Introduction to prcbench

Create a tool set

The create_toolset function generates a tool set with a combination of the five tools.

library(prcbench)

## A single tool
toolsetA <- create_toolset("ROCR")

## Multiple tools
toolsetB <- create_toolset(c("PerfMeas", "PRROC"))
                           
## Tool sets can be manually combined to a single set
toolsetAB <- c(toolsetA, toolsetB)

The create_toolset function accepts partially matched tool names. Tool names may also be case-insensitive.

library(prcbench)

## A single tool - lower case
toolsetA2 <- create_toolset("rocr")

## Multiple tools - lower case and partially matched 
toolsetB2 <- create_toolset(c("perf", "prr"))

Use predefined tool sets

The create_toolset function takes two additional arguments - calc_auc and store_res. The calc_auc argument makes tools calculate and retrieve the AUC score, and the store_res argument forces tools to calculate and store the curve values.

The following six tool sets are predefined with a different combination of tools and their argument values.

## Use 'set_names'
toolsetC <- create_toolset(set_names = "auc5")

## Multiple sets are automatically combined to a single set
toolsetD <- create_toolset(set_names = c("auc5", "crv4"))

Create a test dataset for benchmarking

The create_testset function uses a naming convention for randomly generated data for benchmarking. The format is a prefix (‘b’ or ‘i’) followed by the number of dataset. The prefix ‘b’ indicates a balanced dataset, whereas ‘i’ indicates an imbalanced dataset. The number can be used with a suffix ‘k’ or ‘m’, indicating respectively 1000 or 1 million.

## A balanced data set with 50 positives and 50 negatives
testset1A <- create_testset("bench", "b100")

## An imbalanced data set with 2500 positives and 7500 negatives
testset1B <- create_testset("bench", "i10k")

## Test data sets can be manually combined to a single set
testset1AB <- c(testset1A, testset1B)

## Multiple sets are automatically combined to a single set
testset1C <- create_testset("bench", c("i10", "b10"))

Create a test dataset for curve evaluation

The create_testset function takes predefined set names for curve evaluation. These data sets contain pre-calculated precision and recall values. The pre-calculated values must be correct so that they can be compared with the results of specified tools.

The following three test sets are currently available.

• c1
• c2
• c3

## C1 test set
testset2A <- create_testset("curve", "c1")

## C2 test set
testset2B <- create_testset("curve", "c2")

## Test data sets can be manually combined to a single set
testset2AB <- c(testset2A, testset2B)

## Multiple sets are automatically combined to a single set
testset2C <- create_testset("curve", c("c1", "c2"))

Evaluation scores

The run_evalcurve function calculates the scores of the test cases and summarizes them to a data frame.

## Evaluate Precision-Recall curves for ROCR and precrec with c1 test set
testset <- create_testset("curve", "c1")
toolset <- create_toolset(c("ROCR", "precrec"))
scores <- run_evalcurve(testset, toolset)
scores

##   testset toolset toolname score
## 1      c1    ROCR     ROCR   5/8
## 2      c1 precrec  precrec   8/8

The result of each test case can be displayed by specifying data_type = all of the print function.

## Print all results
print(scores, data_type = "all")

##    testset toolset toolname testitem testcat success total
## 1       c1    ROCR     ROCR  x_range      Rg       1     1
## 2       c1    ROCR     ROCR  y_range      Rg       1     1
## 3       c1    ROCR     ROCR   fpoint      SE       0     1
## 4       c1    ROCR     ROCR   intpts      Ip       2     4
## 5       c1    ROCR     ROCR   epoint      SE       1     1
## 6       c1 precrec  precrec  x_range      Rg       1     1
## 7       c1 precrec  precrec  y_range      Rg       1     1
## 8       c1 precrec  precrec   fpoint      SE       1     1
## 9       c1 precrec  precrec   intpts      Ip       4     4
## 10      c1 precrec  precrec   epoint      SE       1     1

Visualization of the result

The autoplot shows a plot with the result of the run_evalcurve function.

## ggplot2 is necessary to use autoplot
library(ggplot2)

## Plot base points and the result of precrec on c1, c2, and c3 test sets
testset <- create_testset("curve", c("c1", "c2", "c3"))
toolset <- create_toolset("precrec")
scores1 <- run_evalcurve(testset, toolset)
autoplot(scores1)

## Plot the results of PerfMeas and PRROC on c1, c2, and c3 test sets
toolset <- create_toolset(c("PerfMeas", "PRROC"))
scores2 <- run_evalcurve(testset, toolset)
autoplot(scores2, base_plot = FALSE)

The format of the function for calculating a Precision-Recall curve

The create_example_func function creates an example for the second argument of the create_usrtool function. The actual function should also take a testset generated by the create_testset function and returns a list with three elements - x, y, and auc.

## Show an example of the second argument
calcfunc <- create_example_func()
print(calcfunc)

## function (single_testset) 
## {
##     scores <- single_testset$get_scores()
##     labels <- single_testset$get_labels()
##     pred <- list(x = seq(0, 1, 1/length(scores)), y = seq(0, 
##         1, 1/length(scores)), auc = 0.5)
## }
## <bytecode: 0x556684f08558>
## <environment: 0x556681232520>

The create_testset function produces a testset as either TestDataB or TestDataC object. See the help files of the R6 classes - help(TestDataB) and help(TestDataC) - for the methods that can be used with the Precision-Recall calculation.

Create a user-defined test dataset for benchmarking

The first argument of the create_usrdata function should be “bench” to create a test data for benchmarking. Additionally, scores and labels are required.

## Create a test dataset 'b5' for benchmarking
testsetB <- create_usrdata("bench", scores = c(0.1, 0.2), labels = c(1, 0),
                           tsname = "b5")

It can be used in the same way as the predefined test datasets for benchmarking.

## Run microbenchmark for ROCR and precrec on a predefined test dataset
toolset <- create_toolset(c("ROCR", "precrec"))
res <- run_benchmark(testsetB, toolset)
res

##   testset toolset toolname min  lq mean median  uq max neval
## 1      b5    ROCR     ROCR 1.5 1.6  1.7    1.6 1.8 2.1     5
## 2      b5 precrec  precrec 4.4 4.4  4.6    4.5 4.8 5.1     5

Create a user-defined test dataset for curve evaluation

The first argument of the create_usrdata function should be “curve” to create a test dataset for curve evaluation. Scores and labels as well as pre-calculated recall and precision values are required. These pre-calculated values are used to compare with the corresponding values predicted by the specified tools.

## Create a test dataset 'c5' for benchmarking
testsetC <- create_usrdata("curve", scores = c(0.1, 0.2), labels = c(1, 0),
                           tsname = "c5", base_x = c(0.0, 1.0), 
                           base_y = c(0.0, 0.5))

It can be used in the same way as the predefined test datasets for curve evaluation.

## Run curve evaluation for ROCR and precrec on a predefined test dataset
toolset2 <- create_toolset(c("ROCR", "precrec"))
scores2 <- run_evalcurve(testsetC, toolset2)
autoplot(scores2, base_plot = FALSE)