File size: 3,692 Bytes
b99b9ee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | """Numerical metrics over (observed, predicted) trajectory pairs.
Responsibility: compute scalar fit quality (R-squared), per-variable
residuals, and lightweight diagnostic statistics. Does no parsing, no
simulation, no English-text generation.
"""
from __future__ import annotations
from collections.abc import Iterable
import numpy as np
from pydantic import BaseModel, ConfigDict, Field
class ResidualSummary(BaseModel):
"""Diagnostic statistics derived from per-variable residuals.
Consumed exclusively by :mod:`physix.verifier.mismatch` to render the
English residual summary surfaced to the agent.
"""
model_config = ConfigDict(frozen=True)
per_variable_max_abs_residual: dict[str, float] = Field(default_factory=dict)
per_variable_t_of_max_residual: dict[str, float] = Field(default_factory=dict)
per_variable_late_residual_mean: dict[str, float] = Field(default_factory=dict)
overall_r2: float = 0.0
def compute_match(
observed: dict[str, np.ndarray],
predicted: dict[str, np.ndarray],
state_variables: Iterable[str],
) -> float:
"""Compute the per-step R-squared used as the primary reward signal.
Returns the **average** of per-variable R-squared values, clipped to
``[0, 1]``. This intentionally rewards models that get *some* variables
right even if others diverge.
"""
r2s = [
_r_squared(observed[var], predicted[var])
for var in state_variables
if var in observed and var in predicted
]
if not r2s:
return 0.0
avg = float(np.mean(r2s))
return _clip01(avg)
def residual_summary(
timestamps: np.ndarray,
observed: dict[str, np.ndarray],
predicted: dict[str, np.ndarray],
state_variables: Iterable[str],
) -> ResidualSummary:
"""Build a structured residual summary used downstream by mismatch.py."""
per_max: dict[str, float] = {}
per_t_max: dict[str, float] = {}
per_late_mean: dict[str, float] = {}
r2_values: list[float] = []
for var in state_variables:
if var not in observed or var not in predicted:
continue
obs = observed[var]
pred = predicted[var]
residual = pred - obs
r2_values.append(_r_squared(obs, pred))
i_max = int(np.argmax(np.abs(residual)))
per_max[var] = float(np.abs(residual[i_max]))
per_t_max[var] = float(timestamps[i_max])
# Mean residual magnitude over the last 25% of the trajectory; this
# is the signal the mismatch summariser uses to detect drift /
# plateau-mismatch.
late_start = int(0.75 * len(timestamps))
per_late_mean[var] = float(np.mean(np.abs(residual[late_start:])))
overall = float(np.mean(r2_values)) if r2_values else 0.0
return ResidualSummary(
per_variable_max_abs_residual=per_max,
per_variable_t_of_max_residual=per_t_max,
per_variable_late_residual_mean=per_late_mean,
overall_r2=_clip01(overall),
)
def _r_squared(observed: np.ndarray, predicted: np.ndarray) -> float:
"""Coefficient of determination with a zero floor.
Returns 0.0 when the observed series is constant (degenerate).
Returns 0.0 when the model is worse than the observed mean.
"""
if observed.shape != predicted.shape:
return 0.0
obs_mean = float(np.mean(observed))
ss_res = float(np.sum((observed - predicted) ** 2))
ss_tot = float(np.sum((observed - obs_mean) ** 2))
if ss_tot <= 0.0:
return 0.0
return _clip01(1.0 - ss_res / ss_tot)
def _clip01(value: float) -> float:
if value < 0.0:
return 0.0
if value > 1.0:
return 1.0
return value
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