path: root/python-wmd.spec

%global _empty_manifest_terminate_build 0
Name:		python-wmd
Version:	1.3.2
Release:	1
Summary:	Accelerated functions to calculate Word Mover's Distance
License:	Apache Software License
URL:		https://github.com/src-d/wmd-relax
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/e5/14/e1d122e56607ae49999041f372fa14166eb1e3b838122118d706f9bf1620/wmd-1.3.2.tar.gz
BuildArch:	noarch


%description
Calculates Word Mover's Distance as described in
[From Word Embeddings To Document Distances](http://www.cs.cornell.edu/~kilian/papers/wmd_metric.pdf)
by Matt Kusner, Yu Sun, Nicholas Kolkin and Kilian Weinberger.
<img src="doc/wmd.png" alt="Word Mover's Distance" width="200"/>
The high level logic is written in Python, the low level functions related to
linear programming are offloaded to the bundled native extension. The native
extension can be built as a generic shared library not related to Python at all.
**Python 2.7 and older are not supported.** The heavy-lifting is done by
[google/or-tools](https://github.com/google/or-tools).
### Installation
```
pip3 install wmd
```
Tested on Linux and macOS.
### Usage
You should have the embeddings numpy array and the nbow model - that is,
every sample is a weighted set of items, and every item is embedded.
```python
import numpy
from wmd import WMD
embeddings = numpy.array([[0.1, 1], [1, 0.1]], dtype=numpy.float32)
nbow = {"first":  ("#1", [0, 1], numpy.array([1.5, 0.5], dtype=numpy.float32)),
        "second": ("#2", [0, 1], numpy.array([0.75, 0.15], dtype=numpy.float32))}
calc = WMD(embeddings, nbow, vocabulary_min=2)
print(calc.nearest_neighbors("first"))
```
```
[('second', 0.10606599599123001)]
```
`embeddings` must support `__getitem__` which returns an item by it's
identifier; particularly, `numpy.ndarray` matches that interface.
`nbow` must be iterable - returns sample identifiers - and support
`__getitem__` by those identifiers which returns tuples of length 3.
The first element is the human-readable name of the sample, the
second is an iterable with item identifiers and the third is `numpy.ndarray`
with the corresponding weights. All numpy arrays must be float32. The return
format is the list of tuples with sample identifiers and relevancy
indices (lower the better).
It is possible to use this package with [spaCy](https://github.com/explosion/spaCy):
```python
import spacy
import wmd
nlp = spacy.load('en_core_web_md')
nlp.add_pipe(wmd.WMD.SpacySimilarityHook(nlp), last=True)
doc1 = nlp("Politician speaks to the media in Illinois.")
doc2 = nlp("The president greets the press in Chicago.")
print(doc1.similarity(doc2))
```
Besides, see another [example](spacy_example.py) which finds similar Wikipedia
pages.
### Building from source
Either build it as a Python package:
```
pip3 install git+https://github.com/src-d/wmd-relax
```
or use CMake:
```
git clone --recursive https://github.com/src-d/wmd-relax
cmake -D CMAKE_BUILD_TYPE=Release .
make -j
```
Please note the `--recursive` flag for `git clone`. This project uses source{d}'s
fork of [google/or-tools](https://github.com/google/or-tools) as the git submodule.
### Tests
Tests are in `test.py` and use the stock `unittest` package.
### Documentation
```
cd doc
make html
```
The files are in `doc/doxyhtml` and `doc/html` directories.
### Contributions
### License
[Apache 2.0](LICENSE.md)
#### README {#ignore_this_doxygen_anchor}

%package -n python3-wmd
Summary:	Accelerated functions to calculate Word Mover's Distance
Provides:	python-wmd
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-wmd
Calculates Word Mover's Distance as described in
[From Word Embeddings To Document Distances](http://www.cs.cornell.edu/~kilian/papers/wmd_metric.pdf)
by Matt Kusner, Yu Sun, Nicholas Kolkin and Kilian Weinberger.
<img src="doc/wmd.png" alt="Word Mover's Distance" width="200"/>
The high level logic is written in Python, the low level functions related to
linear programming are offloaded to the bundled native extension. The native
extension can be built as a generic shared library not related to Python at all.
**Python 2.7 and older are not supported.** The heavy-lifting is done by
[google/or-tools](https://github.com/google/or-tools).
### Installation
```
pip3 install wmd
```
Tested on Linux and macOS.
### Usage
You should have the embeddings numpy array and the nbow model - that is,
every sample is a weighted set of items, and every item is embedded.
```python
import numpy
from wmd import WMD
embeddings = numpy.array([[0.1, 1], [1, 0.1]], dtype=numpy.float32)
nbow = {"first":  ("#1", [0, 1], numpy.array([1.5, 0.5], dtype=numpy.float32)),
        "second": ("#2", [0, 1], numpy.array([0.75, 0.15], dtype=numpy.float32))}
calc = WMD(embeddings, nbow, vocabulary_min=2)
print(calc.nearest_neighbors("first"))
```
```
[('second', 0.10606599599123001)]
```
`embeddings` must support `__getitem__` which returns an item by it's
identifier; particularly, `numpy.ndarray` matches that interface.
`nbow` must be iterable - returns sample identifiers - and support
`__getitem__` by those identifiers which returns tuples of length 3.
The first element is the human-readable name of the sample, the
second is an iterable with item identifiers and the third is `numpy.ndarray`
with the corresponding weights. All numpy arrays must be float32. The return
format is the list of tuples with sample identifiers and relevancy
indices (lower the better).
It is possible to use this package with [spaCy](https://github.com/explosion/spaCy):
```python
import spacy
import wmd
nlp = spacy.load('en_core_web_md')
nlp.add_pipe(wmd.WMD.SpacySimilarityHook(nlp), last=True)
doc1 = nlp("Politician speaks to the media in Illinois.")
doc2 = nlp("The president greets the press in Chicago.")
print(doc1.similarity(doc2))
```
Besides, see another [example](spacy_example.py) which finds similar Wikipedia
pages.
### Building from source
Either build it as a Python package:
```
pip3 install git+https://github.com/src-d/wmd-relax
```
or use CMake:
```
git clone --recursive https://github.com/src-d/wmd-relax
cmake -D CMAKE_BUILD_TYPE=Release .
make -j
```
Please note the `--recursive` flag for `git clone`. This project uses source{d}'s
fork of [google/or-tools](https://github.com/google/or-tools) as the git submodule.
### Tests
Tests are in `test.py` and use the stock `unittest` package.
### Documentation
```
cd doc
make html
```
The files are in `doc/doxyhtml` and `doc/html` directories.
### Contributions
### License
[Apache 2.0](LICENSE.md)
#### README {#ignore_this_doxygen_anchor}

%package help
Summary:	Development documents and examples for wmd
Provides:	python3-wmd-doc
%description help
Calculates Word Mover's Distance as described in
[From Word Embeddings To Document Distances](http://www.cs.cornell.edu/~kilian/papers/wmd_metric.pdf)
by Matt Kusner, Yu Sun, Nicholas Kolkin and Kilian Weinberger.
<img src="doc/wmd.png" alt="Word Mover's Distance" width="200"/>
The high level logic is written in Python, the low level functions related to
linear programming are offloaded to the bundled native extension. The native
extension can be built as a generic shared library not related to Python at all.
**Python 2.7 and older are not supported.** The heavy-lifting is done by
[google/or-tools](https://github.com/google/or-tools).
### Installation
```
pip3 install wmd
```
Tested on Linux and macOS.
### Usage
You should have the embeddings numpy array and the nbow model - that is,
every sample is a weighted set of items, and every item is embedded.
```python
import numpy
from wmd import WMD
embeddings = numpy.array([[0.1, 1], [1, 0.1]], dtype=numpy.float32)
nbow = {"first":  ("#1", [0, 1], numpy.array([1.5, 0.5], dtype=numpy.float32)),
        "second": ("#2", [0, 1], numpy.array([0.75, 0.15], dtype=numpy.float32))}
calc = WMD(embeddings, nbow, vocabulary_min=2)
print(calc.nearest_neighbors("first"))
```
```
[('second', 0.10606599599123001)]
```
`embeddings` must support `__getitem__` which returns an item by it's
identifier; particularly, `numpy.ndarray` matches that interface.
`nbow` must be iterable - returns sample identifiers - and support
`__getitem__` by those identifiers which returns tuples of length 3.
The first element is the human-readable name of the sample, the
second is an iterable with item identifiers and the third is `numpy.ndarray`
with the corresponding weights. All numpy arrays must be float32. The return
format is the list of tuples with sample identifiers and relevancy
indices (lower the better).
It is possible to use this package with [spaCy](https://github.com/explosion/spaCy):
```python
import spacy
import wmd
nlp = spacy.load('en_core_web_md')
nlp.add_pipe(wmd.WMD.SpacySimilarityHook(nlp), last=True)
doc1 = nlp("Politician speaks to the media in Illinois.")
doc2 = nlp("The president greets the press in Chicago.")
print(doc1.similarity(doc2))
```
Besides, see another [example](spacy_example.py) which finds similar Wikipedia
pages.
### Building from source
Either build it as a Python package:
```
pip3 install git+https://github.com/src-d/wmd-relax
```
or use CMake:
```
git clone --recursive https://github.com/src-d/wmd-relax
cmake -D CMAKE_BUILD_TYPE=Release .
make -j
```
Please note the `--recursive` flag for `git clone`. This project uses source{d}'s
fork of [google/or-tools](https://github.com/google/or-tools) as the git submodule.
### Tests
Tests are in `test.py` and use the stock `unittest` package.
### Documentation
```
cd doc
make html
```
The files are in `doc/doxyhtml` and `doc/html` directories.
### Contributions
### License
[Apache 2.0](LICENSE.md)
#### README {#ignore_this_doxygen_anchor}

%prep
%autosetup -n wmd-1.3.2

%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-wmd -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri Apr 21 2023 Python_Bot <Python_Bot@openeuler.org> - 1.3.2-1
- Package Spec generated