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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
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.0-1
- Package Spec generated
|
