src.models .ALZeroShotWrapper

class src.models. ALZeroShotWrapper ( classifier , max_iter = 1000 , selection_strategy = 'entropy' , n_initial = 100 , increment = 50 , save_classifiers = False , auto_load = True , evaluation_metric = None , random_state = None , verbose = None )

Active Learning with Zero-Shot classification

__init__ ( classifier , max_iter = 1000 , selection_strategy = 'entropy' , n_initial = 100 , increment = 50 , save_classifiers = False , auto_load = True , evaluation_metric = None , random_state = None , verbose = None )

Performs Active Learning using Zero Shot classification for obtaining a pseudo-ground truth. This method attempts to fit to the ZSCF’s predictions and emulate its behavior in a more simplistic way. This might be a good alternative for situations where the computational power is limited.

Parameters

classifier sklearn obj or similar

Classifier to be trained.

max_iter int, default=1000

Maximum number of iterations

selection_strategy str, default=’entropy’

Strategy used to compute uncertainty. Can be either one of ‘entropy’, ‘margin sampling’ or ‘random’.

n_initial int, default=100

Number of initial training points.

increment int, default=50

Number of additional instances per iteration.

save_classifiers bool, default=False

If True, creates a list of classifiers (one per iteration) in the attribute self.classifiers_ .

auto_load bool, default=True

Wether to use the best found classifier as default classification method. If True, the trained classifier object is found at self.classifier_ .

evaluation_metric function or NoneType, default=None

Evaluation metric used to evaluate the classification outputs on each iteration. If None , Overall Accuracy is used.

random_state int or RandomState, default=None

Control the random number generator used. Setting a value to this parameter should allow the experiment to become reproducible.

verbose int, bool or NoneType, default=None

Controls the verbosity during the training process.