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@@ -0,0 +1 @@ +/diffprivlib-0.6.2.tar.gz diff --git a/python-diffprivlib.spec b/python-diffprivlib.spec new file mode 100644 index 0000000..f0f0491 --- /dev/null +++ b/python-diffprivlib.spec @@ -0,0 +1,485 @@ +%global _empty_manifest_terminate_build 0 +Name: python-diffprivlib +Version: 0.6.2 +Release: 1 +Summary: IBM Differential Privacy Library +License: MIT +URL: https://github.com/IBM/differential-privacy-library +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/48/52/15d7dc925bfedcfab6f63f5fc7513e380afdb56cf949177c06668acdb960/diffprivlib-0.6.2.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-scikit-learn +Requires: python3-scipy +Requires: python3-joblib +Requires: python3-setuptools +Requires: python3-crlibm +Requires: python3-sphinx +Requires: python3-sphinx-rtd-theme + +%description +# Diffprivlib v0.6 + +[](https://pypi.org/project/diffprivlib/) +[](https://pepy.tech/project/diffprivlib) +[](https://pypi.org/project/diffprivlib/) +[](https://pypi.org/project/diffprivlib/) +[](https://github.com/IBM/differential-privacy-library/actions/workflows/general.yml) +[](https://diffprivlib.readthedocs.io/en/latest/?badge=latest) +[](https://github.com/IBM/differential-privacy-library/actions/workflows/codeql.yml) +[](https://codecov.io/gh/IBM/differential-privacy-library) + +Diffprivlib is a general-purpose library for experimenting with, investigating and developing applications in, differential privacy. + +Use diffprivlib if you are looking to: + +- Experiment with differential privacy +- Explore the impact of differential privacy on machine learning accuracy using classification and clustering models +- Build your own differential privacy applications, using our extensive collection of mechanisms + +Diffprivlib supports Python versions 3.8 to 3.11. + +## Getting started: [Machine learning with differential privacy in 30 seconds](https://github.com/IBM/differential-privacy-library/blob/main/notebooks/30seconds.ipynb) +We're using the [Iris dataset](https://archive.ics.uci.edu/ml/datasets/iris), so let's load it and perform an 80/20 train/test split. + +```python +from sklearn import datasets +from sklearn.model_selection import train_test_split + +dataset = datasets.load_iris() +X_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.2) +``` + +Now, let's train a differentially private naive Bayes classifier. Our classifier __runs just like an `sklearn` classifier__, so you can get up and running quickly. + +`diffprivlib.models.GaussianNB` can be run __without any parameters__, although this will throw a warning (we need to specify the `bounds` parameter to avoid this). The privacy level is controlled by the parameter `epsilon`, which is passed to the classifier at initialisation (e.g. `GaussianNB(epsilon=0.1)`). The default is `epsilon = 1.0`. + +```python +from diffprivlib.models import GaussianNB + +clf = GaussianNB() +clf.fit(X_train, y_train) +``` + +We can now classify unseen examples, knowing that the trained model is differentially private and preserves the privacy of the 'individuals' in the training set (flowers are entitled to their privacy too!). + +```python +clf.predict(X_test) +``` + +Every time the model is trained with `.fit()`, a different model is produced due to the randomness of differential privacy. The accuracy will therefore change, even if it's re-trained with the same training data. Try it for yourself to find out! + +```python +print("Test accuracy: %f" % clf.score(X_test, y_test)) +``` + +We can easily evaluate the accuracy of the model for various `epsilon` values and plot it with `matplotlib`. + +```python +import numpy as np +import matplotlib.pyplot as plt + +epsilons = np.logspace(-2, 2, 50) +bounds = ([4.3, 2.0, 1.1, 0.1], [7.9, 4.4, 6.9, 2.5]) +accuracy = list() + +for epsilon in epsilons: + clf = GaussianNB(bounds=bounds, epsilon=epsilon) + clf.fit(X_train, y_train) + + accuracy.append(clf.score(X_test, y_test)) + +plt.semilogx(epsilons, accuracy) +plt.title("Differentially private Naive Bayes accuracy") +plt.xlabel("epsilon") +plt.ylabel("Accuracy") +plt.show() +``` + + + +Congratulations, you've completed your first differentially private machine learning task with the Differential Privacy Library! Check out more examples in the [notebooks](https://github.com/IBM/differential-privacy-library/blob/main/notebooks/) directory, or [dive straight in](https://github.com/IBM/differential-privacy-library/blob/main/diffprivlib/). + +## Contents + +Diffprivlib is comprised of four major components: +1. __Mechanisms:__ These are the building blocks of differential privacy, and are used in all models that implement differential privacy. Mechanisms have little or no default settings, and are intended for use by experts implementing their own models. They can, however, be used outside models for separate investigations, etc. +1. __Models:__ This module includes machine learning models with differential privacy. Diffprivlib currently has models for clustering, classification, regression, dimensionality reduction and pre-processing. +1. __Tools:__ Diffprivlib comes with a number of generic tools for differentially private data analysis. This includes differentially private histograms, following the same format as [Numpy's histogram function](https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html). +1. __Accountant:__ The `BudgetAccountant` class can be used to track privacy budget and calculate total privacy loss using advanced composition techniques. + + +## Setup + +### Installation with `pip` + +The library is designed to run with Python 3. +The library can be installed from the PyPi repository using `pip` (or `pip3`): + +```bash +pip install diffprivlib +``` + +### Manual installation + +For the most recent version of the library, either download the source code or clone the repository in your directory of choice: + +```bash +git clone https://github.com/IBM/differential-privacy-library +``` + +To install `diffprivlib`, do the following in the project folder (alternatively, you can run `python3 -m pip install .`): +```bash +pip install . +``` + +The library comes with a basic set of unit tests for `pytest`. To check your install, you can run all the unit tests by calling `pytest` in the install folder: + +```bash +pytest +``` + +## Citing diffprivlib +If you use diffprivlib for research, please consider citing the following reference paper: +``` +@article{diffprivlib, + title={Diffprivlib: the {IBM} differential privacy library}, + author={Holohan, Naoise and Braghin, Stefano and Mac Aonghusa, P{\'o}l and Levacher, Killian}, + year={2019}, + journal = {ArXiv e-prints}, + archivePrefix = "arXiv", + volume = {1907.02444 [cs.CR]}, + primaryClass = "cs.CR", + month = jul +} +``` + + +%package -n python3-diffprivlib +Summary: IBM Differential Privacy Library +Provides: python-diffprivlib +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-diffprivlib +# Diffprivlib v0.6 + +[](https://pypi.org/project/diffprivlib/) +[](https://pepy.tech/project/diffprivlib) +[](https://pypi.org/project/diffprivlib/) +[](https://pypi.org/project/diffprivlib/) +[](https://github.com/IBM/differential-privacy-library/actions/workflows/general.yml) +[](https://diffprivlib.readthedocs.io/en/latest/?badge=latest) +[](https://github.com/IBM/differential-privacy-library/actions/workflows/codeql.yml) +[](https://codecov.io/gh/IBM/differential-privacy-library) + +Diffprivlib is a general-purpose library for experimenting with, investigating and developing applications in, differential privacy. + +Use diffprivlib if you are looking to: + +- Experiment with differential privacy +- Explore the impact of differential privacy on machine learning accuracy using classification and clustering models +- Build your own differential privacy applications, using our extensive collection of mechanisms + +Diffprivlib supports Python versions 3.8 to 3.11. + +## Getting started: [Machine learning with differential privacy in 30 seconds](https://github.com/IBM/differential-privacy-library/blob/main/notebooks/30seconds.ipynb) +We're using the [Iris dataset](https://archive.ics.uci.edu/ml/datasets/iris), so let's load it and perform an 80/20 train/test split. + +```python +from sklearn import datasets +from sklearn.model_selection import train_test_split + +dataset = datasets.load_iris() +X_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.2) +``` + +Now, let's train a differentially private naive Bayes classifier. Our classifier __runs just like an `sklearn` classifier__, so you can get up and running quickly. + +`diffprivlib.models.GaussianNB` can be run __without any parameters__, although this will throw a warning (we need to specify the `bounds` parameter to avoid this). The privacy level is controlled by the parameter `epsilon`, which is passed to the classifier at initialisation (e.g. `GaussianNB(epsilon=0.1)`). The default is `epsilon = 1.0`. + +```python +from diffprivlib.models import GaussianNB + +clf = GaussianNB() +clf.fit(X_train, y_train) +``` + +We can now classify unseen examples, knowing that the trained model is differentially private and preserves the privacy of the 'individuals' in the training set (flowers are entitled to their privacy too!). + +```python +clf.predict(X_test) +``` + +Every time the model is trained with `.fit()`, a different model is produced due to the randomness of differential privacy. The accuracy will therefore change, even if it's re-trained with the same training data. Try it for yourself to find out! + +```python +print("Test accuracy: %f" % clf.score(X_test, y_test)) +``` + +We can easily evaluate the accuracy of the model for various `epsilon` values and plot it with `matplotlib`. + +```python +import numpy as np +import matplotlib.pyplot as plt + +epsilons = np.logspace(-2, 2, 50) +bounds = ([4.3, 2.0, 1.1, 0.1], [7.9, 4.4, 6.9, 2.5]) +accuracy = list() + +for epsilon in epsilons: + clf = GaussianNB(bounds=bounds, epsilon=epsilon) + clf.fit(X_train, y_train) + + accuracy.append(clf.score(X_test, y_test)) + +plt.semilogx(epsilons, accuracy) +plt.title("Differentially private Naive Bayes accuracy") +plt.xlabel("epsilon") +plt.ylabel("Accuracy") +plt.show() +``` + + + +Congratulations, you've completed your first differentially private machine learning task with the Differential Privacy Library! Check out more examples in the [notebooks](https://github.com/IBM/differential-privacy-library/blob/main/notebooks/) directory, or [dive straight in](https://github.com/IBM/differential-privacy-library/blob/main/diffprivlib/). + +## Contents + +Diffprivlib is comprised of four major components: +1. __Mechanisms:__ These are the building blocks of differential privacy, and are used in all models that implement differential privacy. Mechanisms have little or no default settings, and are intended for use by experts implementing their own models. They can, however, be used outside models for separate investigations, etc. +1. __Models:__ This module includes machine learning models with differential privacy. Diffprivlib currently has models for clustering, classification, regression, dimensionality reduction and pre-processing. +1. __Tools:__ Diffprivlib comes with a number of generic tools for differentially private data analysis. This includes differentially private histograms, following the same format as [Numpy's histogram function](https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html). +1. __Accountant:__ The `BudgetAccountant` class can be used to track privacy budget and calculate total privacy loss using advanced composition techniques. + + +## Setup + +### Installation with `pip` + +The library is designed to run with Python 3. +The library can be installed from the PyPi repository using `pip` (or `pip3`): + +```bash +pip install diffprivlib +``` + +### Manual installation + +For the most recent version of the library, either download the source code or clone the repository in your directory of choice: + +```bash +git clone https://github.com/IBM/differential-privacy-library +``` + +To install `diffprivlib`, do the following in the project folder (alternatively, you can run `python3 -m pip install .`): +```bash +pip install . +``` + +The library comes with a basic set of unit tests for `pytest`. To check your install, you can run all the unit tests by calling `pytest` in the install folder: + +```bash +pytest +``` + +## Citing diffprivlib +If you use diffprivlib for research, please consider citing the following reference paper: +``` +@article{diffprivlib, + title={Diffprivlib: the {IBM} differential privacy library}, + author={Holohan, Naoise and Braghin, Stefano and Mac Aonghusa, P{\'o}l and Levacher, Killian}, + year={2019}, + journal = {ArXiv e-prints}, + archivePrefix = "arXiv", + volume = {1907.02444 [cs.CR]}, + primaryClass = "cs.CR", + month = jul +} +``` + + +%package help +Summary: Development documents and examples for diffprivlib +Provides: python3-diffprivlib-doc +%description help +# Diffprivlib v0.6 + +[](https://pypi.org/project/diffprivlib/) +[](https://pepy.tech/project/diffprivlib) +[](https://pypi.org/project/diffprivlib/) +[](https://pypi.org/project/diffprivlib/) +[](https://github.com/IBM/differential-privacy-library/actions/workflows/general.yml) +[](https://diffprivlib.readthedocs.io/en/latest/?badge=latest) +[](https://github.com/IBM/differential-privacy-library/actions/workflows/codeql.yml) +[](https://codecov.io/gh/IBM/differential-privacy-library) + +Diffprivlib is a general-purpose library for experimenting with, investigating and developing applications in, differential privacy. + +Use diffprivlib if you are looking to: + +- Experiment with differential privacy +- Explore the impact of differential privacy on machine learning accuracy using classification and clustering models +- Build your own differential privacy applications, using our extensive collection of mechanisms + +Diffprivlib supports Python versions 3.8 to 3.11. + +## Getting started: [Machine learning with differential privacy in 30 seconds](https://github.com/IBM/differential-privacy-library/blob/main/notebooks/30seconds.ipynb) +We're using the [Iris dataset](https://archive.ics.uci.edu/ml/datasets/iris), so let's load it and perform an 80/20 train/test split. + +```python +from sklearn import datasets +from sklearn.model_selection import train_test_split + +dataset = datasets.load_iris() +X_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.2) +``` + +Now, let's train a differentially private naive Bayes classifier. Our classifier __runs just like an `sklearn` classifier__, so you can get up and running quickly. + +`diffprivlib.models.GaussianNB` can be run __without any parameters__, although this will throw a warning (we need to specify the `bounds` parameter to avoid this). The privacy level is controlled by the parameter `epsilon`, which is passed to the classifier at initialisation (e.g. `GaussianNB(epsilon=0.1)`). The default is `epsilon = 1.0`. + +```python +from diffprivlib.models import GaussianNB + +clf = GaussianNB() +clf.fit(X_train, y_train) +``` + +We can now classify unseen examples, knowing that the trained model is differentially private and preserves the privacy of the 'individuals' in the training set (flowers are entitled to their privacy too!). + +```python +clf.predict(X_test) +``` + +Every time the model is trained with `.fit()`, a different model is produced due to the randomness of differential privacy. The accuracy will therefore change, even if it's re-trained with the same training data. Try it for yourself to find out! + +```python +print("Test accuracy: %f" % clf.score(X_test, y_test)) +``` + +We can easily evaluate the accuracy of the model for various `epsilon` values and plot it with `matplotlib`. + +```python +import numpy as np +import matplotlib.pyplot as plt + +epsilons = np.logspace(-2, 2, 50) +bounds = ([4.3, 2.0, 1.1, 0.1], [7.9, 4.4, 6.9, 2.5]) +accuracy = list() + +for epsilon in epsilons: + clf = GaussianNB(bounds=bounds, epsilon=epsilon) + clf.fit(X_train, y_train) + + accuracy.append(clf.score(X_test, y_test)) + +plt.semilogx(epsilons, accuracy) +plt.title("Differentially private Naive Bayes accuracy") +plt.xlabel("epsilon") +plt.ylabel("Accuracy") +plt.show() +``` + + + +Congratulations, you've completed your first differentially private machine learning task with the Differential Privacy Library! Check out more examples in the [notebooks](https://github.com/IBM/differential-privacy-library/blob/main/notebooks/) directory, or [dive straight in](https://github.com/IBM/differential-privacy-library/blob/main/diffprivlib/). + +## Contents + +Diffprivlib is comprised of four major components: +1. __Mechanisms:__ These are the building blocks of differential privacy, and are used in all models that implement differential privacy. Mechanisms have little or no default settings, and are intended for use by experts implementing their own models. They can, however, be used outside models for separate investigations, etc. +1. __Models:__ This module includes machine learning models with differential privacy. Diffprivlib currently has models for clustering, classification, regression, dimensionality reduction and pre-processing. +1. __Tools:__ Diffprivlib comes with a number of generic tools for differentially private data analysis. This includes differentially private histograms, following the same format as [Numpy's histogram function](https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html). +1. __Accountant:__ The `BudgetAccountant` class can be used to track privacy budget and calculate total privacy loss using advanced composition techniques. + + +## Setup + +### Installation with `pip` + +The library is designed to run with Python 3. +The library can be installed from the PyPi repository using `pip` (or `pip3`): + +```bash +pip install diffprivlib +``` + +### Manual installation + +For the most recent version of the library, either download the source code or clone the repository in your directory of choice: + +```bash +git clone https://github.com/IBM/differential-privacy-library +``` + +To install `diffprivlib`, do the following in the project folder (alternatively, you can run `python3 -m pip install .`): +```bash +pip install . +``` + +The library comes with a basic set of unit tests for `pytest`. To check your install, you can run all the unit tests by calling `pytest` in the install folder: + +```bash +pytest +``` + +## Citing diffprivlib +If you use diffprivlib for research, please consider citing the following reference paper: +``` +@article{diffprivlib, + title={Diffprivlib: the {IBM} differential privacy library}, + author={Holohan, Naoise and Braghin, Stefano and Mac Aonghusa, P{\'o}l and Levacher, Killian}, + year={2019}, + journal = {ArXiv e-prints}, + archivePrefix = "arXiv", + volume = {1907.02444 [cs.CR]}, + primaryClass = "cs.CR", + month = jul +} +``` + + +%prep +%autosetup -n diffprivlib-0.6.2 + +%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-diffprivlib -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.2-1 +- Package Spec generated @@ -0,0 +1 @@ +840c2ed0edf60554f02768f2d759f904 diffprivlib-0.6.2.tar.gz |