Skip to content
Fetching contributors…
Cannot retrieve contributors at this time
51 lines (42 sloc) 2.02 KB
from sklearn.base import BaseEstimator, TransformerMixin, ClassifierMixin
from scipy.optimize import minimize
import numpy as np
from ._base import AbstractRealIsotonicRegression
from .curves import PiecewiseLinearIsotonicCurve
__all__ = ['LpIsotonicRegression']
class LpIsotonicRegression(AbstractRealIsotonicRegression):
def __init__(self, npoints, power=2, increasing=True, cut_algo='quantile', curve_algo=PiecewiseLinearIsotonicCurve):
super().__init__(npoints, increasing=increasing, cut_algo=cut_algo, curve_algo=curve_algo)
assert (power >= 1), "Power must be bigger than or equal to 1"
self.power = power
def _check_x_y(self, X, y):
assert np.all(np.isfinite(X)), "All x-values must be finite"
assert np.all(np.isfinite(y)), "All y-values must be finite"
def _err_func(self, x_cuts, X, y):
def err(alpha):
gamma = self.gamma_of_alpha(alpha)
curve = self.curve_algo(x=x_cuts, y=gamma)
y_p = curve.f(X)
result = 0
result += np.power(np.abs(y_p-y), self.power).sum()
return result / len(X)
return err
def _grad_err_func(self, x_cuts, X, y):
N = len(X)
grad_y = [] # Part of performance hack, see below
def grad_err(alpha):
gamma = self.gamma_of_alpha(alpha)
curve = self.curve_algo(x=x_cuts, y=gamma)
y_p = curve.f(X)
delta = y_p - y
dE_dgamma = np.zeros(shape=(N,))
if self.power == 1:
dE_dgamma += np.sign(delta)
dE_dgamma += self.power * np.power(np.abs(delta), self.power-1) * np.sign(delta)
if len(grad_y) == 0: # Terrible performance hack
grad_y.append(curve.grad_y(X)) # This value depends only on x_cuts, so if we calculate it once we don't need to recalculate it
dE_dgamma = grad_y[0] @ dE_dgamma
result = self.grad_gamma_of_alpha(alpha) @ dE_dgamma / N
return result
return grad_err
You can’t perform that action at this time.