Feature Mismatch caused by StackingEstimator: X has ___ features, but ________ is expecting ___ features as input.

The way that StackingEstimator is currently coded, it will either add 1 column or (1 + n_classes) columns based on whether predict_proba is an option for the estimator being stacked. This is fantastic! It's the way it should be. When predict_proba isn't around, it just adds one column with the class prediction, and when predict_proba is around it adds both the single column class prediction plus 1 additional column for each class with its probability.

The problem happens with these lines of code in stacking_estimator.py
```
# check all values that should be not infinity or not NAN
if np.all(np.isfinite(y_pred_proba)):
X_transformed = np.hstack((y_pred_proba, X))
```

I'm finding that sometimes when I call fit() and there are no infinity/NAN values in y_pred_proba it adds (1 + n_classes) columns to the original features, and now the pipeline is permanently expecting that number of features.

But if later you just run transform() on a different set of X values, and now at least one row of y_pred_proba is indeed nan, nan you run into problems. This happens sometimes when stacking LogisticRegression and this is known behavior from sklearn LR implementation if things aren't scaled ahead of time.

Here's an example. I'm doing binary classification with 19 features. The exported pipeline that was found is this:
```
exported_pipeline = make_pipeline(
StackingEstimator(estimator=LogisticRegression(C=20.0, dual=False, penalty="l2")),
MaxAbsScaler(),
XGBClassifier(learning_rate=0.1, max_depth=7, min_child_weight=7, n_estimators=100, n_jobs=1, subsample=0.9500000000000001, verbosity=0)
)
```

When I later run predict_proba on data that wasn't in the training set, I get the following (sometimes):
X has 20 features, but MaxAbsScaler is expecting 22 features as input.

What's going on here is that when we did fit(X_train,y_train), there were no rows that blew up the LogisticRegression estimator, so it passed 22 features onto MaxAbsScaler: 19 original features plus 1 class feature plus 2 class probability features (again, I'm doing binary classification). But later when I did exported_pipeline.predict_proba(X), there was a row that blew up the LogisticRegression estimator, so StackingEstimator only passed 20 features onto MaxAbsScaler: 19 original features plus 1 class feature plus 0 class probabilities since `np.all(np.isfinite(y_pred_proba))` was false.

I'm not sure exactly how you'll decide you want to handle this. One idea would be overwrite nans in the predict_proba class columns with 1s or 0s based on the overall prediction. So if predict=0, you would set predict_proba to (1,0) in binary classification and if predict=1, you would set predict_proba to (0,1). Or do some sort of imputer for the nans?

In the end, you must do something to fill the blanks because you can't pass 22 columns when doing stacking_estimator.fit() but only 20 columns later when doing stacking_estimator.transform(). It must continue to pass the number of columns it originally passed when it was fit.

_If this problem applies to you and the maintainers haven't settled on a fix yet, I suggest turning off LogisticRegression in your tpot config, as that's the main thing I've seen sending the occasional nan in the predict_proba class probabilities._