Using SMOTE with NaN values - missing-data

Question

Is there a way one can use SMOTE with NaNs?
Here is a dummy prog to try using SMOTE in presence of NaN values
# Imports
from collections import Counter
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.preprocessing import Imputer
from imblearn.over_sampling import SMOTE
from imblearn.pipeline import make_pipeline
from imblearn.combine import SMOTEENN
# Load data
bc = load_breast_cancer()
X, y = bc.data, bc.target
# Initial number of samples per class
print('Number of samples for both classes: {} and {}.'.format(*Counter(y).values()))
# SMOTEd class distribution
print('Dataset has %s missing values.' % np.isnan(X).sum())
_, y_resampled = SMOTE().fit_sample(X, y)
print('Number of samples for both classes: {} and {}.'.format(*Counter(y_resampled).values()))
# Generate artificial missing values
X[X > 1.0] = np.nan
print('Dataset has %s missing values.' % np.isnan(X).sum())
#_, y_resampled = make_pipeline(Imputer(), SMOTE()).fit_sample(X, y)
sm = SMOTE(ratio = 'auto',k_neighbors = 5, n_jobs = -1)
smote_enn = SMOTEENN(smote = sm)
x_train_res, y_train_res = smote_enn.fit_sample(X, y)
print('Number of samples for both classes: {} and {}.'.format(*Counter(y_resampled).values()))
I get the following output/error:
Number of samples for both classes: 212 and 357.
Dataset has 0 missing values.
Number of samples for both classes: 357 and 357.
Dataset has 6051 missing values.
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

Answer 1

You already include the answer. Notice that fit_resample is used instead of fit_sample. You should use the make_pipeline as follows:
# Imports
import numpy as np
from collections import Counter
from sklearn.datasets import load_breast_cancer
from sklearn.impute import SimpleImputer
from imblearn.over_sampling import SMOTE
from imblearn.pipeline import make_pipeline
from imblearn.combine import SMOTEENN
# Load data
bc = load_breast_cancer()
X, y = bc.data, bc.target
X[X > 1.0] = np.nan
# Over-sampling
smote = SMOTE(ratio='auto',k_neighbors=5, n_jobs=-1)
smote_enn = make_pipeline(SimpleImputer(), SMOTEENN(smote=smote))
_, y_res = smote_enn.fit_resample(X, y)
# Class distribution
print('Number of samples for both classes: {} and {}.'.format(*Counter(y_res).values()))
Check also your imbalanced-learn version.

Answer 2

Generally no, SMOTE is preparing a data set for further model fitting.
Usual models (like random forest, etc.) do not work with NAin the label variable, because what are you actually predicting here? The same goes for NAin the predictor variables where most algorithms either do not work or simply ignore cases with NA.
So the error is pretty much by design because you cannot and should not have missing values in your training data set for the algorithm and logically you do not want to "balance" cases with missing values, you only want to SMOTE cases with valid labels.
If you feel that missing labels still represent valid information that should be balanced (e.g. you actually want to oversample the NAclass because you think it is underpreresented), then it should not be a missing value but rather a defined value called "Unknown" or something else, indicating a known class with the characteristic of "NA" but I do not really see any research questions where this makes sense.
Update 1:
Another way to go is to impute the missing values first so that you actually have three steps in fitting your model:
Imputing missing values (using MICE or similar)
SMOTE to balance training set
Fit algorithm/model