import numpy as np
import torch
from .strategy import Strategy
[docs]class EntropySamplingDropout(Strategy):
"""
Implementation of Entropy Sampling Dropout Strategy.
This class extends :class:`active_learning_strategies.strategy.Strategy`
to include entropy sampling with dropout technique to select data points for active learning.
Least Confidence and Margin Sampling do not make use of all the label probabilities, whereas entropy sampling calculates entropy based on the hypothesised confidence scores for each label and queries for the true label of a data instance with the highest entropy.
.. list-table:: Example
:widths: 50 50
:header-rows: 1
* - Data Instances
- Entropy
* - p1
- 0.2
* - p2
- 0.5
* - p3
- 0.7
From the above table, Entropy sampling will query for the true label data instance p3 since it has the highest entropy.
Let :math:`p_i` denote probability for ith label of data instance p, and let total possible labels be denoted by n, then Entropy for p is calculated as:
.. math::
E = \\sum{p_i*log(p_i)}
where i=1,2,3....n
Thus Entropy Selection can be mathematically shown as:
..math::
\\max{(E)}
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(EntropySamplingDropout, 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)
log_probs = torch.log(probs)
U = (probs*log_probs).sum(1)
U_idx = U.sort()[1][:budget]
return U_idx