import numpy as np
import math
from scipy.stats import norm
from . scw import SCW
[docs]class SCW2(SCW):
"""Soft Confidence Weighted variant 2 model.
Wang, J.; Zhao, P. & Hoi, S. C. H.
Exact Soft Confidence-Weighted learning
CoRR, 112, abs/1206.4612
Attributes:
eta (:obj:`float`, optional): Mean weight value. Defaults to 0.7.
C (:obj:`float`, optional): Initial variance parameter, `C > 0`.
Defaults to 1.
num_iterations (:obj:`int`, optional): Number of iterations
to run the training for. Defaults to 1.
random_state (:obj:`int`, optional): The random seed to use
with the pseudo-random generator. Defaults to `None`.
positive_label (:obj:`int`, optional): The number in the output
field that represents the positive label. The value passed
should be different than -1. Defaults to 1.
class_weight (:obj:`dict`, optional): Represents the relative
weight of the labels in the data. Useful for imbalanced
classification tasks.
Raises:
AssertionError: if `positive_label` is equal to -1.
"""
def _get_alpha(self, m_t, v_t):
"""Computes the alpha for the CW/SCW algorithms.
The `alpha` variable is used to determine the magnitude of
update that needs to be applied to the weights.
Args:
m_t (:obj:`float`): Represents whether there was an error in
prediction or not. 1 for no error, -1 otherwise.
v_t (:obj:`float`): Represents how far the point was from its
actual value.
Returns:
float: the value for `alpha`.
"""
n_t = v_t + 1 / (2 * self._C)
return max(0, ((-(2 * m_t * n_t + self._phi ** 2 * m_t * v_t) +
math.sqrt((self._phi ** 4 * m_t ** 2 * v_t * 2 + 4
* n_t * v_t * self._phi ** 2
* (n_t + v_t * self._phi * 2))))
/ (2 * (n_t ** 2 + n_t * v_t * self._phi ** 2))))