Diversity

This file contains the implementation of key diversity measures found in the ensemble literature:

• Double Fault
• Negative Double fault
• Q-statistics
• Ratio of errors

The implementation are made according to the specifications from the book “Combining Pattern Classifiers”.

deslib.util.diversity.Q_statistic(y, y_pred1, y_pred2)

Calculates the Q-statistics diversity measure between a pair of classifiers. The Q value is in a range [-1, 1]. Classifiers that tend to classify the same object correctly will have positive values of Q, and Q = 0 for two independent classifiers.

Parameters:

y : array of shape (n_samples):

class labels of each sample.

y_pred1 : array of shape (n_samples):

predicted class labels by the classifier 1 for each sample.

y_pred2 : array of shape (n_samples):

predicted class labels by the classifier 2 for each sample.

Returns:

Q : The q-statistic measure between two classifiers

deslib.util.diversity.agreement_measure(y, y_pred1, y_pred2)

Calculates the agreement measure between a pair of classifiers. This measure is calculated by the frequency that both classifiers either obtained the correct or incorrect prediction for any given sample

Parameters:

y : array of shape (n_samples):

class labels of each sample.

y_pred1 : array of shape (n_samples):

predicted class labels by the classifier 1 for each sample.

y_pred2 : array of shape (n_samples):

predicted class labels by the classifier 2 for each sample.

Returns:

agreement : The frequency at which both classifiers agrees

deslib.util.diversity.compute_pairwise_diversity(targets, prediction_matrix, diversity_func)

Computes the pairwise diversity matrix.

Parameters:

targets : array of shape (n_samples):

Class labels of each sample in X.

prediction_matrix : array of shape (n_samples, n_classifiers):

Predicted class labels for each classifier in the pool

diversity_func : Function

Function used to estimate the pairwise diversity

Returns:

diversity : array of shape = [n_classifiers]

The average pairwise diversity matrix calculated for the pool of classifiers

deslib.util.diversity.correlation_coefficient(y, y_pred1, y_pred2)

Calculates the correlation between two classifiers using oracle outputs. Coefficient is a value in a range [-1, 1].

Parameters:

y : array of shape (n_samples):

class labels of each sample.

y_pred1 : array of shape (n_samples):

predicted class labels by the classifier 1 for each sample.

y_pred2 : array of shape (n_samples):

predicted class labels by the classifier 2 for each sample.

Returns:

rho : The correlation coefficient measured between two classifiers

deslib.util.diversity.disagreement_measure(y, y_pred1, y_pred2)

Calculates the disagreement measure between a pair of classifiers. This measure is calculated by the frequency that only one classifier makes the correct prediction.

Parameters:

y : array of shape (n_samples):

class labels of each sample.

y_pred1 : array of shape (n_samples):

predicted class labels by the classifier 1 for each sample.

y_pred2 : array of shape (n_samples):

predicted class labels by the classifier 2 for each sample.

Returns:

disagreement : The frequency at which both classifiers disagrees

deslib.util.diversity.double_fault(y, y_pred1, y_pred2)

Calculates the double fault (df) measure. This measure represents the probability that both classifiers makes the wrong prediction. A lower value of df means the base classifiers are less likely to make the same error. This measure must be minimized to increase diversity.

Parameters:

y : array of shape (n_samples):

class labels of each sample.

y_pred1 : array of shape (n_samples):

predicted class labels by the classifier 1 for each sample.

y_pred2 : array of shape (n_samples):

predicted class labels by the classifier 2 for each sample.

Returns:

df : The double fault measure between two classifiers

References

Giacinto, Giorgio, and Fabio Roli. “Design of effective neural network ensembles for image classification purposes.” Image and Vision Computing 19.9 (2001): 699-707.

deslib.util.diversity.negative_double_fault(y, y_pred1, y_pred2)

The negative of the double fault measure. This measure should be maximized for a higher diversity.

Parameters:

y : array of shape (n_samples):

class labels of each sample.

y_pred1 : array of shape (n_samples):

predicted class labels by the classifier 1 for each sample.

y_pred2 : array of shape (n_samples):

predicted class labels by the classifier 2 for each sample.

Returns:

df : The negative double fault measure between two classifiers

References

Giacinto, Giorgio, and Fabio Roli. “Design of effective neural network ensembles for image classification purposes.” Image and Vision Computing 19.9 (2001): 699-707.

deslib.util.diversity.ratio_errors(y, y_pred1, y_pred2)

Calculates Ratio of errors diversity measure between a pair of classifiers. A higher value means that the base classifiers are less likely to make the same errors. The ratio must be maximized for a higher diversity

Parameters:

y : array of shape (n_samples):

class labels of each sample.

y_pred1 : array of shape (n_samples):

predicted class labels by the classifier 1 for each sample.

y_pred2 : array of shape (n_samples):

predicted class labels by the classifier 2 for each sample.

Returns:

ratio : The q-statistic measure between two classifiers

References

Aksela, Matti. “Comparison of classifier selection methods for improving committee performance.” Multiple Classifier Systems (2003): 159-159.