path: root/python-emmv.spec

%global _empty_manifest_terminate_build 0
Name:		python-emmv
Version:	0.0.4
Release:	1
Summary:	Metrics for unsupervised anomaly detection models
License:	MIT License
URL:		https://github.com/christian-oleary/emmv
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/37/1f/2e9f578680b98196e3aef716e78a63e66cc4b9f39c32563afa7effadce35/emmv-0.0.4.tar.gz
BuildArch:	noarch

Requires:	python3-numpy
Requires:	python3-pandas
Requires:	python3-setuptools
Requires:	python3-scikit-learn

%description
# EMMV

Implementation of EM/MV metrics based on N. Goix et al.

This is a means of evaluating anomaly detection models without anomaly labels

## Installation

```shell
pip install emmv
```

## Example Use

```python
from emmv import emmv_scores

test_scores = emmv_scores(model, features)
```

- Where 'model' is your **trained** scikit-learn, PyOD, or PyCaret model
- Where 'features' is a 2D DataFrame of features (the *X* matrix)

Example resulting object:

```json
{ 
    "em": 0.77586,
    "mv": 0.25367
}
```

If you are using models without a built-in *decision_function* (e.g. Keras or ADTK models), then you need to specify an anomaly scoring function. Please see examples in the examples folder.

## Running Examples

```shell
pip install .
python ./examples/sklearn_example.py
```

## Interpreting scores

- The best model should have the **highest** Excess Mass score
- The best model should have the **lowest** Mass Volume score
- Probably easiest to just use one of the metrics
- Extreme values are possible

## Contact

Please feel free to get in touch at christian.oleary@mtu.ie

## Citation

```latex
@Misc{emmv,
author =   {Christian O'Leary},
title =    {EMMV library},
howpublished = {\url{https://pypi.org/project/emmv/}},
year = {2021--2021}
}
```




%package -n python3-emmv
Summary:	Metrics for unsupervised anomaly detection models
Provides:	python-emmv
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-emmv
# EMMV

Implementation of EM/MV metrics based on N. Goix et al.

This is a means of evaluating anomaly detection models without anomaly labels

## Installation

```shell
pip install emmv
```

## Example Use

```python
from emmv import emmv_scores

test_scores = emmv_scores(model, features)
```

- Where 'model' is your **trained** scikit-learn, PyOD, or PyCaret model
- Where 'features' is a 2D DataFrame of features (the *X* matrix)

Example resulting object:

```json
{ 
    "em": 0.77586,
    "mv": 0.25367
}
```

If you are using models without a built-in *decision_function* (e.g. Keras or ADTK models), then you need to specify an anomaly scoring function. Please see examples in the examples folder.

## Running Examples

```shell
pip install .
python ./examples/sklearn_example.py
```

## Interpreting scores

- The best model should have the **highest** Excess Mass score
- The best model should have the **lowest** Mass Volume score
- Probably easiest to just use one of the metrics
- Extreme values are possible

## Contact

Please feel free to get in touch at christian.oleary@mtu.ie

## Citation

```latex
@Misc{emmv,
author =   {Christian O'Leary},
title =    {EMMV library},
howpublished = {\url{https://pypi.org/project/emmv/}},
year = {2021--2021}
}
```




%package help
Summary:	Development documents and examples for emmv
Provides:	python3-emmv-doc
%description help
# EMMV

Implementation of EM/MV metrics based on N. Goix et al.

This is a means of evaluating anomaly detection models without anomaly labels

## Installation

```shell
pip install emmv
```

## Example Use

```python
from emmv import emmv_scores

test_scores = emmv_scores(model, features)
```

- Where 'model' is your **trained** scikit-learn, PyOD, or PyCaret model
- Where 'features' is a 2D DataFrame of features (the *X* matrix)

Example resulting object:

```json
{ 
    "em": 0.77586,
    "mv": 0.25367
}
```

If you are using models without a built-in *decision_function* (e.g. Keras or ADTK models), then you need to specify an anomaly scoring function. Please see examples in the examples folder.

## Running Examples

```shell
pip install .
python ./examples/sklearn_example.py
```

## Interpreting scores

- The best model should have the **highest** Excess Mass score
- The best model should have the **lowest** Mass Volume score
- Probably easiest to just use one of the metrics
- Extreme values are possible

## Contact

Please feel free to get in touch at christian.oleary@mtu.ie

## Citation

```latex
@Misc{emmv,
author =   {Christian O'Leary},
title =    {EMMV library},
howpublished = {\url{https://pypi.org/project/emmv/}},
year = {2021--2021}
}
```




%prep
%autosetup -n emmv-0.0.4

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

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

%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.4-1
- Package Spec generated