Static Selection

class deslib.static.static_selection.StaticSelection(pool_classifiers=None, pct_classifiers=0.5, scoring=None, random_state=None, n_jobs=-1)

Ensemble model that selects N classifiers with the best performance in a dataset

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.

scoring : string, callable (default = None)

A single string or a callable to evaluate the predictions on the validation set.

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.

pct_classifiers : float (Default = 0.5)

Percentage of base classifier that should be selected by the selection scheme.

n_jobs : int, default=-1

The number of parallel jobs to run. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. Doesn’t affect fit method.

References

Britto, Alceu S., Robert Sabourin, and Luiz ES Oliveira. “Dynamic selection of classifiers—a comprehensive review.” Pattern Recognition 47.11 (2014): 3665-3680.

Kuncheva, Ludmila I. Combining pattern classifiers: methods and algorithms. John Wiley & Sons, 2004.

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.

fit(X, y)

Fit the static selection model by select an ensemble of classifier containing the base classifiers with highest accuracy in the given dataset.

Parameters:

X : array of shape (n_samples, n_features)

Data used to fit the model.

y : array of shape (n_samples)

class labels of each example in X.

Returns:

self : object

Returns self.

predict(X)

Predict the label of each sample in X and returns the predicted label.

Parameters:

X : array of shape (n_samples, n_features)

The data to be classified

Returns:

predicted_labels : array of shape (n_samples)

Predicted class 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)

Return 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 of shape (n_samples, n_features)

Test samples.

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

True labels for X.

sample_weight : array-like of shape (n_samples,), default=None

Sample weights.

Returns:

score : float

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