python implementation of elastic-net regularized generalized linear models
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+|License| |Travis| |Codecov| |Circle| |Gitter| |DOI|
+`[Documentation (stable version)]`_ `[Documentation (development version)]`_
+`Generalized linear
+models <https://en.wikipedia.org/wiki/Generalized_linear_model>`__ are
+well-established tools for regression and classification and are widely
+applied across the sciences, economics, business, and finance. They are
+uniquely identifiable due to their convex loss and easy to interpret due
+to their point-wise non-linearities and well-defined noise models.
+In the era of exploratory data analyses with a large number of predictor
+variables, it is important to regularize. Regularization prevents
+overfitting by penalizing the negative log likelihood and can be used to
+articulate prior knowledge about the parameters in a structured form.
+Despite the attractiveness of regularized GLMs, the available tools in
+the Python data science eco-system are highly fragmented. More
+specifically,
+-  `statsmodels <http://statsmodels.sourceforge.net/devel/glm.html>`__
+   provides a wide range of link functions but no regularization.
+-  `scikit-learn <http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html>`__
+   provides elastic net regularization but only for linear models.
+-  `lightning <https://github.com/scikit-learn-contrib/lightning>`__
+   provides elastic net and group lasso regularization, but only for
+   linear and logistic regression.
+**Pyglmnet** is a response to this fragmentation. It runs on Python 3.5+,
+and here are some of the highlights.
+-  Pyglmnet provides a wide range of noise models (and paired canonical
+   link functions): ``'gaussian'``, ``'binomial'``, ``'probit'``,
+   ``'gamma'``, '``poisson``', and ``'softplus'``.
+-  It supports a wide range of regularizers: ridge, lasso, elastic net,
+   `group
+   lasso <https://en.wikipedia.org/wiki/Proximal_gradient_methods_for_learning#Group_lasso>`__,
+   and `Tikhonov
+   regularization <https://en.wikipedia.org/wiki/Tikhonov_regularization>`__.
+-  Pyglmnet's API is designed to be compatible with scikit-learn, so you
+   can deploy ``Pipeline`` tools such as ``GridSearchCV()`` and
+   ``cross_val_score()``.
+-  We follow the same approach and notations as in `Friedman, J.,
+   Hastie, T., & Tibshirani, R.
+   (2010) <https://core.ac.uk/download/files/153/6287975.pdf>`__ and the
+   accompanying widely popular `R
+   package <https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html>`__.
+-  We have implemented a cyclical coordinate descent optimizer with
+   Newton update, active sets, update caching, and warm restarts. This
+   optimization approach is identical to the one used in R package.
+-  A number of Python wrappers exist for the R glmnet package (e.g.
+   `here <https://github.com/civisanalytics/python-glmnet>`__ and
+   `here <https://github.com/dwf/glmnet-python>`__) but in contrast to
+   these, Pyglmnet is a pure python implementation. Therefore, it is
+   easy to modify and introduce additional noise models and regularizers
+   in the future.
+Installation
+~~~~~~~~~~~~
+Install the stable PyPI version with ``pip``
+    $ pip install pyglmnet
+For the bleeding edge development version:
+Clone the repository.
+    $ pip install https://api.github.com/repos/glm-tools/pyglmnet/zipball/master
+Getting Started
+~~~~~~~~~~~~~~~
+Here is an example on how to use the ``GLM`` estimator.
+   import numpy as np
+   import scipy.sparse as sps
+   from pyglmnet import GLM, simulate_glm
+   n_samples, n_features = 1000, 100
+   distr = 'poisson'
+   # sample a sparse model
+   beta0 = np.random.rand()
+   beta = np.random.random(n_features)
+   beta[beta < 0.9] = 0
+   # simulate data
+   Xtrain = np.random.normal(0.0, 1.0, [n_samples, n_features])
+   ytrain = simulate_glm('poisson', beta0, beta, Xtrain)
+   Xtest = np.random.normal(0.0, 1.0, [n_samples, n_features])
+   ytest = simulate_glm('poisson', beta0, beta, Xtest)
+   # create an instance of the GLM class
+   glm = GLM(distr='poisson', score_metric='deviance')
+   # fit the model on the training data
+   glm.fit(Xtrain, ytrain)
+   # predict using fitted model on the test data
+   yhat = glm.predict(Xtest)
+   # score the model on test data
+   deviance = glm.score(Xtest, ytest)
+`More pyglmnet examples and use
+cases <http://glm-tools.github.io/pyglmnet/auto_examples/index.html>`__.
+Tutorial
+~~~~~~~~
+Here is an `extensive
+tutorial <http://glm-tools.github.io/pyglmnet/tutorial.html>`__ on GLMs,
+optimization and pseudo-code.
+Here are
+`slides <https://pavanramkumar.github.io/pydata-chicago-2016>`__ from a
+talk at `PyData Chicago
+2016 <http://pydata.org/chicago2016/schedule/presentation/15/>`__,
+corresponding `tutorial
+notebooks <http://github.com/pavanramkumar/pydata-chicago-2016>`__ and a
+`video <https://www.youtube.com/watch?v=zXec96KD1uA>`__.
+How to contribute?
+~~~~~~~~~~~~~~~~~~
+We welcome pull requests. Please see our `developer documentation
+page <http://glm-tools.github.io/pyglmnet/developers.html>`__ for more
+details.
+Acknowledgments
+~~~~~~~~~~~~~~~
+-  `Konrad Kording <http://kordinglab.com>`__ for funding and support
+-  `Sara
+   Solla <http://www.physics.northwestern.edu/people/joint-faculty/sara-solla.html>`__
+   for masterful GLM lectures
+License
+~~~~~~~
+MIT License Copyright (c) 2016-2019 Pavan Ramkumar
+
+%prep
+%autosetup -n pyglmnet-1.1
+
+%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-pyglmnet -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..9625aad
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+e7a90f472d7613162ad1deb372087b09  pyglmnet-1.1.tar.gz