Machine learning explainability and unexpectated behaviors detection
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.
pip install mlbugdetection-
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)
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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)
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Calibration:
from mlbugdetection.calibration import calibration_check
Usage:
calibration_check(model, samples, target, pos_label=1)
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Sanity:
from mlbugdetection.sanity import sanity_check, sanity_check_with_indexes
Usage:
sanity_check(model, samples, target)
Same as sanity check, but return the failed indexes
sanity_check_with_indexes(model, samples, target)
python3 -m virtualenv venv
source venv/bin/activate
python -m pip install --upgrade pip
pip install -r requirements.txt