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How do we measure the degradation of a machine learning process? Why does the performance of our predictive models decrease? Maybe it is that a data source has changed (one or more variables) or maybe what changes is the relationship of these variables with the target we want to predict. `pydrift` tries to facilitate this task to the data scientist, performing this kind of checks and somehow measuring that degradation.

License: MIT License

Makefile 0.01% Python 0.75% Jupyter Notebook 99.25%

pydrift's Issues

Problems with the packages versions

Hi, I'm trying to run the notebooks provided in the repo and I'm not being able to. I've created a new conda virtual environment with Python 3.7

conda create -n pydrift python=3.7
conda activate pydrift
conda install -c conda-forge pydrift

When I try to run a simple import, I get an error:

>>> import pandas as pd
>>> from sklearn.model_selection import train_test_split
>>> from pydrift import DataDriftChecker
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/ubuntu/anaconda3/envs/pydrift/lib/python3.7/site-packages/pydrift/__init__.py", line 1, in <module>
    from .core.drift_checker import (DriftChecker,
  File "/home/ubuntu/anaconda3/envs/pydrift/lib/python3.7/site-packages/pydrift/core/drift_checker.py", line 16, in <module>
    from .interpretable_drift import InterpretableDrift
  File "/home/ubuntu/anaconda3/envs/pydrift/lib/python3.7/site-packages/pydrift/core/interpretable_drift.py", line 19, in <module>
    from shap.common import SHAPError
ModuleNotFoundError: No module named 'shap.common'

What am I doing wrong?

Thanks

// M.A. Monjas

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