FeatureTransformer
Description
A FeatureTransformer
object contains information about the
feature transformations generated from a training data set. To better understand the generated
features, you can use the describe
object
function. To apply the same training set feature transformations to a test set, you can use
the transform
object
function.
Creation
Create a FeatureTransformer
object by using the gencfeatures
or
genrfeatures
function.
Properties
Type
— Type of model
'classification'
| 'regression'
This property is read-only.
Type of model, returned as 'classification'
or
'regression'
.
TargetLearner
— Expected learner type
'linear'
| 'bag'
| 'gaussian-svm'
This property is read-only.
Expected learner type, returned as 'linear'
,
'bag'
, or 'gaussian-svm'
. The software creates
and selects new features assuming that they will be used to train a linear model, a
bagged ensemble, or a support vector machine (SVM) model with a Gaussian kernel,
respectively.
NumEngineeredFeatures
— Number of engineered features
nonnegative scalar
This property is read-only.
Number of engineered features stored in FeatureTransformer
,
returned as a nonnegative scalar.
Data Types: double
NumOriginalFeatures
— Number of original features
nonnegative scalar
This property is read-only.
Number of original features stored in FeatureTransformer
, returned
as a nonnegative scalar.
Data Types: double
TotalNumFeatures
— Total number of features
nonnegative scalar
This property is read-only.
Total number of features stored in FeatureTransformer
, returned as
a nonnegative scalar. TotalNumFeatures
equals the sum of
NumEngineeredFeatures
and
NumOriginalFeatures
.
Data Types: double
Object Functions
Examples
Generate and Inspect Features for Regression Problem
Generate features from a table of predictor data by using genrfeatures
. Inspect the generated features by using the describe
object function.
Read power outage data into the workspace as a table. Remove observations with missing values, and display the first few rows of the table.
outages = readtable("outages.csv");
Tbl = rmmissing(outages);
head(Tbl)
Region OutageTime Loss Customers RestorationTime Cause _____________ ________________ ______ __________ ________________ ___________________ {'SouthWest'} 2002-02-01 12:18 458.98 1.8202e+06 2002-02-07 16:50 {'winter storm' } {'SouthEast'} 2003-02-07 21:15 289.4 1.4294e+05 2003-02-17 08:14 {'winter storm' } {'West' } 2004-04-06 05:44 434.81 3.4037e+05 2004-04-06 06:10 {'equipment fault'} {'MidWest' } 2002-03-16 06:18 186.44 2.1275e+05 2002-03-18 23:23 {'severe storm' } {'West' } 2003-06-18 02:49 0 0 2003-06-18 10:54 {'attack' } {'NorthEast'} 2003-07-16 16:23 239.93 49434 2003-07-17 01:12 {'fire' } {'MidWest' } 2004-09-27 11:09 286.72 66104 2004-09-27 16:37 {'equipment fault'} {'SouthEast'} 2004-09-05 17:48 73.387 36073 2004-09-05 20:46 {'equipment fault'}
Some of the variables, such as OutageTime
and RestorationTime
, have data types that are not supported by regression model training functions like fitrensemble
.
Generate 25 features from the predictors in Tbl
that can be used to train a bagged ensemble. Specify the Loss
table variable as the response.
rng("default") % For reproducibility Transformer = genrfeatures(Tbl,"Loss",25,TargetLearner="bag")
Transformer = FeatureTransformer with properties: Type: 'regression' TargetLearner: 'bag' NumEngineeredFeatures: 22 NumOriginalFeatures: 3 TotalNumFeatures: 25
The Transformer
object contains the information about the generated features and the transformations used to create them.
To better understand the generated features, use the describe
object function.
Info = describe(Transformer)
Info=25×4 table
Type IsOriginal InputVariables Transformations
___________ __________ ___________________________ ___________________________________________________________________
c(Region) Categorical true Region "Variable of type categorical converted from a cell data type"
Customers Numeric true Customers ""
c(Cause) Categorical true Cause "Variable of type categorical converted from a cell data type"
kmd2 Numeric false Customers "Euclidean distance to centroid 2 (kmeans clustering with k = 10)"
kmd1 Numeric false Customers "Euclidean distance to centroid 1 (kmeans clustering with k = 10)"
kmd4 Numeric false Customers "Euclidean distance to centroid 4 (kmeans clustering with k = 10)"
kmd5 Numeric false Customers "Euclidean distance to centroid 5 (kmeans clustering with k = 10)"
kmd9 Numeric false Customers "Euclidean distance to centroid 9 (kmeans clustering with k = 10)"
cos(Customers) Numeric false Customers "cos( )"
RestorationTime-OutageTime Numeric false OutageTime, RestorationTime "Elapsed time in seconds between OutageTime and RestorationTime"
kmd6 Numeric false Customers "Euclidean distance to centroid 6 (kmeans clustering with k = 10)"
kmi Categorical false Customers "Cluster index encoding (kmeans clustering with k = 10)"
kmd7 Numeric false Customers "Euclidean distance to centroid 7 (kmeans clustering with k = 10)"
kmd3 Numeric false Customers "Euclidean distance to centroid 3 (kmeans clustering with k = 10)"
kmd10 Numeric false Customers "Euclidean distance to centroid 10 (kmeans clustering with k = 10)"
hour(RestorationTime) Numeric false RestorationTime "Hour of the day"
⋮
The first three generated features are original to Tbl
, although the software converts the original Region
and Cause
variables to categorical
variables.
Info(1:3,:) % describe(Transformer,1:3)
ans=3×4 table
Type IsOriginal InputVariables Transformations
___________ __________ ______________ ______________________________________________________________
c(Region) Categorical true Region "Variable of type categorical converted from a cell data type"
Customers Numeric true Customers ""
c(Cause) Categorical true Cause "Variable of type categorical converted from a cell data type"
The OutageTime
and RestorationTime
variables are not included as generated features because they are datetime
variables, which cannot be used to train a bagged ensemble model. However, the software derives some generated features from these variables, such as the tenth feature RestorationTime-OutageTime
.
Info(10,:) % describe(Transformer,10)
ans=1×4 table
Type IsOriginal InputVariables Transformations
_______ __________ ___________________________ ________________________________________________________________
RestorationTime-OutageTime Numeric false OutageTime, RestorationTime "Elapsed time in seconds between OutageTime and RestorationTime"
Some generated features are a combination of multiple transformations. For example, the software generates the nineteenth feature fenc(c(Cause))
by converting the Cause
variable to a categorical variable with 10 categories and then calculating the frequency of the categories.
Info(19,:) % describe(Transformer,19)
ans=1×4 table
Type IsOriginal InputVariables Transformations
_______ __________ ______________ ____________________________________________________________________________________________________________
fenc(c(Cause)) Numeric false Cause "Variable of type categorical converted from a cell data type -> Frequency encoding (number of levels = 10)"
Train Model Using Subset of Generated Features
Train a linear classifier using only the numeric generated features returned by gencfeatures
.
Load the patients
data set. Create a table from a subset of the variables.
load patients Tbl = table(Age,Diastolic,Height,SelfAssessedHealthStatus, ... Smoker,Systolic,Weight,Gender);
Partition the data into training and test sets. Use approximately 70% of the observations as training data, and 30% of the observations as test data. Partition the data using cvpartition
.
rng("default")
c = cvpartition(Tbl.Gender,Holdout=0.30);
TrainTbl = Tbl(training(c),:);
TestTbl = Tbl(test(c),:);
Use the training data to generate 25 new features. Specify the minimum redundancy maximum relevance (MRMR) feature selection method for selecting new features.
Transformer = gencfeatures(TrainTbl,"Gender",25, ... FeatureSelectionMethod="mrmr")
Transformer = FeatureTransformer with properties: Type: 'classification' TargetLearner: 'linear' NumEngineeredFeatures: 23 NumOriginalFeatures: 2 TotalNumFeatures: 25
Inspect the generated features.
Info = describe(Transformer)
Info=25×4 table
Type IsOriginal InputVariables Transformations
___________ __________ ________________________ __________________________________________________________________________________________
zsc(Weight) Numeric true Weight "Standardization with z-score (mean = 153.1571, std = 26.8229)"
eb5(Weight) Categorical false Weight "Equal-width binning (number of bins = 5)"
c(SelfAssessedHealthStatus) Categorical true SelfAssessedHealthStatus "Variable of type categorical converted from a cell data type"
zsc(sqrt(Systolic)) Numeric false Systolic "sqrt( ) -> Standardization with z-score (mean = 11.086, std = 0.29694)"
zsc(sin(Systolic)) Numeric false Systolic "sin( ) -> Standardization with z-score (mean = -0.1303, std = 0.72575)"
zsc(Systolic./Weight) Numeric false Systolic, Weight "Systolic ./ Weight -> Standardization with z-score (mean = 0.82662, std = 0.14555)"
zsc(Age+Weight) Numeric false Age, Weight "Age + Weight -> Standardization with z-score (mean = 191.1143, std = 28.6976)"
zsc(Age./Weight) Numeric false Age, Weight "Age ./ Weight -> Standardization with z-score (mean = 0.25424, std = 0.062486)"
zsc(Diastolic.*Weight) Numeric false Diastolic, Weight "Diastolic .* Weight -> Standardization with z-score (mean = 12864.6857, std = 2731.1613)"
q6(Height) Categorical false Height "Equiprobable binning (number of bins = 6)"
zsc(Systolic+Weight) Numeric false Systolic, Weight "Systolic + Weight -> Standardization with z-score (mean = 276.1429, std = 28.7111)"
zsc(Diastolic-Weight) Numeric false Diastolic, Weight "Diastolic - Weight -> Standardization with z-score (mean = -69.4286, std = 26.2411)"
zsc(Age-Weight) Numeric false Age, Weight "Age - Weight -> Standardization with z-score (mean = -115.2, std = 27.0113)"
zsc(Height./Weight) Numeric false Height, Weight "Height ./ Weight -> Standardization with z-score (mean = 0.44797, std = 0.067992)"
zsc(Height.*Weight) Numeric false Height, Weight "Height .* Weight -> Standardization with z-score (mean = 10291.0714, std = 2111.9071)"
zsc(Diastolic+Weight) Numeric false Diastolic, Weight "Diastolic + Weight -> Standardization with z-score (mean = 236.8857, std = 29.2439)"
⋮
Transform the training and test sets, but retain only the numeric predictors.
numericIdx = (Info.Type == "Numeric");
NewTrainTbl = transform(Transformer,TrainTbl,numericIdx);
NewTestTbl = transform(Transformer,TestTbl,numericIdx);
Train a linear model using the transformed training data. Visualize the accuracy of the model's test set predictions by using a confusion matrix.
Mdl = fitclinear(NewTrainTbl,TrainTbl.Gender); testLabels = predict(Mdl,NewTestTbl); confusionchart(TestTbl.Gender,testLabels)
Version History
Introduced in R2021a
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