Source code for caliber.binary_classification.minimizing.mixins.fit.smooth_fit
import abc
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from scipy.optimize import minimize
class SmoothFitBinaryClassificationMixin(abc.ABC):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def fit(self, probs: np.ndarray, targets: np.ndarray) -> dict:
self._check_targets(targets)
self._check_probs(probs)
def _loss_fn(params):
return self._loss_fn(targets, self._get_output_for_loss(params, probs))
status = minimize(_loss_fn, **self._minimize_options)
self._params = status.x
return status
@staticmethod
def _get_x0() -> Union[np.ndarray, float]:
pass
def _config_minimize_options(self, minimize_options: Optional[dict]) -> dict:
minimize_options = deepcopy(minimize_options) or dict()
if "x0" not in minimize_options:
minimize_options["x0"] = self._get_x0()
return minimize_options