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
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
|
%global _empty_manifest_terminate_build 0
Name: python-kpplus
Version: 0.0.3
Release: 1
Summary: A JIT optimized K-Prototype algorithm
License: MIT License
URL: https://github.com/youbao88/KPrototypes_plus
Source0: https://mirrors.aliyun.com/pypi/web/packages/a2/5c/df60622dab8168d875947c28cee33c63e72f47c6559af6baccdabac5c97f/kpplus-0.0.3.tar.gz
BuildArch: noarch
Requires: python3-pandas
Requires: python3-numpy
Requires: python3-numba
Requires: python3-joblib
%description
# KPrototype plus (kpplus)
[](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity) [](https://www.python.org/) [](https://pypi.org/project/kpplus/)
## Description
K-prototype is a clustering method invented to support both categorical and numerical variables[1]
**KPrototype plus (kpplus)** is a Python 3 package that is designed to increase the performance of [nivoc's KPrototypes function](https://github.com/nicodv/kmodes) by using [Numba](http://numba.pydata.org/).
This code is part of [Stockholms diabetespreventiva program](https://www.folkhalsoguiden.se/amnesomraden1/analys-och-kartlaggning/sdpp/).
### Performance improvement
As an [example](example/example.ipynb), I used one of the [Heart Disease Data Sets](https://archive.ics.uci.edu/ml/datasets/Heart+Disease) from [UCI](https://archive.ics.uci.edu/ml/index.php) to test the performance.
This data set contains 4455 rows, 7 categorical variables, and 5 numerical variables.
We compare the performance between nicodv's kprototype function and k_prototype_plus.
~~~~
< nicodv's kprototype >
CPU times: user 2.14 s, sys: 18.2 ms, total: 2.16 s
Wall time: 1min 41s
~~~~
~~~~
< k_prototype_plus >
CPU times: user 298 ms, sys: 9.24 ms, total: 308 ms
Wall time: 13.4 s
~~~~
**Notice:** Only Cao initiation is supported as the initiation method[2].
## System requirement
[](https://www.python.org/) [](https://pandas.pydata.org/) [](https://numpy.org/) [](https://joblib.readthedocs.io/en/latest/) [](http://numba.pydata.org/)
## Installiation
```
pip install kpplus
```
## Usage
```python
from kpplus import KPrototypes_plus
model = KPrototypes_plus(n_clusters = 3, n_init = 4, gamma = None, n_jobs = -1) #initialize the model
model.fit_predict(X=df, categorical = [0,1]) #fit the data and categorical into the mdoel
model.labels_ #return the cluster_labels
model.cluster_centroids_ #return the cluster centroid points(prototypes)
model.n_iter_ #return the number of iterations
model.cost_ #return the costs
```
**n_clusters:** the number of clusters
**n_init:** the number of parallel oprations by using different initializations
**gamma (optional):** A value that controls how algorithm favours categorical variables. (By default, it is the mean std of all numeric variables)
**n_jobs (optional, default=-1):** The number of parallel processors. ('-1' means using all the processor)
**X:** 2-D numpy array (dataset)
**types:** A numpy array that indicates if the variable is categorical or numerical.
For example: ```types = [1,1,0,0,0,0]``` means the first two variables are categorical and the last four variables are numerical.
## Acknowledgement
I'm extremely grateful to [Dr. Diego Yacaman Mendez](https://staff.ki.se/people/dieyac?_ga=2.70810192.1199119869.1588953123-1873461028.1579027503) and [Dr. David Ebbevi](https://www.linkedin.com/in/debbevi/?originalSubdomain=se) for their support. They are two brilliant researchers who started this project with excellent knowledge of medical science, epidemiology, statistics and programming.
## Reference
[1] Huang Z. Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining and Knowledge Discovery. 1998;2(3):283-304.
[2] Cao F, Liang J, Bai LJESwA. A new initialization method for categorical data clustering. 2009;36(7):10223-8.
%package -n python3-kpplus
Summary: A JIT optimized K-Prototype algorithm
Provides: python-kpplus
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-kpplus
# KPrototype plus (kpplus)
[](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity) [](https://www.python.org/) [](https://pypi.org/project/kpplus/)
## Description
K-prototype is a clustering method invented to support both categorical and numerical variables[1]
**KPrototype plus (kpplus)** is a Python 3 package that is designed to increase the performance of [nivoc's KPrototypes function](https://github.com/nicodv/kmodes) by using [Numba](http://numba.pydata.org/).
This code is part of [Stockholms diabetespreventiva program](https://www.folkhalsoguiden.se/amnesomraden1/analys-och-kartlaggning/sdpp/).
### Performance improvement
As an [example](example/example.ipynb), I used one of the [Heart Disease Data Sets](https://archive.ics.uci.edu/ml/datasets/Heart+Disease) from [UCI](https://archive.ics.uci.edu/ml/index.php) to test the performance.
This data set contains 4455 rows, 7 categorical variables, and 5 numerical variables.
We compare the performance between nicodv's kprototype function and k_prototype_plus.
~~~~
< nicodv's kprototype >
CPU times: user 2.14 s, sys: 18.2 ms, total: 2.16 s
Wall time: 1min 41s
~~~~
~~~~
< k_prototype_plus >
CPU times: user 298 ms, sys: 9.24 ms, total: 308 ms
Wall time: 13.4 s
~~~~
**Notice:** Only Cao initiation is supported as the initiation method[2].
## System requirement
[](https://www.python.org/) [](https://pandas.pydata.org/) [](https://numpy.org/) [](https://joblib.readthedocs.io/en/latest/) [](http://numba.pydata.org/)
## Installiation
```
pip install kpplus
```
## Usage
```python
from kpplus import KPrototypes_plus
model = KPrototypes_plus(n_clusters = 3, n_init = 4, gamma = None, n_jobs = -1) #initialize the model
model.fit_predict(X=df, categorical = [0,1]) #fit the data and categorical into the mdoel
model.labels_ #return the cluster_labels
model.cluster_centroids_ #return the cluster centroid points(prototypes)
model.n_iter_ #return the number of iterations
model.cost_ #return the costs
```
**n_clusters:** the number of clusters
**n_init:** the number of parallel oprations by using different initializations
**gamma (optional):** A value that controls how algorithm favours categorical variables. (By default, it is the mean std of all numeric variables)
**n_jobs (optional, default=-1):** The number of parallel processors. ('-1' means using all the processor)
**X:** 2-D numpy array (dataset)
**types:** A numpy array that indicates if the variable is categorical or numerical.
For example: ```types = [1,1,0,0,0,0]``` means the first two variables are categorical and the last four variables are numerical.
## Acknowledgement
I'm extremely grateful to [Dr. Diego Yacaman Mendez](https://staff.ki.se/people/dieyac?_ga=2.70810192.1199119869.1588953123-1873461028.1579027503) and [Dr. David Ebbevi](https://www.linkedin.com/in/debbevi/?originalSubdomain=se) for their support. They are two brilliant researchers who started this project with excellent knowledge of medical science, epidemiology, statistics and programming.
## Reference
[1] Huang Z. Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining and Knowledge Discovery. 1998;2(3):283-304.
[2] Cao F, Liang J, Bai LJESwA. A new initialization method for categorical data clustering. 2009;36(7):10223-8.
%package help
Summary: Development documents and examples for kpplus
Provides: python3-kpplus-doc
%description help
# KPrototype plus (kpplus)
[](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity) [](https://www.python.org/) [](https://pypi.org/project/kpplus/)
## Description
K-prototype is a clustering method invented to support both categorical and numerical variables[1]
**KPrototype plus (kpplus)** is a Python 3 package that is designed to increase the performance of [nivoc's KPrototypes function](https://github.com/nicodv/kmodes) by using [Numba](http://numba.pydata.org/).
This code is part of [Stockholms diabetespreventiva program](https://www.folkhalsoguiden.se/amnesomraden1/analys-och-kartlaggning/sdpp/).
### Performance improvement
As an [example](example/example.ipynb), I used one of the [Heart Disease Data Sets](https://archive.ics.uci.edu/ml/datasets/Heart+Disease) from [UCI](https://archive.ics.uci.edu/ml/index.php) to test the performance.
This data set contains 4455 rows, 7 categorical variables, and 5 numerical variables.
We compare the performance between nicodv's kprototype function and k_prototype_plus.
~~~~
< nicodv's kprototype >
CPU times: user 2.14 s, sys: 18.2 ms, total: 2.16 s
Wall time: 1min 41s
~~~~
~~~~
< k_prototype_plus >
CPU times: user 298 ms, sys: 9.24 ms, total: 308 ms
Wall time: 13.4 s
~~~~
**Notice:** Only Cao initiation is supported as the initiation method[2].
## System requirement
[](https://www.python.org/) [](https://pandas.pydata.org/) [](https://numpy.org/) [](https://joblib.readthedocs.io/en/latest/) [](http://numba.pydata.org/)
## Installiation
```
pip install kpplus
```
## Usage
```python
from kpplus import KPrototypes_plus
model = KPrototypes_plus(n_clusters = 3, n_init = 4, gamma = None, n_jobs = -1) #initialize the model
model.fit_predict(X=df, categorical = [0,1]) #fit the data and categorical into the mdoel
model.labels_ #return the cluster_labels
model.cluster_centroids_ #return the cluster centroid points(prototypes)
model.n_iter_ #return the number of iterations
model.cost_ #return the costs
```
**n_clusters:** the number of clusters
**n_init:** the number of parallel oprations by using different initializations
**gamma (optional):** A value that controls how algorithm favours categorical variables. (By default, it is the mean std of all numeric variables)
**n_jobs (optional, default=-1):** The number of parallel processors. ('-1' means using all the processor)
**X:** 2-D numpy array (dataset)
**types:** A numpy array that indicates if the variable is categorical or numerical.
For example: ```types = [1,1,0,0,0,0]``` means the first two variables are categorical and the last four variables are numerical.
## Acknowledgement
I'm extremely grateful to [Dr. Diego Yacaman Mendez](https://staff.ki.se/people/dieyac?_ga=2.70810192.1199119869.1588953123-1873461028.1579027503) and [Dr. David Ebbevi](https://www.linkedin.com/in/debbevi/?originalSubdomain=se) for their support. They are two brilliant researchers who started this project with excellent knowledge of medical science, epidemiology, statistics and programming.
## Reference
[1] Huang Z. Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining and Knowledge Discovery. 1998;2(3):283-304.
[2] Cao F, Liang J, Bai LJESwA. A new initialization method for categorical data clustering. 2009;36(7):10223-8.
%prep
%autosetup -n kpplus-0.0.3
%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-kpplus -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.3-1
- Package Spec generated
|
