summit.multiview_platform.multiview_classifiers.mumbo
mumbo
- classifier_class_name = 'Mumbo'
- class Mumbo(estimator=None, n_estimators=50, random_state=None, best_view_mode='edge', **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', 'best_view_mode']
- distribs
- set_params(estimator=None, **params)
Sets the estimator from a dict. :param estimator: :param params: :return:
- 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 byX[:, 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 viewn
, which is then given byX[:, 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
- 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,)
- 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)