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autotst-1.2
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مشاهده بیشترتوضیحات
Two-samples Testing and Distribution Shift Detection with AutoML
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | autotst-1.2 |
| نام | autotst |
| نسخه کتابخانه | 1.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Jonas M. Kübler |
| ایمیل نویسنده | jonas.m.kuebler@tuebingen.mpg.de |
| آدرس صفحه اصلی | https://jmkuebler.github.io/auto-tst/ |
| آدرس اینترنتی | https://pypi.org/project/autotst/ |
| مجوز | - |
# AutoML Two-Sample Test
[](https://github.com/python/mypy)
[](https://github.com/ambv/black)
[](https://github.com/jmkuebler/auto-tst/actions/workflows/tests.yml)
[](https://opensource.org/licenses/MIT)
[](https://pypi.org/project/autotst/)
[](https://pepy.tech/project/autotst)
[](https://arxiv.org/abs/2206.08843)
`autotst` is a Python package for easy-to-use two-sample testing and distribution shift detection.
Given two datasets `sample_P` and `sample_Q` drawn from distributions $P$ and $Q$, the
goal is to estimate a $p$-value for the null hypothesis $P=Q$.
`autotst` achieves this by learning a witness function and taking its mean discrepancy as a test statistic
(see References).
The package provides functionalities to prepare the data, an interface to train an ML model, and methods
to evaluate $p$-values and interpret results.
By default, autotst uses the Tabular Predictor of [AutoGluon](https://auto.gluon.ai/), but it is easy
to wrap and use your own favorite ML framework (see below).
The full documentation of the package can be found [here](https://jmkuebler.github.io/auto-tst/).
## Installation
Requires at least Python 3.7. Since the installation also installs AutoGluon, it can take a few moments.
```
pip install autotst
```
## How to use `autotst`
We provide worked out examples in the 'Example' directory. In the following assume that
`sample_P` and `sample_Q` are two `numpy` arrays containing samples from $P$ and $Q$.
### Default Usage:
The easiest way to compute a $p$-value is to use the default settings
```python
import autotst
tst = autotst.AutoTST(sample_P, sample_Q)
p_value = tst.p_value()
```
You would then reject the null hypothesis if `p_value` is smaller or equal to your significance level.
### Customizing the testing pipeline
We highly recommend to use the pipeline step by step, which would look like this:
```python
import autotst
from autotst.model import AutoGluonTabularPredictor
tst = autotst.AutoTST(sample_P, sample_Q, split_ratio=0.5, model=AutoGluonTabularPredictor)
tst.split_data()
tst.fit_witness(time_limit=60) # time limit adjustable to your needs (in seconds)
p_value = tst.p_value_evaluate(permutations=10000) # control number of permutations in the estimation
```
This allows you to change the time limit for fitting the witness function and you can also pass other
arguments to the fit model (see [AutoGluon](https://auto.gluon.ai/) documentation).
Since the permutations are very cheap, the default number of permutations is relatively high and should work for most
use-cases. If your significance level is, say, smaller than 1/1000, consider increasing it further.
### Customizing the machine learning model
If you have good domain knowledge about your problem and believe that a specific ML framework will work well,
it is easy to wrap your model.
Therefore, simply inherit from the class `Model` and wrap the methods
(see our implementation in [`model.py`](autotst/model.py)).
You can then run the test simply by importing your model and initializing the test accordingly.
```python
import autotst
tst = autotst.AutoTST(sample_P, sample_Q, model=YourCustomModel)
...
... etc.
```
We also provide a wrapper for `AutoGluonImagePredictor`. However, it seems that this should not be used
with small datasets and small training times.
## References
If you use this package, please cite this paper:
Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf: "AutoML Two-Sample Test", [arXiv 2206.08843](https://arxiv.org/abs/2206.08843) (2022)
Bibtex:
```
@misc{kubler2022autotst,
doi = {10.48550/ARXIV.2206.08843},
url = {https://arxiv.org/abs/2206.08843},
author = {Kübler, Jonas M. and Stimper, Vincent and Buchholz, Simon and Muandet, Krikamol and Schölkopf, Bernhard},
title = {AutoML Two-Sample Test},
publisher = {arXiv},
year = {2022},
}
```
نیازمندی
| مقدار | نام |
|---|---|
| >=0.4.2,<0.5.0 | autogluon |
| >=1.3,<2.0 | pandas |
| >=1.4.4,<2.0.0 | nptyping |
| >=1.21,<2.0 | numpy |
| >=7.1.2,<8.0.0 | pytest |
| ==0.11.3 | torchvision |
| >=1.4 | importlib-metadata |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7.1,<3.10 | Python |
نحوه نصب
نصب پکیج whl autotst-1.2:
pip install autotst-1.2.whl
نصب پکیج tar.gz autotst-1.2:
pip install autotst-1.2.tar.gz