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
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
|
%global _empty_manifest_terminate_build 0
Name: python-drifter-ml
Version: 0.25
Release: 1
Summary: Testing for models confirming to the scikit-learn api
License: MIT
URL: https://github.com/EricSchles/drifter_ml
Source0: https://mirrors.aliyun.com/pypi/web/packages/08/36/335e44943f5f2056cf8dbb698efaf056bdccdf46b0fa005035e29e7c9611/drifter_ml-0.25.tar.gz
BuildArch: noarch
Requires: python3-sklearn
Requires: python3-scipy
Requires: python3-numpy
Requires: python3-statsmodels
Requires: python3-mlxtend
Requires: python3-pytest
Requires: python3-energyusage
Requires: python3-backtester
%description
# ML Testing
The goal of this module is to create a flexible and easy to use module for testing machine learning models, specifically those in scikit-learn.
The tests will be readable enough that anyone can extend them to other frameworks and APIs with the major notions kept the same, but more or less the ideas will be extended, no work will be taken in this library to extend passed the scikit-learn API.
You can [read the docs](https://drifter-ml.readthedocs.io/en/latest/) for a more detailed explaination.
[](https://drifter-ml.readthedocs.io/en/latest/?badge=latest)
[](https://circleci.com/gh/EricSchles/drifter_ml)
[](https://pypi.org/project/drifter-ml/)
[](https://pypi.org/project/drifter-ml/)
[](https://codecov.io/gh/EricSchles/drifter_ml)
## Tests Covered
* Testing Against Metrics
* Classification Tests
* Rule Based Testing:
* precision lower boundary
* recall lower boundary
* f1 score lower boundary
* AUC lower boundary
* precision lower boundary per class
* recall lower boundary per class
* f1 score lower boundary per class
* AUC lower boundary per class
* Decision Based Testing:
* precision fold below average
* recall fold below average
* f1 fold below average
* AUC fold below average
* precision fold below average per class
* recall fold below average per class
* f1 fold below average per class
* AUC fold below average per class
* Against New Predictions
* proportion of predictions per class
* class imbalance tests
* probability distribution similarity tests
* calibration tests
* environmental impact tests
* [energyusage](https://pypi.org/project/energyusage/) upper bound test
* Regression Tests
* Rule Based Testing:
* Mean Squared Error upper boundary
* Median Absolute Error upper boundary
* Decision Based Testing:
* Mean Squared Error fold above average
* Median Absolute Error fold above average
* Testing Against Run Time Performance
* prediction run time for simulated samples of size X
* Testing Against Input Data
* percentage of correct imputes for any columns requiring imputation
* dataset testing - http://www.vldb.org/pvldb/vol11/p1781-schelter.pdf
* Memoryful Tests
* cluster testing - this is about the overall structure of the data
If the number of clusters increases or decreases substantially that
should be an indicator that the data has changed enough that things
should possibly be rerun
* correlation testing - this is about ensuring that the correlation for a given column with previous data collected in the past does not change very much. If the data does change then the model should possibly be rerun.
* shape testing - this is about ensuring the general shape of for the given column does not change much over time. The idea here is the same as the correlation tests.
## Possible Issues
Some known issues with this, any machine learning tests are going to require human interaction because of type 1 and type 2 error for statistical tests. Additionally, one simply needs to interrogate models from a lot of angles. It can't be from just one angle. So please use with care!
## Future Features
* cross validation score testing
* add custom loss function
* add custom accuracy function
* add these tests: https://www.datasciencecentral.com/profiles/blogs/a-plethora-of-original-underused-statistical-tests
* clustering for classification
* Unsupervised and semi supervised tests
* verify similarity in clusters to similarity in labels
* generate a small representative set of labels and then propagate other labels
## References
* https://dzone.com/articles/quality-assurancetesting-the-machine-learning-mode
* https://medium.com/datadriveninvestor/how-to-perform-quality-assurance-for-ml-models-cef77bbbcfb
* Explaination of UAT: https://www.techopedia.com/definition/3887/user-acceptance-testing-uat
* https://mice.cs.columbia.edu/getTechreport.php?techreportID=419&format=pdf
* https://www.xenonstack.com/blog/unit-testing-tdd-bdd-deep-machine-learning/
%package -n python3-drifter-ml
Summary: Testing for models confirming to the scikit-learn api
Provides: python-drifter-ml
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-drifter-ml
# ML Testing
The goal of this module is to create a flexible and easy to use module for testing machine learning models, specifically those in scikit-learn.
The tests will be readable enough that anyone can extend them to other frameworks and APIs with the major notions kept the same, but more or less the ideas will be extended, no work will be taken in this library to extend passed the scikit-learn API.
You can [read the docs](https://drifter-ml.readthedocs.io/en/latest/) for a more detailed explaination.
[](https://drifter-ml.readthedocs.io/en/latest/?badge=latest)
[](https://circleci.com/gh/EricSchles/drifter_ml)
[](https://pypi.org/project/drifter-ml/)
[](https://pypi.org/project/drifter-ml/)
[](https://codecov.io/gh/EricSchles/drifter_ml)
## Tests Covered
* Testing Against Metrics
* Classification Tests
* Rule Based Testing:
* precision lower boundary
* recall lower boundary
* f1 score lower boundary
* AUC lower boundary
* precision lower boundary per class
* recall lower boundary per class
* f1 score lower boundary per class
* AUC lower boundary per class
* Decision Based Testing:
* precision fold below average
* recall fold below average
* f1 fold below average
* AUC fold below average
* precision fold below average per class
* recall fold below average per class
* f1 fold below average per class
* AUC fold below average per class
* Against New Predictions
* proportion of predictions per class
* class imbalance tests
* probability distribution similarity tests
* calibration tests
* environmental impact tests
* [energyusage](https://pypi.org/project/energyusage/) upper bound test
* Regression Tests
* Rule Based Testing:
* Mean Squared Error upper boundary
* Median Absolute Error upper boundary
* Decision Based Testing:
* Mean Squared Error fold above average
* Median Absolute Error fold above average
* Testing Against Run Time Performance
* prediction run time for simulated samples of size X
* Testing Against Input Data
* percentage of correct imputes for any columns requiring imputation
* dataset testing - http://www.vldb.org/pvldb/vol11/p1781-schelter.pdf
* Memoryful Tests
* cluster testing - this is about the overall structure of the data
If the number of clusters increases or decreases substantially that
should be an indicator that the data has changed enough that things
should possibly be rerun
* correlation testing - this is about ensuring that the correlation for a given column with previous data collected in the past does not change very much. If the data does change then the model should possibly be rerun.
* shape testing - this is about ensuring the general shape of for the given column does not change much over time. The idea here is the same as the correlation tests.
## Possible Issues
Some known issues with this, any machine learning tests are going to require human interaction because of type 1 and type 2 error for statistical tests. Additionally, one simply needs to interrogate models from a lot of angles. It can't be from just one angle. So please use with care!
## Future Features
* cross validation score testing
* add custom loss function
* add custom accuracy function
* add these tests: https://www.datasciencecentral.com/profiles/blogs/a-plethora-of-original-underused-statistical-tests
* clustering for classification
* Unsupervised and semi supervised tests
* verify similarity in clusters to similarity in labels
* generate a small representative set of labels and then propagate other labels
## References
* https://dzone.com/articles/quality-assurancetesting-the-machine-learning-mode
* https://medium.com/datadriveninvestor/how-to-perform-quality-assurance-for-ml-models-cef77bbbcfb
* Explaination of UAT: https://www.techopedia.com/definition/3887/user-acceptance-testing-uat
* https://mice.cs.columbia.edu/getTechreport.php?techreportID=419&format=pdf
* https://www.xenonstack.com/blog/unit-testing-tdd-bdd-deep-machine-learning/
%package help
Summary: Development documents and examples for drifter-ml
Provides: python3-drifter-ml-doc
%description help
# ML Testing
The goal of this module is to create a flexible and easy to use module for testing machine learning models, specifically those in scikit-learn.
The tests will be readable enough that anyone can extend them to other frameworks and APIs with the major notions kept the same, but more or less the ideas will be extended, no work will be taken in this library to extend passed the scikit-learn API.
You can [read the docs](https://drifter-ml.readthedocs.io/en/latest/) for a more detailed explaination.
[](https://drifter-ml.readthedocs.io/en/latest/?badge=latest)
[](https://circleci.com/gh/EricSchles/drifter_ml)
[](https://pypi.org/project/drifter-ml/)
[](https://pypi.org/project/drifter-ml/)
[](https://codecov.io/gh/EricSchles/drifter_ml)
## Tests Covered
* Testing Against Metrics
* Classification Tests
* Rule Based Testing:
* precision lower boundary
* recall lower boundary
* f1 score lower boundary
* AUC lower boundary
* precision lower boundary per class
* recall lower boundary per class
* f1 score lower boundary per class
* AUC lower boundary per class
* Decision Based Testing:
* precision fold below average
* recall fold below average
* f1 fold below average
* AUC fold below average
* precision fold below average per class
* recall fold below average per class
* f1 fold below average per class
* AUC fold below average per class
* Against New Predictions
* proportion of predictions per class
* class imbalance tests
* probability distribution similarity tests
* calibration tests
* environmental impact tests
* [energyusage](https://pypi.org/project/energyusage/) upper bound test
* Regression Tests
* Rule Based Testing:
* Mean Squared Error upper boundary
* Median Absolute Error upper boundary
* Decision Based Testing:
* Mean Squared Error fold above average
* Median Absolute Error fold above average
* Testing Against Run Time Performance
* prediction run time for simulated samples of size X
* Testing Against Input Data
* percentage of correct imputes for any columns requiring imputation
* dataset testing - http://www.vldb.org/pvldb/vol11/p1781-schelter.pdf
* Memoryful Tests
* cluster testing - this is about the overall structure of the data
If the number of clusters increases or decreases substantially that
should be an indicator that the data has changed enough that things
should possibly be rerun
* correlation testing - this is about ensuring that the correlation for a given column with previous data collected in the past does not change very much. If the data does change then the model should possibly be rerun.
* shape testing - this is about ensuring the general shape of for the given column does not change much over time. The idea here is the same as the correlation tests.
## Possible Issues
Some known issues with this, any machine learning tests are going to require human interaction because of type 1 and type 2 error for statistical tests. Additionally, one simply needs to interrogate models from a lot of angles. It can't be from just one angle. So please use with care!
## Future Features
* cross validation score testing
* add custom loss function
* add custom accuracy function
* add these tests: https://www.datasciencecentral.com/profiles/blogs/a-plethora-of-original-underused-statistical-tests
* clustering for classification
* Unsupervised and semi supervised tests
* verify similarity in clusters to similarity in labels
* generate a small representative set of labels and then propagate other labels
## References
* https://dzone.com/articles/quality-assurancetesting-the-machine-learning-mode
* https://medium.com/datadriveninvestor/how-to-perform-quality-assurance-for-ml-models-cef77bbbcfb
* Explaination of UAT: https://www.techopedia.com/definition/3887/user-acceptance-testing-uat
* https://mice.cs.columbia.edu/getTechreport.php?techreportID=419&format=pdf
* https://www.xenonstack.com/blog/unit-testing-tdd-bdd-deep-machine-learning/
%prep
%autosetup -n drifter_ml-0.25
%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-drifter-ml -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.25-1
- Package Spec generated
|
