Dynamic Ensemble Selection performance (DES-P)

class deslib.des.des_p.DESP(pool_classifiers=None, k=7, DFP=False, with_IH=False, safe_k=None, IH_rate=0.3, mode='selection', random_state=None, knn_classifier='knn', DSEL_perc=0.5)

Dynamic ensemble selection-Performance(DES-P).

This method selects all base classifiers that achieve a classification performance, in the region of competence, that is higher than the random classifier (RC). The performance of the random classifier is defined by RC = 1/L, where L is the number of classes in the problem. If no base classifier is selected, the whole pool is used for classification.

Parameters:

pool_classifiers : list of classifiers (Default = None)

The generated_pool of classifiers trained for the corresponding classification problem. Each base classifiers should support the method “predict”. If None, then the pool of classifiers is a bagging classifier.

k : int (Default = 7)

Number of neighbors used to estimate the competence of the base classifiers.

DFP : Boolean (Default = False)

Determines if the dynamic frienemy pruning is applied.

with_IH : Boolean (Default = False)

Whether the hardness level of the region of competence is used to decide between using the DS algorithm or the KNN for classification of a given query sample.

safe_k : int (default = None)

The size of the indecision region.

IH_rate : float (default = 0.3)

Hardness threshold. If the hardness level of the competence region is lower than the IH_rate the KNN classifier is used. Otherwise, the DS algorithm is used for classification.

mode : String (Default = “selection”)

Whether the technique will perform dynamic selection, dynamic weighting or an hybrid approach for classification.

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

knn_classifier : {‘knn’, ‘faiss’, None} (Default = ‘knn’)

The algorithm used to estimate the region of competence:

• ‘knn’ will use KNeighborsClassifier from sklearn
• ‘faiss’ will use Facebook’s Faiss similarity search through the class FaissKNNClassifier
• None, will use sklearn KNeighborsClassifier.

DSEL_perc : float (Default = 0.5)

Percentage of the input data used to fit DSEL. Note: This parameter is only used if the pool of classifier is None or unfitted.

References

Woloszynski, Tomasz, et al. “A measure of competence based on random classification for dynamic ensemble selection.” Information Fusion 13.3 (2012): 207-213.

Woloszynski, Tomasz, and Marek Kurzynski. “A probabilistic model of classifier competence for dynamic ensemble selection.” Pattern Recognition 44.10 (2011): 2656-2668.

R. M. O. Cruz, R. Sabourin, and G. D. Cavalcanti, “Dynamic classifier selection: Recent advances and perspectives,” Information Fusion, vol. 41, pp. 195 – 216, 2018.

estimate_competence(query, neighbors, distances=None, predictions=None)

estimate the competence of each base classifier \(c_{i}\) for the classification of the query sample base on its local performance.

\[\delta_{i,j} = \hat{P}(c_{i} \mid \theta_{j} ) - \frac{1}{L}\]

Parameters:

query : array of shape = [n_samples, n_features]

The test examples.

neighbors : array of shale = [n_samples, n_neighbors]

Indices of the k nearest neighbors according for each test sample.

distances : array of shale = [n_samples, n_neighbors]

Distances of the k nearest neighbors according for each test sample.

predictions : array of shape = [n_samples, n_classifiers]

Predictions of the base classifiers for all test examples.

Returns:

competences : array of shape = [n_samples, n_classifiers]

Competence level estimated for each base classifier and test example.

fit(X, y)

Prepare the DS model by setting the KNN algorithm and pre-processing the information required to apply the DS methods

Parameters:

X : array of shape = [n_samples, n_features]

The input data.

y : array of shape = [n_samples]

class labels of each example in X.

Returns:

self

predict(X)

Predict the class label for each sample in X.

Parameters:

X : array of shape = [n_samples, n_features]

The input data.

Returns:

predicted_labels : array of shape = [n_samples]

Predicted class label for each sample in X.

predict_proba(X)

Estimates the posterior probabilities for sample in X.

Parameters:

X : array of shape = [n_samples, n_features]

The input data.

Returns:

predicted_proba : array of shape = [n_samples, n_classes]

Probabilities estimates for each sample in X.

score(X, y, sample_weight=None)

Returns the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.

Returns:

score : float

Mean accuracy of self.predict(X) wrt. y.

select(competences)

Selects all base classifiers that obtained a local classification accuracy higher than the Random Classifier. The performance of the random classifier is denoted 1/L, where L is the number of classes in the problem.

Parameters:

competences : array of shape = [n_samples, n_classifiers]

Competence level estimated for each base classifier and test example.

Returns:

selected_classifiers : array of shape = [n_samples, n_classifiers]

Boolean matrix containing True if the base classifier is selected, False otherwise.