CGAL 6.2 - Classification

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CGAL::Classification::Evaluation Class Reference
Reference Manual » Common Data Structures

`#include <CGAL/Classification/Evaluation.h>`

## Definition

Class to compute several measurements to evaluate the quality of a classification output.

## Constructors
| Evaluation (const Label_set &labels)
| instantiates an empty evaluation object.
template<typename GroundTruthIndexRange , typename ResultIndexRange >
| Evaluation (const Label_set &labels, const GroundTruthIndexRange &ground_truth, const ResultIndexRange &result)
| instantiates an evaluation object and computes all measurements.

## Modification
template<typename GroundTruthIndexRange , typename ResultIndexRange >
void | append (const GroundTruthIndexRange &ground_truth, const ResultIndexRange &result)
| appends more items to the evaluation object.

## Label Evaluation
std::size_t | confusion (Label_handle ground_truth, Label_handle result)
| returns the number of items whose ground truth is `ground_truth` and which were classified as `result`.
float | precision (Label_handle label) const
| returns the precision of the training for the given label.
float | recall (Label_handle label) const
| returns the recall of the training for the given label.
float | f1_score (Label_handle label) const
| returns the \(F_1\) score of the training for the given label.
float | intersection_over_union (Label_handle label) const
| returns the intersection over union of the training for the given label.

## Global Evaluation
std::size_t | number_of_misclassified_items () const
| returns the number of misclassified items.
std::size_t | number_of_items () const
| returns the total number of items used for evaluation.
float | accuracy () const
| returns the accuracy of the training.
float | mean_f1_score () const
| returns the mean \(F_1\) score of the training over all labels (see `f1_score()`).
float | mean_intersection_over_union () const
| returns the mean intersection over union of the training over all labels (see `intersection_over_union()`).

## Output Formatting Functions
std::ostream & | operator<< (std::ostream &os, const Evaluation &evaluation)
| outputs the evaluation in a simple ASCII format to the stream `os`.
static std::ostream & | output_to_html (std::ostream &os, const Evaluation &evaluation)
| outputs the evaluation as an HTML page to the stream `os`.

## Constructor & Destructor Documentation

## ◆ Evaluation() [1/2]

CGAL::Classification::Evaluation::Evaluation | ( | const Label_set & | labels | ) |

instantiates an empty evaluation object.

Parameters

  labels | labels used.

## ◆ Evaluation() [2/2]

template<typename GroundTruthIndexRange , typename ResultIndexRange >

CGAL::Classification::Evaluation::Evaluation | ( | const Label_set & | labels,
|  | const GroundTruthIndexRange & | ground_truth,
|  | const ResultIndexRange & | result
| ) |  |

instantiates an evaluation object and computes all measurements.

Parameters

  labels | labels used.
  ground_truth | vector of label indices: it should contain the index of the corresponding label in the `Label_set` provided in the constructor. Input items that do not have a ground truth information should be given the value `-1`.
  result | similar to `ground_truth` but contained the result of a classification.

## Member Function Documentation

## ◆ accuracy()

float CGAL::Classification::Evaluation::accuracy | ( |  | ) | const

returns the accuracy of the training.

Accuracy is the total number of true positives divided by the total number of provided inliers.

## ◆ append()

template<typename GroundTruthIndexRange , typename ResultIndexRange >

void CGAL::Classification::Evaluation::append | ( | const GroundTruthIndexRange & | ground_truth,
|  | const ResultIndexRange & | result
| ) |  |

appends more items to the evaluation object.

Parameters

  ground_truth | vector of label indices: it should contain the index of the corresponding label in the `Label_set` provided in the constructor. Input items that do not have a ground truth information should be given the value `-1`.
  result | similar to `ground_truth` but contained the result of a classification.

## ◆ f1_score()

float CGAL::Classification::Evaluation::f1_score | ( | Label_handle | label | ) | const

returns the \(F_1\) score of the training for the given label.

\(F_1\) score is the harmonic mean of `precision()` and `recall()`:

\[ F_1 = 2 \times \frac{precision \times recall}{precision + recall} \]

## ◆ intersection_over_union()

float CGAL::Classification::Evaluation::intersection_over_union | ( | Label_handle | label | ) | const

returns the intersection over union of the training for the given label.

Intersection over union is the number of true positives divided by the sum of the true positives, of the false positives and of the false negatives.

## ◆ precision()

float CGAL::Classification::Evaluation::precision | ( | Label_handle | label | ) | const

returns the precision of the training for the given label.

Precision is the number of true positives divided by the sum of the true positives and the false positives.

## ◆ recall()

float CGAL::Classification::Evaluation::recall | ( | Label_handle | label | ) | const

returns the recall of the training for the given label.

Recall is the number of true positives divided by the sum of the true positives and the false negatives.