miml.classifier.mimlTOmi.miml_to_mi_br_classifier.MIMLtoMIBRClassifier#
- class miml.classifier.mimlTOmi.miml_to_mi_br_classifier.MIMLtoMIBRClassifier(mi_classifier)#
Bases:
MIMLtoMIClassifierClass to represent a multi-instance classifier using a binary relevance transformation
- evaluate(dataset_test: MIMLDataset) ndarray#
Evaluate the model on a test dataset
Parameters#
- dataset_testMIMLDataset
Test dataset to evaluate the model on
Returns#
- resultsndarray of shape (n_bags, n_labels)
Predicted labels of dataset_test
- fit(dataset_train: MIMLDataset) None#
Training the classifier
Parameters#
- dataset_trainMIMLDataset
Dataset to train the classifier
- fit_internal(dataset_train: MIMLDataset) None#
Training the classifier
Parameters#
- dataset_train: MIMLDataset
Dataset to train the classifier
- predict(x: ndarray) ndarray#
Predict labels of given data
Parameters#
- xndarray of shape (n_instances, n_labels)
Data to predict their labels
Returns#
- resultsndarray of shape (n_labels)
Predicted labels
- predict_bag(bag: Bag) ndarray#
Predict labels of a given bag
Parameters#
- bagBag
Bag to predict their labels
Returns#
- resultsndarray of shape (n_labels)
Predicted labels of the bag
- predict_proba(dataset_test: MIMLDataset)#
Predict probabilities of given dataset of having a positive label
- Parameters
- dataset_testMIMLDataset
Dataset to predict probabilities
- results: np.ndarray of shape (n_instances, n_labels)
Predicted probabilities for given dataset