import numpy as np
from . __base import OnlineLearningModel
[docs]class AROW(OnlineLearningModel):
"""The Adaptive Regularization of Weight vectors model.
Crammer, K.; Kulesza, A. & Dredze, M.
Adaptive regularization of weight vectors
Advances in neural information processing systems, 109, 414-422
Attributes:
r (:obj:`int`, optional): AROW's parameter with `r` being
strictly bigger than 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,
r=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._r = r
self._sigma = None
def _update(self, x: np.ndarray, y: int):
"""Updates the weight vector in case a mistake occured.
When presented with a data point, this method evaluates
the error and based on the result, updates or not the
weights vector.
Args:
x (:obj:`np.ndarray` or `list`): An array representing
one single data point. Array needs to be 2D.
y (`int`): Output value for the data point. Takes value
between 1 and -1.
Returns:
None
Raises:
IndexError: if the value x is not 2D.
"""
f_t = self.weights.dot(x)
v_t = x @ self._sigma @ x.T
loss = max(0, 1 - f_t * y) * self.class_weight_[y]
if loss > 0:
beta_t = (1 / (v_t + self._r)) * self.class_weight_[y]
alpha_t = loss * beta_t
sigma = np.expand_dims(x @ self._sigma.T, axis=0)
self.weights += alpha_t * y * np.squeeze(sigma)
self._sigma -= beta_t * sigma.T @ sigma
def _setup(self, X: np.ndarray):
"""Initializes the values for the model' parameters.
Based on the data in argument, this method initializes
the covariance matrix `sigma`.
Args:
X (:obj:`numpy.ndarray`): Input data with n rows and
m columns
Returns:
None
"""
self._sigma = np.identity(X.shape[1])
[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['r'] = self._r
return params