import numpy as np
from numpy import linalg as LA
from . pa import PA
[docs]class PA_II(PA):
"""Passive Aggressive-II Model.
Crammer, K. et al., Online Passive-Aggressive algorithms,
Journal of Machine Learning Research, 106, 7, 551-585
Attributes:
C (:obj:`float`, optional): Aggressiveness parameter with `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 __init__(
self,
C=1,
num_iterations=1,
random_state=None,
positive_label=1,
class_weight=None
):
super().__init__(
num_iterations=num_iterations,
random_state=random_state,
positive_label=positive_label,
class_weight=class_weight
)
self._C = C
def _get_gamma(self, loss, s_t):
"""Computes the coefficient used to update the weight vector.
Args:
loss(:obj:`float`): Loss incurred on the current instance.
s_t (:obj:`float`): the L2-norm of the vector representing the
current instance.
Returns:
float: the value of gamma to be used.
"""
return loss / (s_t + (1/(2 * self._C)))
[docs] def get_params(self, deep=True):
"""Get parameters for this estimator.
This function is for use with hyper-parameter tuning utilities
such as `GridSearchCV`_.
Args:
deep(:obj:`bool`, optional): If True, will return the parameters
for this estimator and contained sub-objects that are
estimators. Defaults to True.
.. _GridSearchCV:
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
"""
params = super().get_params()
params['C'] = self._C
return params