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"