NaiveSoft

class NaiveSoft(answers, n_classes=2, **kwargs)

Naive soft: Frequency distribution

__init__(answers, n_classes=2, **kwargs)

Naive soft: Frequency distribution of labels

\[\mathrm{NaiveSoft}(i, \mathcal{D}) = \left(\sum_{j\in\mathcal{A}(x_i)}\mathbf{1}(y_i^{(j)} = k)\right)_{k\in[K]}\]

Parameters:

• answers (dict) –

  Dictionary of workers answers with format

  {
      task0: {worker0: label, worker1: label},
      task1: {worker1: label}
  }
  

• n_classes (int, optional) – Number of possible classes, defaults to 2

get_probas()

Get soft labels distribution for each task

Returns:

Label frequency for each task

Return type:

numpy.ndarray(n_task, n_classes)

get_answers()

Argmax of soft labels, in this case corresponds to a majority vote

Returns:

Hard labels (majority vote)

Return type:

numpy.ndarray