https://github.com/ddaskan/mlqa
Package to perform QA on data flows for Machine Learning
https://github.com/ddaskan/mlqa
data-flows machine-learning mlqa quality-assurance
Last synced: about 1 month ago
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Package to perform QA on data flows for Machine Learning
- Host: GitHub
- URL: https://github.com/ddaskan/mlqa
- Owner: ddaskan
- License: mit
- Created: 2020-05-17T12:43:40.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-09-03T21:05:14.000Z (over 1 year ago)
- Last Synced: 2025-12-17T03:35:31.414Z (4 months ago)
- Topics: data-flows, machine-learning, mlqa, quality-assurance
- Language: Python
- Homepage: https://mlqa.readthedocs.io/
- Size: 397 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MLQA
[](https://pypi.org/project/mlqa/)
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[](https://codecov.io/gh/ddaskan/mlqa/)
[](https://mlqa.readthedocs.io/en/latest/?badge=latest)
[](https://pypi.org/project/mlqa/)
[](https://github.com/ddaskan/mlqa)
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A Package to perform QA on data flows for Machine Learning.
## Introduction
MLQA is a Python package that is created to help data scientists, analysts and developers to perform quality assurance (i.e. QA) on [pandas dataframes](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) and 1d arrays, especially for machine learning modeling data flows. It's designed to work with [logging](https://docs.python.org/3/library/logging.html) library to log and notify QA steps in a descriptive way. It includes stand alone functions (i.e. [checkers](mlqa/checkers.py)) for different QA activities and [DiffChecker](mlqa/identifiers.py) class for integrated QA capabilities on data.
## Installation
You can install MLQA with pip.
`pip install mlqa`
MLQA depends on Pandas and Numpy and works in Python 3.7+.
## Quickstart
[DiffChecker](mlqa/identifiers.py) is designed to perform QA on data flows for ML. You can easily save statistics from the origin data such as missing value rate, mean, min/max, percentile, outliers, etc., then to compare against the new data. This is especially important if you want to keep the prediction data under the same assumptions with the training data.
Below is a quick example on how it works, just initiate and save statistics from the input data.
```python
>>> from mlqa.identifiers import DiffChecker
>>> import pandas as pd
>>> dc = DiffChecker()
>>> dc.fit(pd.DataFrame({'mean_col':[1, 2]*50, 'na_col':[None]*50+[1]*50}))
```
Then, you can check on new data if it's okay for given criteria. Below, you can see some data that is very similar in column `mean_col` but increased NA count in column `na_col`. The default threshold is 0.5 which means it should be okay if NA rate is 50% more than the origin data. NA rate is 50% in the origin data so up to 75% (i.e. 50*(1+0.5)) should be okay. NA rate is 70% in the new data and, as expected, the QA passes.
```python
>>> dc.check(pd.DataFrame({'mean_col':[.99, 2.1]*50, 'na_col':[None]*70+[1]*30}))
True
```
See more examples at [Documentation/Quickstart](https://mlqa.readthedocs.io/en/latest/source/quickstart.html). You can also read the full documentation [here](https://mlqa.readthedocs.io/).
## Tests
Tests are written with [unittest](https://docs.python.org/3/library/unittest.html) and can be located in the [tests](tests/) folder. There are also some tests in docstring to be run by [doctest](https://docs.python.org/3/library/doctest.html).
## License
[MIT](https://github.com/ddaskan/mlqa/blob/master/LICENSE)