1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
|
%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
|
