ML::ROCFunctions

This repository has the code of a Raku package for Receiver Operating Characteristic (ROC) functions.

The ROC framework is used for analysis and tuning of binary classifiers, [Wk1]. (The classifiers are assumed to classify into a positive/true label or a negative/false label. )

For computational introduction to ROC utilization (in Mathematica) see the article "Basic example of using ROC with Linear regression", [AA1].

The examples below use the packages "Data::Generators", "Data::Reshapers", and "Data::Summarizers", described in the article "Introduction to data wrangling with Raku", [AA2].

Installation

Via zef-ecosystem:

zef install ML::ROCFunctions

From GitHub:

zef install https://github.com/antononcube/Raku-ML-ROCFunctions

Usage examples

Properties

Here are some retrieval functions:

use ML::ROCFunctions;
say roc-functions('properties');
say roc-functions('FPR');

Single ROC record

Here we generate a random "dataset" with columns "Actual" and "Predicted" that have the values "true" and "false" and show the summary:

use Data::Generators;
use Data::Summarizers;
my @dfRandomLabels = random-tabular-dataset(200, <Actual Predicted>, generators=>{Actual => <true false>, Predicted => <true false>});
records-summary(@dfRandomLabels)

Here is a sample of the dataset:

use Data::Reshapers;
to-pretty-table(@dfRandomLabels.pick(6))

Here we make the corresponding ROC hash-map:

to-roc-hash('true', 'false', @dfRandomLabels.map({$_<Actual>}), @dfRandomLabels.map({$_<Predicted>}))

Multiple ROC records

Here we make random dataset with entries that associated with a certain threshold parameter with three unique values:

my @dfRandomLabels2 = random-tabular-dataset(200, <Threshold Actual Predicted>, generators=>{Threshold => (0.2, 0.4, 0.6), Actual => <true false>, Predicted => <true false>});
records-summary(@dfRandomLabels2)

Remark: Threshold parameters are typically used while tuning Machine Learning (ML) classifiers.

Here we group the rows of the dataset by the unique threshold values:

my %groups = group-by(@dfRandomLabels2, 'Threshold');
records-summary(%groups)

Here we find and print the ROC records (hash-maps) for each unique threshold value:

my @rocs = do for %groups.kv -> $k, $v {
  to-roc-hash('true', 'false', $v.map({$_<Actual>}), $v.map({$_<Predicted>}))
}
.say for @rocs;

Application of ROC functions

Here we define a list of ROC functions:

my @funcs = (&PPV, &NPV, &TPR, &ACC, &SPC, &MCC);

Here we apply each ROC function to each of the ROC records obtained above:

my @rocRes = @rocs.map( -> $r { @funcs.map({ $_.name => $_($r) }).Hash });
say to-pretty-table(@rocRes);

References

Articles

[Wk1] Wikipedia entry, "Receiver operating characteristic".

[AA1] Anton Antonov, "Basic example of using ROC with Linear regression", (2016), MathematicaForPrediction at WordPress.

[AA2] Anton Antonov, "Introduction to data wrangling with Raku", (2021), RakuForPrediction at WordPress.

Packages

[AAp1] Anton Antonov, ROCFunctions Mathematica package, (2016-2022), MathematicaForPrediction at GitHub/antononcube.

[AAp2] Anton Antonov, ROCFunctions R package, (2021), R-packages at GitHub/antononcube.

[AAp1] Anton Antonov,