automatic import of python-sklearn-json

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diff --git a/.gitignore b/.gitignore
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+/sklearn-json-0.1.0.tar.gz
diff --git a/python-sklearn-json.spec b/python-sklearn-json.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-sklearn-json
+Version:	0.1.0
+Release:	1
+Summary:	A safe, transparent way to share and deploy scikit-learn models.
+License:	MIT License
+URL:		https://github.com/mlrequest/sklearn-json
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/95/eb/2097ec853072efec5a52a3ebdaaf70f3fae5d6df3c4dc050556397734509/sklearn-json-0.1.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-scikit-learn
+
+%description
+# sklearn-json
+Export scikit-learn model files to JSON for sharing or deploying predictive models with peace of mind.
+
+# Why sklearn-json?
+Other methods for exporting scikit-learn models require Pickle or Joblib (based on Pickle). Serializing model files with Pickle provide a simple attack vector for malicious users-- they give an attacker the ability to execute arbitrary code wherever the file is deserialized. (For an example see: https://www.smartfile.com/blog/python-pickle-security-problems-and-solutions/).
+
+sklearn-json is a safe and transparent solution for exporting scikit-learn model files.
+
+### Safe
+Export model files to 100% JSON which cannot execute code on deserialization.
+
+### Transparent
+Model files are serialized in JSON (i.e., not binary), so you have the ability to see exactly what's inside.
+
+# Getting Started
+
+sklearn-json makes exporting model files to JSON simple.
+
+## Install
+```
+pip install sklearn-json
+```
+## Example Usage
+
+```python
+import sklearn_json as skljson
+from sklearn.ensemble import RandomForestClassifier
+
+model = RandomForestClassifier(n_estimators=10, max_depth=5, random_state=0).fit(X, y)
+
+skljson.to_json(model, file_name)
+deserialized_model = skljson.from_json(file_name)
+
+deserialized_model.predict(X)
+```
+
+# Features
+The list of supported models is rapidly growing. If you have a request for a model or feature, please reach out to support@mlrequest.com.
+
+sklearn-json requires scikit-learn >= 0.21.3.
+
+## Supported scikit-learn Models
+
+* Classification
+    * `sklearn.linear_model.LogisticRegression`
+    * `sklearn.linear_model.Perceptron`
+    * `sklearn.discriminant_analysis.LinearDiscriminantAnalysis`
+    * `sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis`
+    * `sklearn.svm.SVC`
+    * `sklearn.naive_bayes.GaussianNB`
+    * `sklearn.naive_bayes.MultinomialNB`
+    * `sklearn.naive_bayes.ComplementNB`
+    * `sklearn.naive_bayes.BernoulliNB`
+    * `sklearn.tree.DecisionTreeClassifier`
+    * `sklearn.ensemble.RandomForestClassifier`
+    * `sklearn.ensemble.GradientBoostingClassifier`
+    * `sklearn.neural_network.MLPClassifier`
+
+* Regression
+    * `sklearn.linear_model.LinearRegression`
+    * `sklearn.linear_model.Ridge`
+    * `sklearn.linear_model.Lasso`
+    * `sklearn.svm.SVR`
+    * `sklearn.tree.DecisionTreeRegressor`
+    * `sklearn.ensemble.RandomForestRegressor`
+    * `sklearn.ensemble.GradientBoostingRegressor`
+    * `sklearn.neural_network.MLPRegressor`
+
+
+
+
+%package -n python3-sklearn-json
+Summary:	A safe, transparent way to share and deploy scikit-learn models.
+Provides:	python-sklearn-json
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-sklearn-json
+# sklearn-json
+Export scikit-learn model files to JSON for sharing or deploying predictive models with peace of mind.
+
+# Why sklearn-json?
+Other methods for exporting scikit-learn models require Pickle or Joblib (based on Pickle). Serializing model files with Pickle provide a simple attack vector for malicious users-- they give an attacker the ability to execute arbitrary code wherever the file is deserialized. (For an example see: https://www.smartfile.com/blog/python-pickle-security-problems-and-solutions/).
+
+sklearn-json is a safe and transparent solution for exporting scikit-learn model files.
+
+### Safe
+Export model files to 100% JSON which cannot execute code on deserialization.
+
+### Transparent
+Model files are serialized in JSON (i.e., not binary), so you have the ability to see exactly what's inside.
+
+# Getting Started
+
+sklearn-json makes exporting model files to JSON simple.
+
+## Install
+```
+pip install sklearn-json
+```
+## Example Usage
+
+```python
+import sklearn_json as skljson
+from sklearn.ensemble import RandomForestClassifier
+
+model = RandomForestClassifier(n_estimators=10, max_depth=5, random_state=0).fit(X, y)
+
+skljson.to_json(model, file_name)
+deserialized_model = skljson.from_json(file_name)
+
+deserialized_model.predict(X)
+```
+
+# Features
+The list of supported models is rapidly growing. If you have a request for a model or feature, please reach out to support@mlrequest.com.
+
+sklearn-json requires scikit-learn >= 0.21.3.
+
+## Supported scikit-learn Models
+
+* Classification
+    * `sklearn.linear_model.LogisticRegression`
+    * `sklearn.linear_model.Perceptron`
+    * `sklearn.discriminant_analysis.LinearDiscriminantAnalysis`
+    * `sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis`
+    * `sklearn.svm.SVC`
+    * `sklearn.naive_bayes.GaussianNB`
+    * `sklearn.naive_bayes.MultinomialNB`
+    * `sklearn.naive_bayes.ComplementNB`
+    * `sklearn.naive_bayes.BernoulliNB`
+    * `sklearn.tree.DecisionTreeClassifier`
+    * `sklearn.ensemble.RandomForestClassifier`
+    * `sklearn.ensemble.GradientBoostingClassifier`
+    * `sklearn.neural_network.MLPClassifier`
+
+* Regression
+    * `sklearn.linear_model.LinearRegression`
+    * `sklearn.linear_model.Ridge`
+    * `sklearn.linear_model.Lasso`
+    * `sklearn.svm.SVR`
+    * `sklearn.tree.DecisionTreeRegressor`
+    * `sklearn.ensemble.RandomForestRegressor`
+    * `sklearn.ensemble.GradientBoostingRegressor`
+    * `sklearn.neural_network.MLPRegressor`
+
+
+
+
+%package help
+Summary:	Development documents and examples for sklearn-json
+Provides:	python3-sklearn-json-doc
+%description help
+# sklearn-json
+Export scikit-learn model files to JSON for sharing or deploying predictive models with peace of mind.
+
+# Why sklearn-json?
+Other methods for exporting scikit-learn models require Pickle or Joblib (based on Pickle). Serializing model files with Pickle provide a simple attack vector for malicious users-- they give an attacker the ability to execute arbitrary code wherever the file is deserialized. (For an example see: https://www.smartfile.com/blog/python-pickle-security-problems-and-solutions/).
+
+sklearn-json is a safe and transparent solution for exporting scikit-learn model files.
+
+### Safe
+Export model files to 100% JSON which cannot execute code on deserialization.
+
+### Transparent
+Model files are serialized in JSON (i.e., not binary), so you have the ability to see exactly what's inside.
+
+# Getting Started
+
+sklearn-json makes exporting model files to JSON simple.
+
+## Install
+```
+pip install sklearn-json
+```
+## Example Usage
+
+```python
+import sklearn_json as skljson
+from sklearn.ensemble import RandomForestClassifier
+
+model = RandomForestClassifier(n_estimators=10, max_depth=5, random_state=0).fit(X, y)
+
+skljson.to_json(model, file_name)
+deserialized_model = skljson.from_json(file_name)
+
+deserialized_model.predict(X)
+```
+
+# Features
+The list of supported models is rapidly growing. If you have a request for a model or feature, please reach out to support@mlrequest.com.
+
+sklearn-json requires scikit-learn >= 0.21.3.
+
+## Supported scikit-learn Models
+
+* Classification
+    * `sklearn.linear_model.LogisticRegression`
+    * `sklearn.linear_model.Perceptron`
+    * `sklearn.discriminant_analysis.LinearDiscriminantAnalysis`
+    * `sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis`
+    * `sklearn.svm.SVC`
+    * `sklearn.naive_bayes.GaussianNB`
+    * `sklearn.naive_bayes.MultinomialNB`
+    * `sklearn.naive_bayes.ComplementNB`
+    * `sklearn.naive_bayes.BernoulliNB`
+    * `sklearn.tree.DecisionTreeClassifier`
+    * `sklearn.ensemble.RandomForestClassifier`
+    * `sklearn.ensemble.GradientBoostingClassifier`
+    * `sklearn.neural_network.MLPClassifier`
+
+* Regression
+    * `sklearn.linear_model.LinearRegression`
+    * `sklearn.linear_model.Ridge`
+    * `sklearn.linear_model.Lasso`
+    * `sklearn.svm.SVR`
+    * `sklearn.tree.DecisionTreeRegressor`
+    * `sklearn.ensemble.RandomForestRegressor`
+    * `sklearn.ensemble.GradientBoostingRegressor`
+    * `sklearn.neural_network.MLPRegressor`
+
+
+
+
+%prep
+%autosetup -n sklearn-json-0.1.0
+
+%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-sklearn-json -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
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+++ b/sources
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+1fe1a380448c6be73edb50b5b4fd31f7  sklearn-json-0.1.0.tar.gz