updateMetricsAndFit
Update performance metrics in ECOC incremental learning classification model given new data and train model
Since R2022a
Description
Given streaming data, updateMetricsAndFit
first evaluates the
performance of a configured multiclass error-correcting output codes (ECOC) classification
model for incremental learning (incrementalClassificationECOC
object) by calling updateMetrics
on
incoming data. Then updateMetricsAndFit
fits the model to that data by calling
fit
. In other
words, updateMetricsAndFit
performs prequential evaluation
because it treats each incoming chunk of data as a test set, and tracks performance metrics
measured cumulatively and over a specified window [1].
updateMetricsAndFit
provides a simple way to update model performance metrics
and train the model on each chunk of data. Alternatively, you can perform the operations
separately by calling updateMetrics
and then fit
,
which allows for more flexibility (for example, you can decide whether you need to train the
model based on its performance on a chunk of data).
returns an incremental learning model Mdl
= updateMetricsAndFit(Mdl
,X
,Y
)Mdl
, which is the input incremental learning model Mdl
with the following modifications:
updateMetricsAndFit
measures the model performance on the incoming predictor and response data,X
andY
respectively. When the input model is warm (Mdl.IsWarm
istrue
),updateMetricsAndFit
overwrites previously computed metrics, stored in theMetrics
property, with the new values. Otherwise,updateMetricsAndFit
storesNaN
values inMetrics
instead.updateMetricsAndFit
fits the modified model to the incoming data and stores the updated binary learners and configurations in the output modelMdl
.
Examples
Update Performance Metrics and Train Model on Data Stream
Prepare an incremental ECOC learner by specifying the maximum number of classes. Track the model performance on streaming data and fit the model to the data in one call by using the updateMetricsAndFit
function.
Create an ECOC classification model for incremental learning by calling incrementalClassificationECOC
and specifying a maximum of 5 expected classes in the data.
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 object. All its properties are read-only.
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.
Implement incremental learning by performing the following actions at each iteration:
Simulate a data stream by processing a chunk of 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 , cumulative metrics, and window metrics to see how they evolve during incremental learning.
% Preallocation numObsPerChunk = 50; nchunk = floor(n/numObsPerChunk); mc = array2table(zeros(nchunk,2),VariableNames=["Cumulative","Window"]); beta11 = 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 = updateMetricsAndFit(Mdl,X(idx,:),Y(idx)); mc{j,:} = Mdl.Metrics{"ClassificationError",:}; beta11(j) = Mdl.BinaryLearners{1}.Beta(1); end
Mdl
is an incrementalClassificationECOC
model object trained on all the data in the stream. During incremental learning and after the model is warmed up, updateMetricsAndFit
checks the performance of the model on the incoming observations, and then fits the model to those observations.
To see how the performance metrics and evolve during training, plot them on separate tiles.
t = tiledlayout(2,1); nexttile plot(beta11) ylabel("\beta_{11}") xlim([0 nchunk]) nexttile plot(mc.Variables) xlim([0 nchunk]) ylabel("Classification Error") xline(Mdl.MetricsWarmupPeriod/numObsPerChunk,"--") legend(mc.Properties.VariableNames) xlabel(t,"Iteration")
The plot indicates that updateMetricsAndFit
performs the following actions:
Fit during all incremental learning iterations.
Compute the performance metrics after the metrics warm-up period only.
Compute the cumulative metrics during each iteration.
Compute the window metrics after processing 200 observations (4 iterations).
Specify Orientation of Observations and Observation Weights
Train an ECOC classification model by using fitcecoc
and convert it to an incremental learner by using incrementalLearner
. Track the model performance on streaming data and fit the model to streaming data in one call by using updateMetricsAndFit
. Specify the orientation of observations and the observation weights when you call updateMetricsAndFit
.
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 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.
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 object. Because class names are specified in IncrementalMdl.ClassNames
, labels encountered during incremental learning must be in IncrementalMdl.ClassNames
.
Track Performance Metrics and Fit Model
Perform incremental learning on the rest of the data by using the updateMetricsAndFit
function. Transpose the predictor matrix, and specify the data orientation when you call updateMetricsAndFit
. At each iteration:
Simulate a data stream by processing 50 observations at a time.
Call
updateMetricsAndFit
to update the cumulative and window performance metrics of the model given the incoming chunk of observations, and then fit the model to the data. Overwrite the previous incremental model with a new one. Specify that the observations are oriented in columns, and specify the observation weights.Store the misclassification error rate.
% 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 = updateMetricsAndFit(IncrementalMdl,Xil(:,idx),Yil(idx), ... Weights=Wil(idx),ObservationsIn="columns"); mc{j,:} = IncrementalMdl.Metrics{"ClassificationError",:}; end
IncrementalMdl
is an incrementalClassificationECOC
model object trained on all the data in the stream.
Create a trace plot of the misclassification error rate.
plot(mc.Variables) xlim([0 nchunk]) ylabel("Classification Error") legend(mc.Properties.VariableNames) xlabel("Iteration")
The cumulative loss initially jumps, but stabilizes around 0.03, whereas the window loss jumps throughout the training.
Input Arguments
Mdl
— Incremental learning model
incrementalClassificationECOC
model object
Incremental learning model whose performance is measured and then the model is fit
to 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.
If Mdl.IsWarm
is false
,
updateMetricsAndFit
does not track the performance of the model. For more
details, see Performance Metrics.
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,updateMetricsAndFit
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
,updateMetricsAndFit
issues an error.updateMetricsAndFit
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
.
updateMetricsAndFit
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, updateMetricsAndFit
ignores the
observation. Consequently, updateMetricsAndFit
uses fewer than n
observations to compute the model performance and 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.
updateMetricsAndFit
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
, incrementalClassificationECOC
.
If the model is not warm, updateMetricsAndFit
does not compute
performance metrics. As a result, the Metrics
property of
Mdl
remains completely composed of NaN
values.
If the model is warm, updateMetricsAndFit
computes the cumulative and
window performance metrics on the new data X
and
Y
, and overwrites the corresponding elements of
Mdl.Metrics
. For more details, see Performance Metrics.
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 updateMetricsAndFit
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
.
Algorithms
Performance Metrics
updateMetrics
andupdateMetricsAndFit
track model performance metrics, specified by the row labels of the table inMdl.Metrics
, from new data only when the incremental model is warm (IsWarm
property istrue
).If you create an incremental model by using
incrementalLearner
andMetricsWarmupPeriod
is 0 (default forincrementalLearner
), the model is warm at creation.Otherwise, an incremental model becomes warm after the
fit
orupdateMetricsAndFit
function performs both of these actions:Fit the incremental model to
Mdl.MetricsWarmupPeriod
observations, which is the metrics warm-up period.Fit the incremental model to all expected classes (see the
MaxNumClasses
andClassNames
arguments ofincrementalClassificationECOC
).
The
Mdl.Metrics
property stores two forms of each performance metric as variables (columns) of a table,Cumulative
andWindow
, with individual metrics in rows. When the incremental model is warm,updateMetrics
andupdateMetricsAndFit
update the metrics at the following frequencies:Cumulative
— The functions compute cumulative metrics since the start of model performance tracking. The functions update metrics every time you call the functions and base the calculation on the entire supplied data set.Window
— The functions compute metrics based on all observations within a window determined by theMdl.MetricsWindowSize
property.Mdl.MetricsWindowSize
also determines the frequency at which the software updatesWindow
metrics. For example, ifMdl.MetricsWindowSize
is 20, the functions compute metrics based on the last 20 observations in the supplied data (X((end – 20 + 1):end,:)
andY((end – 20 + 1):end)
).Incremental functions that track performance metrics within a window use the following process:
Store a buffer of length
Mdl.MetricsWindowSize
for each specified metric, and store a buffer of observation weights.Populate elements of the metrics buffer with the model performance based on batches of incoming observations, and store corresponding observation weights in the weights buffer.
When the buffer is filled, overwrite
Mdl.Metrics.Window
with the weighted average performance in the metrics window. If the buffer is overfilled when the function processes a batch of observations, the latest incomingMdl.MetricsWindowSize
observations enter the buffer, and the earliest observations are removed from the buffer. For example, supposeMdl.MetricsWindowSize
is 20, the metrics buffer has 10 values from a previously processed batch, and 15 values are incoming. To compose the length 20 window, the function uses the measurements from the 15 incoming observations and the latest 5 measurements from the previous batch.
The software omits an observation with a
NaN
score when computing theCumulative
andWindow
performance metric values.
Observation Weights
If the prior class probability distribution is known (in other words, the prior distribution is not empirical), updateMetricsAndFit
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 updateMetricsAndFit
.
References
[1] Bifet, Albert, Ricard Gavaldá, Geoffrey Holmes, and Bernhard Pfahringer. Machine Learning for Data Streams with Practical Example in MOA. Cambridge, MA: The MIT Press, 2007.
Version History
Introduced in R2022a
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