pusion.core.k_nearest_neighbors_combiner.py

class pusion.core.k_nearest_neighbors_combiner.KNNCombiner

Bases: pusion.core.combiner.TrainableCombiner

The KNNCombiner (kNN) is a learning and classifier-based combiner that converts multiple decision outputs into new features, which in turn are used to train this combiner. The kNN combiner (k=5) uses uniform weights for all neighbors and the standard Euclidean metric for the distance.

train(decision_tensor, true_assignments)

Train the kNN combiner by fitting the k nearest neighbors (k=5) model with given decision outputs and true class assignments. Both continuous and crisp classification outputs are supported. This procedure transforms decision outputs into a new feature space.

Parameters

• decision_tensor – numpy.array of shape (n_classifiers, n_samples, n_classes). Tensor of either crisp or continuous decision outputs by different classifiers per sample.

• true_assignments – numpy.array of shape (n_samples, n_classes). Matrix of either crisp or continuous class assignments which are considered true for each sample during the training procedure.

combine(decision_tensor)

Combine decision outputs by the k nearest neighbors (k=5) model. Both continuous and crisp classification outputs are supported. Combining requires a trained DecisionTreeCombiner. This procedure transforms decision outputs into a new feature space.

Parameters

decision_tensor – numpy.array of shape (n_classifiers, n_samples, n_classes). Tensor of either crisp or continuous decision outputs by different classifiers per sample.

Returns

A matrix (numpy.array) of either crisp or continuous class assignments which represents fused decisions obtained by kNN. Axis 0 represents samples and axis 1 the class assignments which are aligned with axis 2 in decision_tensor input tensor.

class pusion.core.k_nearest_neighbors_combiner.CRKNNCombiner

Bases: pusion.core.k_nearest_neighbors_combiner.KNNCombiner

The CRKNNCombiner is a modification of KNNCombiner that also supports complementary-redundant decision outputs. Therefore the input is transformed, such that all missing classification assignments are considered as a constant, respectively. To use methods train() and combine() a coverage needs to be set first by the inherited set_coverage() method.

train(decision_outputs, true_assignments)

Train the kNN combiner model by fitting the k nearest neighbors (k=5) model with given decision outputs and true class assignments. Both continuous and crisp classification outputs are supported. This procedure transforms decision outputs into a new feature space.

Parameters

• decision_outputs – list of numpy.array matrices, each of shape (n_samples, n_classes’), where n_classes’ is classifier-specific and described by the coverage. Each matrix corresponds to one of n_classifiers classifiers and contains either crisp or continuous decision outputs per sample.

• true_assignments – numpy.array of shape (n_samples, n_classes). Matrix of either crisp or continuous class assignments which are considered true for each sample during the training procedure.

combine(decision_outputs)

Combine decision outputs by the k nearest neighbors (k=5) model. Both continuous and crisp classification outputs are supported. Combining requires a trained DecisionTreeCombiner. This procedure transforms decision outputs into a new feature space.

Parameters

decision_outputs – list of numpy.array matrices, each of shape (n_samples, n_classes’), where n_classes’ is classifier-specific and described by the coverage. Each matrix corresponds to one of n_classifiers classifiers and contains crisp or continuous decision outputs per sample.

Returns

A matrix (numpy.array) of crisp or continuous class assignments which represents fused decisions. Axis 0 represents samples and axis 1 the class labels which are aligned with axis 2 in decision_tensor input tensor.