Classification Learner is well suited for choosing and training classification models interactively, but it does not generate C/C++ code that labels data based on a trained model. The Generate Function button in the Export section of the Classification Learner app generates MATLAB code for training a model but does not generate C/C++ code. This example shows how to generate C code from a function that predicts labels using an exported classification model. The example builds a model that predicts the credit rating of a business given various financial ratios, according to these steps:
Use the credit rating data set in the file
CreditRating_Historical.dat, which is included with Statistics and Machine Learning Toolbox™.
Reduce the data dimensionality using principal component analysis (PCA).
Train a set of models that support code generation for label prediction.
Export the model with the minimum 5-fold, cross-validated classification accuracy.
Generate C code from an entry-point function that transforms the new predictor data and then predicts corresponding labels using the exported model.
Load sample data and import the data into the Classification Learner app. Review the data using scatter plots and remove unnecessary predictors.
readtable to load the historical credit rating data set in
CreditRating_Historical.dat into a table.
creditrating = readtable('CreditRating_Historical.dat');
On the Apps tab, click Classification Learner.
In Classification Learner, on the Classification Learner tab, in the File section, click New Session and select From Workspace.
In the New Session dialog box, select the table
variables, except the one identified as the response, are double-precision numeric
vectors. Click Start Session to compare classification models
based on the 5-fold, cross-validated classification accuracy.
Classification Learner loads the data and plots a scatter plot of the variables
ID. Because identification numbers
are not helpful to display in a plot, choose
X under Predictors.
The scatter plot suggests that the two variables can separate the classes
CCC fairly well. However, the observations corresponding to the
remaining classes are mixed into these classes.
Identification numbers are not helpful for prediction. Therefore, in the Features section, click Feature Selection and then clear the ID check box. You can also remove unnecessary predictors from the beginning by using the check boxes in the New Session dialog box. This example shows how to remove unused predictors for code generation when you have included all predictors.
Enable PCA to reduce the data dimensionality.
In the Features section, click PCA, and then select Enable PCA. This action applies PCA to the predictor data, and then transforms the data before training the models. Classification Learner uses only components that collectively explain 95% of the variability.
Train a set of models that support code generation for label prediction.
Select the following classification models and options, which support code generation for label prediction, and then perform cross-validation (for more details, see Code Generation Support, Usage Notes, and Limitations). To select each model, in the Model Type section, click the Show more arrow, and then click the model. After selecting a model and specifying any options, close any open menus, and then click Train in the Training section.
|Models and Options to Select||Description|
|Under Decision Trees, select All Trees||Classification trees of various complexities|
|Under Support Vector Machines, select All SVMs||SVMs of various complexities and using various kernels. Complex SVMs require time to fit.|
|Under Ensemble Classifiers, select Boosted Trees. In the Model Type section, click Advanced. Reduce Maximum number of splits to 5 and increase Number of learners to 100.||Boosted ensemble of classification trees|
|Under Ensemble Classifiers, select Bagged Trees. In the Model Type section, click Advanced. Increase Maximum number of splits to 50 and increase Number of learners to 100.||Random forest of classification trees|
After cross-validating each model type, the Data Browser displays each model and its 5-fold, cross-validated classification accuracy, and highlights the model with the best accuracy.
Select the model that yields the maximum 5-fold, cross-validated classification accuracy, which is the error-correcting output codes (ECOC) model of Fine Gaussian SVM learners. With PCA enabled, Classification Learner uses two predictors out of six.
In the Plots section, click Confusion Matrix.
The model does well distinguishing between
C classes. However, the model does not do
as well distinguishing between particular levels within those groups, the lower B levels
Export the model to the MATLAB® Workspace and save the model using
In the Export section, click Export Model, and then select Export Compact Model. Click OK in the dialog box.
trainedModel appears in the MATLAB Workspace. The field
trainedModel contains the compact model.
At the command line, save the compact model to a file called
ClassificationLearnerModel.mat in your current folder.
Prediction using the object functions requires a trained model object, but the
-args option of
codegen does not accept such objects. Work around this limitation by using
loadLearnerForCoder. Save a trained model by using
saveLearnerForCoder. Then, define an entry-point function that loads
the saved model by using
loadLearnerForCoder and calls the
predict function. Finally, use
generate code for the entry-point function.
Preprocess new data in the same way you preprocess the training data.
To preprocess, you need the following three model parameters:
removeVars — Column vector of at most
p elements identifying indices of variables to remove from
the data, where
p is the number of predictor variables in the
pcaCenters — Row vector of exactly
q PCA centers
r matrix of PCA coefficients, where
r is at most
Specify the indices of predictor variables that you removed while selecting data
using Feature Selection in Classification Learner. Extract the
PCA statistics from
removeVars = 1; pcaCenters = trainedModel.PCACenters; pcaCoefficients = trainedModel.PCACoefficients;
Save the model parameters to a file named
in your current folder.
An entry-point function is a function you define for code
generation. Because you cannot call any function at the top level using
codegen, you must define an entry-point function that calls
code-generation-enabled functions, and then generate C/C++ code for the entry-point
function by using
In your current folder, define a function named
Accepts a numeric matrix (
X) of raw observations
containing the same predictor variables as the ones passed into Classification
Loads the classification model in
ClassificationLearnerModel.mat and the model parameters in
Removes the predictor variables corresponding to the indices in
Transforms the remaining predictor data using the PCA centers
pcaCenters) and coefficients
pcaCoefficients) estimated by Classification
Returns predicted labels using the model
function label = mypredictCL(X) %#codegen %MYPREDICTCL Classify credit rating using model exported from %Classification Learner % MYPREDICTCL loads trained classification model (SVM) and model % parameters (removeVars, pcaCenters, and pcaCoefficients), removes the % columns of the raw matrix of predictor data in X corresponding to the % indices in removeVars, transforms the resulting matrix using the PCA % centers in pcaCenters and PCA coefficients in pcaCoefficients, and then % uses the transformed data to classify credit ratings. X is a numeric % matrix with n rows and 7 columns. label is an n-by-1 cell array of % predicted labels. % Load trained classification model and model parameters SVM = loadLearnerForCoder('ClassificationLearnerModel'); data = coder.load('ModelParameters'); removeVars = data.removeVars; pcaCenters = data.pcaCenters; pcaCoefficients = data.pcaCoefficients; % Remove unused predictor variables keepvars = 1:size(X,2); idx = ~ismember(keepvars,removeVars); keepvars = keepvars(idx); XwoID = X(:,keepvars); % Transform predictors via PCA Xpca = bsxfun(@minus,XwoID,pcaCenters)*pcaCoefficients; % Generate label from SVM label = predict(SVM,Xpca); end
Because C and C++ are statically typed languages, you must
determine the properties of all variables in the entry-point function at compile time.
Specify variable-size arguments using
coder.typeof and generate code using the arguments.
Create a double-precision matrix called
x for code generation
coder.typeof. Specify that the number of
x is arbitrary, but that
x must have
p = size(creditrating,2) - 1; x = coder.typeof(0,[Inf,p],[1 0]);
For more details about specifying variable-size arguments, see Specify Variable-Size Arguments for Code Generation.
Generate a MEX function from
mypredictCL.m. Use the
-args option to specify
x as an
codegen mypredictCL -args x
codegen generates the MEX file
mypredictCL_mex.mexw64 in your current folder. The file extension
depends on your platform.
Verify that the MEX function returns the expected labels.
Remove the response variable from the original data set, and then randomly draw 15 observations.
rng('default'); % For reproducibility m = 15; testsampleT = datasample(creditrating(:,1:(end - 1)),m);
Predict corresponding labels by using
predictFcn in the
classification model trained by Classification Learner.
testLabels = trainedModel.predictFcn(testsampleT);
Convert the resulting table to a matrix.
testsample = table2array(testsampleT);
The columns of
testsample correspond to the columns of the
predictor data loaded by Classification Learner.
Pass the test data to
mypredictCL. The function
mypredictCL predicts corresponding labels by using
predict and the classification model trained by Classification
testLabelsPredict = mypredictCL(testsample);
Predict corresponding labels by using the generated MEX function
testLabelsMEX = mypredictCL_mex(testsample);
Compare the sets of predictions.
ans = logical 1
isequal returns logical 1 (true) if all
the inputs are equal.
and the MEX function return the same values.