AttributeError: 'SMOTE' object has no attribute '_validate_data'

Question

I'm resampling my data (multiclass) by using SMOTE.

sm = SMOTE(random_state=1)
X_res, Y_res = sm.fit_resample(X_train, Y_train)

However, I'm getting this attribute error. Can anyone help?

Answer 1

Short answer

You need to upgrade scikit-learn to version 0.23.1.

Long answer

The newest version 0.7.0 of imbalanced-learn seems to have an undocumented dependency on scikit-learn v0.23.1. It would give you AttributeError: 'SMOTE' object has no attribute '_validate_data' if your scikit-learnis 0.22 or below.

If you are using Anaconda, installing scikit-learn version 0.23.1 might be tricky. conda update scikit-learn might not update scikit-learn version 0.23 or higher because the newest scikit-learn version Conda has at this point of time is 0.22.1. If you try to install it using conda install scikit-learn=0.23.1 or pip install scikit-learn==0.23.1, you will get tons of compatibility checks and installation might not be quick. Therefore the easiest way to install scikit-learn version 0.23.1 in Anaconda is to create a new virtual environment with minimum packages so that there are less or no conflict issues. Then, in the new virtual environment install scikit-learn version 0.23.1 followed by version 0.7.0 of imbalanced-learn.

conda create -n test python=3.7.6
conda activate test
pip install scikit-learn==0.23.1
pip install imbalanced-learn==0.7.0

Finally, you need to reinstall your IDE in the new virtual environment in order to use these packages.

However, once scikit-learn version 0.23.1 becomes available in Conda and there are no compatibility issues, you can install it in the base environment directly.

Answer 2

Step 1- Open your jupyter notebook

Step 2 - type pip install --upgrade scikit-learn

Step 3 - Restart the kernel

Follow all the steps as it is and it's done!!(upgraded)

Answer 3

Welcome to SO! For your next question like this, you'll probably want to include the versions of python, sklearn, and imblearn you are using.

I ran into this same problem myself and the developers have noticed it: https://github.com/scikit-learn-contrib/imbalanced-learn/issues/727

Might want to follow this page to see if a solution is posted in the next few days. It seems to be about the sklearn library not being cleaned up properly after installing imblearn.

UPDATE
This can be fixed by updating your sklearn to Version 0.23 or higher. This should be possible for you through either:
pip update scikit-learn
OR
conda update scikit-learn