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Auto generate table input type for predictFcn exported model

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I have already read the documentaion about the methods requiervedVariable and hotToPredict of the exported model.
I also manually created the required input format for predictFcn.
My dataset is a table with lots of categorical and double data. I select a few of the columns as feature in regression lerarner app.
I am looking for an easier way to create a input data table for testing and predicting with my model after exporing. Its a little hard to each time create a table manually with the variable names and variable types.
This is my Method now:
if trained == true
load trainedModel.mat
varNames = ["BlockTime","FlightTime","dayOfYear","hourOfDay","payloadKg","totalWeightKg"];
varTypes = ["double","double","double","double","double","double"];
inT = table('Size',size(varNames),'VariableTypes',varTypes,'VariableNames',varNames);
intT(1,:) = array2table([BlockTime FlightTime dayofYear PayloadKG TotalKG]);
fuelPredict = trainedModel.predictFcn(inT)
but my main data has lots of other columns so I cant use that for input template
opts.Sheet = "Sheet1";
opts.DataRange = "A2:AM5115";
% Specify column names and types
opts.VariableNames = ["ACType", "ACReg", "FlightDateM", "FlightNo", "Origin", "Destination", "Airborn", "OffBlock", "TouchDown", "OnBlock", "BaggageKG", "BaggagePD", "AdultCount", "InfantCount", "ChildCount", "FlightTime", "BlockTime", "SitaFlightTime", "RemainingFuelKG", "RemainingFuelLt", "RemainingFuelPD", "UpliftFuelKG", "UpliftFuelLT", "UpliftFuelPD", "ArrFuelKG", "ArrFuelLT", "ArrFuelPD", "DiffFuelKG", "DiffFuelLT", "DiffFuelPD", "RampFuelKG", "RampFuelLT", "RampFuelPD", "SitaFuelKG", "SitaFuelLT", "SitaFuelPD", "TaxiFuelKG", "TaxiFuelLT", "TaxiFuelPD"];
opts.VariableTypes = ["categorical", "categorical", "datetime", "string", "categorical", "categorical", "datetime", "datetime", "datetime", "datetime", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double", "double"];
  1 comentario
Siddharth Bhutiya
Siddharth Bhutiya el 22 de Jul. de 2022
Can you share a sample MAT file of what the initial data looks like and what you want the final table to look like ?

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Respuesta aceptada

Alireza Ghaderi
Alireza Ghaderi el 11 de Mayo de 2023
I wrote this function for myself once for ever:)
just share it here if anyone need it:
function prediction = smartPredict(model, inputTable)
% smartPredict Predict the output using the given model and input table
% Syntax: prediction = smartPredict(model, inputTable)
% Inputs:
% model: A regression learner model created by the Regression Learner App
% inputTable: A table containing the input data, which may or may not have
% all the required variables
% Outputs:
% prediction: A scalar or vector containing the predicted output(s)
% based on the given input data
% Example:
% mdl = fitlm(X, y); % Create a linear regression model
% input_data = table(X1, X2); % Create an input table
% pred = smartPredict(mdl, input_data); % Get the prediction
% Notes:
% - This function will report missing required variables and ignore
% any extra columns in the input table.
% - The output is a scalar if the input data has one row and a vector if
% the input data has multiple rows.
% Get the required variables from the model
requiredVariables = model.RequiredVariables;
% Check if the input table contains all the required variables
missingVariables = setdiff(requiredVariables, inputTable.Properties.VariableNames);
if ~isempty(missingVariables)
% Report missing variables
error('smartPredict:missingVariables', ...
'The input table is missing the following required variables: %s', ...
strjoin(missingVariables, ', '));
% Select only the required variables from the input table
selectedInputTable = inputTable(:, requiredVariables);
% Make the prediction
prediction = model.predictFcn(selectedInputTable);
catch e
% Report errors during prediction
error('smartPredict:predictionError', ...
'An error occurred during prediction: %s', e.message);

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