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
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
|
%global _empty_manifest_terminate_build 0
Name: python-active-semi-supervised-clustering
Version: 0.0.1
Release: 1
Summary: Active semi-supervised clustering algorithms for scikit-learn
License: MIT License
URL: https://github.com/datamole-ai/active-semi-supervised-clustering
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/84/cc/8189ebe735cd7b6c53869775969d89c6fe2d68a872ddd1cc24df3a38d1ba/active-semi-supervised-clustering-0.0.1.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-scikit-learn
Requires: python3-metric-learn
%description
# active-semi-supervised-clustering
Active semi-supervised clustering algorithms for scikit-learn.
## Algorithms
### Semi-supervised clustering
* Seeded-KMeans
* Constrainted-KMeans
* COP-KMeans
* Pairwise constrained K-Means (PCK-Means)
* Metric K-Means (MK-Means)
* Metric pairwise constrained K-Means (MPCK-Means)
### Active learning of pairwise clustering
* Explore & Consolidate
* Min-max
* Normalized point-based uncertainty (NPU) method
## Installation
```
pip install active-semi-supervised-clustering
```
## Usage
```python
from sklearn import datasets, metrics
from active_semi_clustering.semi_supervised.pairwise_constraints import PCKMeans
from active_semi_clustering.active.pairwise_constraints import ExampleOracle, ExploreConsolidate, MinMax
```
```python
X, y = datasets.load_iris(return_X_y=True)
```
First, obtain some pairwise constraints from an oracle.
```python
# TODO implement your own oracle that will, for example, query a domain expert via GUI or CLI
oracle = ExampleOracle(y, max_queries_cnt=10)
active_learner = MinMax(n_clusters=3)
active_learner.fit(X, oracle=oracle)
pairwise_constraints = active_learner.pairwise_constraints_
```
Then, use the constraints to do the clustering.
```python
clusterer = PCKMeans(n_clusters=3)
clusterer.fit(X, ml=pairwise_constraints[0], cl=pairwise_constraints[1])
```
Evaluate the clustering using Adjusted Rand Score.
```python
metrics.adjusted_rand_score(y, clusterer.labels_)
```
%package -n python3-active-semi-supervised-clustering
Summary: Active semi-supervised clustering algorithms for scikit-learn
Provides: python-active-semi-supervised-clustering
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-active-semi-supervised-clustering
# active-semi-supervised-clustering
Active semi-supervised clustering algorithms for scikit-learn.
## Algorithms
### Semi-supervised clustering
* Seeded-KMeans
* Constrainted-KMeans
* COP-KMeans
* Pairwise constrained K-Means (PCK-Means)
* Metric K-Means (MK-Means)
* Metric pairwise constrained K-Means (MPCK-Means)
### Active learning of pairwise clustering
* Explore & Consolidate
* Min-max
* Normalized point-based uncertainty (NPU) method
## Installation
```
pip install active-semi-supervised-clustering
```
## Usage
```python
from sklearn import datasets, metrics
from active_semi_clustering.semi_supervised.pairwise_constraints import PCKMeans
from active_semi_clustering.active.pairwise_constraints import ExampleOracle, ExploreConsolidate, MinMax
```
```python
X, y = datasets.load_iris(return_X_y=True)
```
First, obtain some pairwise constraints from an oracle.
```python
# TODO implement your own oracle that will, for example, query a domain expert via GUI or CLI
oracle = ExampleOracle(y, max_queries_cnt=10)
active_learner = MinMax(n_clusters=3)
active_learner.fit(X, oracle=oracle)
pairwise_constraints = active_learner.pairwise_constraints_
```
Then, use the constraints to do the clustering.
```python
clusterer = PCKMeans(n_clusters=3)
clusterer.fit(X, ml=pairwise_constraints[0], cl=pairwise_constraints[1])
```
Evaluate the clustering using Adjusted Rand Score.
```python
metrics.adjusted_rand_score(y, clusterer.labels_)
```
%package help
Summary: Development documents and examples for active-semi-supervised-clustering
Provides: python3-active-semi-supervised-clustering-doc
%description help
# active-semi-supervised-clustering
Active semi-supervised clustering algorithms for scikit-learn.
## Algorithms
### Semi-supervised clustering
* Seeded-KMeans
* Constrainted-KMeans
* COP-KMeans
* Pairwise constrained K-Means (PCK-Means)
* Metric K-Means (MK-Means)
* Metric pairwise constrained K-Means (MPCK-Means)
### Active learning of pairwise clustering
* Explore & Consolidate
* Min-max
* Normalized point-based uncertainty (NPU) method
## Installation
```
pip install active-semi-supervised-clustering
```
## Usage
```python
from sklearn import datasets, metrics
from active_semi_clustering.semi_supervised.pairwise_constraints import PCKMeans
from active_semi_clustering.active.pairwise_constraints import ExampleOracle, ExploreConsolidate, MinMax
```
```python
X, y = datasets.load_iris(return_X_y=True)
```
First, obtain some pairwise constraints from an oracle.
```python
# TODO implement your own oracle that will, for example, query a domain expert via GUI or CLI
oracle = ExampleOracle(y, max_queries_cnt=10)
active_learner = MinMax(n_clusters=3)
active_learner.fit(X, oracle=oracle)
pairwise_constraints = active_learner.pairwise_constraints_
```
Then, use the constraints to do the clustering.
```python
clusterer = PCKMeans(n_clusters=3)
clusterer.fit(X, ml=pairwise_constraints[0], cl=pairwise_constraints[1])
```
Evaluate the clustering using Adjusted Rand Score.
```python
metrics.adjusted_rand_score(y, clusterer.labels_)
```
%prep
%autosetup -n active-semi-supervised-clustering-0.0.1
%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-active-semi-supervised-clustering -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.1-1
- Package Spec generated
|
