transform {CatEncoders}

transform transforms a new data set using the fitted encoder

Description

transform transforms a new data set using the fitted encoder

Usage

transform(enc, ...)

## S4 method for signature 'LabelEncoder.Numeric'
transform(enc, y)

## S4 method for signature 'LabelEncoder.Character'
transform(enc, y)

## S4 method for signature 'LabelEncoder.Factor'
transform(enc, y)

## S4 method for signature 'OneHotEncoder'
transform(enc, X, sparse = TRUE,
  new.feature.error = TRUE)

Arguments

enc

A fitted encoder, i.e., LabelEncoder or OneHotEncoder

...

Additional argument list

y

A vector of character, factor or numeric values

X

A data.frame or matrix

sparse

If TRUE then return a sparse matrix, default = TRUE

new.feature.error

If TRUE then throw an error for new feature values; otherwise the new feature values are ignored, default = TRUE

Value

If enc is an OneHotEncoder, the returned value is a sparse or dense matrix. If enc is a LabelEncoder, the returned value is a vector.

Examples

# matrix X
X1 <- matrix(c(0, 1, 0, 1, 0, 1, 2, 0, 3, 0, 1, 2),c(4,3),byrow=FALSE)
oenc <- OneHotEncoder.fit(X1)
z <- transform(oenc,X1,sparse=TRUE)
# return a sparse matrix
print(z)

# data.frame X
X2 <- cbind(data.frame(X1),X4=c('a','b','d',NA),X5=factor(c(1,2,3,1)))
oenc <- OneHotEncoder.fit(X2)
z <- transform(oenc,X2,sparse=FALSE)
# return a dense matrix
print(z)

# factor vector y
y <- factor(c('a','d','e',NA),exclude=NULL)
lenc <- LabelEncoder.fit(y)
# new values are transformed to NA
z <- transform(lenc,factor(c('d','d',NA,'f')))
print(z)

# character vector y
y <- c('a','d','e',NA)
lenc <- LabelEncoder.fit(y)
# new values are transformed to NA
z <- transform(lenc,c('d','d',NA,'f'))
print(z)

# numeric vector y
set.seed(123)
y <- sample(c(1:10,NA),5)
lenc <- LabelEncoder.fit(y)
# new values are transformed to NA
z <-transform(lenc,sample(c(1:10,NA),5))
print(z)