Package 'prcbench'

Title: Testing Workbench for Precision-Recall Curves
Description: A testing workbench to evaluate tools that calculate precision-recall curves. Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.
Authors: Takaya Saito [aut, cre] (ORCID: <https://orcid.org/0000-0002-0154-8452>), Marc Rehmsmeier [aut] (ORCID: <https://orcid.org/0000-0002-5021-7721>), The scikit-learn developers [cph] (Python code in inst/python, derived from scikit-learn (BSD-3-Clause); see inst/COPYRIGHTS)
Maintainer: Takaya Saito <[email protected]>
License: GPL-3
Version: 1.1.16
Built: 2026-09-26 13:21:53 UTC
Source: https://github.com/evalclass/prcbench

Help Index

Plot the result of Precision-Recall curve evaluation

Description

The plot_eval_results function validates Precision-Recall curves and creates a plot.

Usage

## S3 method for class 'evalcurve'
autoplot(
  object,
  base_plot = TRUE,
  ret_grob = FALSE,
  ncol = NULL,
  nrow = NULL,
  use_category = FALSE,
  multiplot_lib = "patchwork",
  ...
)

Arguments

object

An S3 object that contains evaluation results of Precision-Recall curves.
base_plot

A Boolean value to specify whether the base points are plotted.
ret_grob

A Boolean value to specify whether the function returns a grob object.
ncol

An integer used for the column size of multiple panes.
nrow

An integer used for the row size of multiple panes.
use_category

A Boolean value to specify whether the categorical summary instead of the total summary.
multiplot_lib

A string to decide which library is used to combine multiple plots. Either "patchwork" or "grid".
...

Not used by this function.

Value

A data frame with validation results.

Examples

library(ggplot2)

## Plot evaluation results on test datasets r1, r2, and r3
testset <- create_testset("curve", c("c1", "c2", "c3"))
toolset <- create_toolset(set_names = "crv5")
eres1 <- run_evalcurve(testset, toolset)
autoplot(eres1)

C1: Pre-calculated Precision-Recall curve

Description

A list contains scores, labels, and pre-calculated recall and precision values as x and y.

Usage

data(C1DATA)

Format

A list with 5 items.

scores

input scores

labels

input labels

bp_x

pre-calculated recall values for curve evaluation

bp_y

pre-calculated precision values for curve evaluation

tp_x

x position for displaying the test result in a plot

tp_y

y position for displaying the test result in a plot

C2: Pre-calculated Precision-Recall curve

Description

A list contains scores, labels, and pre-calculated recall and precision values as x and y.

Usage

data(C2DATA)

Format

See C1DATA.

C3: Pre-calculated Precision-Recall curve

Description

A list contains scores, labels, and pre-calculated recall and precision values as x and y.

Usage

data(C3DATA)

Format

See C1DATA.

C4: Pre-calculated Precision-Recall curve

Description

A list contains scores, labels, and pre-calculated recall and precision values as x and y.

Usage

data(C4DATA)

Format

See C1DATA.

Create an example for the func argument of the create_usrtool function

Description

The create_example_func function creates an example for the create_usrtool function.

Usage

create_example_func()

Value

A function as an example for create_usrtool

See Also

create_usrtool requires the same format. create_testset for testset.

Examples

## Create a function
func <- create_example_func()
func

Create a list of test datasets

Description

The create_testset function creates test datasets either for benchmarking or curve evaluation.

Usage

create_testset(test_type, set_names = NULL)

Arguments

test_type

A single string to specify the type of dataset generated by this function.

"bench"

Create test datasets for benchmarking

"curve"

Create test datasets for curve evaluation
set_names

A character vector to specify the names of test datasets.

  1. For benchmarking (test_type = "bench")

    This function uses a naming convention for randomly generated data for benchmarking. The format is a prefix ('b', 'i' or 'r') followed by the total number of data points in the set. The prefix 'b' indicates a balanced data set (half positives), 'i' indicates an imbalanced one (a quarter positives), and 'r' uses a randomly chosen ratio. The number can be used with a suffix 'k' or 'm', indicating respectively 1000 or 1 million.

    Below are some examples.

    "b100"

    A balanced data set with 50 positives and 50 negatives.

    "b10k"

    A balanced data set with 5000 positives and 5000 negatives.

    "b1m"

    A balanced data set with 500,000 positives and 500,000 negatives.

    "i100"

    An imbalanced data set with 25 positives and 75 negatives.

    The function returns a list of TestDataB objects.

  2. For curve evaluation (test_type = "curve")

    The following three predefined datasets can be specified for curve evaluation.

    set name S3 object data source
    c1 or C1 TestDataC C1DATA
    c2 or C2 TestDataC C2DATA
    c3 or C3 TestDataC C3DATA
    c4 or C4 TestDataC C4DATA

    The function returns a list of TestDataC objects.

Value

A list of R6 test dataset objects.

See Also

run_benchmark and run_evalcurve require the list of the datasets generated by this function. TestDataB for benchmarking test data. TestDataC, C1DATA, C2DATA, C3DATA, and C4DATA for curve evaluation test data. create_usrdata for creating a user-defined test set.

Examples

## Create a balanced data set with 50 positives and 50 negatives
tset1 <- create_testset("bench", "b100")
tset1

## Create an imbalanced data set with 25 positives and 75 negatives
tset2 <- create_testset("bench", "i100")
tset2

## Create P1 dataset
tset3 <- create_testset("curve", "c1")
tset3

## Create P1 dataset
tset4 <- create_testset("curve", c("c1", "c2"))
tset4

Create a set of tools

Description

The create_toolset function takes names of predefined tools and generates a list of wrapper functions for Precision-Recall curve calculations.

Usage

create_toolset(
  tool_names = NULL,
  set_names = NULL,
  calc_auc = TRUE,
  store_res = TRUE
)

Arguments

tool_names

A character vector to specify the names of performance evaluation tools. The names for the following seven tools can be currently used.

  • ROCR

  • AUCCalculator

  • PerfMeas

  • PRROC

  • precrec

  • yardstick

  • sklearn

The sklearn tool requires the reticulate package together with a working Python installation and numpy. When they are unavailable, the tool is created without them and returns a flat dummy curve, in the same way as AUCCalculator does without rJava.
set_names

A character vector to specify a predefined set name. Following twelve sets are currently available. The digit is the number of tools in the set, and each smaller set drops one more of the slower tools.

"def7"

ROCR, AUCCalculator, PerfMeas, PRROC, precrec, yardstick, and sklearn with calc_auc = TRUE and store_res = TRUE

"auc7"

The 7 tools of "def7" with calc_auc = TRUE and store_res = FALSE

"crv7"

The 7 tools of "def7" with calc_auc = FALSE and store_res = TRUE

"def6"

ROCR, AUCCalculator, PRROC, precrec, yardstick, and sklearn with calc_auc = TRUE and store_res = TRUE

"auc6"

The 6 tools of "def6" with calc_auc = TRUE and store_res = FALSE

"crv6"

The 6 tools of "def6" with calc_auc = FALSE and store_res = TRUE

"def5"

ROCR, PRROC, precrec, yardstick, and sklearn with calc_auc = TRUE and store_res = TRUE

"auc5"

The 5 tools of "def5" with calc_auc = TRUE and store_res = FALSE

"crv5"

The 5 tools of "def5" with calc_auc = FALSE and store_res = TRUE

"def4"

ROCR, precrec, yardstick, and sklearn with calc_auc = TRUE and store_res = TRUE

"auc4"

The 4 tools of "def4" with calc_auc = TRUE and store_res = FALSE

"crv4"

The 4 tools of "def4" with calc_auc = FALSE and store_res = TRUE
calc_auc

A Boolean value to specify whether the AUC score should be calculated.
store_res

A Boolean value to specify whether the calculated curve is retrieved and stored

Value

A list of R6 tool objects.

See Also

run_benchmark and run_evalcurve require the list of the tools generated by this function ToolROCR, ToolAUCCalculator, ToolPerfMeas, ToolPRROC, Toolprecrec, Toolyardstick, and Toolsklearn as R6 tool classes.

Examples

## Create ROCR and precrec
toolset1 <- create_toolset(c("ROCR", "precrec"))
toolset1

## Create auc7 tools
toolset2 <- create_toolset(set_names = "auc7")
toolset2

Create a user-defined test dataset

Description

The create_usrdata function creates various types of test datasets.

Usage

create_usrdata(
  test_type,
  scores = NULL,
  labels = NULL,
  tsname = NULL,
  base_x = NULL,
  base_y = NULL,
  text_x = NULL,
  text_y = NULL,
  text_x2 = text_x,
  text_y2 = text_y
)

Arguments

test_type

A single string to specify the type of dataset generated by this function.

"bench"

Create a test dataset for benchmarking

"curve"

Create a test dataset for curve evaluation
scores

A numeric vector to set scores.
labels

A numeric vector to set labels.
tsname

A single string to specify the name of the dataset.
base_x

A numeric vector to set pre-calculated recall values for curve evaluation.
base_y

A numeric vector to set pre-calculated precision values for curve evaluation.
text_x

A single numeric value to set the x position for displaying the test result in a plot
text_y

A single numeric value to set the y position for displaying the test result in a plot
text_x2

A single numeric value to set the x position for displaying the test result (group into categories) in a plot
text_y2

A single numeric value to set the y position for displaying the test result (group into categories) in a plot

Value

A list of R6 test dataset objects.

See Also

create_testset for creating a predefined test set. TestDataB for benchmarking test data. TestDataC for curve evaluation test data.

Examples

## Create a test dataset for benchmarking
testset2 <- create_usrdata("bench",
  scores = c(0.1, 0.2), labels = c(1, 0),
  tsname = "m1"
)
testset2

## Create a test dataset for curve evaluation
testset <- create_usrdata("curve",
  scores = c(0.1, 0.2), labels = c(1, 0),
  base_x = c(0, 1.0), base_y = c(0, 0.5)
)
testset

Create a set of tools

Description

The create_toolset function takes names of predefined tools and generates a list of wrapper functions for Precision-Recall curve calculations.

Usage

create_usrtool(
  tool_name,
  func,
  calc_auc = TRUE,
  store_res = TRUE,
  x = NA,
  y = NA
)

Arguments

tool_name

A single string to specify the name of a user-defined tool.
func

A function to calculate a Precision-Recall curve and the AUC. It should take an element of the test dataset generated by create_testset as an argument. It also should return a list with three elements - 'x', 'y', and 'auc' that represent calculated recall and precision values plus the AUC score. See create_example_func for an example.
calc_auc

A Boolean value to specify whether the AUC score should be calculated.
store_res

A Boolean value to specify whether the calculated curve is retrieved and stored.
x

Set pre-calculated recall values.
y

Set pre-calculated precision values.

Value

A list of R6 tool objects.

See Also

create_toolset to create a predefined tool set. create_testset for testset. create_example_func to create an example function.

Examples

## Create a new tool interface called "xyz"
efunc <- create_example_func()
toolset1 <- create_usrtool("xyz", efunc)
toolset1

## Example function with a correct argument
testset <- create_usrdata("bench", scores = c(0.1, 0.2), labels = c(1, 0))
retf <- efunc(testset[[1]])
retf

prcbench: A package to provide a testing workbench for precision-recall curves

Description

The prcbench package provides four categories of important functions: tool interface, test data interface, benchmarking, and curve evaluation.

Tool interface

The create_toolset function creates a common interface for seven different tools that calculate Precision-Recall curves. These tools are ROCR, AUCCalculator, PerfMeas, PRROC, precrec, yardstick, and scikit-learn.

The sklearn tool is calculated by a standalone Python module that is bundled with prcbench and derived from the scikit-learn source code. It requires reticulate, a working Python installation and numpy. Without them it returns a flat dummy curve rather than raising an error, so the predefined tool sets that contain it stay usable.

The create_usrtool function helps users to make the same interface of the predefined ones for their own tools.

Test data interface

The create_testset function creates two different types of test data sets. The first type is for benchmarking, and the second type is for curve evaluation.

The create_usrdata function helps users to make their own test data sets.

Benchmarking

The run_benchmark function takes a tool set and a test data set and run microbenchmark for them.

The timing of the sklearn tool includes the cost of crossing the R/Python boundary, so it is not comparable with the timings of the tools written in R.

Curve evaluation

The run_evalcurve function takes a tool set and a test data set and evaluates the accuracy of Precision-Recall curves for them.

Author(s)

Maintainer: Takaya Saito [email protected] (ORCID)

Authors:

Other contributors:

  • The scikit-learn developers (Python code in inst/python, derived from scikit-learn (BSD-3-Clause); see inst/COPYRIGHTS) [copyright holder]

See Also

Useful links:

Run microbenchmark with specified tools and test sets

Description

The run_benchmark function runs microbenchmark for specified tools and test datasets

Usage

run_benchmark(testset, toolset, times = 5, unit = "ms", use_sys_time = FALSE)

Arguments

testset

A character vector to specify a test set generated by create_testset.
toolset

A character vector to specify a tool set generated by create_toolset.
times

The number of iteration used in microbenchmark.
unit

A single string to specify the unit used in summary.microbenchmark.
use_sys_time

A Boolean value to specify system.time is used instead of summary.microbenchmark.

Details

The timing of the sklearn tool is not comparable with the timings of the tools written in R. Every call crosses the R/Python boundary and converts the input and output vectors, and that overhead is counted as part of the measurement. On a small test set it often dominates the curve calculation itself, so the sklearn row measures the cost of the round trip to Python rather than the speed of the scikit-learn algorithm. See Toolsklearn for the tool itself, and run_evalcurve for an evaluation that this does not affect.

Value

A data frame of microbenchmark results with additional columns.

See Also

create_testset to generate a test dataset. create_toolset to generate a tool set. microbenchmark for benchmarking details.

Examples

## Not run: 
## Benchmarking for b10 and i10 test sets and crv5, auc5, and def5 tool sets
testset <- create_testset("bench", c("b10", "i10"))
toolset <- create_toolset(set_names = "def5")
res1 <- run_benchmark(testset, toolset)
res1

## End(Not run)

Evaluate Precision-Recall curves with specified tools and test sets

Description

The run_evalcurve function runs several tests to evaluate the accuracy of Precision-Recall curves.

Usage

run_evalcurve(testset, toolset, auto_combo = TRUE)

Arguments

testset

A character vector to specify a test set generated by create_testset.
toolset

A character vector to specify a tool set generated by create_toolset.
auto_combo

A Boolean value to specify whether a combination of test and tool sets is automatically created.

Value

A data frame with validation results.

See Also

create_testset to generate a test dataset. create_toolset to generate a tool set.

Examples

## Evaluate curves for c1, c2, c3 test sets and crv5 tool set
testset <- create_testset("curve", c("c1", "c2", "c3"))
toolset <- create_toolset(set_names = "crv5")
res1 <- run_evalcurve(testset, toolset)
res1

TestDataB

Description

R6 class of test data set for performance evaluation tools.

Format

An R6 class object.

Details

TestDataB is a class that contains scores and label for performance evaluation tools. It provides necessary methods for benchmarking.

Methods

Public methods

TestDataB$new()

Default class initialization method.

Usage
TestDataB$new(scores = NULL, labels = NULL, tsname = NA)
Arguments
scores

A vector of scores.

labels

A vector of labels.

tsname

A dataset name.

TestDataB$get_tsname()

Get the dataset name.

Usage
TestDataB$get_tsname()

TestDataB$get_scores()

Get a vector of scores.

Usage
TestDataB$get_scores()

TestDataB$get_labels()

Get a vector of labels.

Usage
TestDataB$get_labels()

TestDataB$get_fg()

Get a vector of positive scores.

Usage
TestDataB$get_fg()

TestDataB$get_bg()

Get a vector of negative scores.

Usage
TestDataB$get_bg()

TestDataB$get_fname()

Get a file name that contains scores and labels.

Usage
TestDataB$get_fname()

TestDataB$del_file()

Delete the file with scores and labels.

Usage
TestDataB$del_file()

TestDataB$print()

Pretty print of the test dataset.

Usage
TestDataB$print(...)
Arguments
...

Not used.

TestDataB$clone()

The objects of this class are cloneable with this method.

Usage
TestDataB$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

create_testset for creating a list of test datasets. TestDataC is derived from this class for curve evaluation.

Examples

## Initialize with scores, labels, and a dataset name
testset <- TestDataB$new(c(0.1, 0.2, 0.3), c(0, 1, 1), "m1")
testset

TestDataC

Description

R6 class of test dataset for Precision-Recall curve evaluation.

Format

An R6 class object.

Details

TestDataC is a class that contains scores and label for performance evaluation tools. It provides necessary methods for curve evaluation.

Super class

TestDataB -> TestDataC

Methods

Public methods

Inherited methods

TestDataC$set_basepoints_x()

Set pre-calculated recall values for curve evaluation.

Usage
TestDataC$set_basepoints_x(x)
Arguments
x

A recall value.

TestDataC$set_basepoints_y()

Set pre-calculated precision values for curve evaluation.

Usage
TestDataC$set_basepoints_y(y)
Arguments
y

A precision value.

TestDataC$get_basepoints_x()

Get pre-calculated recall values for curve evaluation.

Usage
TestDataC$get_basepoints_x()

TestDataC$get_basepoints_y()

Get pre-calculated precision values for curve evaluation.

Usage
TestDataC$get_basepoints_y()

TestDataC$set_textpos_x()

Set the position x for displaying the test result in a plot.

Usage
TestDataC$set_textpos_x(x)
Arguments
x

Position x of the test result.

TestDataC$set_textpos_y()

Set the y position for displaying the test result in a plot.

Usage
TestDataC$set_textpos_y(y)
Arguments
y

Position y of the test result.

TestDataC$set_textpos_x2()

Set the x position for displaying the test result in a plot.

Usage
TestDataC$set_textpos_x2(x)
Arguments
x

Position x of the test result.

TestDataC$set_textpos_y2()

Set the y position for displaying the test result in a plot.

Usage
TestDataC$set_textpos_y2(y)
Arguments
y

Position y of the test result.

TestDataC$get_textpos_x()

Get the position x for displaying the test result in a plot.

Usage
TestDataC$get_textpos_x()

TestDataC$get_textpos_y()

Get the position y for displaying the test result in a plot.

Usage
TestDataC$get_textpos_y()

TestDataC$get_textpos_x2()

Get the x position for displaying the test result in a plot.

Usage
TestDataC$get_textpos_x2()

TestDataC$get_textpos_y2()

Get the y position for displaying the test result in a plot.

Usage
TestDataC$get_textpos_y2()

TestDataC$clone()

The objects of this class are cloneable with this method.

Usage
TestDataC$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

create_testset for creating a list of test datasets. It is derived from TestDataB.

Examples

## Initialize with scores, labels, and a dataset name
testset <- TestDataC$new(c(0.1, 0.2), c(1, 0), "c4")
testset

## Set base points
testset$set_basepoints_x(c(0.13, 0.2))
testset$set_basepoints_y(c(0.5, 0.6))
testset

ToolAUCCalculator

Description

R6 class of the AUCCalculator tool

Format

An R6 class object.

Details

ToolAUCCalculator is a wrapper class for the AUCCalculator tool, which is a Java library that provides calculations of ROC and Precision-Recall curves.

Super class

ToolIFBase -> ToolAUCCalculator

Methods

Public methods

Inherited methods

ToolAUCCalculator$new()

Default class initialization method.

Usage
ToolAUCCalculator$new(...)
Arguments
...

set value for jarpath.

ToolAUCCalculator$set_jarpath()

It sets an AUCCalculator jar file.

Usage
ToolAUCCalculator$set_jarpath(jarpath = NULL)
Arguments
jarpath

File path of the AUCCalculator jar file, e.g. "/path1/path2/auc2.jar".

ToolAUCCalculator$set_curvetype()

It sets the type of curve.

Usage
ToolAUCCalculator$set_curvetype(curvetype = "SPR")
Arguments
curvetype

"SPR", "PR", or "ROC"

ToolAUCCalculator$set_auctype()

It sets the type of calculation method

Usage
ToolAUCCalculator$set_auctype(auctype)
Arguments
auctype

"java" or "r"

ToolAUCCalculator$clone()

The objects of this class are cloneable with this method.

Usage
ToolAUCCalculator$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

This class is derived from ToolIFBase. create_toolset for creating a list of tools.

Examples

## Initialization
toolauccalc <- ToolAUCCalculator$new()

## Show object info
toolauccalc

## create_toolset should be used for benchmarking and curve evaluation
toolauccalc2 <- create_toolset("AUCCalculator")

ToolIFBase

Description

Base class of performance evaluation tools.

Format

An R6 class object

Details

ToolIFBase is an abstract class to provide a uniform interface for performance evaluation tools.

Methods

Public methods

ToolIFBase$new()

Default class initialization method.

Usage
ToolIFBase$new(...)
Arguments
...

set value for setname, calc_auc, store_res, x, y.

ToolIFBase$call()

It calls the tool to calculate precision-recall curves.

Usage
ToolIFBase$call(testset, calc_auc, store_res)
Arguments
testset

R6 object generated by the create_testset function.

calc_auc

A Boolean value to specify whether the AUC score should be calculated.

store_res

A Boolean value to specify whether the calculated curve is retrieved and stored.

ToolIFBase$get_toolname()

Get the name of the tool.

Usage
ToolIFBase$get_toolname()

ToolIFBase$set_toolname()

Set the name of the tool.

Usage
ToolIFBase$set_toolname(toolname)
Arguments
toolname

Name of the tool.

ToolIFBase$get_setname()

Get the name of the tool set.

Usage
ToolIFBase$get_setname()

ToolIFBase$set_setname()

Set the name of the tool set.

Usage
ToolIFBase$set_setname(setname)
Arguments
setname

Name of the tool set.

ToolIFBase$get_result()

Get a list with curve values and the AUC score.

Usage
ToolIFBase$get_result()

ToolIFBase$get_x()

Get calculated recall values.

Usage
ToolIFBase$get_x()

ToolIFBase$get_y()

Get calculated precision values.

Usage
ToolIFBase$get_y()

ToolIFBase$get_auc()

Get tne AUC score.

Usage
ToolIFBase$get_auc()

ToolIFBase$print()

Pretty print of the tool interface

Usage
ToolIFBase$print(...)
Arguments
...

Not used.

ToolIFBase$clone()

The objects of this class are cloneable with this method.

Usage
ToolIFBase$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

ToolROCR, ToolAUCCalculator, ToolPerfMeas, ToolPRROC, Toolprecrec, Toolyardstick, and Toolsklearn are derived from this class. create_toolset for creating a list of tools.

ToolPerfMeas

Description

R6 class of the PerfMeas tool

Format

An R6 class object.

Details

ToolPerfMeas is a wrapper class for the PerfMeas tool, which is an R library that provides several performance measures.

Super class

ToolIFBase -> ToolPerfMeas

Methods

Public methods

Inherited methods

ToolPerfMeas$clone()

The objects of this class are cloneable with this method.

Usage
ToolPerfMeas$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

This class is derived from ToolIFBase. create_toolset for creating a list of tools.

Examples

## Initialization
toolperf <- ToolPerfMeas$new()

## Show object info
toolperf

## create_toolset should be used for benchmarking and curve evaluation
toolperf2 <- create_toolset("PerfMeas")

Toolprecrec

Description

R6 class of the precrec tool

Format

An R6 class object.

Details

Toolprecrec is a wrapper class for the precrec tool, which is an R library that provides calculations of ROC and Precision-Recall curves.

Super class

ToolIFBase -> Toolprecrec

Methods

Public methods

Inherited methods

Toolprecrec$new()

Default class initialization method.

Usage
Toolprecrec$new(...)
Arguments
...

set value for x_bins.

Toolprecrec$set_x_bins()

Set the number of supporting points as the number of bins.

Usage
Toolprecrec$set_x_bins(x_bins)
Arguments
x_bins

set value for x_bins.

Toolprecrec$clone()

The objects of this class are cloneable with this method.

Usage
Toolprecrec$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

This class is derived from ToolIFBase. create_toolset for creating a list of tools.

Examples

## Initialization
toolprecrec <- Toolprecrec$new()

## Show object info
toolprecrec

## create_toolset should be used for benchmarking and curve evaluation
toolprecrec2 <- create_toolset("precrec")

ToolPRROC

Description

R6 class of the PRROC tool

Format

An R6 class object.

Details

ToolPRROC is a wrapper class for the PRROC tool, which is an R library that provides calculations of ROC and Precision-Recall curves.

Super class

ToolIFBase -> ToolPRROC

Methods

Public methods

Inherited methods

ToolPRROC$new()

Default class initialization method.

Usage
ToolPRROC$new(...)
Arguments
...

set value for curve, minStepSize, aucType.

ToolPRROC$set_curve()

A Boolean value to specify whether precision-recall curve is calculated.

Usage
ToolPRROC$set_curve(val)
Arguments
val

TRUE: calculate, FALSE: not calculate.

ToolPRROC$set_minStepSize()

A numeric value to specify the minimum step size between two intermediate points.

Usage
ToolPRROC$set_minStepSize(val)
Arguments
val

Step size between two points.

ToolPRROC$set_aucType()

Set the AUC calculation method

Usage
ToolPRROC$set_aucType(val)
Arguments
val

1: integral, 2: Davis Goadrich

ToolPRROC$clone()

The objects of this class are cloneable with this method.

Usage
ToolPRROC$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

This class is derived from ToolIFBase. create_toolset for creating a list of tools.

Examples

## Initialization
toolprroc <- ToolPRROC$new()

## Show object info
toolprroc

## create_toolset should be used for benchmarking and curve evaluation
toolprroc2 <- create_toolset("PRROC")

ToolROCR

Description

R6 class of the ROCR tool

Format

An R6 class object.

Details

ToolROCR is a wrapper class for the ROCR tool, which is an R library that provides calculations of various performance evaluation measures.

Super class

ToolIFBase -> ToolROCR

Methods

Public methods

Inherited methods

ToolROCR$clone()

The objects of this class are cloneable with this method.

Usage
ToolROCR$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

This class is derived from ToolIFBase. create_toolset for creating a list of tools.

Examples

## Initialization
toolrocr <- ToolROCR$new()

## Show object info
toolrocr

## create_toolset should be used for benchmarking and curve evaluation
toolrocr2 <- create_toolset("ROCR")

Toolsklearn

Description

R6 class of the scikit-learn tool

Format

An R6 class object.

Details

Toolsklearn is a wrapper class for the precision-recall curve calculation of scikit-learn, which is a machine learning library for Python.

The calculation is performed by a standalone Python module that is bundled with prcbench and derived from the scikit-learn source code. As a result, scikit-learn itself is not required, but reticulate, a working Python installation, and numpy are. The tool can be created without them, whereas the actual calculation cannot be performed. In that case the tool returns a flat dummy curve instead of raising an error, in the same way as ToolAUCCalculator does without rJava, so that the predefined tool sets keep working on a machine without Python.

Initialising Python imports numpy, and the BLAS library behind numpy starts a thread pool sized to the number of cores. Those threads cost CPU time that the import itself never spends, so the pool is capped to two threads while the import runs. Set OMP_NUM_THREADS, OPENBLAS_NUM_THREADS, MKL_NUM_THREADS, or NUMEXPR_NUM_THREADS before the first tool is created to choose the size of the pool instead.

Two AUC calculation methods are available. aucType = 1 uses average precision, which is the summary scikit-learn recommends for precision-recall curves, whereas aucType = 2 uses the trapezoidal rule. The scikit-learn documentation discourages the use of the trapezoidal rule for precision-recall curves.

Timings of this tool are not comparable with those of the tools written in R. Every call crosses the R/Python boundary and converts the input and output vectors, and run_benchmark counts that overhead as part of the measurement. On a small test set it often dominates the curve calculation itself. The accuracy evaluation of run_evalcurve is unaffected.

Super class

ToolIFBase -> Toolsklearn

Methods

Public methods

Inherited methods

Toolsklearn$new()

Default class initialization method.

Usage
Toolsklearn$new(...)
Arguments
...

set value for drop_intermediate, aucType.

Toolsklearn$set_drop_intermediate()

A Boolean value to specify whether suboptimal thresholds are dropped.

Usage
Toolsklearn$set_drop_intermediate(val)
Arguments
val

TRUE: drop, FALSE: keep.

Toolsklearn$set_aucType()

Set the AUC calculation method

Usage
Toolsklearn$set_aucType(val)
Arguments
val

1: average precision, 2: trapezoidal rule

Toolsklearn$clone()

The objects of this class are cloneable with this method.

Usage
Toolsklearn$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

This class is derived from ToolIFBase. create_toolset for creating a list of tools.

Examples

## Initialization
toolsklearn <- Toolsklearn$new()

## Show object info
toolsklearn

## create_toolset should be used for benchmarking and curve evaluation
toolsklearn2 <- create_toolset("sklearn")

Toolyardstick

Description

R6 class of the yardstick tool

Format

An R6 class object.

Details

Toolyardstick is a wrapper class for the yardstick tool, which is an R library of the tidymodels ecosystem that provides calculations of various model performance measures.

Super class

ToolIFBase -> Toolyardstick

Methods

Public methods

Inherited methods

Toolyardstick$clone()

The objects of this class are cloneable with this method.

Usage
Toolyardstick$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

This class is derived from ToolIFBase. create_toolset for creating a list of tools.

Examples

## Initialization
toolyardstick <- Toolyardstick$new()

## Show object info
toolyardstick

## create_toolset should be used for benchmarking and curve evaluation
toolyardstick2 <- create_toolset("yardstick")