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ad12dda | 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 | """Tests for the latent-state simulator modules."""
from models import ActionType, DrugTargetAction, OutputType
from server.simulator.latent_state import (
CreditState,
DataQualityState,
FullLatentState,
TargetProfile,
ValidationProgress,
)
from server.simulator.noise import NoiseModel
from server.simulator.output_generator import OutputGenerator
from server.simulator.transition import TransitionEngine
def _make_state() -> FullLatentState:
return FullLatentState(
target=TargetProfile(
expression_level="high_specific",
tissue_specificity=0.8,
disease_overexpression=3.0,
druggability_score=0.7,
binding_pocket_quality="good",
has_known_ligands=True,
allosteric_site_available=True,
selectivity_ratio=8.0,
off_target_count=2,
off_target_genes=["OFF1", "OFF2"],
toxicity_profile="mild",
toxicity_tissues=[],
clinical_precedent="positive",
clinical_stage_reached="phase2",
in_vitro_ic50_nM=10.0,
in_vivo_efficacy="strong",
crispr_essentiality=-1.0,
true_viability_score=0.8,
correct_decision="go",
key_evidence_dimensions=["expression", "druggability"],
),
data_quality=DataQualityState(noise_level=0.1),
progress=ValidationProgress(),
credits=CreditState(credits_total=50),
)
class TestNoiseModel:
def test_deterministic_with_seed(self):
n1 = NoiseModel(seed=42)
n2 = NoiseModel(seed=42)
assert n1.sample_qc_metric(0.5, 0.1) == n2.sample_qc_metric(0.5, 0.1)
def test_false_positives(self):
n = NoiseModel(seed=0)
fps = n.generate_false_positives(1000, 0.01)
assert all(g.startswith("FP_GENE_") for g in fps)
def test_quality_degradation_bounded(self):
n = NoiseModel(seed=0)
for _ in range(100):
q = n.quality_degradation(0.9, [0.8, 0.7])
assert 0.0 <= q <= 1.0
class TestOutputGenerator:
def test_query_expression_returns_expression_result(self):
gen = OutputGenerator(NoiseModel(seed=1))
out = gen.generate(
DrugTargetAction(
action_type=ActionType.QUERY_EXPRESSION,
parameters={"database": "GTEx"},
),
_make_state(),
1,
)
assert out.output_type == OutputType.EXPRESSION_RESULT
assert out.data["expression_level"] == "high_specific"
def test_druggability_screen_returns_druggability_result(self):
gen = OutputGenerator(NoiseModel(seed=42))
out = gen.generate(
DrugTargetAction(action_type=ActionType.DRUGGABILITY_SCREEN),
_make_state(),
2,
)
assert out.output_type == OutputType.DRUGGABILITY_RESULT
assert "druggability_score" in out.data
def test_binding_site_allosteric_flag(self):
gen = OutputGenerator(NoiseModel(seed=7))
out = gen.generate(
DrugTargetAction(
action_type=ActionType.BINDING_SITE_ANALYSIS,
parameters={"include_allosteric": True},
),
_make_state(),
3,
)
assert out.output_type == OutputType.BINDING_SITE_RESULT
assert out.data["allosteric_site_detected"] is True
class TestTransitionEngine:
def test_progress_flag_set_after_action(self):
engine = TransitionEngine(NoiseModel(seed=0))
result = engine.step(
_make_state(),
DrugTargetAction(action_type=ActionType.QUERY_EXPRESSION),
)
assert result.next_state.progress.expression_queried is True
def test_credit_deduction(self):
engine = TransitionEngine(NoiseModel(seed=0))
state = _make_state()
result = engine.step(
state,
DrugTargetAction(action_type=ActionType.IN_VIVO_MODEL),
)
assert result.next_state.credits.credits_used == 8
def test_hard_violation_blocks(self):
engine = TransitionEngine(NoiseModel(seed=0))
result = engine.step(
_make_state(),
DrugTargetAction(action_type=ActionType.QUERY_EXPRESSION),
hard_violations=["test_block"],
)
assert result.output.success is False
assert result.output.output_type == OutputType.FAILURE_REPORT
def test_submit_validation_report_ends_episode(self):
engine = TransitionEngine(NoiseModel(seed=0))
result = engine.step(
_make_state(),
DrugTargetAction(
action_type=ActionType.SUBMIT_VALIDATION_REPORT,
final_decision="go",
confidence=0.8,
),
)
assert result.done is True
def test_credit_exhaustion_ends_episode(self):
engine = TransitionEngine(NoiseModel(seed=0))
state = _make_state()
state.credits.credits_used = state.credits.credits_total - 1
result = engine.step(
state,
DrugTargetAction(action_type=ActionType.QUERY_EXPRESSION),
)
assert result.done is True
|