%global _empty_manifest_terminate_build 0
Name:		python-ml-insights
Version:	1.0.2
Release:	1
Summary:	Package to calibrate and understand ML Models
License:	MIT license
URL:		http://ml-insights.readthedocs.io/en/latest/
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/21/a3/b43ed7c627ceabb7c30c583e3bd67a7d15be10722da2a1d8c0320c99ff82/ml_insights-1.0.2.tar.gz
BuildArch:	noarch

Requires:	python3-pandas
Requires:	python3-numpy
Requires:	python3-matplotlib
Requires:	python3-scikit-learn
Requires:	python3-scipy
Requires:	python3-splinecalib

%description
Welcome to ML-Insights!
This package contains two core sets of functions:
1) Calibration
2) Interpreting Models
For probability calibration, the main class is `SplineCalib`.  Given a set of model outputs and the "true" classes, you can `fit` a SplineCalib object.  That object can then be used to `calibrate` future model predictions post-hoc.
    >>> model.fit(X_train, y_train)
    >>> sc = mli.SplineCalib()
    >>> sc.fit(X_valid, y_valid)
    >>> uncalib_preds = model.predict_proba(X_test)
    >>> calib_preds = sc.calibrate(uncalib_preds)
    >>> cv_preds = mli.cv_predictions(model, X_train, y_train)
    >>> model.fit(X_train, y_train)
    >>> sc = mli.SplineCalib()
    >>> sc.fit(cv_preds, y_train)
    >>> uncalib_preds = model.predict_proba(X_test)
    >>> calib_preds = sc.calibrate(uncalib_preds)
For model interpretability, we provide the `ice_plot` and `histogram_pair` functions as well as other tools.
    >>> rd = mli.get_range_dict(X_train)
    >>> mli.ice_plot(model, X_test.sample(3), X_train.columns, rd)
    >>> mli.histogram_pair(df.outcome, df.feature, bins=np.linspace(0,100,11))
Please see the documentation and examples at the links below.
- `Documentation <https://ml-insights.readthedocs.io>`_
- `Notebook Examples and Usage <https://github.com/numeristical/introspective/tree/master/examples>`_

%package -n python3-ml-insights
Summary:	Package to calibrate and understand ML Models
Provides:	python-ml-insights
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-ml-insights
Welcome to ML-Insights!
This package contains two core sets of functions:
1) Calibration
2) Interpreting Models
For probability calibration, the main class is `SplineCalib`.  Given a set of model outputs and the "true" classes, you can `fit` a SplineCalib object.  That object can then be used to `calibrate` future model predictions post-hoc.
    >>> model.fit(X_train, y_train)
    >>> sc = mli.SplineCalib()
    >>> sc.fit(X_valid, y_valid)
    >>> uncalib_preds = model.predict_proba(X_test)
    >>> calib_preds = sc.calibrate(uncalib_preds)
    >>> cv_preds = mli.cv_predictions(model, X_train, y_train)
    >>> model.fit(X_train, y_train)
    >>> sc = mli.SplineCalib()
    >>> sc.fit(cv_preds, y_train)
    >>> uncalib_preds = model.predict_proba(X_test)
    >>> calib_preds = sc.calibrate(uncalib_preds)
For model interpretability, we provide the `ice_plot` and `histogram_pair` functions as well as other tools.
    >>> rd = mli.get_range_dict(X_train)
    >>> mli.ice_plot(model, X_test.sample(3), X_train.columns, rd)
    >>> mli.histogram_pair(df.outcome, df.feature, bins=np.linspace(0,100,11))
Please see the documentation and examples at the links below.
- `Documentation <https://ml-insights.readthedocs.io>`_
- `Notebook Examples and Usage <https://github.com/numeristical/introspective/tree/master/examples>`_

%package help
Summary:	Development documents and examples for ml-insights
Provides:	python3-ml-insights-doc
%description help
Welcome to ML-Insights!
This package contains two core sets of functions:
1) Calibration
2) Interpreting Models
For probability calibration, the main class is `SplineCalib`.  Given a set of model outputs and the "true" classes, you can `fit` a SplineCalib object.  That object can then be used to `calibrate` future model predictions post-hoc.
    >>> model.fit(X_train, y_train)
    >>> sc = mli.SplineCalib()
    >>> sc.fit(X_valid, y_valid)
    >>> uncalib_preds = model.predict_proba(X_test)
    >>> calib_preds = sc.calibrate(uncalib_preds)
    >>> cv_preds = mli.cv_predictions(model, X_train, y_train)
    >>> model.fit(X_train, y_train)
    >>> sc = mli.SplineCalib()
    >>> sc.fit(cv_preds, y_train)
    >>> uncalib_preds = model.predict_proba(X_test)
    >>> calib_preds = sc.calibrate(uncalib_preds)
For model interpretability, we provide the `ice_plot` and `histogram_pair` functions as well as other tools.
    >>> rd = mli.get_range_dict(X_train)
    >>> mli.ice_plot(model, X_test.sample(3), X_train.columns, rd)
    >>> mli.histogram_pair(df.outcome, df.feature, bins=np.linspace(0,100,11))
Please see the documentation and examples at the links below.
- `Documentation <https://ml-insights.readthedocs.io>`_
- `Notebook Examples and Usage <https://github.com/numeristical/introspective/tree/master/examples>`_

%prep
%autosetup -n ml-insights-1.0.2

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

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

%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.2-1
- Package Spec generated