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+%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