Source code for caliber.binary_classification.minimizing.base_brute_fit_mixin
import abc
from copy import deepcopy
from typing import List, Optional, Tuple
import numpy as np
from scipy.optimize import brute
class BruteFitBinaryClassificationMixin(abc.ABC):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def fit(self, probs: np.ndarray, targets: np.ndarray):
self._check_targets(targets)
self._check_probs(probs)
def _loss_fn(params):
return self._loss_fn(targets, self._get_output_for_loss(params, probs))
self._params = brute(_loss_fn, **self._minimize_options)
@staticmethod
def _get_ranges() -> List[Tuple]:
pass
@staticmethod
def _get_Ns() -> int:
pass
def _config_minimize_options(self, minimize_options: Optional[dict]) -> dict:
minimize_options = deepcopy(minimize_options) or dict()
if "ranges" not in minimize_options:
minimize_options["ranges"] = self._get_ranges()
if "Ns" not in minimize_options:
minimize_options["Ns"] = self._get_Ns()
return minimize_options