samleenz/exprso

An R package to rapidly build and deploy supervised machine learning models

Quick start

Welcome to the exprso GitHub page! Let's get started.

library(devtools)
devtools::install_github("tpq/exprso")
library(exprso)

Importing data

To import data, we use the exprso function. This function has two arguments.

data(iris)
array <- exprso(iris[1:80, 1:4], iris[1:80, 5])

## [1] "Preparing data for binary classification."

Pre-processing data

Functions with a mod prefix pre-process the data.

array <- modTransform(array)
array <- modNormalize(array, c(1, 2))

Split data

Functions with a split prefix split the data into training and test sets.

arrays <- splitSample(array, percent.include = 67)
array.train <- arrays$array.train
array.test <- arrays$array.valid

Select features

Functions with a fs prefix select features.

array.train <- fsStats(array.train, top = 0, how = "t.test")

Build models

Functions with a build prefix build models.

mach <- buildSVM(array.train,
                 top = 50,
                 kernel = "linear",
                 cost = 1)

## Setting probability to TRUE (forced behavior, cannot override)...
## Setting cross to 0 (forced behavior, cannot override)...

pred <- predict(mach, array.train)

## Individual classifier performance:
## Arguments not provided in an ROCR AUC format. Calculating accuracy outside of ROCR...
## Classification confusion table:
##          actual
## predicted Control Case
##   Control      29    0
##   Case          0   25
##   acc sens spec
## 1   1    1    1

pred <- predict(mach, array.test)

## Individual classifier performance:
## Arguments not provided in an ROCR AUC format. Calculating accuracy outside of ROCR...
## Classification confusion table:
##          actual
## predicted Control Case
##   Control      21    0
##   Case          0    5
##   acc sens spec
## 1   1    1    1

calcStats(pred)

Deploy pipelines

Functions with a pl prefix deploy high-throughput learning pipelines.

pl <- plGrid(array.train,
             array.test,
             how = "buildSVM",
             top = c(2, 4),
             kernel = "linear",
             cost = 10^(-3:3),
             fold = NULL)

pl

## Accuracy summary (complete summary stored in @summary slot):
## 
##      build top kernel  cost train.acc train.sens train.spec train.auc
## 1 buildSVM   2 linear 0.001  0.537037          0          1         0
## 2 buildSVM   4 linear 0.001  0.537037          0          1         0
## 3 buildSVM   2 linear 0.010  1.000000          1          1         1
## 4 buildSVM   4 linear 0.010  1.000000          1          1         1
##   valid.acc valid.sens valid.spec valid.auc
## 1 0.8076923          0          1         0
## 2 0.8076923          0          1         0
## 3 1.0000000          1          1         1
## 4 1.0000000          1          1         1
## ...
##       build top kernel cost train.acc train.sens train.spec train.auc
## 11 buildSVM   2 linear  100         1          1          1         1
## 12 buildSVM   4 linear  100         1          1          1         1
## 13 buildSVM   2 linear 1000         1          1          1         1
## 14 buildSVM   4 linear 1000         1          1          1         1
##    valid.acc valid.sens valid.spec valid.auc
## 11         1          1          1         1
## 12         1          1          1         1
## 13         1          1          1         1
## 14         1          1          1         1
## 
## Machine summary (all machines stored in @machs slot):
## 
## ##Number of classes: 2 
## @preFilter summary: 4 2 
## @reductionModel summary: logical logical 
## @mach class: svm.formula svm 
## ...
## ##Number of classes: 2 
## @preFilter summary: 4 4 
## @reductionModel summary: logical logical 
## @mach class: svm.formula svm

Read the exprso vignettes for more details.