nmoo.denoisers.resample_average

 1"""
 2Averaging: a naive method to reduce 0-mean noise.
 3"""
 4__docformat__ = "google"
 5
 6from typing import List
 7
 8from pymoo.core.problem import Problem
 9import numpy as np
10
11from nmoo.wrapped_problem import WrappedProblem
12
13
14class ResampleAverage(WrappedProblem):
15    """
16    A resampler tries to denoise a noisy problem by evaluating a solution
17    multiple times and averaging the outputs.
18    """
19
20    _n_evaluations: int
21
22    def __init__(
23        self,
24        problem: Problem,
25        n_evaluations: int = 5,
26        *,
27        name: str = "resample_avg",
28        **kwargs,
29    ):
30        """
31        Constructor.
32
33        Args:
34            problem (pymoo `Problem`): Noisy pymoo problem (or
35                `nmoo.wrapped_problem.WrappedProblem`).
36            n_evaluations (int): Number of times to evaluate the problem on
37                each solution. Defaults to 5.
38            name (str): An optional name for this problem. This will be used
39                when creating history dump files. Defaults to `resample_avg`.
40        """
41        super().__init__(problem, name=name, **kwargs)
42
43        if n_evaluations <= 0:
44            raise ValueError("The number of evaluations should be at least 1.")
45        self._n_evaluations = n_evaluations
46
47    def _evaluate(self, x, out, *args, **kwargs):
48        outs: List[dict] = []
49        for _ in range(self._n_evaluations):
50            outs.append({})
51            self._problem._evaluate(x, outs[-1], *args, **kwargs)
52        for k in outs[0]:  # By assumption, self._n_evaluations > 0
53            # TODO: What if the type is not consistent across evaluations?
54            if isinstance(outs[0][k], (int, float, np.ndarray)):
55                out[k] = np.average([o[k] for o in outs], axis=0)
56            else:
57                # TODO: Deal with different non numeric outputs
58                out[k] = outs[-1][k]
59        self.add_to_history_x_out(x, out)