def __init__(self,
a=2.155,
alpha=0.94,
b=0.789,
beta=1.0,
c=0.835,
gamma=2.0,
shuffle=True,
random_state=None):
"""Initialize the triple balance strategy.
Arguments
---------
a: float
Governs the weight of the 1's. Higher values mean linearly more 1's
in your training sample.
alpha: float
Governs the scaling the weight of the 1's, as a function of the
ratio of ones to zeros. A positive value means that the lower the
ratio of zeros to ones, the higher the weight of the ones.
b: float
Governs how strongly we want to sample depending on the total
number of samples. A value of 1 means no dependence on the total
number of samples, while lower values mean increasingly stronger
dependence on the number of samples.
beta: float
Governs the scaling of the weight of the zeros depending on the
number of samples. Higher values means that larger samples are more
strongly penalizing zeros.
c: float
Value between one and zero that governs the weight of samples done
with maximal sampling. Higher values mean higher weight.
gamma: float
Governs the scaling of the weight of the max samples as a function
of the % of papers read. Higher values mean stronger scaling.
"""
super(TripleBalance, self).__init__()
self.a = a
self.alpha = alpha
self.b = b
self.beta = beta
self.c = c
self.gamma = gamma
self.shuffle = shuffle
self.fallback_model = DoubleBalance(a=a,
alpha=alpha,
b=b,
beta=beta,
random_state=random_state)
self._random_state = get_random_state(random_state)
def __init__(self,
a=2.155,
alpha=0.94,
b=0.789,
beta=1.0,
c=0.835,
gamma=2.0,
shuffle=True,
random_state=None):
"""Initialize the triple balance strategy."""
super(TripleBalance, self).__init__()
self.a = a
self.alpha = alpha
self.b = b
self.beta = beta
self.c = c
self.gamma = gamma
self.shuffle = shuffle
self.fallback_model = DoubleBalance(a=a,
alpha=alpha,
b=b,
beta=beta,
random_state=random_state)
self._random_state = get_random_state(random_state)