def __init__(self, unlabeled_datasets = [], models=None): # # call the BaseLearner constructor to initialize various globals and process the # datasets, etc.; of course, these can subsequently be overwritten. BaseNBLearner.__init__(self, unlabeled_datasets=unlabeled_datasets, models=models) # use the random query function (i.e., ask for labels at random) self.query_function = self.get_random_unlabeled_ids self.name = "Random Naive Bayes"
def __init__(self, unlabeled_datasets = [], models=None, undersample_before_eval=False): # # call the BaseLearner constructor to initialize various globals and process the # datasets, etc.; of course, these can subsequently be overwritten. BaseNBLearner.__init__(self, unlabeled_datasets=unlabeled_datasets, models=models, undersample_before_eval=undersample_before_eval) # set the query function to uncertainty sampling self.query_function = self.uncertainty_sample self.name = "Uncertain Naive Bayes"
def __init__(self, unlabeled_datasets=[], models=None): # # call the BaseLearner constructor to initialize various globals and process the # datasets, etc.; of course, these can subsequently be overwritten. BaseNBLearner.__init__(self, unlabeled_datasets=unlabeled_datasets, models=models) # use the random query function (i.e., ask for labels at random) self.query_function = self.get_random_unlabeled_ids self.name = "Random Naive Bayes"
def __init__(self, unlabeled_datasets=[], models=None): # # call the BaseLearner constructor to initialize various globals and process the # datasets, etc.; of course, these can subsequently be overwritten. BaseNBLearner.__init__(self, unlabeled_datasets=unlabeled_datasets, models=models) # set the query function to uncertainty sampling pdb.set_trace() self.query_function = self.uncertainty_sample self.name = "Uncertain Naive Bayes"