| Type: | Package |
| Title: | Encoders for Categorical Variables |
| Version: | 0.1.1 |
| Author: | nl zhang |
| Maintainer: | nl zhang <setseed2016@gmail.com> |
| Description: | Contains some commonly used categorical variable encoders, such as 'LabelEncoder' and 'OneHotEncoder'. Inspired by the encoders implemented in Python 'sklearn.preprocessing' package (see http://scikit-learn.org/stable/modules/preprocessing.html). |
| License: | GPL-2 | GPL-3 |
| LazyData: | TRUE |
| Imports: | Matrix (≥ 1.2-6), data.table (≥ 1.9.6), methods |
| RoxygenNote: | 5.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2017-03-08 03:14:25 UTC; nl |
| Repository: | CRAN |
| Date/Publication: | 2017-03-08 08:22:03 |
An S4 class to represent a LabelEncoder.
Description
An S4 class to represent a LabelEncoder.
Slots
typeA character to denote the input type, either character, factor or numeric
mappingA data.frame to store the mapping table
An S4 class to represent a LabelEncoder with character input.
Description
An S4 class to represent a LabelEncoder with character input.
Slots
classesA character vector to store the unique values of classes
An S4 class to represent a LabelEncoder with factor input.
Description
An S4 class to represent a LabelEncoder with factor input.
Slots
classesA factor vector to store the unique values of classes
An S4 class to represent a LabelEncoder with numeric input.
Description
An S4 class to represent a LabelEncoder with numeric input.
Slots
classesA numeric vector to store the unique values of classes
LabelEncoder.fit fits a LabelEncoder object
Description
LabelEncoder.fit fits a LabelEncoder object
Usage
LabelEncoder.fit(y)
Arguments
y |
A vector of characters, factors, or numerics, which can include NA as well |
Value
Returns an object of S4 class LabelEncoder.
Examples
# factor 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 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 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)
An S4 class to represent a OneHotEncoder
Description
An S4 class to represent a OneHotEncoder
Slots
n_columnsAn integer value to store the number of columns of input data
n_valuesA numeric vector to store the number of unique values in each column of input data
column_encodersA list that stores the LabelEncoder for each column of input data
OneHotEncoder.fit fits an OneHotEncoder object
Description
OneHotEncoder.fit fits an OneHotEncoder object
Usage
OneHotEncoder.fit(X)
Arguments
X |
A matrix or data.frame, which can include NA |
Value
Returns an object of S4 class OneHotEncoder
Examples
# matrix input
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
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)
inverse.transform transforms an integer vector back to the original vector
Description
inverse.transform transforms an integer vector back to the original vector
Usage
inverse.transform(enc, z)
## S4 method for signature 'LabelEncoder,numeric'
inverse.transform(enc, z)
Arguments
enc |
A fitted LabelEncoder |
z |
A vector of integers |
Value
A vector of characters, factors or numerics.
Examples
# 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)
inverse.transform(lenc,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('a','d',NA,'f')))
inverse.transform(lenc,z)
# numeric vector y
set.seed(123)
y <- c(1:10,NA)
lenc <- LabelEncoder.fit(y)
# new values are transformed to NA
newy <- sample(c(1:10,NA),5)
print(newy)
z <-transform(lenc,newy)
inverse.transform(lenc, z)
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)