updateMetricsAndFit
Update performance metrics in kernel incremental learning model given new data and train model
Since R2022a
Description
Given streaming data, updateMetricsAndFit
first evaluates the
performance of a configured incremental learning model for kernel regression (incrementalRegressionKernel
object) or binary kernel classification (incrementalClassificationKernel
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 by following this procedure:
The input and output models have the same data type.
Examples
Update Performance Metrics and Train Model on Data Stream
Create an incremental kernel model for binary classification by calling incrementalClassificationKernel
directly. Track the model performance and fit the model to streaming data in one call by using updateMetricsAndFit
.
Create a default incremental kernel model for binary classification.
Mdl = incrementalClassificationKernel()
Mdl = incrementalClassificationKernel IsWarm: 0 Metrics: [1x2 table] ClassNames: [1x0 double] ScoreTransform: 'none' NumExpansionDimensions: 0 KernelScale: 1
Mdl
is an incrementalClassificationKernel
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.
Responses can be one of five classes: Sitting, Standing, Walking, Running, or Dancing. Dichotomize the response by identifying whether the subject is moving (actid
> 2).
Y = Y > 2;
Fit the incremental model to the training data by using the updateMetricsAndFit
function. 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 cumulative metrics, window metrics, and number of training observations to see how they evolve during incremental learning.
% Preallocation numObsPerChunk = 50; nchunk = floor(n/numObsPerChunk); ce = array2table(zeros(nchunk,2),VariableNames=["Cumulative","Window"]); numtrainobs = [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)); ce{j,:} = Mdl.Metrics{"ClassificationError",:}; numtrainobs(j) = Mdl.NumTrainingObservations; end
Mdl
is an incrementalClassificationKernel
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 number of observations and performance metrics evolve during training, plot them on separate tiles.
t = tiledlayout(2,1); nexttile plot(numtrainobs) ylabel("Number of Training Observations") xlim([0 nchunk]) nexttile plot(ce.Variables) xlim([0 nchunk]) ylabel("Classification Error") xline((Mdl.EstimationPeriod + Mdl.MetricsWarmupPeriod)/numObsPerChunk,"--"); legend(ce.Properties.VariableNames) xlabel(t,"Iteration")
The plot suggests that updateMetricsAndFit
does the following:
Fit the model 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 Observation Weights
Train a kernel regression model by using fitrkernel
, and convert it to an incremental learner by using incrementalLearner
. Track the model performance, and fit the model to streaming data in one call by using updateMetricsAndFit
. Specify the observation weights when you call updateMetricsAndFit
.
Load and Preprocess Data
Load the 2015 NYC housing data set, and shuffle the data. For more details on the data, see NYC Open Data.
load NYCHousing2015 rng(1) % For reproducibility n = size(NYCHousing2015,1); idxshuff = randsample(n,n); NYCHousing2015 = NYCHousing2015(idxshuff,:);
Suppose that the data collected from Manhattan (BOROUGH
= 1
) was collected using a new method that doubles its quality. Create a weight variable that attributes 2 to observations collected from Manhattan, and 1 to all other observations.
n = size(NYCHousing2015,1); NYCHousing2015.W = ones(n,1) + (NYCHousing2015.BOROUGH == 1);
Extract the response variable SALEPRICE
from the table. For numerical stability, scale SALEPRICE
by 1e6
.
Y = NYCHousing2015.SALEPRICE/1e6; NYCHousing2015.SALEPRICE = [];
To reduce computational cost for this example, remove the NEIGHBORHOOD
column, which contains a categorical variable with 254 categories.
NYCHousing2015.NEIGHBORHOOD = [];
Create dummy variable matrices from the other categorical predictors.
catvars = ["BOROUGH","BUILDINGCLASSCATEGORY"]; dumvarstbl = varfun(@(x)dummyvar(categorical(x)),NYCHousing2015, ... InputVariables=catvars); dumvarmat = table2array(dumvarstbl); NYCHousing2015(:,catvars) = [];
Treat all other numeric variables in the table as predictors of sales price. Concatenate the matrix of dummy variables to the rest of the predictor data.
idxnum = varfun(@isnumeric,NYCHousing2015,OutputFormat="uniform");
X = [dumvarmat NYCHousing2015{:,idxnum}];
Train Kernel Regression Model
Fit a kernel regression model to a random sample of half the data.
idxtt = randsample([true false],n,true); TTMdl = fitrkernel(X(idxtt,:),Y(idxtt),Weights=NYCHousing2015.W(idxtt))
TTMdl = RegressionKernel ResponseName: 'Y' Learner: 'svm' NumExpansionDimensions: 2048 KernelScale: 1 Lambda: 2.1977e-05 BoxConstraint: 1 Epsilon: 0.0547
TTMdl
is a RegressionKernel
model object representing a traditionally trained kernel regression model.
Convert Trained Model
Convert the traditionally trained kernel regression model to a model for incremental learning.
IncrementalMdl = incrementalLearner(TTMdl)
IncrementalMdl = incrementalRegressionKernel IsWarm: 1 Metrics: [1x2 table] ResponseTransform: 'none' NumExpansionDimensions: 2048 KernelScale: 1
IncrementalMdl
is an incrementalRegressionKernel
model object. All its properties are read-only.
Track Performance Metrics and Fit Model
Perform incremental learning on the rest of the data by using the updateMetricsAndFit
function. At each iteration:
Simulate a data stream by processing a chunk of 500 observations.
Call
updateMetricsAndFit
to update the cumulative and window epsilon insensitive loss 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 the observation weights.Store the losses.
% Preallocation idxil = ~idxtt; nil = sum(idxil); numObsPerChunk = 500; nchunk = floor(nil/numObsPerChunk); ei = array2table(zeros(nchunk,2),VariableNames=["Cumulative","Window"]); Xil = X(idxil,:); Yil = Y(idxil); Wil = NYCHousing2015.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)); ei{j,:} = IncrementalMdl.Metrics{"EpsilonInsensitiveLoss",:}; end
IncrementalMdl
is an incrementalRegressionKernel
model object trained on all the data in the stream.
Plot a trace plot of the performance metrics.
plot(ei.Variables) xlim([0 nchunk]) ylabel("Epsilon Insensitive Loss") legend(ei.Properties.VariableNames) xlabel("Iteration")
The cumulative loss gradually changes with each iteration (chunk of 500 observations), whereas the window loss jumps. Because the metrics window is 200 by default, updateMetricsAndFit
measures the performance based on the latest 200 observations in each 500 observation chunk.
Input Arguments
Mdl
— Incremental learning model
incrementalClassificationKernel
model object | incrementalRegressionKernel
model object
Incremental learning model whose performance is measured and then the model is fit
to data, specified as an incrementalClassificationKernel
or incrementalRegressionKernel
model object. You can create
Mdl
directly or by converting a supported, traditionally trained
machine learning model using the incrementalLearner
function. For
more details, see the corresponding reference page.
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 length of the observation labels Y
and the number of observations in X
must be equal; Y(
is the label of observation j (row) 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 responses (labels)
categorical array | character array | string array | logical vector | floating-point vector | cell array of character vectors
Chunk of responses (labels), specified as a categorical, character, or string array, a logical or floating-point vector, or a cell array of character vectors for classification problems; or a floating-point vector for regression problems.
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) in j
)X
.
For classification problems:
updateMetricsAndFit
supports binary classification only.When the
ClassNames
property of the input modelMdl
is nonempty, the following conditions apply:If
Y
contains a label that is not a member ofMdl.ClassNames
,updateMetricsAndFit
issues an error.The data type of
Y
andMdl.ClassNames
must be the same.
Data Types: char
| string
| cell
| categorical
| logical
| single
| double
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, the number of
observations in X
.
By default, weights
is
ones(
.n
,1)
For more details, including normalization schemes, see Observation Weights.
Data Types: double
| single
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 inX
.The chunk size n and the stochastic gradient descent (SGD) hyperparameter mini-batch size (
Mdl.SolverOptions.BatchSize
) can be different values, and n does not have to be an exact multiple of the mini-batch size.updateMetricsAndFit
uses theBatchSize
observations when it applies SGD for each learning cycle. The number of observations in the last mini-batch for the last learning cycle can be less than or equal toMdl.SolverOptions.BatchSize
.
Output Arguments
Mdl
— Updated incremental learning model
incrementalClassificationKernel
model object | incrementalRegressionKernel
model object
Updated incremental learning model, returned as an incremental learning model object
of the same data type as the input model Mdl
, either incrementalClassificationKernel
or incrementalRegressionKernel
.
When you call updateMetricsAndFit
, the following conditions
apply:
If the model is not warm,
updateMetricsAndFit
does not compute performance metrics. As a result, theMetrics
property ofMdl
remains completely composed ofNaN
values. For more details, see Performance Metrics.If
Mdl.EstimationPeriod
> 0,updateMetricsAndFit
estimates hyperparameters using the firstMdl.EstimationPeriod
observations passed to it; the function does not train the input model using that data. However, if an incoming chunk of n observations is greater than or equal to the number of observations remaining in the estimation period m,updateMetricsAndFit
estimates hyperparameters using the first n – m observations, and fits the input model to the remaining m observations. Consequently, the software updates model parameters, hyperparameter properties, and recordkeeping properties such asNumTrainingObservations
.
For classification problems, if the ClassNames
property of the input model Mdl
is an empty array, updateMetricsAndFit
sets the ClassNames
property of the output model Mdl
to unique(Y)
.
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
). An incremental model is warm afterfit
orupdateMetricsAndFit
fits the incremental model toMdl.MetricsWarmupPeriod
observations, which is the metrics warm-up period.If
Mdl.EstimationPeriod
> 0, thefit
andupdateMetricsAndFit
functions estimate hyperparameters before fitting the model to data. Therefore, the functions must process an additionalEstimationPeriod
observations before the model starts the metrics warm-up period.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
prediction (score for classification and response for regression) when computing theCumulative
andWindow
performance metric values.
Observation Weights
For classification problems, 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 observation weights are the respective prior class probabilities by default.
For regression problems or 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
See Also
Objects
Functions
Topics
- Incremental Learning Overview
- Configure Incremental Learning Model
- Implement Incremental Learning for Classification Using Succinct Workflow
- Initialize Incremental Learning Model from Logistic Regression Model Trained in Classification Learner
- Initialize Incremental Learning Model from SVM Regression Model Trained in Regression Learner
MATLAB 명령
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