mlbugdetection 0.0.12

A package for identify Machine Learning bugs

MLBugDetection

Machine learning explainability and unexpectated behaviors detection

Overview

Most machine learning explainability packages requires both trained models and the training data to create Explainer objects that explain the model's behavior. This package allows ceteris paribus analysis of features using only the trained model and one or more input samples.

Documentation

• https://jpgianfaldoni.github.io/MLBugDetection/

Installation:

pip install mlbugdetection

How to use

Functions

1. Monotonic:

   from mlbugdetection.monotonic import check_monotonicity_single_sample, check_monotonicity_multiple_samples
   

   Usage: For 1 sample

   check_monotonicity_single_sample(model, sample, feature, start, stop, step=1)
   

   For more than 1 sample:

   check_monotonicity_multiple_samples(model, samples, feature, start, stop, step=1) 
   

2. Critical Values:

   from mlbugdetection.critical_values import find_critical_values, find_several_critical_values
   

   Usage: For 1 sample

   find_critical_values(model, sample, feature, start, stop, step=1)
   

   For more than 1 sample:

   find_several_critical_values(model, samples, feature, start, stop, step=1, bins=15, keep_n=5, log=False)
   

3. Calibration:

   from mlbugdetection.calibration import calibration_check
   

   Usage:

   calibration_check(model, samples, target, pos_label=1)
   

4. Sanity:

   from mlbugdetection.sanity import sanity_check, sanity_check_with_indexes
   

   Usage:

   sanity_check(model, samples, target)
   

   If test not pass, check the indexes

   sanity_check_with_indexes(model, samples, target)
   

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Virtual Environment with Jupyter Notebook

python3 -m virtualenv venv 
source venv/bin/activate
python -m pip install --upgrade pip
pip install -r requirements.txt