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|
%global _empty_manifest_terminate_build 0
Name: python-MLLytics
Version: 0.2.2
Release: 1
Summary: A library of tools for easier evaluation of ML models.
License: MIT
URL: https://github.com/scottclay/MLLytics
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/5f/f2/5a26529eb02ab005060644781f7f6b28717cc1f16e5c62cbe3a95bfd0fbc/MLLytics-0.2.2.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-matplotlib
Requires: python3-seaborn
Requires: python3-pandas
Requires: python3-scikit-learn
%description
[](https://github.com/scottclay/MLLytics/actions/workflows/python-publish.yml)
# MLLytics
## Installation instructions
```pip install MLLytics```
or
```python setup.py install```
or
``` conda env create -f environment.yml```
## Future
### Improvements and cleanup
* Comment all functions and classes
* Add type hinting to all functions and classes (https://mypy.readthedocs.io/en/latest/cheat_sheet_py3.html)
* Scoring functions
* More output stats in overviews
* Update reliability plot https://machinelearningmastery.com/calibrated-classification-model-in-scikit-learn/
* Tests
* Switch from my metrics to sklearn metrics where it makes sense? aka
```fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])```
and more general macro/micro average metrics from: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score
* Additional metrics (sensitivity, specificity, precision, negative predictive value, FPR, FNR,
false discovery rate, accuracy, F1 score
### Cosmetic
* Fix size of confusion matrix
* Check works with matplotlib 3
* Tidy up legends and annotation text on plots
* Joy plots
* Brier score for calibration plot
* Tidy up cross validation and plots (also repeated cross-validation)
* Acc-thresholds graph
### Recently completed
* ~Allow figure size and font sizes to be passed into plotting functions~
* ~Example guides for each function in jupyter notebooks~
* ~MultiClassMetrics class to inherit from ClassMetrics and share common functions~
* ~REGRESSION~
## Contributing Authors
* Scott Clay
* David Sullivan
%package -n python3-MLLytics
Summary: A library of tools for easier evaluation of ML models.
Provides: python-MLLytics
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-MLLytics
[](https://github.com/scottclay/MLLytics/actions/workflows/python-publish.yml)
# MLLytics
## Installation instructions
```pip install MLLytics```
or
```python setup.py install```
or
``` conda env create -f environment.yml```
## Future
### Improvements and cleanup
* Comment all functions and classes
* Add type hinting to all functions and classes (https://mypy.readthedocs.io/en/latest/cheat_sheet_py3.html)
* Scoring functions
* More output stats in overviews
* Update reliability plot https://machinelearningmastery.com/calibrated-classification-model-in-scikit-learn/
* Tests
* Switch from my metrics to sklearn metrics where it makes sense? aka
```fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])```
and more general macro/micro average metrics from: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score
* Additional metrics (sensitivity, specificity, precision, negative predictive value, FPR, FNR,
false discovery rate, accuracy, F1 score
### Cosmetic
* Fix size of confusion matrix
* Check works with matplotlib 3
* Tidy up legends and annotation text on plots
* Joy plots
* Brier score for calibration plot
* Tidy up cross validation and plots (also repeated cross-validation)
* Acc-thresholds graph
### Recently completed
* ~Allow figure size and font sizes to be passed into plotting functions~
* ~Example guides for each function in jupyter notebooks~
* ~MultiClassMetrics class to inherit from ClassMetrics and share common functions~
* ~REGRESSION~
## Contributing Authors
* Scott Clay
* David Sullivan
%package help
Summary: Development documents and examples for MLLytics
Provides: python3-MLLytics-doc
%description help
[](https://github.com/scottclay/MLLytics/actions/workflows/python-publish.yml)
# MLLytics
## Installation instructions
```pip install MLLytics```
or
```python setup.py install```
or
``` conda env create -f environment.yml```
## Future
### Improvements and cleanup
* Comment all functions and classes
* Add type hinting to all functions and classes (https://mypy.readthedocs.io/en/latest/cheat_sheet_py3.html)
* Scoring functions
* More output stats in overviews
* Update reliability plot https://machinelearningmastery.com/calibrated-classification-model-in-scikit-learn/
* Tests
* Switch from my metrics to sklearn metrics where it makes sense? aka
```fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])```
and more general macro/micro average metrics from: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score
* Additional metrics (sensitivity, specificity, precision, negative predictive value, FPR, FNR,
false discovery rate, accuracy, F1 score
### Cosmetic
* Fix size of confusion matrix
* Check works with matplotlib 3
* Tidy up legends and annotation text on plots
* Joy plots
* Brier score for calibration plot
* Tidy up cross validation and plots (also repeated cross-validation)
* Acc-thresholds graph
### Recently completed
* ~Allow figure size and font sizes to be passed into plotting functions~
* ~Example guides for each function in jupyter notebooks~
* ~MultiClassMetrics class to inherit from ClassMetrics and share common functions~
* ~REGRESSION~
## Contributing Authors
* Scott Clay
* David Sullivan
%prep
%autosetup -n MLLytics-0.2.2
%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-MLLytics -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.2-1
- Package Spec generated
|
