summit.multiview_platform.multiview_classifiers.mucombo

mucombo

classifier_class_name = 'MuCombo'
class MuCombo(estimator=None, n_estimators=50, random_state=None, **kwargs)

BaseMultiviewClassifier base of Multiview classifiers

Parameters:

random_state (int seed, RandomState instance, or None (default=None)) – The seed of the pseudo random number multiview_generator to use when shuffling the data.

param_names = ['estimator', 'n_estimators', 'random_state']
distribs
fit(X, y, train_indices=None, view_indices=None)

Build a multimodal boosted classifier from the training set (X, y).

Parameters:
  • X (dict dictionary with all views) – or MultiModalData , MultiModalArray, MultiModalSparseArray or {array-like, sparse matrix}, shape = (n_samples, n_features) Training multi-view input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK and LIL are converted to CSR.

  • y (array-like, shape = (n_samples,)) – Target values (class labels).

  • views_ind (array-like (default=[0, n_features//2, n_features])) –

    Paramater specifying how to extract the data views from X:

    • If views_ind is a 1-D array of sorted integers, the entries indicate the limits of the slices used to extract the views, where view n is given by X[:, views_ind[n]:views_ind[n+1]].

      With this convention each view is therefore a view (in the NumPy sense) of X and no copy of the data is done.

    • If views_ind is an array of arrays of integers, then each array of integers views_ind[n] specifies the indices of the view n, which is then given by X[:, views_ind[n]].

      With this convention each view creates therefore a partial copy of the data in X. This convention is thus more flexible but less efficient than the previous one.

Returns:

self – Returns self.

Return type:

object

Raises:
  • ValueError estimator must support sample_weight

  • ValueError where X and view_ind are not compatibles

predict(X, sample_indices=None, view_indices=None)

Predict classes for X.

The predicted class of an input sample is computed as the weighted mean prediction of the classifiers in the ensemble.

Parameters:

X ({array-like, sparse matrix}, shape = (n_samples, n_features)) – Multi-view input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK and LIL are converted to CSR.

Returns:

y – Predicted classes.

Return type:

numpy.ndarray, shape = (n_samples,)

Raises:

ValueError 'X' input matrix must be have the same total number of features – of ‘X’ fit data

get_interpretation(directory, base_file_name, y_test, feature_ids, multi_class=False)

Base method that returns an empty string if there is not interpretation method in the classifier’s module

set_base_estim_from_dict(dict)