Raku Land

ML::ROCFunctions

zef:antononcube

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');
# (FunctionInterpretations FunctionNames Functions Methods Properties)
roc-functions('FunctionInterpretations')
# {ACC => accuracy, AUROC => area under the ROC curve, Accuracy => same as ACC, F1 => F1 score, FDR => false discovery rate, FNR => false negative rate, FOR => false omission rate, FPR => false positive rate, MCC => Matthews correlation coefficient, NPV => negative predictive value, PPV => positive predictive value, Precision => same as PPV, Recall => same as TPR, SPC => specificity, Sensitivity => same as TPR, TNR => true negative rate, TPR => true positive rate}
say roc-functions('FPR');
# &FPR

Single ROC record

Definition: A ROC record (ROC-hash or ROC-hash-map) is an object of type Associative that has the keys: "FalseNegative", "FalsePositive", "TrueNegative", "TruePositive". Here is an example:

{FalseNegative => 50, FalsePositive => 51, TrueNegative => 60, TruePositive => 39}

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)
# +--------------+--------------+
# | Actual       | Predicted    |
# +--------------+--------------+
# | true  => 109 | false => 110 |
# | false => 91  | true  => 90  |
# +--------------+--------------+

Here is a sample of the dataset:

use Data::Reshapers;
to-pretty-table(@dfRandomLabels.pick(6))
# +--------+-----------+
# | Actual | Predicted |
# +--------+-----------+
# |  true  |    true   |
# |  true  |    true   |
# | false  |   false   |
# |  true  |    true   |
# |  true  |   false   |
# |  true  |   false   |
# +--------+-----------+

Here we make the corresponding ROC hash-map:

to-roc-hash('true', 'false', @dfRandomLabels.map({$_<Actual>}), @dfRandomLabels.map({$_<Predicted>}))
# {FalseNegative => 57, FalsePositive => 38, TrueNegative => 53, TruePositive => 52}

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)
# +---------------+--------------+--------------+
# | Threshold     | Predicted    | Actual       |
# +---------------+--------------+--------------+
# | Min    => 0.2 | true  => 104 | false => 103 |
# | 1st-Qu => 0.2 | false => 96  | true  => 97  |
# | Mean   => 0.4 |              |              |
# | Median => 0.4 |              |              |
# | 3rd-Qu => 0.6 |              |              |
# | Max    => 0.6 |              |              |
# +---------------+--------------+--------------+

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)
# summary of 0.6 =>
# +-------------+---------------+-------------+
# | Actual      | Threshold     | Predicted   |
# +-------------+---------------+-------------+
# | false => 35 | Min    => 0.6 | true  => 37 |
# | true  => 29 | 1st-Qu => 0.6 | false => 27 |
# |             | Mean   => 0.6 |             |
# |             | Median => 0.6 |             |
# |             | 3rd-Qu => 0.6 |             |
# |             | Max    => 0.6 |             |
# +-------------+---------------+-------------+
# summary of 0.2 =>
# +-------------+---------------+-------------+
# | Predicted   | Threshold     | Actual      |
# +-------------+---------------+-------------+
# | false => 33 | Min    => 0.2 | false => 34 |
# | true  => 31 | 1st-Qu => 0.2 | true  => 30 |
# |             | Mean   => 0.2 |             |
# |             | Median => 0.2 |             |
# |             | 3rd-Qu => 0.2 |             |
# |             | Max    => 0.2 |             |
# +-------------+---------------+-------------+
# summary of 0.4 =>
# +-------------+---------------+-------------+
# | Actual      | Threshold     | Predicted   |
# +-------------+---------------+-------------+
# | true  => 38 | Min    => 0.4 | false => 36 |
# | false => 34 | 1st-Qu => 0.4 | true  => 36 |
# |             | Mean   => 0.4 |             |
# |             | Median => 0.4 |             |
# |             | 3rd-Qu => 0.4 |             |
# |             | Max    => 0.4 |             |
# +-------------+---------------+-------------+

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;
# {FalseNegative => 16, FalsePositive => 24, TrueNegative => 11, TruePositive => 13}
# {FalseNegative => 18, FalsePositive => 19, TrueNegative => 15, TruePositive => 12}
# {FalseNegative => 17, FalsePositive => 15, TrueNegative => 19, TruePositive => 21}

Application of ROC functions

Here we define a list of ROC functions:

my @funcs = (&PPV, &NPV, &TPR, &ACC, &SPC, &MCC);
# [&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);
# +----------+-----------+----------+----------+----------+----------+
# |   ACC    |    MCC    |   NPV    |   TPR    |   PPV    |   SPC    |
# +----------+-----------+----------+----------+----------+----------+
# | 0.375000 | -0.239599 | 0.407407 | 0.448276 | 0.351351 | 0.314286 |
# | 0.421875 | -0.158958 | 0.454545 | 0.400000 | 0.387097 | 0.441176 |
# | 0.555556 |  0.111457 | 0.527778 | 0.552632 | 0.583333 | 0.558824 |
# +----------+-----------+----------+----------+----------+----------+

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.

[AAp3] Anton Antonov, Data::Generators Raku package, (2021), GitHub/antononcube.

[AAp4] Anton Antonov, Data::Reshapers Raku package, (2021), GitHub/antononcube.

[AAp5] Anton Antonov, Data::Summarizers Raku package, (2021), GitHub/antononcube.