Source code for distil.active_learning_strategies.margin_sampling_dropout

import numpy as np
import torch
from .strategy import Strategy

[docs]class MarginSamplingDropout(Strategy): """ Implementation of Margin Sampling Dropout Strategy. This class extends :class:`active_learning_strategies.strategy.Strategy` to include margin sampling dropout technique to select data points for active learning. While least confidence only takes into consideration the maximum probability, margin sampling considers the difference between the confidence of first and the second most probable labels. .. list-table:: Example :widths: 25 25 25 25 :header-rows: 1 * - Data Instances - Label 1 - Label 2 - Label 3 * - p1 - 0.1 - 0.55 - 0.45 * - p2 - 0.2 - 0.3 - 0.5 * - p3 - 0.1 - 0.1 - 0.8 From the above table, the difference between the probability first and the second labels for p1, p2, p3 are 0.1, 0.2, 0.7 respectively. The margin sampling will query the true label for the data instance p1 since it has the smallest difference among all the different data instances. Let :math:`p_i` represent probability for ith label and let there be n possible labels for data instance p. Let :math:`\\max{(t)}` represent the maximum value in t and :math:`max1{(t)}` represent second maximum value in t then, mathematically it can be written as: .. math:: \\min{(\\max{(P)} - \\max1{(P)})} where P=[ :math:`p_1, p_2,… p_n`] The drop out version uses the predict probability dropout function from the base strategy class to find the hypothesised labels. User can pass n_drop argument which denotes the number of times the probabilities will be calculated. The final probability is calculated by averaging probabilities obtained in all iteraitons. Parameters ---------- X: numpy array Present training/labeled data y: numpy array Labels of present training data unlabeled_x: numpy array Data without labels net: class Pytorch Model class handler: class Data Handler, which can load data even without labels. nclasses: int Number of unique target variables args: dict Specify optional parameters batch_size Batch size to be used inside strategy class (int, optional) n_drop Dropout value to be used (int, optional) """ def __init__(self, X, Y, unlabeled_x, net, handler, nclasses, args={}): """ Constructor method """ if 'n_drop' in args: self.n_drop = args['n_drop'] else: self.n_drop = 10 super(MarginSamplingDropout, self).__init__(X, Y, unlabeled_x, net, handler, nclasses, args)
[docs] def select(self, budget): """ Select next set of points Parameters ---------- budget: int Number of indexes to be returned for next set Returns ---------- U_idx: list List of selected data point indexes with respect to unlabeled_x """ probs = self.predict_prob_dropout(self.unlabeled_x, self.n_drop) probs_sorted, idxs = probs.sort(descending=True) U = probs_sorted[:, 0] - probs_sorted[:,1] U_idx = U.sort()[1][:budget] return U_idx