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1a74e1c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | """
Progressive Overload Engine — standalone, stdlib only.
Given exercise performance history, determines whether the agent's
proposed prescription follows correct double-progression rules.
Used by the grader to score the 'progressive_overload' dimension.
"""
from __future__ import annotations
from typing import Optional
def _parse_rep_range(s: str) -> tuple[int, int]:
if not s:
return (8, 12)
s = s.strip()
if "-" in s:
parts = s.split("-", 1)
try:
return (int(parts[0]), int(parts[1]))
except ValueError:
return (8, 12)
try:
n = int(s)
return (n, n)
except ValueError:
return (8, 12)
def _parse_reps_completed(s: str) -> list[int]:
if not s:
return []
out = []
for part in s.split(","):
try:
out.append(int(part.strip()))
except ValueError:
pass
return out
def _is_compound(name: str) -> bool:
compound = ["squat", "deadlift", "bench", "press", "row",
"pull-up", "pullup", "chin-up", "dip", "lunge", "thrust"]
isolation = ["curl", "extension", "raise", "fly", "flye", "kickback"]
nl = name.lower()
if any(k in nl for k in isolation):
return False
return any(k in nl for k in compound)
def expected_progression(
exercise_name: str,
last_weight_kg: float,
last_reps_str: str,
target_reps: str = "8-12",
target_sets: int = 3,
) -> dict:
"""
Compute the correct next-session prescription given last performance.
Returns {"progression_type", "expected_weight_kg", "expected_reps"}.
"""
last_reps = _parse_reps_completed(last_reps_str)
target_lo, target_hi = _parse_rep_range(target_reps)
compound = _is_compound(exercise_name)
if not last_reps:
return {"progression_type": "repeat",
"expected_weight_kg": last_weight_kg,
"expected_reps": target_reps}
all_hit_top = (
len(last_reps) >= target_sets
and all(r >= target_hi for r in last_reps[:target_sets])
)
heavy_miss = sum(1 for r in last_reps if r < target_lo) >= 2
single_miss = any(r < target_lo for r in last_reps) and not all_hit_top
if heavy_miss:
dw = round(last_weight_kg * 0.9 / 2.5) * 2.5 if last_weight_kg > 0 else 0
return {"progression_type": "deload",
"expected_weight_kg": dw,
"expected_reps": target_reps}
if single_miss:
return {"progression_type": "repeat",
"expected_weight_kg": last_weight_kg,
"expected_reps": target_reps}
if all_hit_top:
if last_weight_kg == 0:
new_hi = target_hi + 2
return {"progression_type": "add_reps",
"expected_weight_kg": 0,
"expected_reps": f"{target_lo + 1}-{new_hi}"}
inc = 2.5 if compound else 1.25
nw = round((last_weight_kg + inc) / 2.5) * 2.5
return {"progression_type": "add_weight",
"expected_weight_kg": nw,
"expected_reps": target_reps}
return {"progression_type": "repeat",
"expected_weight_kg": last_weight_kg,
"expected_reps": target_reps}
def verify_agent_overload(
exercise_name: str,
agent_weight_kg: float,
agent_reps: str,
last_weight_kg: float,
last_reps_str: str,
target_reps: str = "8-12",
target_sets: int = 3,
) -> tuple[bool, str]:
"""
Check whether agent's prescription follows correct overload logic.
Returns (is_correct, explanation).
"""
expected = expected_progression(
exercise_name, last_weight_kg, last_reps_str, target_reps, target_sets
)
ptype = expected["progression_type"]
exp_w = expected["expected_weight_kg"]
if ptype == "add_weight":
min_ok = last_weight_kg + 1.0 # must be meaningfully heavier
if agent_weight_kg >= min_ok:
return True, (
f"Correct: added weight "
f"(agent={agent_weight_kg}kg, expected≥{exp_w}kg)."
)
return False, (
f"Should add weight to {exp_w}kg "
f"(agent submitted {agent_weight_kg}kg, last was {last_weight_kg}kg)."
)
if ptype == "deload":
if agent_weight_kg <= last_weight_kg * 0.95:
return True, f"Correct deload (agent={agent_weight_kg}kg < {last_weight_kg}kg)."
return False, f"Should deload to ~{exp_w}kg, agent kept {agent_weight_kg}kg."
if ptype == "add_reps":
_, target_hi = _parse_rep_range(target_reps)
_, agent_hi = _parse_rep_range(agent_reps)
if agent_hi > target_hi:
return True, f"Correct rep progression (agent={agent_reps})."
return False, (
f"Should increase rep target above {target_hi}, "
f"agent submitted {agent_reps}."
)
# repeat — weight should stay same ±2.5kg
if abs(agent_weight_kg - last_weight_kg) <= 2.5:
return True, f"Correct: repeating prescription ({last_weight_kg}kg)."
return False, (
f"Should repeat {last_weight_kg}kg, agent changed to {agent_weight_kg}kg."
) |