EnsembleVoteClassifier with neural network
I have a trained neural networks in which I am trying to average their prediction using EnsembleVoteClassifier from mlxtend.classifier. The problem is my neural network don't share the same input, (I performed feature reduction and feature select algorithms randomly and stored the results on new different variables, so I have something like X_test_algo1, X_test_algo2 and X_test_algo3 and Y_test). 

I am trying to average the weights, but as I said, I don't have the same X, and I didn't any example on the documentation. How can I average the predictions for my three models model1, model2 and model3

eclf = EnsembleVoteClassifier(clfs=[model1, model2, model3], weights=[1,1,1], refit=False)
names = ['NN1', 'NN2', 'NN2', 'Ensemble']

eclf.fit(X_train_algo1, Ytrain) #????


If it's not possible, that is okay. I am only interested on how to calculate the formulas of Hard Voting, Hard Voting and Weighted Voting, or if there is anther library that is more flexible or the explicit expressions of the formulas could be helpful too.

Why would you need a library to do that?

Simply pass the same examples through all your neural networks and get the predictions (either logits or probabilities or labels).


Hard voting choose the label predicted most often by classifiers. 
Soft voting, average probabilities predicted by classifiers and choose the label having the highest.
Weighted voting - either of the above can be weighted. Just assign weights to each classifier and multiply their predictions by them. Weights are usually normalized to (0, 1] range.


In principle you could also sum logits and choose the label with highest.

Oh, and weight averaging is different technique and requires you to have the same model and usually is done for the same initialization but at different training timesteps. You can read about it in this blog post.
