diff --git a/summit/multiview_platform/multiview_classifiers/mucombo.py b/summit/multiview_platform/multiview_classifiers/mucombo.py index ac2e4fe131a436ccb9f777de164673e3d92d2851..ad5268e6e052ce0b61bbbfb7c171b96facd9f1ad 100644 --- a/summit/multiview_platform/multiview_classifiers/mucombo.py +++ b/summit/multiview_platform/multiview_classifiers/mucombo.py @@ -1,7 +1,7 @@ from sklearn.tree import DecisionTreeClassifier -from multimodal.boosting.cumbo import MuCumboClassifier +from multimodal.boosting.combo import MuComboClassifier from ..multiview.multiview_utils import BaseMultiviewClassifier from ..utils.hyper_parameter_search import CustomRandint from ..utils.dataset import get_samples_views_indices @@ -10,14 +10,14 @@ from ..utils.base import base_boosting_estimators classifier_class_name = "MuCumbo" -class MuCumbo(BaseMultiviewClassifier, MuCumboClassifier): +class MuCumbo(BaseMultiviewClassifier, MuComboClassifier): def __init__(self, base_estimator=None, n_estimators=50, random_state=None,**kwargs): BaseMultiviewClassifier.__init__(self, random_state) base_estimator = self.set_base_estim_from_dict(base_estimator, **kwargs) - MuCumboClassifier.__init__(self, base_estimator=base_estimator, + MuComboClassifier.__init__(self, base_estimator=base_estimator, n_estimators=n_estimators, random_state=random_state,) self.param_names = ["base_estimator", "n_estimators", "random_state",] @@ -31,7 +31,7 @@ class MuCumbo(BaseMultiviewClassifier, MuCumboClassifier): self.used_views = view_indices numpy_X, view_limits = X.to_numpy_array(sample_indices=train_indices, view_indices=view_indices) - return MuCumboClassifier.fit(self, numpy_X, y[train_indices], + return MuComboClassifier.fit(self, numpy_X, y[train_indices], view_limits) def predict(self, X, sample_indices=None, view_indices=None): @@ -41,7 +41,7 @@ class MuCumbo(BaseMultiviewClassifier, MuCumboClassifier): self._check_views(view_indices) numpy_X, view_limits = X.to_numpy_array(sample_indices=sample_indices, view_indices=view_indices) - return MuCumboClassifier.predict(self, numpy_X) + return MuComboClassifier.predict(self, numpy_X) def get_interpretation(self, directory, base_file_name, labels, multiclass=False):