| Name | icecube-voka JSON |
| Version |
0.1.2
JSON |
| download |
| home_page | None |
| Summary | Sanity checks on sequences/histograms using ML techniques. |
| upload_time | 2024-11-18 21:08:07 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.14,>=3.9 |
| license | MIT License Copyright (c) 2020 IceCube Open Source Software Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
histogram comparison
outlier detection
statistical tests
empirical p-value threshold
poissonian statistics
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<!--- Top of README Badges (automated) --->
[](https://pypi.org/project/icecube-voka/) [](https://github.com/icecube/voka/) [](https://pypi.org/project/icecube-voka) [](https://github.com/icecube/voka/blob/main/LICENSE) [](https://github.com/icecube/voka/issues?q=is%3Aissue+sort%3Aupdated-desc+is%3Aopen) [](https://github.com/icecube/voka/pulls?q=is%3Apr+sort%3Aupdated-desc+is%3Aopen)
<!--- End of README Badges (automated) --->
# voka
Histograms comparisons using statistical tests as input to an outlier detection algorithm.
## Problem Statement
Let's say you have a large number of histograms produced by a complex system (e.g. scientific simulation chain
for a large-scale physics experiment) and you want to compare one large set of histograms to another to determine
differences. When the number of histograms becomes large (>100) it can be difficult for human observers to
efficiently scan them for subtle differences buried in statistical flucuations. The project is a tool that
can help detect those differences.
**This method can be viewed as emperically determining a p-value threshold from benchmark sets, valid for both
discrete and continuous distributions, and both Poissonian and non-Poissonian statistics.**
See the [wiki](https://github.com/icecube/voka/wiki) for more details.
# Dependencies
* numpy
* matplotlib
* scipy (optional)
```
numpy (basic_example,classic_fit_example,standard_distribution_comparisons,stochastic_example,test.test_lof,test.test_metrics,test.test_voka,vanilla_gaussian,voka.lof)
pylab (classic_fit_example,standard_distribution_comparisons,stochastic_example,vanilla_gaussian)
scipy
\-optimize (classic_fit_example,standard_distribution_comparisons,stochastic_example,vanilla_gaussian)
\-special (voka.metrics.llh)
\-stats (standard_distribution_comparisons,stochastic_example,vanilla_gaussian)
voka
\-compare (test.test_metrics)
\-lof (test.test_lof)
\-metrics
| \-ad (test.test_metrics)
| \-bdm (test.test_metrics)
| \-chisq (standard_distribution_comparisons,stochastic_example,test.test_metrics,vanilla_gaussian)
| \-cvm (test.test_metrics)
| \-ks (test.test_metrics)
| \-llh (test.test_metrics)
\-model (basic_example,test.test_voka)
```
# Test Coverage
Measured with [coverage](https://coverage.readthedocs.io/en/6.2/).
As of January 14th, 2022:
```
Name Stmts Miss Cover Missing
--------------------------------------------------
voka/__init__.py 0 0 100%
voka/compare.py 12 2 83% 37-38
voka/lof.py 26 0 100%
voka/metrics.py 115 17 85% 39-42, 60, 80, 89, 113, 141, 154, 162-163, 165-166, 168-169, 184
voka/model.py 36 6 83% 78-87
voka/two_sample.py 38 38 0% 2-90
--------------------------------------------------
TOTAL 227 63 72%
```
## Running Tests
```sh
$ python3 -m unittest
$ coverage run --source=voka -m unittest
$ coverage report -m
```
Raw data
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"description": "<!--- Top of README Badges (automated) --->\n[](https://pypi.org/project/icecube-voka/) [](https://github.com/icecube/voka/) [](https://pypi.org/project/icecube-voka) [](https://github.com/icecube/voka/blob/main/LICENSE) [](https://github.com/icecube/voka/issues?q=is%3Aissue+sort%3Aupdated-desc+is%3Aopen) [](https://github.com/icecube/voka/pulls?q=is%3Apr+sort%3Aupdated-desc+is%3Aopen)\n<!--- End of README Badges (automated) --->\n# voka\nHistograms comparisons using statistical tests as input to an outlier detection algorithm.\n\n## Problem Statement\nLet's say you have a large number of histograms produced by a complex system (e.g. scientific simulation chain \nfor a large-scale physics experiment) and you want to compare one large set of histograms to another to determine \ndifferences. When the number of histograms becomes large (>100) it can be difficult for human observers to \nefficiently scan them for subtle differences buried in statistical flucuations. The project is a tool that\ncan help detect those differences.\n\n**This method can be viewed as emperically determining a p-value threshold from benchmark sets, valid for both \ndiscrete and continuous distributions, and both Poissonian and non-Poissonian statistics.**\n\nSee the [wiki](https://github.com/icecube/voka/wiki) for more details.\n\n# Dependencies\n\n* numpy\n* matplotlib\n* scipy (optional)\n\n```\n numpy (basic_example,classic_fit_example,standard_distribution_comparisons,stochastic_example,test.test_lof,test.test_metrics,test.test_voka,vanilla_gaussian,voka.lof)\n pylab (classic_fit_example,standard_distribution_comparisons,stochastic_example,vanilla_gaussian)\n scipy \n \\-optimize (classic_fit_example,standard_distribution_comparisons,stochastic_example,vanilla_gaussian)\n \\-special (voka.metrics.llh)\n \\-stats (standard_distribution_comparisons,stochastic_example,vanilla_gaussian)\n voka \n \\-compare (test.test_metrics)\n \\-lof (test.test_lof)\n \\-metrics \n | \\-ad (test.test_metrics)\n | \\-bdm (test.test_metrics)\n | \\-chisq (standard_distribution_comparisons,stochastic_example,test.test_metrics,vanilla_gaussian)\n | \\-cvm (test.test_metrics)\n | \\-ks (test.test_metrics)\n | \\-llh (test.test_metrics)\n \\-model (basic_example,test.test_voka)\n\n```\n\n\n# Test Coverage\nMeasured with [coverage](https://coverage.readthedocs.io/en/6.2/).\n\nAs of January 14th, 2022:\n```\nName Stmts Miss Cover Missing\n--------------------------------------------------\nvoka/__init__.py 0 0 100%\nvoka/compare.py 12 2 83% 37-38\nvoka/lof.py 26 0 100%\nvoka/metrics.py 115 17 85% 39-42, 60, 80, 89, 113, 141, 154, 162-163, 165-166, 168-169, 184\nvoka/model.py 36 6 83% 78-87\nvoka/two_sample.py 38 38 0% 2-90\n--------------------------------------------------\nTOTAL 227 63 72%\n```\n\n## Running Tests\n```sh\n$ python3 -m unittest\n$ coverage run --source=voka -m unittest\n$ coverage report -m\n```\n",
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"license": "MIT License Copyright (c) 2020 IceCube Open Source Software Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ",
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