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How can I work with hybrid inputs (numerical + categorical variables) to create a neural network

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I want to create a neural network model with two inputs:
Input 1: numerical values
Input 2: categorical dummy variables (calendar date: Sunday, Monday,..., January, february,...)
The first input is vector. The second input is matrix.
How to deal with this hybrid input data to create a NN model or any other machine learning technique.

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Tomaso Cetto
Tomaso Cetto il 30 Set 2021
Modificato: Tomaso Cetto il 30 Set 2021
Hi Ismael,
One thing you could do is create a very simple neural network using two featureInputLayers. Let's break down the workflow into steps.
Preparing the raw data
The standard data format for the featureInputLayer is numObservations x numFeatures. If I understand your data correctly, it seems like your first input has a single feature, so it will be a numObservations x 1 vector, and your second input will be a matrix of dummified categorical inputs (neural networks can't process categorical inputs so we need to turn them into numeric matrices by dummifying, or 'one-hot encoding', them). A very useful function you can use to transform your categorical array to a dummifed matrix is the onehotencode function. The idea is that you will have a numObservations x numFeatures matrix, where numFeatures is equal to the total number of categories of your categorical array.
Creating the datastores
The way to feed in multiple inputs to trainNetwork is using a combinedDatastore. A combinedDatstore objects hold multiple underlying datastores, and reads from them all at training time. Because your inputs are 2D arrays, you can have arrayDatastores as the underlying datastores. Attached below is some code where I create some dummy data and responses, store them in individual arrayDatastores, and then combine them into one combinedDatastore:
% Create data
XTrain1 = rand(10, 1); % numObservations (10) x numFeatures (1)
XTrain2 = rand(10, 7); % numObservations (10) x numFeatures (7)
YTrain = rand(10, 1); % numObservations (10) x numResponses(1)
% Create arrayDatastores. We transpose the arrays because the datastore needs to read out
% predictors in the format numFeatures x 1, and so the 'IterationDimension' becomes the
% second one.
dsX1 = arrayDatastore(XTrain1',"IterationDimension", 2,'OutputType','cell');
dsX2 = arrayDatastore(XTrain2',"IterationDimension", 2,'OutputType','cell');
dsY = arrayDatastore(YTrain',"IterationDimension", 2,'OutputType','cell');
% Create the combined datastore.
ds = combine(dsX1, dsX2, dsY);
% Read one observation from the datastore
For more information regarding how to build datastores for feature input, see the appropriate section of the trainNetwork doc.
Creating the network
I've attached code below to build a simple network that fully-connects on each input branch, concatenates both branches, and then fully-connects one more time before output. Feel free to modify this network to suit your needs!
inputOne = [
fullyConnectedLayer(20, 'Name', 'fc1')];
inputTwo = [
fullyConnectedLayer(10, 'Name', 'fc2')];
concat = concatenationLayer(1,2,'Name','concat');
lgraph = layerGraph(inputOne);
lgraph = addLayers(lgraph, inputTwo);
lgraph = addLayers(lgraph, concat);
lgraph = connectLayers(lgraph, 'fc1', 'concat/in1');
lgraph = connectLayers(lgraph, 'fc2', 'concat/in2');
outputLayers = [
regressionLayer('name', 'class')];
lgraph = addLayers(lgraph, outputLayers);
lgraph = connectLayers(lgraph, 'concat', 'fc3');
% Visualize the network architecture
Training the network
All that's left to do is train the network!
options = trainingOptions('sgdm', ...
'MaxEpochs', 5, ...
'MiniBatchSize', 8, ...
'Verbose', true);
net = trainNetwork(ds, lgraph, options);
Let me know if this solves your problem and if there is anything more I can do to help!
  4 Commenti
Ismaeel il 1 Ott 2021
This is exactly what I was looking for;
An energy-forecasting problem with multiple inputs:
1. Energy measurement per day (nonrepeatable numerical values)
2. Weather information (nonrepeatable numerical values)
2. Date: mm-dd-yyyy (repeatable dummified variables)
3. Date: Weekdays (repeatable dummified variables)
4. Date type: working days\holidays (repeatable dummified variables)
The aim is to solve a forecasting problem given the above information and reaching an estimation of one vector of data, energy forecasting.
I believe training a network with the input like above (large matrix input) would be my next challenging problem but at least, I have a method now to start with. Since I have many zeros\ones in the input data, I will think about using a sparse matrix if possible.
Thank you very much Tomaso for the help.

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