SemiSupervisedGraphModel

Semi-supervised graph-based model for classification

Since R2020b

Description

You can use a semi-supervised graph-based method to label unlabeled data by using the fitsemigraph function. The resulting SemiSupervisedGraphModel object contains the fitted labels for the unlabeled observations (FittedLabels) and their scores (LabelScores). You can also use the SemiSupervisedGraphModel object as a classifier, trained on both the labeled and unlabeled data, to classify new data by using the predict function.

Creation

Create a SemiSupervisedGraphModel object by using fitsemigraph.

Properties

FittedLabels — Labels fitted to unlabeled data

categorical array | character array | logical vector | numeric vector | cell array of character vectors

This property is read-only.

Labels fitted to the unlabeled data, specified as a categorical or character array, logical or numeric vector, or cell array of character vectors. FittedLabels has the same data type as the class labels in the response variable in the call to fitsemigraph. (The software treats string arrays as cell arrays of character vectors.)

Each row of FittedLabels represents the fitted label of the corresponding row of UnlabeledX or UnlabeledTbl.

For more information on how fitsemigraph fits labels, see Algorithms.

Data Types: single | double | logical | char | cell | categorical

LabelScores — Scores for fitted labels

numeric matrix

This property is read-only.

Scores for the fitted labels, specified as a numeric matrix. LabelScores has size u-by-K, where u is the number of observations (or rows) in the unlabeled data and K is the number of classes in ClassNames.

score(u,k) is the likelihood that the observation u belongs to class k, where a higher score value indicates a higher likelihood.

For more information on how fitsemigraph computes label scores, see Algorithms.

Data Types: double

Method — Labeling technique

'labelpropagation' | 'labelpropagationexact' | 'labelspreading' | 'labelspreadingexact'

This property is read-only.

Labeling technique used to label the unlabeled data, specified as 'labelpropagation', 'labelpropagationexact', 'labelspreading', or 'labelspreadingexact'.

Data Types: char

CategoricalPredictors — Categorical predictor indices

positive integer vector | []

This property is read-only.

Categorical predictor indices, specified as a positive integer vector. CategoricalPredictors contains index values corresponding to the columns of the predictor data that contain categorical predictors. If none of the predictors are categorical, then this property is empty ([]).

Data Types: single | double

ClassNames — Unique class labels

categorical array | character array | logical vector | numeric vector | cell array of character vectors

This property is read-only.

Unique class labels used to label the unlabeled data, specified as a categorical or character array, logical or numeric vector, or cell array of character vectors. The order of the elements of ClassNames determines the order of the classes.

Data Types: single | double | logical | char | cell | categorical

PredictorNames — Predictor variable names

cell array of character vectors

This property is read-only.

Predictor variable names, specified as a cell array of character vectors. The order of the elements of PredictorNames corresponds to the order in which the predictor names appear in the predictor data.

Data Types: cell

ResponseName — Response variable name

character vector

This property is read-only.

Response variable name, specified as a character vector.

Data Types: char

Object Functions

predict

Label new data using semi-supervised graph-based classifier

Examples

Fit Labels to Unlabeled Data

Fit labels to unlabeled data by using a semi-supervised graph-based method.

Randomly generate 60 observations of labeled data, with 20 observations in each of three classes.

rng('default') % For reproducibility

labeledX = [randn(20,2)*0.25 + ones(20,2);
            randn(20,2)*0.25 - ones(20,2);
            randn(20,2)*0.5];
Y = [ones(20,1); ones(20,1)*2; ones(20,1)*3];

Visualize the labeled data by using a scatter plot. Observations in the same class have the same color. Notice that the data is split into three clusters with very little overlap.

scatter(labeledX(:,1),labeledX(:,2),[],Y,'filled')
title('Labeled Data')

Randomly generate 300 additional observations of unlabeled data, with 100 observations per class. For the purposes of validation, keep track of the true labels for the unlabeled data.

unlabeledX = [randn(100,2)*0.25 + ones(100,2);
              randn(100,2)*0.25 - ones(100,2);
              randn(100,2)*0.5];
trueLabels = [ones(100,1); ones(100,1)*2; ones(100,1)*3];

Fit labels to the unlabeled data by using a semi-supervised graph-based method. The function fitsemigraph returns a SemiSupervisedGraphModel object whose FittedLabels property contains the fitted labels for the unlabeled data and whose LabelScores property contains the associated label scores.

Mdl = fitsemigraph(labeledX,Y,unlabeledX)

Mdl = 
  SemiSupervisedGraphModel with properties:

             FittedLabels: [300x1 double]
              LabelScores: [300x3 double]
               ClassNames: [1 2 3]
             ResponseName: 'Y'
    CategoricalPredictors: []
                   Method: 'labelpropagation'

Visualize the fitted label results by using a scatter plot. Use the fitted labels to set the color of the observations, and use the maximum label scores to set the transparency of the observations. Observations with less transparency are labeled with greater confidence. Notice that observations that lie closer to the cluster boundaries are labeled with more uncertainty.

maxLabelScores = max(Mdl.LabelScores,[],2);
rescaledScores = rescale(maxLabelScores,0.05,0.95);
scatter(unlabeledX(:,1),unlabeledX(:,2),[],Mdl.FittedLabels,'filled', ...
    'MarkerFaceAlpha','flat','AlphaData',rescaledScores);
title('Fitted Labels for Unlabeled Data')

Determine the accuracy of the labeling by using the true labels for the unlabeled data.

numWrongLabels = sum(trueLabels ~= Mdl.FittedLabels)

numWrongLabels = 
10

Only 10 of the 300 observations in unlabeledX are mislabeled.

Classify New Data Using Model Trained on Labeled and Unlabeled Data

Use both labeled and unlabeled data to train a SemiSupervisedGraphModel object. Label new data using the trained model.

Randomly generate 15 observations of labeled data, with 5 observations in each of three classes.

rng('default') % For reproducibility
labeledX = [randn(5,2)*0.25 + ones(5,2);
            randn(5,2)*0.25 - ones(5,2);
            randn(5,2)*0.5];
Y = [ones(5,1); ones(5,1)*2; ones(5,1)*3];

Randomly generate 300 additional observations of unlabeled data, with 100 observations per class.

unlabeledX = [randn(100,2)*0.25 + ones(100,2);
              randn(100,2)*0.25 - ones(100,2);
              randn(100,2)*0.5];

Fit labels to the unlabeled data by using a semi-supervised graph-based method. Specify label spreading as the labeling algorithm, and use an automatically selected kernel scale factor. The function fitsemigraph returns a SemiSupervisedGraphModel object whose FittedLabels property contains the fitted labels for the unlabeled data and whose LabelScores property contains the associated label scores.

Mdl = fitsemigraph(labeledX,Y,unlabeledX,'Method','labelspreading', ...
    'KernelScale','auto')

Mdl = 
  SemiSupervisedGraphModel with properties:

             FittedLabels: [300x1 double]
              LabelScores: [300x3 double]
               ClassNames: [1 2 3]
             ResponseName: 'Y'
    CategoricalPredictors: []
                   Method: 'labelspreading'

Randomly generate 150 observations of new data, with 50 observations per class. For the purposes of validation, keep track of the true labels for the new data.

newX = [randn(50,2)*0.25 + ones(50,2);
        randn(50,2)*0.25 - ones(50,2);
        randn(50,2)*0.5];
trueLabels = [ones(50,1); ones(50,1)*2; ones(50,1)*3];

Predict the labels for the new data by using the predict function of the SemiSupervisedGraphModel object. Compare the true labels to the predicted labels by using a confusion matrix.

predictedLabels = predict(Mdl,newX);
confusionchart(trueLabels,predictedLabels)

Only 3 of the 150 observations in newX are mislabeled.

Tips

• You can use interpretability features, such as lime, shapley, partialDependence, and plotPartialDependence, to interpret how predictors contribute to predictions. You must define a custom function and pass it to the interpretability functions. The custom function must return labels for lime, scores of a single class for shapley, and scores of one or more classes for partialDependence and plotPartialDependence. For an example, see Specify Model Using Function Handle.

Version History

Introduced in R2020b