fit
Description
The fit
function fits a configured multiclass
error-correcting output codes (ECOC) classification model for incremental learning (incrementalClassificationECOC
object) to streaming data. To additionally track
performance metrics using the data as it arrives, use updateMetricsAndFit
instead.
To fit or cross-validate an ECOC classification model to an entire batch of data at once,
see fitcecoc
.
returns an incremental learning model Mdl
= fit(Mdl
,X
,Y
)Mdl
, which represents the input incremental learning model Mdl
trained using the predictor and response data, X
and
Y
respectively. Specifically, fit
fits
the model to the incoming data and stores the updated binary learners and configurations in
the output model Mdl
.
Examples
Incrementally Train Model with Little Prior Information
Fit an incremental ECOC learner when you know only the expected maximum number of classes in the data.
Create an incremental ECOC model. Specify that the maximum number of expected classes is 5.
Mdl = incrementalClassificationECOC(MaxNumClasses=5)
Mdl = incrementalClassificationECOC IsWarm: 0 Metrics: [1x2 table] ClassNames: [1x0 double] ScoreTransform: 'none' BinaryLearners: {10x1 cell} CodingName: 'onevsone' Decoding: 'lossweighted'
Mdl
is an incrementalClassificationECOC
model. All its properties are read-only. Mdl
can process at most 5 unique classes. By default, the prior class distribution Mdl.Prior
is empirical, which means the software updates the prior distribution as it encounters labels.
Mdl
must be fit to data before you can use it to perform any other operations.
Load the human activity data set. Randomly shuffle the data.
load humanactivity n = numel(actid); rng(1) % For reproducibility idx = randsample(n,n); X = feat(idx,:); Y = actid(idx);
For details on the data set, enter Description
at the command line.
Fit the incremental model to the training data, in chunks of 50 observations at a time, by using the fit
function. At each iteration:
Simulate a data stream by processing 50 observations.
Overwrite the previous incremental model with a new one fitted to the incoming observations.
Store the first model coefficient of the first binary learner and the prior probability that the subject is moving (
Y
> 2) to see how these parameters evolve during incremental learning.
% Preallocation numObsPerChunk = 50; nchunk = floor(n/numObsPerChunk); beta11 = zeros(nchunk,1); priormoved = zeros(nchunk,1); % Incremental fitting for j = 1:nchunk ibegin = min(n,numObsPerChunk*(j-1) + 1); iend = min(n,numObsPerChunk*j); idx = ibegin:iend; Mdl = fit(Mdl,X(idx,:),Y(idx)); beta11(j) = Mdl.BinaryLearners{1}.Beta(1); priormoved(j) = sum(Mdl.Prior(Mdl.ClassNames > 2)); end
Mdl
is an incrementalClassificationECOC
model object trained on all the data in the stream.
To see how the parameters evolve during incremental learning, plot them on separate tiles.
t = tiledlayout(2,1); nexttile plot(beta11) xlim([0 nchunk]) ylabel("\beta_{11}") nexttile plot(priormoved) xlim([0 nchunk]) ylabel("\pi(Subject Is Moving)") xlabel(t,"Iteration")
fit
updates the coefficient as it processes each chunk. Because the prior class distribution is empirical, (subject is moving) changes as fit
processes each chunk.
Specify All Class Names Before Fitting
Fit an incremental ECOC learner when you know all the class names in the data.
Consider training a device to predict whether a subject is sitting, standing, walking, running, or dancing based on biometric data measured on the subject. The class names map 1 through 5 to an activity. Also, suppose that the researchers plan to expose the device to each class uniformly.
Create an incremental ECOC model for multiclass learning. Specify the class names and the uniform prior class distribution.
classnames = 1:5;
Mdl = incrementalClassificationECOC(ClassNames=classnames,Prior="uniform")
Mdl = incrementalClassificationECOC IsWarm: 0 Metrics: [1x2 table] ClassNames: [1 2 3 4 5] ScoreTransform: 'none' BinaryLearners: {10x1 cell} CodingName: 'onevsone' Decoding: 'lossweighted'
Mdl
is an incrementalClassificationECOC
model object. All its properties are read-only. During training, observed labels must be in Mdl.ClassNames
.
Mdl
must be fit to data before you can use it to perform any other operations.
Load the human activity data set. Randomly shuffle the data.
load humanactivity n = numel(actid); rng(1) % For reproducibility idx = randsample(n,n); X = feat(idx,:); Y = actid(idx);
For details on the data set, enter Description
at the command line.
Fit the incremental model to the training data by using the fit
function. Simulate a data stream by processing chunks of 50 observations at a time. At each iteration:
Process 50 observations.
Overwrite the previous incremental model with a new one fitted to the incoming observations.
Store the first model coefficient of the first binary learner and the prior probability that the subject is moving (
Y
> 2) to see how these parameters evolve during incremental learning.
% Preallocation numObsPerChunk = 50; nchunk = floor(n/numObsPerChunk); beta11 = zeros(nchunk,1); priormoved = zeros(nchunk,1); % Incremental fitting for j = 1:nchunk ibegin = min(n,numObsPerChunk*(j-1) + 1); iend = min(n,numObsPerChunk*j); idx = ibegin:iend; Mdl = fit(Mdl,X(idx,:),Y(idx)); beta11(j) = Mdl.BinaryLearners{1}.Beta(1); priormoved(j) = sum(Mdl.Prior(Mdl.ClassNames > 2)); end
Mdl
is an incrementalClassificationECOC
model object trained on all the data in the stream.
To see how the parameters evolve during incremental learning, plot them on separate tiles.
t = tiledlayout(2,1); nexttile plot(beta11) xlim([0 nchunk]) ylabel("\beta_{11}") nexttile plot(priormoved) xlim([0 nchunk]) ylabel("\pi(Subject Is Moving)") xlabel(t,"Iteration")
fit
updates the posterior mean of the predictor distribution as it processes each chunk. Because the prior class distribution is specified as uniform, (subject is moving) = 0.6 and does not change as fit
processes each chunk.
Specify Orientation of Observations and Observation Weights
Train an ECOC classification model by using fitcecoc
, convert it to an incremental learner, track its performance on streaming data, and then fit the model to the data. For incremental learning functions, orient the observations in columns, and specify observation weights.
Load and Preprocess Data
Load the human activity data set. Randomly shuffle the data.
load humanactivity rng(1); % For reproducibility n = numel(actid); idx = randsample(n,n); X = feat(idx,:); Y = actid(idx);
For details on the data set, enter Description
at the command line.
Suppose that the data from a stationary subject (Y
<= 2) has double the quality of the data from a moving subject. Create a weight variable that assigns a weight of 2 to observations from a stationary subject and 1 to a moving subject.
W = ones(n,1) + (Y <=2);
Train ECOC Classification Model
Fit an ECOC classification model to a random sample of half the data. Specify observation weights.
idxtt = randsample([true false],n,true); TTMdl = fitcecoc(X(idxtt,:),Y(idxtt),Weights=W(idxtt))
TTMdl = ClassificationECOC ResponseName: 'Y' CategoricalPredictors: [] ClassNames: [1 2 3 4 5] ScoreTransform: 'none' BinaryLearners: {10x1 cell} CodingName: 'onevsone'
TTMdl
is a ClassificationECOC
model object representing a traditionally trained ECOC classification model.
Convert Trained Model
Convert the traditionally trained model to a model for incremental learning.
IncrementalMdl = incrementalLearner(TTMdl)
IncrementalMdl = incrementalClassificationECOC IsWarm: 1 Metrics: [1x2 table] ClassNames: [1 2 3 4 5] ScoreTransform: 'none' BinaryLearners: {10x1 cell} CodingName: 'onevsone' Decoding: 'lossweighted'
IncrementalMdl
is an incrementalClassificationECOC
model. Because class names are specified in IncrementalMdl.ClassNames
, labels encountered during incremental learning must be in IncrementalMdl.ClassNames
.
Separately Track Performance Metrics and Fit Model
Perform incremental learning on the rest of the data by using the updateMetrics
and fit
functions. For incremental learning, orient the observations of the predictor data in columns. At each iteration:
Simulate a data stream by processing 50 observations at a time.
Call
updateMetrics
to update the cumulative and window classification error of the model given the incoming chunk of observations. Overwrite the previous incremental model to update the losses in theMetrics
property. Note that the function does not fit the model to the chunk of data—the chunk is "new" data for the model. Specify that the observations are oriented in columns, and specify the observation weights.Store the classification error.
Call
fit
to fit the model to the incoming chunk of observations. Overwrite the previous incremental model to update the model parameters. Specify that the observations are oriented in columns, and specify the observation weights.
% Preallocation idxil = ~idxtt; nil = sum(idxil); numObsPerChunk = 50; nchunk = floor(nil/numObsPerChunk); mc = array2table(zeros(nchunk,2),VariableNames=["Cumulative","Window"]); Xil = X(idxil,:)'; Yil = Y(idxil); Wil = W(idxil); % Incremental fitting for j = 1:nchunk ibegin = min(nil,numObsPerChunk*(j-1) + 1); iend = min(nil,numObsPerChunk*j); idx = ibegin:iend; IncrementalMdl = updateMetrics(IncrementalMdl,Xil(:,idx),Yil(idx), ... Weights=Wil(idx),ObservationsIn="columns"); mc{j,:} = IncrementalMdl.Metrics{"ClassificationError",:}; IncrementalMdl = fit(IncrementalMdl,Xil(:,idx),Yil(idx), ... Weights=Wil(idx),ObservationsIn="columns"); end
IncrementalMdl
is an incrementalClassificationECOC
model object trained on all the data in the stream.
Alternatively, you can use updateMetricsAndFit
to update performance metrics of the model given a new chunk of data, and then fit the model to the data.
Plot a trace plot of the performance metrics.
plot(mc.Variables) xlim([0 nchunk]) legend(mc.Properties.VariableNames) ylabel("Classification Error") xlabel("Iteration")
The cumulative loss gradually stabilizes, whereas the window loss jumps throughout the training.
Perform Conditional Training
Incrementally train an ECOC classification model only when its performance degrades.
Load the human activity data set. Randomly shuffle the data.
load humanactivity n = numel(actid); rng(1) % For reproducibility idx = randsample(n,n); X = feat(idx,:); Y = actid(idx);
For details on the data set, enter Description
at the command line.
Configure an ECOC classification model for incremental learning so that the maximum number of expected classes is 5, and the metrics window size is 1000. Prepare the model for updateMetrics
by fitting the model to the first 1000 observations.
Mdl = incrementalClassificationECOC(MaxNumClasses=5,MetricsWindowSize=1000); initobs = 1000; Mdl = fit(Mdl,X(1:initobs,:),Y(1:initobs));
Mdl
is an incrementalClassificationECOC
model object.
Determine whether the model is warm by querying the model property.
isWarm = Mdl.IsWarm
isWarm = logical
1
Mdl.IsWarm
is 1; therefore, Mdl
is warm.
Perform incremental learning, with conditional fitting, by following this procedure for each iteration:
Simulate a data stream by processing a chunk of 100 observations at a time.
Update the model performance on the incoming chunk of data.
Fit the model to the chunk of data only when the misclassification error rate is greater than 0.05.
When tracking performance and fitting, overwrite the previous incremental model.
Store the misclassification error rate and the first model coefficient of the first binary learner to see how they evolve during training.
Track when
fit
trains the model.
% Preallocation numObsPerChunk = 100; nchunk = floor((n - initobs)/numObsPerChunk); beta11 = zeros(nchunk,1); ce = array2table(nan(nchunk,2),VariableNames=["Cumulative","Window"]); trained = false(nchunk,1); % Incremental fitting for j = 1:nchunk ibegin = min(n,numObsPerChunk*(j-1) + 1 + initobs); iend = min(n,numObsPerChunk*j + initobs); idx = ibegin:iend; Mdl = updateMetrics(Mdl,X(idx,:),Y(idx)); ce{j,:} = Mdl.Metrics{"ClassificationError",:}; if ce{j,2} > 0.05 Mdl = fit(Mdl,X(idx,:),Y(idx)); trained(j) = true; end beta11(j) = Mdl.BinaryLearners{1}.Beta(1); end
Mdl
is an incrementalClassificationECOC
model object trained on all the data in the stream.
To see how the model performance and evolve during training, plot them on separate tiles.
t = tiledlayout(2,1); nexttile plot(beta11) hold on plot(find(trained),beta11(trained),"r.") xlim([0 nchunk]) ylabel("\beta_{11}") legend("\beta_{11}","Training occurs",Location="best") hold off nexttile plot(ce.Variables) yline(0.05,"--") xlim([0 nchunk]) ylabel("Misclassification Error Rate") legend(ce.Properties.VariableNames,Location="best") xlabel(t,"Iteration")
The trace plot of shows periods of constant values, during which the loss within the previous observation window is at most 0.05.
Input Arguments
Mdl
— Incremental learning model
incrementalClassificationECOC
model object
Incremental learning model to fit to streaming data, specified as an incrementalClassificationECOC
model object. You can create
Mdl
by calling incrementalClassificationECOC
directly, or by converting a supported, traditionally trained machine learning model
using the incrementalLearner
function.
X
— Chunk of predictor data
floating-point matrix
Chunk of predictor data, specified as a floating-point matrix of n
observations and Mdl.NumPredictors
predictor
variables. The value of the ObservationsIn
name-value
argument determines the orientation of the variables and observations. The default
ObservationsIn
value is "rows"
, which indicates that
observations in the predictor data are oriented along the rows of
X
.
The length of the observation labels Y
and the number of observations in X
must be equal; Y(
is the label of observation j (row or column) in j
)X
.
Note
If
Mdl.NumPredictors
= 0,fit
infers the number of predictors fromX
, and sets the corresponding property of the output model. Otherwise, if the number of predictor variables in the streaming data changes fromMdl.NumPredictors
,fit
issues an error.fit
supports only floating-point input predictor data. If your input data includes categorical data, you must prepare an encoded version of the categorical data. Usedummyvar
to convert each categorical variable to a numeric matrix of dummy variables. Then, concatenate all dummy variable matrices and any other numeric predictors. For more details, see Dummy Variables.
Data Types: single
| double
Y
— Chunk of labels
categorical array | character array | string array | logical vector | floating-point vector | cell array of character vectors
Chunk of labels, specified as a categorical, character, or string array, a logical or floating-point vector, or a cell array of character vectors.
The length of the observation labels Y
and the number of
observations in X
must be equal;
Y(
is the label of observation
j (row or column) in j
)X
.
fit
issues an error when one or both of these conditions
are met:
Y
contains a new label and the maximum number of classes has already been reached (see theMaxNumClasses
andClassNames
arguments ofincrementalClassificationECOC
).The
ClassNames
property of the input modelMdl
is nonempty, and the data types ofY
andMdl.ClassNames
are different.
Data Types: char
| string
| cell
| categorical
| logical
| single
| double
Note
If an observation (predictor or label) or weight contains at
least one missing (NaN
) value, fit
ignores the
observation. Consequently, fit
uses fewer than n
observations to create an updated model, where n is the number of
observations in X
.
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Example: ObservationsIn="columns",Weights=W
specifies that the columns
of the predictor matrix correspond to observations, and the vector W
contains observation weights to apply during incremental learning.
ObservationsIn
— Predictor data observation dimension
"rows"
(default) | "columns"
Predictor data observation dimension, specified as "rows"
or
"columns"
.
Example: ObservationsIn="columns"
Data Types: char
| string
Weights
— Chunk of observation weights
floating-point vector of positive values
Chunk of observation weights, specified as a floating-point vector of positive values.
fit
weighs the observations in X
with the corresponding values in Weights
. The size of
Weights
must equal n, which is the number of
observations in X
.
By default, Weights
is ones(
.n
,1)
For more details, including normalization schemes, see Observation Weights.
Example: Weights=W
specifies the observation weights as the vector
W
.
Data Types: double
| single
Output Arguments
Mdl
— Updated ECOC classification model for incremental learning
incrementalClassificationECOC
model object
Updated ECOC classification model for incremental learning, returned as an
incremental learning model object of the same data type as the input model Mdl
, an incrementalClassificationECOC
object.
If you do not specify all expected classes by using the
ClassNames
name-value argument when you create the input model
Mdl
using incrementalClassificationECOC
, and Y
contains expected, but
unprocessed, classes, then fit
performs the following actions:
Append any new labels in
Y
to the tail ofMdl.ClassNames
.Expand
Mdl.Prior
to a length c vector of an updated empirical class distribution, where c is the number of classes inMdl.ClassNames
.
Tips
Unlike traditional training, incremental learning might not have a separate test (holdout) set. Therefore, to treat each incoming chunk of data as a test set, pass the incremental model and each incoming chunk to
updateMetrics
before training the model on the same data.
Algorithms
Observation Weights
If the prior class probability distribution is known (in other words, the prior distribution is not empirical), fit
normalizes observation weights to sum to the prior class probabilities in the respective classes. This action implies that the default observation weights are the respective prior class probabilities.
If the prior class probability distribution is empirical, the software normalizes the specified observation weights to sum to 1 each time you call fit
.
Version History
Introduced in R2022a
MATLAB Command
You clicked a link that corresponds to this MATLAB command:
Run the command by entering it in the MATLAB Command Window. Web browsers do not support MATLAB commands.
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)