ConfusionMatrix Class Reference

A confusion matrix tracks sample predictions in a two-dimensional table based both on the predicted class and on the actual ground-truth class label. More...

#include <ConfusionMatrix.h>

Collaboration diagram for ConfusionMatrix:

[legend]List of all members.

Public Member Functions
	ConfusionMatrix (NameList *classNames)
	Creates an empty confusion matrix object.
void	clear ()
	Clears the confusion matrix, removing any existing data.
int	getSize ()
	Accessor method.
const string &	getClass (int i)
	Accessor method.
void	addEntry (std::valarray< float > &predictions, int expectedClass)
	Adds a data sample to the confusion matrix.
float	get (int predictedClass, int expectedClass)
	Provides direct access to the confusion matrix's raw data.
float	getPredictedTot (int i)
	Accessor method.
int	getExpectedTot (int i)
	Accessor method.
float	getPercentCorrect ()
	Accessor method.
int	getEntryCount ()
	Accessor method.
void	pgmGraph (ostream &ost)
	Generates a portable gray map (.pgm) version of the confusion matrix.
TxtPad *	charPlot ()
	Generates a TxtPad object holding a formatted confusion matrix in tabular form.

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Detailed Description

A confusion matrix tracks sample predictions in a two-dimensional table based both on the predicted class and on the actual ground-truth class label.

The table has size L x L, where L is the number of class labels. Such a representation offers class-specific insight into the classification performance of the model used to fill the confusion matrix. In other words, it tells not just the percentage of classes that are classified correctly, but when samples are misclassified, which classes tend to be confused for each other more often. This implementation allows distributed predictions to contribute; in other words, the prediction for a sample does not need to be a discrete class, but can be a probability distribution over the set of class labels.

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Constructor & Destructor Documentation

ConfusionMatrix::ConfusionMatrix (  NameList *  classNames  )

Creates an empty confusion matrix object.

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Member Function Documentation

void ConfusionMatrix::addEntry (  std::valarray< float > &  predictions, int  expectedClass )

Adds a data sample to the confusion matrix. Since a probability distribution is specified for the prediction, these values are distributed across the entire row specified by the expectedClass index. Parameters: predictions  The distributed set of predictions, a vector over the set of class labels. expectedClass  The index of the ground truth label associated with the sample.

TxtPad * ConfusionMatrix::charPlot (   )

Generates a TxtPad object holding a formatted confusion matrix in tabular form. All columns have the same width, which is that of the longest class name, so short class names are essential. In addition to the basic data, the table also specifies the totals number of predictions per class (bottom row), and the total number of ground-truth samples per class label, along with the percentage classified correctly, both overall, and per class (right hand side, in parentheses). Returns: A pointer to the TxtPad object.

void ConfusionMatrix::clear (   )

Clears the confusion matrix, removing any existing data.

float ConfusionMatrix::get (  int  predictedClass, int  expectedClass )  [inline]

Provides direct access to the confusion matrix's raw data. Returns: The count for the given cell, that is the number of samples that are predicted to be of class predictedClass, and whose ground truth information specifies that they're of class expectedClass.

const string& ConfusionMatrix::getClass (  int  i  )  [inline]

Accessor method. Parameters: i  The row or column of interest. Returns: The name of the class in column or row i.

int ConfusionMatrix::getEntryCount (   )  [inline]

Accessor method. Returns: The number of samples that have been added to the confusion matrix.

int ConfusionMatrix::getExpectedTot (  int  i  )  [inline]

Accessor method. Parameters: i  Index of the class of interest. Returns: The number of samples that are known to be of class i.

float ConfusionMatrix::getPercentCorrect (   )  [inline]

Accessor method. Returns: The percentage of samples classified correctly.

float ConfusionMatrix::getPredictedTot (  int  i  )  [inline]

Accessor method. Parameters: i  Index of the class of interest. Returns: The number of samples predicted to be of class i.

int ConfusionMatrix::getSize (   )  [inline]

Accessor method. Returns: The number of classes, or number of rows and columns.

void ConfusionMatrix::pgmGraph (  ostream &  ost  )

Generates a portable gray map (.pgm) version of the confusion matrix. This graphical form can be easier to read than the equivalent text-based table when there are many classes. It should be written to a file with the extension '.pgm'. The rows and columns are interpreted the same way as for the results of charPlot(), that is, predictions for the same class fall in the same column, whereas samples with the same ground truth label are placed in the same row. The darker a cell's shading, the larger the fraction of the samples of the particular class it represents. Perfect predictions thus consist of black cells on the diagonal of the matrix.

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The documentation for this class was generated from the following files:

• ConfusionMatrix.h
• ConfusionMatrix.cpp

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