kernlab: Interfaces for kernlab package for data science pipelines.

Description Usage Arguments Details Value Author(s) Examples

Description

Interfaces to kernlab functions that can be used in a pipeline implemented by magrittr.

Usage

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Arguments

data

data frame, tibble, list, ...

...

Other arguments passed to the corresponding interfaced function.

Details

Interfaces call their corresponding interfaced function.

Value

Object returned by interfaced function.

Author(s)

Roberto Bertolusso

Examples

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## Not run: 
library(intubate)
library(magrittr)
library(kernlab)

##  ntbt_gausspr: Gaussian processes for regression and classification
data(iris)

## Original function to interface
gausspr(Species ~ ., data = iris, var = 2)

## The interface puts data as first parameter
ntbt_gausspr(iris, Species ~ ., var = 2)

## so it can be used easily in a pipeline.
iris %>%
  ntbt_gausspr(Species ~ ., var = 2)


## ntbt_kfa: Kernel Feature Analysis
data(promotergene)

## Original function to interface
kfa(~ ., data = promotergene)

## The interface puts data as first parameter
ntbt_kfa(promotergene, ~ .)

## so it can be used easily in a pipeline.
promotergene %>%
  ntbt_kfa(~ .)


## ntbt_kha: Kernel Principal Components Analysis
data(iris)
test <- sample(1:150,70)

## Original function to interface
kpc <- kha(~ ., data = iris[-test, -5], kernel = "rbfdot", kpar = list(sigma=0.2),
           features = 2, eta = 0.001, maxiter = 65)
pcv(kpc)

## The interface puts data as first parameter
kpc <- ntbt_kha(iris[-test, -5], ~ ., kernel = "rbfdot", kpar = list(sigma=0.2),
                features = 2, eta = 0.001, maxiter = 65)
pcv(kpc)

## so it can be used easily in a pipeline.
iris[-test, -5] %>%
  ntbt_kha(~ ., kernel = "rbfdot", kpar = list(sigma=0.2),
           features = 2, eta = 0.001, maxiter = 65) %>%
  pcv()


## ntbt_kkmeans: Kernel k-means
## Original function to interface
sc <- kkmeans(~ ., data = iris[-test, -5], centers = 3)
centers(sc)

## The interface puts data as first parameter
sc <- ntbt_kkmeans(iris[-test, -5], ~ ., centers = 3)
centers(sc)

## so it can be used easily in a pipeline.
iris[-test, -5] %>%
  ntbt_kkmeans(~ ., centers = 3) %>%
  centers()


## ntbt_kpca: Kernel Principal Components Analysis
data(iris)
test <- sample(1:150,20)

## Original function to interface
kpc <- kpca(~ ., data = iris[-test, -5], kernel = "rbfdot",
            kpar = list(sigma = 0.2), features = 2)
pcv(kpc)

## The interface puts data as first parameter
kpc <- ntbt_kpca(iris[-test, -5], ~ ., kernel = "rbfdot",
                 kpar = list(sigma = 0.2), features = 2)
pcv(kpc)

## so it can be used easily in a pipeline.
iris[-test, -5] %>%
  ntbt_kpca(~ ., kernel = "rbfdot",
            kpar = list(sigma = 0.2), features = 2) %>%
  pcv()


## ntbt_kqr: Kernel Quantile Regression
## Not found example using formula interface, and I am
## completely ignorant to construct one.
x <- sort(runif(300))
y <- sin(pi*x) + rnorm(300,0,sd=exp(sin(2*pi*x)))

dkqr <- data.frame(x, y)

## Original function to interface
set.seed(1)
kqr(x, y, tau = 0.5, C = 0.15)

## The interface puts data as first parameter
set.seed(1)
ntbt_kqr(dkqr, x, y, tau = 0.5, C = 0.15)

## so it can be used easily in a pipeline.
set.seed(1)
dkqr %>%
  ntbt_kqr(x, y, tau = 0.5, C = 0.15)


## ntbt_ksvm: Support Vector Machines
data(spam)
index <- sample(1:dim(spam)[1])
spamtrain <- spam[index[1:floor(dim(spam)[1]/2)], ]
spamtest <- spam[index[((ceiling(dim(spam)[1]/2)) + 1):dim(spam)[1]], ]

## Original function to interface
set.seed(1)
ksvm(type ~ ., data = spamtrain, kernel = "rbfdot",
     kpar = list(sigma = 0.05), C = 5, cross = 3)

## The interface puts data as first parameter
set.seed(1)
ntbt_ksvm(spamtrain, type ~ ., kernel = "rbfdot",
          kpar = list(sigma = 0.05), C = 5, cross = 3)

## so it can be used easily in a pipeline.
set.seed(1)
spamtrain %>%
  ntbt_ksvm(type ~ ., kernel = "rbfdot",
            kpar = list(sigma = 0.05), C = 5, cross = 3)


## ntbt_lssvm: Least Squares Support Vector Machine
data(iris)

## Original function to interface
set.seed(1)
lssvm(Species ~ ., data = iris)

## The interface puts data as first parameter
set.seed(1)
ntbt_lssvm(iris, Species ~ .)

## so it can be used easily in a pipeline.
set.seed(1)
iris %>%
  ntbt_lssvm(Species ~ .)


## ntbt_rvm: Relevance Vector Machine
## Not found example using formula interface, and I am
## completely ignorant to construct one.
x <- seq(-20,20,0.1)
y <- sin(x)/x + rnorm(401,sd=0.05)

drvm <- data.frame(x, y)

## Original function to interface
set.seed(1)
rvm(x, y, tau = 0.5, C = 0.15)

## The interface puts data as first parameter
set.seed(1)
ntbt_rvm(drvm, x, y, tau = 0.5, C = 0.15)

## so it can be used easily in a pipeline.
set.seed(1)
drvm %>%
  ntbt_rvm(x, y, tau = 0.5, C = 0.15)


## ntbt_sigest: Hyperparameter estimation for the Gaussian Radial Basis kernel
data(promotergene)

## Original function to interface
set.seed(1)
sigest(Class ~ ., data = promotergene)

## The interface puts data as first parameter
set.seed(1)
ntbt_sigest(promotergene, Class ~ .)

## so it can be used easily in a pipeline.
set.seed(1)
promotergene %>%
  ntbt_sigest(Class ~ .)

## ntbt_specc: Spectral Clustering
## Not found example using formula interface, and I am
## completely ignorant to construct one.

## End(Not run)

intubate documentation built on May 2, 2019, 2:46 p.m.