from .strategy import Strategy
[docs]class MarginSampling(Strategy):
"""
Implements the Margin Sampling Strategy a active learning strategy similar to Least Confidence
Sampling Strategy. 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.
Suppose the model has `nclasses` output nodes denoted by :math:`\\overrightarrow{\\boldsymbol{z}}`
and each output node is denoted by :math:`z_j`. Thus, :math:`j \\in [1, nclasses]`.
Then for a output node :math:`z_i` from the model, the corresponding softmax would be
.. math::
\\sigma(z_i) = \\frac{e^{z_i}}{\\sum_j e^{z_j}}
Let,
.. math::
m = \\mbox{argmax}_j{(\\sigma(\\overrightarrow{\\boldsymbol{z}}))}
Then using softmax, Margin Sampling Strategy would pick `budget` no. of elements as follows,
.. math::
\\mbox{argmin}_{{S \\subseteq {\\mathcal U}, |S| \\leq k}}{\\sum_S(\\mbox{argmax}_j {(\\sigma(\\overrightarrow{\\boldsymbol{z}}))}) - (\\mbox{argmax}_{j \\ne m} {(\\sigma(\\overrightarrow{\\boldsymbol{z}}))})}
where :math:`\\mathcal{U}` denotes the Data without lables i.e. `unlabeled_x` and :math:`k` is the `budget`.
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