updateMetrics
Update performance metrics in incremental drift-aware learning model given new data
Since R2022b
Description
returns an incremental drift-aware learning model Mdl
= updateMetrics(Mdl
,X
,Y
)Mdl
, which is the
input incremental drift-aware learning model Mdl
modified to contain
the model performance metrics on the incoming predictor and response data,
X
and Y
respectively.
When the input model is warm (Mdl.IsWarm
is
true
), updateMetrics
overwrites previously computed
metrics, stored in the Metrics
property, with the new values. Otherwise,
updateMetrics
stores NaN
values in
Metrics
instead.
The input and output models have the same data type.
uses additional options specified by one or more name-value arguments. For example, you can
specify that the columns of the predictor data matrix correspond to observations, and set
observation weights.Mdl
= updateMetrics(Mdl
,X
,Y
,Name=Value
)
Examples
Compute and Visualize Performance Metrics During Incremental Learning
Create the random concept data using the HelperSineGenerator
and concept drift generator HelperConceptDriftGenerator
.
concept1 = HelperSineGenerator("ClassificationFunction",1,"IrrelevantFeatures",true,"TableOutput",false); concept2 = HelperSineGenerator("ClassificationFunction",3,"IrrelevantFeatures",true,"TableOutput",false); driftGenerator = HelperConceptDriftGenerator(concept1,concept2,15000,1000);
When ClassificationFunction
is 1, HelperSineGenerator
labels all points that satisfy x1 < sin(x2) as 1, otherwise the function labels them as 0. When ClassificationFunction
is 3, this is reversed. That is, HelperSineGenerator
labels all points that satisfy x1 >= sin(x2) as 1, otherwise the function labels them as 0.
HelperConceptDriftGenerator
establishes the concept drift. The object uses a sigmoid function 1./(1+exp(-4*(numobservations-position)./width))
to decide the probability of choosing the first stream when generating data [1]. In this case, the position argument is 15000 and the width argument is 1000. As the number of observations exceeds the position value minus half of the width, the probability of sampling from the first stream when generating data decreases. The sigmoid function allows a smooth transition from one stream to the other. Larger width values indicate a larger transition period where both streams are approximately equally likely to be selected.
Initiate an incremental drift-aware model as follows:
Create an incremental Naive Bayes classification model for binary classification.
Initiate an incremental concept drift detector that uses the Hoeffding's Bounds Drift Detection Method with moving average (HDDMA).
Using the incremental linear model and the concept drift detector, instantiate an incremental drift-aware model. Specify the training period as 5000 observations.
BaseLearner = incrementalClassificationLinear(Solver="sgd"); dd = incrementalConceptDriftDetector("hddma"); idaMdl = incrementalDriftAwareLearner(BaseLearner,DriftDetector=dd,TrainingPeriod=5000);
Preallocate the number of variables in each chunk and number of iterations for creating a stream of data.
numObsPerChunk = 10; numIterations = 4000;
Preallocate the variables for tracking the drift status and drift time, and storing the classification error.
dstatus = zeros(numIterations,1); statusname = strings(numIterations,1); ce = array2table(zeros(numIterations,2),VariableNames=["Cumulative" "Window"]); driftTimes = [];
Simulate a data stream with incoming chunks of 10 observations each and perform incremental drift-aware learning. At each iteration:
Simulate predictor data and labels, and update the drift generator using the helper function
hgenerate
.Call
updateMetrics
to measure the cumulative performance and the performance within a window of observations. Overwrite the previous incremental model with a new one to track performance metrics.Call
fit
to fit the model to the incoming chunk. Overwrite the previous incremental model with a new one fitted to the incoming observations.Track and record the drift status and the classification error for visualization purposes.
rng(12); % For reproducibility for j = 1:numIterations % Generate data [driftGenerator,X,Y] = hgenerate(driftGenerator,numObsPerChunk); % Update performance metrics and fit the model idaMdl = updateMetrics(idaMdl,X,Y); idaMdl = fit(idaMdl,X,Y); % Record drift status and classification error statusname(j) = string(idaMdl.DriftStatus); ce{j,:} = idaMdl.Metrics{"ClassificationError",:}; if idaMdl.DriftDetected dstatus(j) = 2; driftTimes(end+1) = j; elseif idaMdl.WarningDetected dstatus(j) = 1; else dstatus(j) = 0; end end
Plot the cumulative and per window classification error. Mark the warmup and training periods, and where the drift was introduced.
h = plot(ce.Variables); xlim([0 numIterations]) ylim([0 0.08]) ylabel("Classification Error") xlabel("Iteration") xline((idaMdl.BaseLearner.EstimationPeriod+idaMdl.MetricsWarmupPeriod)/numObsPerChunk,"g-.","Estimation + Warmup Period",LineWidth=1.5) xline((idaMdl.MetricsWarmupPeriod+idaMdl.BaseLearner.EstimationPeriod)/numObsPerChunk+driftTimes,"g-.","Estimation Period + Warmup Period",LineWidth=1.5) xline(driftTimes,"m--","Drift",LabelVerticalAlignment="middle",LineWidth=1.5) legend(h,ce.Properties.VariableNames) legend(h,Location="best")
The plot suggests the following:
updateMetrics
computes the performance metrics after the estimation and metrics warm-up period only.updateMetrics
computes the cumulative metrics during each iteration.updateMetrics
computes the window metrics after processing the default metrics window size (200) observations.After drift detection
updateMetrics
function waits foridaMdl.BaseLearner.EstimationPeriod+idaMdl.MetricsWarmupPeriod
observations to start updating model performance metrics again.
Plot the drift status versus the iteration number.
gscatter(1:numIterations,dstatus,statusname,'gmr','o',4,'on',"Iteration","Drift Status","Filled")
Input Arguments
Mdl
— Incremental drift-aware learning model
incrementalDriftAwareLearner
model object
Incremental drift-aware learning model fit to streaming data, specified as an incrementalDriftAwareLearner
model object. You can create
Mdl
using the incrementalDriftAwareLearner
function. For more details, see the object reference page.
X
— Chunk of predictor data
floating-point matrix
Chunk of predictor data to which the model is fit, specified as a floating-point matrix of n observations and Mdl.BaseLearner.NumPredictors
predictor variables.
When Mdl.BaseLearner
accepts the ObservationsIn
name-value argument, the value of ObservationsIn
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 responses (or labels) Y
and the number of observations in X
must be equal; Y(
is the response (or label) of observation j (row or column) in j
)X
.
Note
If
Mdl.BaseLearner.NumPredictors
= 0,updateMetrics
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.BaseLearner.NumPredictors
,updateMetrics
issues an error.updateMetrics
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 observed responses (or labels)
floating-point vector | categorical array | character array | string array | logical vector | cell array of character vectors
Chunk of responses (or labels) to which the model is fit, specified as one of the following:
Floating-point vector of n elements for regression models, where n is the number of rows in
X
.Categorical, character, or string array, logical vector, or cell array of character vectors for classification models. If
Y
is a character array, it must have one class label per row. Otherwise,Y
must be a vector with n elements.
The length of Y
and the number of observations in
X
must be equal;
Y(
is the response (or label) of
observation j (row or column) in j
)X
.
For classification problems:
When
Mdl.BaseLearner.ClassNames
is nonempty, the following conditions apply:If
Y
contains a label that is not a member ofMdl.BaseLearner.ClassNames
,updateMetrics
issues an error.The data type of
Y
andMdl.BaseLearner.ClassNames
must be the same.
When
Mdl.BaseLearner.ClassNames
is empty,updateMetrics
infersMdl.BaseLearner.ClassNames
from data.
Data Types: single
| double
| categorical
| char
| string
| logical
| cell
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 the comma-separated pair
consisting of 'ObservationsIn'
and 'columns'
or
'rows'
.
updateMetrics
supports ObservationsIn
only
if Mdl.BaseLearner
supports ObservationsIn
name-value argument.
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. updateMetrics
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)
Example: Weights=w
Data Types: double
| single
Output Arguments
Mdl
— Updated incremental drift-aware learning model
incrementalDriftAwareLearner
model object
Updated incremental drift-aware learning model, returned as an incremental learning
model object of the same data type as the input model Mdl
,
incrementalDriftAwareLearner
.
If the model is not warm, updateMetrics
does not compute
performance metrics. As a result, the Metrics
property of
Mdl
remains completely composed of NaN
values.
If the model is warm, updateMetrics
computes the cumulative and
window performance metrics on the new data X
and
Y
, and overwrites the corresponding elements of
Mdl.Metrics
. All other properties of the input model
Mdl
carry over to the output model Mdl
. For
more details, see Performance Metrics.
Algorithms
Performance Metrics
The
updateMetrics
andupdateMetricsAndFit
functions track model performance metrics (Metrics
) from new data when the incremental model is warm (Mdl.BaseLearner.IsWarm
property). An incremental model becomes warm afterfit
orupdateMetricsAndFit
fits the incremental model toMetricsWarmupPeriod
observations, which is the metrics warm-up period.If
Mdl.BaseLearner.EstimationPeriod
> 0, the 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
Metrics
property of the incremental model 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 until a model reset.Window
— The functions compute metrics based on all observations within a window determined by theMetricsWindowSize
name-value argument.MetricsWindowSize
also determines the frequency at which the software updatesWindow
metrics. For example, ifMetricsWindowSize
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
MetricsWindowSize
amount of values for each specified metric, and store the same amount of observation weights.Populate elements of the metrics values with the model performance based on batches of incoming observations, and store the corresponding observation weights.
When the window of observations is filled, overwrite
Mdl.Metrics.Window
with the weighted average performance in the metrics window. If the window is overfilled when the function processes a batch of observations, the latest incomingMetricsWindowSize
observations are stored, and the earliest observations are removed from the window. For example, supposeMetricsWindowSize
is 20, there are 10 stored values from a previously processed batch, and 15 values are incoming. To compose the length 20 window, the functions use 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), updateMetrics
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 updateMetrics
.
Version History
Introduced in R2022b
See Also
predict
| perObservationLoss
| fit
| incrementalDriftAwareLearner
| updateMetricsAndFit
| loss
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