A Package to perform QA on data flows for Machine Learning.
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 and 1d arrays, especially for machine learning modeling data flows. It's designed to work with logging library to log and notify QA steps in a descriptive way. It includes stand alone functions (i.e. checkers) for different QA activities and DiffChecker class for integrated QA capabilities on data.
You can install MLQA with pip.
pip install mlqa
MLQA depends on Pandas and Numpy and works in Python 3.7+.
DiffChecker 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.
>>> 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.
>>> dc.check(pd.DataFrame({'mean_col':[.99, 2.1]*50, 'na_col':[None]*70+[1]*30}))
TrueSee more examples at Documentation/Quickstart. You can also read the full documentation here.
Tests are written with unittest and can be located in the tests folder. There are also some tests in docstring to be run by doctest.