ens_stacking_algorithm.py

from sklearn.model_selection import KFold

def stackingModel(base_models, meta_model, features, target, nfolds=10):
    #Split data into folds
    kfold = KFold(n_splits=nfolds, shuffle=True, random_state=rand_seed)
    #initialize arrays to hold predictions
    test_predictions = np.zeros((features.shape[0], len(base_models)))
    train_predictions = np.zeros((features.shape[0], len(base_models)))
    
    # Train base models
    for i, model in enumerate(base_models):
        for train_index, test_index in kfold.split(features, target):
            #Fit train data on the model
            model.fit(np.array(features)[train_index], np.array(target)[train_index])
            
            #Make prediction on the holdout data
            y_pred = model.predict(np.array(features)[test_index])
            #make predictions on train data
            t_pred = model.predict(np.array(features)[train_index])
            
            #Append the prediction to out of folds
            test_predictions[test_index, i] = y_pred
            #Append predictions to train predictions
            train_predictions[train_index, i] = t_pred


    # Now train the meta-model using the train predictions as new feature
    meta_model.fit(train_predictions, target)
    #Make fianl predictions on the average of out of fold predictions
    final_preds = meta_model.predict(np.mean([test_predictions], axis=0))
    
    return final_preds