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CGAL 5.3 - Classification
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CGAL 5.3 - Classification
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#include <CGAL/Classification/Evaluation.h>
Class to compute several measurements to evaluate the quality of a classification output.
Constructors | |
| Evaluation (const Label_set &labels) | |
| instantiates an empty evaluation object. More... | |
| 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. More... | |
Modification | |
| template<typename GroundTruthIndexRange , typename ResultIndexRange > | |
| void | append (const GroundTruthIndexRange &ground_truth, const ResultIndexRange &result) |
| appends more items to the evaluation object. More... | |
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. More... | |
| float | recall (Label_handle label) const |
| returns the recall of the training for the given label. More... | |
| float | f1_score (Label_handle label) const |
| returns the \(F_1\) score of the training for the given label. More... | |
| float | intersection_over_union (Label_handle label) const |
| returns the intersection over union of the training for the given label. More... | |
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. More... | |
| 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. | |
| CGAL::Classification::Evaluation::Evaluation | ( | const Label_set & | labels | ) |
instantiates an empty evaluation object.
| labels | labels used. |
| CGAL::Classification::Evaluation::Evaluation | ( | const Label_set & | labels, |
| const GroundTruthIndexRange & | ground_truth, | ||
| const ResultIndexRange & | result | ||
| ) |
instantiates an evaluation object and computes all measurements.
| 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. |
| 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.
| void CGAL::Classification::Evaluation::append | ( | const GroundTruthIndexRange & | ground_truth, |
| const ResultIndexRange & | result | ||
| ) |
appends more items to the evaluation object.
| 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. |
| 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():
\[ F_1 = 2 \times \frac{precision \times recall}{precision + recall} \]
| 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.
| 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.
| 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.
1.8.13