learnerCoderConfigurer

Create coder configurer of machine learning model

Description

After training a machine learning model, create a coder configurer for the model by using learnerCoderConfigurer. Use the object functions and properties of the configurer to specify code generation options and to generate C/C++ code for the predict and update functions of the machine learning model. Generating C/C++ code requires MATLAB® Coder™.

This flow chart shows the code generation workflow using a coder configurer. Use learnerCoderConfigurer for the highlighted step.

example

configurer = learnerCoderConfigurer(Mdl,X) returns the coder configurer configurer for the machine learning model Mdl. Specify the predictor data X for the predict function of Mdl.

example

configurer = learnerCoderConfigurer(Mdl,X,Name,Value) returns a coder configurer with additional options specified by one or more name-value pair arguments. For example, you can specify the number of output arguments in the predict function, the file name of generated C/C++ code, and the verbosity level of the coder configurer.

Examples

collapse all

Train a machine learning model, and then generate code for the predict and update functions of the model by using a coder configurer.

Load the carsmall data set and train a support vector machine (SVM) regression model.

load carsmall
X = [Horsepower,Weight];
Y = MPG;
Mdl = fitrsvm(X,Y);

Mdl is a RegressionSVM object.

Create a coder configurer for the RegressionSVM model by using learnerCoderConfigurer. Specify the predictor data X. The learnerCoderConfigurer function uses the input X to configure the coder attributes of the predict function input.

configurer = learnerCoderConfigurer(Mdl,X)
configurer = 
  RegressionSVMCoderConfigurer with properties:

   Update Inputs:
             Alpha: [1x1 LearnerCoderInput]
    SupportVectors: [1x1 LearnerCoderInput]
             Scale: [1x1 LearnerCoderInput]
              Bias: [1x1 LearnerCoderInput]

   Predict Inputs:
                 X: [1x1 LearnerCoderInput]

   Code Generation Parameters:
        NumOutputs: 1
    OutputFileName: 'RegressionSVMModel'


  Properties, Methods

configurer is a RegressionSVMCoderConfigurer object, which is a coder configurer of a RegressionSVM object.

To generate C/C++ code, you must have access to a C/C++ compiler that is configured properly. MATLAB Coder locates and uses a supported, installed compiler. You can use mex -setup to view and change the default compiler. For more details, see Change Default Compiler (MATLAB).

Generate code for the predict and update functions of the SVM regression model (Mdl) with default settings.

generateCode(configurer)
generateCode creates these files in output folder:
'initialize.m', 'predict.m', 'update.m', 'RegressionSVMModel.mat'

The generateCode function completes these actions:

  • Generate the MATLAB files required to generate code, including the two entry-point functions predict.m and update.m for the predict and update functions of Mdl, respectively.

  • Create a MEX function named RegressionSVMModel for the two entry-point functions.

  • Create the code for the MEX function in the codegen\mex\RegressionSVMModel folder.

  • Copy the MEX function to the current folder.

Display the contents of the predict.m, update.m, and initialize.m files by using the type function.

type predict.m
function varargout = predict(X,varargin) %#codegen
% Autogenerated by MATLAB, 26-Aug-2019 17:27:24
[varargout{1:nargout}] = initialize('predict',X,varargin{:});
end
type update.m
function update(varargin) %#codegen
% Autogenerated by MATLAB, 26-Aug-2019 17:27:24
initialize('update',varargin{:});
end
type initialize.m
function [varargout] = initialize(command,varargin) %#codegen
% Autogenerated by MATLAB, 26-Aug-2019 17:27:24
coder.inline('always')
persistent model
if isempty(model)
    model = loadLearnerForCoder('RegressionSVMModel.mat');
end
switch(command)
    case 'update'
        % Update struct fields: Alpha
        %                       SupportVectors
        %                       Scale
        %                       Bias
        model = update(model,varargin{:});
    case 'predict'
        % Predict Inputs: X
        X = varargin{1};
        if nargin == 2
            [varargout{1:nargout}] = predict(model,X);
        else
            PVPairs = cell(1,nargin-2);
            for i = 1:nargin-2
                PVPairs{1,i} = varargin{i+1};
            end
            [varargout{1:nargout}] = predict(model,X,PVPairs{:});
        end
end
end

Train a SVM model using a partial data set and create a coder configurer for the model. Use the properties of the coder configurer to specify coder attributes of the SVM model parameters. Use the object function of the coder configurer to generate C code that predicts labels for new predictor data. Then retrain the model using the whole data set and update parameters in the generated code without regenerating the code.

Train Model

Load the ionosphere data set. This data set has 34 predictors and 351 binary responses for radar returns, either bad ('b') or good ('g'). Train a binary SVM classification model using the first 50 observations.

load ionosphere
Mdl = fitcsvm(X(1:50,:),Y(1:50));

Mdl is a ClassificationSVM object.

Create Coder Configurer

Create a coder configurer for the ClassificationSVM model by using learnerCoderConfigurer. Specify the predictor data X. The learnerCoderConfigurer function uses the input X to configure the coder attributes of the predict function input. Also, set the number of outputs to 2 so that the generated code returns predicted labels and scores.

configurer = learnerCoderConfigurer(Mdl,X(1:50,:),'NumOutputs',2);

configurer is a ClassificationSVMCoderConfigurer object, which is a coder configurer of a ClassificationSVM object.

Specify Coder Attributes of Parameters

Specify the coder attributes of the SVM classification model parameters so that you can update the parameters in the generated code after retraining the model. This example specifies the coder attributes of predictor data that you want to pass to the generated code and the coder attributes of the support vectors of the SVM model.

First, specify the coder attributes of X so that the generated code accepts any number of observations. Modify the SizeVector and VariableDimensions attributes. The SizeVector attribute specifies the upper bound of the predictor data size, and the VariableDimensions attribute specifies whether each dimension of the predictor data has a variable size or fixed size.

configurer.X.SizeVector = [Inf 34];
configurer.X.VariableDimensions = [true false];

The size of the first dimension is the number of observations. In this case, the code specifies that the upper bound of the size is Inf and the size is variable, meaning that X can have any number of observations. This specification is convenient if you do not know the number of observations when generating code.

The size of the second dimension is the number of predictor variables. This value must be fixed for a machine learning model. X contains 34 predictors, so the value of the SizeVector attribute must be 34 and the value of the VariableDimensions attribute must be false.

If you retrain the SVM model using new data or different settings, the number of support vectors can vary. Therefore, specify the coder attributes of SupportVectors so that you can update the support vectors in the generated code.

configurer.SupportVectors.SizeVector = [250 34];
SizeVector attribute for Alpha has been modified to satisfy configuration constraints.
SizeVector attribute for SupportVectorLabels has been modified to satisfy configuration constraints.
configurer.SupportVectors.VariableDimensions = [true false];
VariableDimensions attribute for Alpha has been modified to satisfy configuration constraints.
VariableDimensions attribute for SupportVectorLabels has been modified to satisfy configuration constraints.

If you modify the coder attributes of SupportVectors, then the software modifies the coder attributes of Alpha and SupportVectorLabels to satisfy configuration constraints. If the modification of the coder attributes of one parameter requires subsequent changes to other dependent parameters to satisfy configuration constraints, then the software changes the coder attributes of the dependent parameters.

Generate Code

To generate C/C++ code, you must have access to a C/C++ compiler that is configured properly. MATLAB Coder locates and uses a supported, installed compiler. You can use mex -setup to view and change the default compiler. For more details, see Change Default Compiler (MATLAB).

Use generateCode to generate code for the predict and update functions of the SVM classification model (Mdl) with default settings.

generateCode(configurer)
generateCode creates these files in output folder:
'initialize.m', 'predict.m', 'update.m', 'ClassificationSVMModel.mat'

generateCode generates the MATLAB files required to generate code, including the two entry-point functions predict.m and update.m for the predict and update functions of Mdl, respectively. Then generateCode creates a MEX function named ClassificationSVMModel for the two entry-point functions in the codegen\mex\ClassificationSVMModel folder and copies the MEX function to the current folder.

Verify Generated Code

Pass some predictor data to verify whether the predict function of Mdl and the predict function in the MEX function return the same labels. To call an entry-point function in a MEX function that has more than one entry point, specify the function name as the first input argument.

[label,score] = predict(Mdl,X);
[label_mex,score_mex] = ClassificationSVMModel('predict',X);

Compare label and label_mex by using isequal.

isequal(label,label_mex)
ans = logical
   1

isequal returns logical 1 (true) if all the inputs are equal. The comparison confirms that the predict function of Mdl and the predict function in the MEX function return the same labels.

score_mex might include round-off differences compared with score. In this case, compare score_mex and score, allowing a small tolerance.

find(abs(score-score_mex) > 1e-8)
ans =

  0x1 empty double column vector

The comparison confirms that score and score_mex are equal within the tolerance 1e–8.

Retrain Model and Update Parameters in Generated Code

Retrain the model using the entire data set.

retrainedMdl = fitcsvm(X,Y);

Extract parameters to update by using validatedUpdateInputs. This function detects the modified model parameters in retrainedMdl and validates whether the modified parameter values satisfy the coder attributes of the parameters.

params = validatedUpdateInputs(configurer,retrainedMdl);

Update parameters in the generated code.

ClassificationSVMModel('update',params)

Verify Generated Code

Compare the outputs from the predict function of retrainedMdl and the predict function in the updated MEX function.

[label,score] = predict(retrainedMdl,X);
[label_mex,score_mex] = ClassificationSVMModel('predict',X);
isequal(label,label_mex)
ans = logical
   1

find(abs(score-score_mex) > 1e-8)
ans =

  0x1 empty double column vector

The comparison confirms that labels and labels_mex are equal, and the score values are equal within the tolerance.

Input Arguments

collapse all

Machine learning model, specified as a full or compact model object, as given in this table of supported models.

ModelFull/Compact Model ObjectTraining Function
Binary decision tree for multiclass classificationClassificationTree, CompactClassificationTreefitctree
SVM for one-class and binary classificationClassificationSVM, CompactClassificationSVMfitcsvm
Linear model for binary classificationClassificationLinearfitclinear
Multiclass model for SVMs and linear modelsClassificationECOC, CompactClassificationECOCfitcecoc
Binary decision tree for regressionRegressionTree, CompactRegressionTreefitrtree
Support vector machine (SVM) regressionRegressionSVM, CompactRegressionSVMfitrsvm
Linear regressionRegressionLinearfitrlinear

For the code generation usage notes and limitations of a machine learning model, see the Code Generation section of the model object page.

Predictor data for the predict function of Mdl, specified as an n-by-p numeric matrix, where n is the number of observations and p is the number of predictor variables. To instead specify X as a p-by-n matrix, where the observations correspond to columns, you must set the 'ObservationsIn' name-value pair argument to 'columns'. This option is available only for linear models and ECOC models with linear binary learners.

The predict function of a machine learning model predicts labels for classification and responses for regression for given predictor data. After creating the coder configurer configurer, you can use the generateCode function to generate C/C++ code for the predict function of Mdl. The generated code accepts predictor data that has the same size and data type of X. You can specify whether each dimension has a variable size or fixed size after creating configurer.

For example, if you want to generate C/C++ code that predicts labels using 100 observations with three predictor variables, then specify X as zeros(100,3). The learnerCoderConfigurer function uses only the size and data type of X, not its values. Therefore, X can be predictor data or a MATLAB expression that represents the set of values with a certain data type. The output configurer stores the size and data type of X in the X property of configurer. You can modify the size and data type of X after creating configurer. For example, change the number of observations to 200 and the data type to single.

configurer.X.SizeVector = [200 3];
configurer.X.DataType = 'single';

To allow the generated C/C++ code to accept predictor data with up to 100 observations, specify X as zeros(100,3) and change the VariableDimensions property.

configurer.X.VariableDimensions = [1 0];
[1 0] indicates that the first dimension of X (number of observations) has a variable size and the second dimension of X (number of predictor variables) has a fixed size. The specified number of observations, 100 in this example, becomes the maximum allowed number of observations in the generated C/C++ code. To allow any number of observations, specify the bound as Inf.
configurer.X.SizeVector = [Inf 3];

Data Types: single | double

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: configurer = learnerCoderConfigurer(Mdl,X,'NumOutputs',2,'OutputFileName','myModel') sets the number of outputs in predict to 2 and specifies the file name 'myModel' for the generated C/C++ code.

Number of output arguments in the predict function of the machine learning model Mdl, specified as the comma-separated pair consisting of 'NumOutputs' and a positive integer n.

This table lists the outputs for the predict function of different models. predict in the generated C/C++ code returns the first n outputs of the predict function in the order given in the Outputs column.

Modelpredict Function of ModelOutputs
Binary decision tree for multiclass classificationpredictlabel (predicted class labels), score (posterior probabilities), node (node numbers for predicted classes), cnum (class numbers of predicted labels)
SVM for one-class and binary classificationpredictlabel (predicted class labels), score (scores or posterior probabilities)
Linear model for binary classificationpredictLabel (predicted class labels), Score (classification scores)
Multiclass model for SVMs and linear modelspredictlabel (predicted class labels), NegLoss (negated average binary losses), PBScore (positive-class scores)
Binary decision tree for regressionpredictYfit (predicted responses), node (node numbers for predictions)
SVM regressionpredictyfit (predicted responses)
Linear regressionpredictYHat (predicted responses)

For example, if you specify 'NumOutputs',1 for an SVM classification model, then predict returns predicted class labels in the generated C/C++ code.

After creating the coder configurer configurer, you can modify the number of outputs by using dot notation.

configurer.NumOutputs = 2;

The 'NumOutputs' name-value pair argument is equivalent to the '-nargout' compiler option of codegen. This option specifies the number of output arguments in the entry-point function of code generation. The object function generateCode of a coder configurer generates two entry-point functions—predict.m and update.m for the predict and update functions of Mdl, respectively—and generates C/C++ code for the two entry-point functions. The specified value for 'NumOutputs' corresponds to the number of output arguments in predict.m.

Example: 'NumOutputs',2

Data Types: single | double

File name of the generated C/C++ code, specified as the comma-separated pair consisting of 'OutputFileName' and a character vector or string scalar.

The object function generateCode of a coder configurer generates C/C++ code using this file name.

The file name must not contain spaces because they can lead to code generation failures in certain operating system configurations. Also, the name must be a valid MATLAB function name.

The default file name is the object name of Mdl followed by 'Model'. For example, if Mdl is a CompactClassificationSVM or ClassificationSVM object, then the default name is 'ClassificationSVMModel'.

After creating the coder configurer configurer, you can modify the file name by using dot notation.

configurer.OutputFileName = 'myModel';

Example: 'OutputFileName','myModel'

Data Types: char | string

Verbosity level, specified as the comma-separated pair consisting of 'Verbose' and either true (logical 1) or false (logical 0). The verbosity level controls the display of notification messages at the command line for the coder configurer configurer.

ValueDescription
true (logical 1)The software displays notification messages when your changes to the coder attributes of a parameter result in changes for other dependent parameters.
false (logical 0)The software does not display notification messages.

To enable updating machine learning model parameters in the generated code, you need to configure the coder attributes of the parameters before generating code. The coder attributes of parameters are dependent on each other, so the software stores the dependencies as configuration constraints. If you modify the coder attributes of a parameter by using a coder configurer, and the modification requires subsequent changes to other dependent parameters to satisfy configuration constraints, then the software changes the coder attributes of the dependent parameters. The verbosity level determines whether or not the software displays notification messages for these subsequent changes.

After creating the coder configurer configurer, you can modify the verbosity level by using dot notation.

configurer.Verbose = false;

Example: 'Verbose',false

Data Types: logical

Predictor data observation dimension, specified as the comma-separated pair consisting of 'ObservationsIn' and either 'rows' or 'columns'. If you set 'ObservationsIn' to 'columns', then the predictor data X must be oriented so that the observations correspond to columns.

Note

The 'columns' option is available only for linear models and ECOC models with linear binary learners.

Example: 'ObservationsIn','columns'

Output Arguments

collapse all

Coder configurer of a machine learning model, returned as one of the coder configurer objects in this table.

ModelCoder Configurer Object
Binary decision tree for multiclass classificationClassificationTreeCoderConfigurer
SVM for one-class and binary classificationClassificationSVMCoderConfigurer
Linear model for binary classificationClassificationLinearCoderConfigurer
Multiclass model for SVMs and linear modelsClassificationECOCCoderConfigurer
Binary decision tree for regressionRegressionTreeCoderConfigurer
Support vector machine (SVM) regressionRegressionSVMCoderConfigurer
Linear regressionRegressionLinearCoderConfigurer

Use the object functions and properties of a coder configurer object to configure code generation options and to generate C/C++ code for the predict and update functions of the machine learning model.

Introduced in R2018b