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from dataclasses import dataclass, field
from functools import total_ordering
from typing import Any
import numpy as np
from dataclasses_json import DataClassJsonMixin
@dataclass
@total_ordering
class MetricValue(DataClassJsonMixin):
"""
Represents the value of a metric to be optimized, which can be compared to other metric values.
Comparisons (and max, min) are based on which value is better, not which is larger.
"""
value: float | int | np.number | np.floating | np.ndarray | None
maximize: bool | None = field(default=None, kw_only=True)
def __post_init__(self):
if self.value is not None:
assert isinstance(self.value, (float, int, np.number, np.floating))
self.value = float(self.value)
def __gt__(self, other) -> bool:
"""True if self is a _better_ (not necessarily larger) metric value than other"""
if self.value is None:
return False
if other.value is None:
return True
assert type(self) is type(other) and (self.maximize == other.maximize)
if self.value == other.value:
return False
comp = self.value > other.value
return comp if self.maximize else not comp # type: ignore
def __eq__(self, other: Any) -> bool:
return self.value == other.value
def __repr__(self) -> str:
return str(self)
def __str__(self) -> str:
if self.maximize is None:
opt_dir = "?"
elif self.maximize:
opt_dir = "↑"
else:
opt_dir = "↓"
return f"Metric{opt_dir}({self.value_npsafe:.4f})"
@property
def is_worst(self):
"""True if the metric value is the worst possible value."""
return self.value is None
@property
def value_npsafe(self):
return self.value if self.value is not None else float("nan")
@dataclass
class WorstMetricValue(MetricValue):
"""
Represents an invalid metric value, e.g. when the agent creates a buggy solution.
Always compares worse than any valid metric value.
"""
value: None = None
def __repr__(self):
return super().__repr__()
def __str__(self):
return super().__str__()
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