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ModuleNotFoundError: No module named 'sklearn.preprocessing._label'

See original GitHub issue

on executing the following code

import os
import joblib
import pandas as pd

INPUT_PATH = os.environ.get('INPUT_PATH')
META_PATH = os.environ.get('META_PATH')
LOGS_PATH = os.environ.get('LOGS_PATH')

all_data_pipe = joblib.load(META_PATH + 'all_data_pipe.pkl')

I am getting the following error at last line joblib.load(META_PATH + 'all_data_pipe.pkl')

Traceback (most recent call last):
  File "/home/winter_soilder/anaconda3/lib/python3.7/runpy.py", line 183, in _run_module_as_main
    mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
  File "/home/winter_soilder/anaconda3/lib/python3.7/runpy.py", line 109, in _get_module_details
    __import__(pkg_name)
  File "/home/winter_soilder/Desktop/MPG/Modelling/src/automl.py", line 16, in <module>
    all_data_pipe = joblib.load(META_PATH + 'all_data_pipe.pkl')
  File "/home/winter_soilder/my_envs/mpg/lib/python3.7/site-packages/joblib/numpy_pickle.py", line 605, in load
    obj = _unpickle(fobj, filename, mmap_mode)
  File "/home/winter_soilder/my_envs/mpg/lib/python3.7/site-packages/joblib/numpy_pickle.py", line 529, in _unpickle
    obj = unpickler.load()
  File "/home/winter_soilder/anaconda3/lib/python3.7/pickle.py", line 1085, in load
    dispatch[key[0]](self)
  File "/home/winter_soilder/anaconda3/lib/python3.7/pickle.py", line 1373, in load_global
    klass = self.find_class(module, name)
  File "/home/winter_soilder/anaconda3/lib/python3.7/pickle.py", line 1423, in find_class
    __import__(module, level=0)
ModuleNotFoundError: No module named 'sklearn.preprocessing._label'

Issue Analytics

State:
Created 3 years ago
Comments:10 (5 by maintainers)

Top GitHub Comments

13reactions

EgoPingvinacommented, Mar 28, 2021

@rth I have scikit-learn 0.24.1, but I still have the same problem

10reactions

rthcommented, Apr 5, 2020

Looks like you created this pickle with scikit-learn >=0.22.0 and are trying to load it with scikit-learn <0.22.0.

Pickles are not necessarily compatible across scikit-learn versions so this behavior is expected (and the use case is not supported). For more details, see https://scikit-learn.org/dev/modules/model_persistence.html#model-persistence

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