Source code for distil.active_learning_strategies.margin_sampling

import numpy as np
from .strategy import Strategy
import pdb

[docs]class MarginSampling(Strategy): """ Implementation of Margin Sampling Strategy. This class extends :class:`active_learning_strategies.strategy.Strategy` to include margin sampling 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`] 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) """ def __init__(self, X, Y, unlabeled_x, net, handler, nclasses, args={}): """ Constructor method """ super(MarginSampling, 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(self.unlabeled_x) probs_sorted, idxs = probs.sort(descending=True) U = probs_sorted[:, 0] - probs_sorted[:,1] U_idx = U.sort()[1].numpy()[:budget] return U_idx