%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 - 0.1.0-1 - Package Spec generated