File size: 3,433 Bytes
cc6e5ac | 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 | from __future__ import annotations
import numpy as np
from .tcga_params import TCGA_PARAMS
DIFFICULTY_SCALES = {
"easy": 0.6,
"medium": 1.0,
"hard": 1.5,
}
ARCHETYPES = ["immune_hot", "immune_cold", "high_mutation"]
DIFFICULTIES = ["easy", "medium", "hard"]
VISIBILITY_NOISE_STD = 0.05
SUPPRESSION_NOISE_STD = 0.05
VISIBILITY_MUTATION_COUNT_MAX = 5000.0
def _clip(value: float, low: float, high: float) -> float:
return float(np.clip(value, low, high))
def _sigmoid(value: float) -> float:
return float(1.0 / (1.0 + np.exp(-value)))
def _normalize(value: float, low: float, high: float) -> float:
span = high - low
if span <= 0:
return 0.0
return float(np.clip((value - low) / span, 0.0, 1.0))
def sample_tumor_params(archetype: str, difficulty: str) -> dict:
if archetype not in TCGA_PARAMS:
raise ValueError(f"Unknown archetype: {archetype}")
if difficulty not in DIFFICULTY_SCALES:
raise ValueError(f"Unknown difficulty: {difficulty}")
# Difficulty scaling is intentionally simple: it amplifies the same underlying
# cohort-shaped distributions rather than introducing a separate "hard mode" tumor.
scale = DIFFICULTY_SCALES[difficulty]
archetype_params = TCGA_PARAMS[archetype]
# We sample cohort-backed variables first (TMB, mutation count, genomic instability),
# then map them into environment-facing dynamics (mutation, visibility, suppression).
tmb = float(
np.random.lognormal(
archetype_params["tmb"]["mean_log"],
archetype_params["tmb"]["std_log"],
)
)
mutation_count = float(
np.random.lognormal(
archetype_params["mutation_count"]["mean_log"],
archetype_params["mutation_count"]["std_log"],
)
)
genomic_instability = _clip(
float(
np.random.normal(
archetype_params["genomic_instability"]["mean"],
archetype_params["genomic_instability"]["std"],
)
),
0.0,
1.0,
)
# Mutation rate is a bounded, monotonic function of TMB: higher burden → more adaptation pressure.
mutation_rate = _clip(_sigmoid(tmb / 50.0) * 0.3 * scale, 0.01, 0.5)
visibility = _clip(
_normalize(mutation_count, 0.0, VISIBILITY_MUTATION_COUNT_MAX)
+ float(np.random.normal(0.0, VISIBILITY_NOISE_STD)),
0.05,
0.95,
)
# Suppression is modeled as a noisy proxy for immune evasion (PD‑L1-like effect).
pdl1_suppression = _clip(
genomic_instability + float(np.random.normal(0.0, SUPPRESSION_NOISE_STD)),
0.0,
0.9,
)
growth_rate = float(0.05 * scale)
resistance = float(0.30 * scale)
mutation_impact = float(genomic_instability)
return {
"archetype": archetype,
"difficulty": difficulty,
"tmb": tmb,
"mutation_count": mutation_count,
"genomic_instability": genomic_instability,
"mutation_rate": mutation_rate,
"visibility": visibility,
"pdl1_suppression": pdl1_suppression,
"growth_rate": growth_rate,
"resistance": resistance,
"mutation_impact": mutation_impact,
}
def get_random_episode_params() -> dict:
archetype = str(np.random.choice(ARCHETYPES))
difficulty = str(np.random.choice(DIFFICULTIES))
return sample_tumor_params(archetype, difficulty)
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