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%global _empty_manifest_terminate_build 0
Name: python-naive-bayes
Version: 0.1.1
Release: 1
Summary: Naive Bayes Text Classification
License: MIT
URL: https://github.com/itdxer/naive-bayes
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ab/aa/75158e517a7678485e9a748b3117ce5edb750af643b2a916b061ee53ba4d/naive-bayes-0.1.1.tar.gz
BuildArch: noarch
%description
# Naive Bayes Text Classifier
Text classifier based on Naive Bayes.
## Instalation
```bash
$ pip install naive-bayes
```
## Usage example
```python
from naivebayes import NaiveBayesTextClassifier
classifier = NaiveBayesTextClassifier(
categories=categories_list,
stop_words=stopwords_list
)
classifier.train(train_docs, train_classes)
predicted_classes = classifier.classify(test_docs)
```
`NaiveBayesTextClassifier` is a simple wrapper around `scikit-learn` class `CountVectorizer`. You can put all arguments which support this class. For more information please check `scikit-learn` official documentation.
## More examples
Check examples at `examples` folder. Before run them, install requirements in this folder.
Clone repository from github
```bash
$ git clone git@github.com:itdxer/naive-bayes.git
$ cd naive-bayes/examples
$ pip install -r requirements.txt
```
And run some example
### Usenet 20 newsgroup
```bash
$ python 20newsgroup
```
### Kaggle IMDB reviews competition
```bash
$ python imdb_reviews
```
%package -n python3-naive-bayes
Summary: Naive Bayes Text Classification
Provides: python-naive-bayes
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-naive-bayes
# Naive Bayes Text Classifier
Text classifier based on Naive Bayes.
## Instalation
```bash
$ pip install naive-bayes
```
## Usage example
```python
from naivebayes import NaiveBayesTextClassifier
classifier = NaiveBayesTextClassifier(
categories=categories_list,
stop_words=stopwords_list
)
classifier.train(train_docs, train_classes)
predicted_classes = classifier.classify(test_docs)
```
`NaiveBayesTextClassifier` is a simple wrapper around `scikit-learn` class `CountVectorizer`. You can put all arguments which support this class. For more information please check `scikit-learn` official documentation.
## More examples
Check examples at `examples` folder. Before run them, install requirements in this folder.
Clone repository from github
```bash
$ git clone git@github.com:itdxer/naive-bayes.git
$ cd naive-bayes/examples
$ pip install -r requirements.txt
```
And run some example
### Usenet 20 newsgroup
```bash
$ python 20newsgroup
```
### Kaggle IMDB reviews competition
```bash
$ python imdb_reviews
```
%package help
Summary: Development documents and examples for naive-bayes
Provides: python3-naive-bayes-doc
%description help
# Naive Bayes Text Classifier
Text classifier based on Naive Bayes.
## Instalation
```bash
$ pip install naive-bayes
```
## Usage example
```python
from naivebayes import NaiveBayesTextClassifier
classifier = NaiveBayesTextClassifier(
categories=categories_list,
stop_words=stopwords_list
)
classifier.train(train_docs, train_classes)
predicted_classes = classifier.classify(test_docs)
```
`NaiveBayesTextClassifier` is a simple wrapper around `scikit-learn` class `CountVectorizer`. You can put all arguments which support this class. For more information please check `scikit-learn` official documentation.
## More examples
Check examples at `examples` folder. Before run them, install requirements in this folder.
Clone repository from github
```bash
$ git clone git@github.com:itdxer/naive-bayes.git
$ cd naive-bayes/examples
$ pip install -r requirements.txt
```
And run some example
### Usenet 20 newsgroup
```bash
$ python 20newsgroup
```
### Kaggle IMDB reviews competition
```bash
$ python imdb_reviews
```
%prep
%autosetup -n naive-bayes-0.1.1
%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-naive-bayes -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.1-1
- Package Spec generated
|
