FeatureTransformer

Generated feature transformations

Since R2021a

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

describe	Describe generated features
transform	Transform new data using generated features

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