NNET

nnet.R

# function to find optimal hidden nodes and threshold
cv_nnet_models = function(train, validation,  start, end) {
    print(paste('Cross validating neural network on ams score with nodes from',start,'to',end))
    # create data frame to return best model for each hidden node level
    neural_df = data.frame(hidden_nodes= NULL, threshold=NULL, ams_sum = NULL)
    # iterate with increasing number of hidden nodes
    for (hidden_nodes in seq(start,end,1)){
        print(paste('Training with', hidden_nodes,'hidden nodes'))
        # train model
        model = train_neural(train, hidden_nodes)
        bst_ams= 0
        bst_model = NULL
        # check which threhsold between 0 and 1, with intervasl of .025 yields highest ams
        for (i in seq(0,1,.025)) {
            print(paste('Predicting layer with',hidden_nodes,'nodes & threshold: ', i))
            predicted = get_prediction(validation, i, model, 'nnet')
            ams_sum = get_ams(validation, predicted)
            # if ams for threshold is highest, make it best model for this hidden node level
            if (ams_sum > bst_ams) {
                vec = c(hidden_nodes, i, ams_sum)
                print(vec)
                bst_ams = ams_sum
                bst_model = vec
            }
        }
        print(paste('Choosing best model with ams score:', bst_ams))
        # add best threshold model to data frame of models with different node level
        neural_df = rbind(neural_df, bst_model) 
    }
    return(neural_df)
}