ImportError: cannot import name 'joblib' from 'sklearn.externals'

Question

I am trying to load my saved model from s3 using joblib

import pandas as pd 
import numpy as np
import json
import subprocess
import sqlalchemy
from sklearn.         


        

Answer 1

It looks like your existing pickle save file (model_d2v_version_002) encodes a reference module in a non-standard location – a joblib that's in sklearn.externals.joblib rather than at top-level.

The current scikit-learn documentation only talks about a top-level joblib – eg in 3.4.1 Persistence example – but I do see a reference in someone else's old issue to a DeprecationWarning in scikit-learn version 0.21 about an older scikit.external.joblib variant going away:

Python37\lib\site-packages\sklearn\externals\joblib_init_.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.

'Deprecation' means marking something as inadvisable to rely-upon, as it is likely to be discontinued in a future release (often, but not always, with a recommended newer way to do the same thing).

I suspect your model_d2v_version_002 file was saved from an older version of scikit-learn, and you're now using scikit-learn (aka sklearn) version 0.23+ which has totally removed the sklearn.external.joblib variation. Thus your file can't be directly or easily loaded to your current environment.

But, per the DeprecationWarning, you can probably temporarily use an older scikit-learn version to load the file the old way once, then re-save it with the now-preferred way. Given the warning info, this would probably require scikit-learn version 0.21.x or 0.22.x, but if you know exactly which version your model_d2v_version_002 file was saved from, I'd try to use that. The steps would roughly be:

• create a temporary working environment (or roll back your current working environment) with the older sklearn

• do imports something like:

import sklearn.external.joblib as extjoblib
import joblib

• extjoblib.load() your old file as you'd planned, but then immediately re-joblib.dump() the file using the top-level joblib. (You likely want to use a distinct name, to keep the older file around, just in case.)

• move/update to your real, modern environment, and only import joblib (top level) to use joblib.load() - no longer having any references to `sklearn.external.joblib' in either your code, or your stored pickle files.