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3458215 cc86172 3458215 848a7a5 3458215 848a7a5 3458215 848a7a5 3458215 cc86172 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 | from __future__ import annotations
from pino.substitution_v11 import evaluate_formula, evaluate_material, evaluate_retriever, score_candidate, substitute, wilson_interval
def tiny_profiles():
return [
{
"pimt_version": "v11",
"cas": "1",
"name": "Target Floral",
"descriptor_distribution": {"F": 1.0},
"substantivity_log10_predicted": 1.0,
"log10_vapor_pressure_pa": 0.5,
"boiling_point_k": 450.0,
"vapor_pressure_pa": 3.0,
"enrichment_pending": ["IFRA/allergen/use-level"],
},
{
"pimt_version": "v11",
"cas": "2",
"name": "Documented Floral Substitute",
"descriptor_distribution": {"F": 0.9, "G": 0.1},
"substantivity_log10_predicted": 1.05,
"log10_vapor_pressure_pa": 0.4,
"boiling_point_k": 455.0,
"vapor_pressure_pa": 2.5,
"enrichment_pending": ["IFRA/allergen/use-level"],
},
{
"pimt_version": "v11",
"cas": "3",
"name": "Wrong Citrus",
"descriptor_distribution": {"A": 1.0},
"substantivity_log10_predicted": 3.0,
"log10_vapor_pressure_pa": 4.0,
"boiling_point_k": 330.0,
"vapor_pressure_pa": 1000.0,
"enrichment_pending": ["IFRA/allergen/use-level"],
},
]
def test_substitute_ranks_documented_like_profile_first():
ranked = substitute("1", tiny_profiles(), mode="function_match", top_k=2)
assert ranked[0]["candidate_cas"] == "2"
assert ranked[0]["score"] > ranked[1]["score"]
def test_evaluate_retriever_uses_eval_rows_without_training():
rows = [
{
"pair_id": "pair-1",
"target": "Target Floral",
"target_cas": "1",
"substitute": "Documented Floral Substitute",
"substitute_cas": "2",
"grade": "acceptable",
"mode": "odor_match",
}
]
result = evaluate_retriever(rows, tiny_profiles(), top_k=1)
assert result["n_evaluable"] == 1
assert result["n_hit"] == 1
assert result["n_missed"] == 0
assert result["n_excluded"] == 0
assert result["top_k_hit"] == wilson_interval(1, 1)
assert result["mode"] == "per-row"
assert result["mode_counts"] == {"odor_match": 1}
assert result["by_mode_top_k_hit"] == {"odor_match": wilson_interval(1, 1)}
assert "trained" not in result
def test_evaluate_retriever_uses_each_rows_mode():
rows = [
{
"pair_id": "odor-row",
"target_cas": "1",
"substitute_cas": "2",
"grade": "acceptable",
"mode": "odor_match",
},
{
"pair_id": "function-row",
"target_cas": "1",
"substitute_cas": "2",
"grade": "acceptable",
"mode": "function_match",
},
]
result = evaluate_retriever(rows, tiny_profiles(), top_k=1)
assert result["mode_counts"] == {"function_match": 1, "odor_match": 1}
assert [item["mode"] for item in result["evaluated"]] == ["odor_match", "function_match"]
def test_accord_requires_every_component_and_reports_component_recall():
rows = [
{
"pair_id": "accord-row",
"target_cas": "1",
"grade": "acceptable",
"mode": "accord_rebuild",
"answer": {
"shape": "accord",
"materials": [{"cas": "2"}, {"cas": "3"}],
},
}
]
missed = evaluate_retriever(rows, tiny_profiles(), top_k=1)
hit = evaluate_retriever(rows, tiny_profiles(), top_k=2)
assert missed["n_missed"] == 1
assert missed["evaluated"][0]["answer_components_retrieved"] == 1
assert missed["evaluated"][0]["answer_components_total"] == 2
assert hit["n_hit"] == 1
def test_any_of_accepts_one_preregistered_alternative():
rows = [
{
"pair_id": "any-of-row",
"target_cas": "1",
"grade": "acceptable",
"mode": "function_match",
"answer": {
"shape": "any_of",
"materials": [{"cas": "2"}, {"cas": "3"}],
},
}
]
result = evaluate_retriever(rows, tiny_profiles(), top_k=1)
assert result["n_hit"] == 1
assert result["evaluated"][0]["answer_components_retrieved"] == 1
def test_excluded_and_missed_are_separate_first_class_counts():
rows = [
{
"pair_id": "excluded",
"target_cas": "missing-target",
"substitute_cas": "2",
"grade": "acceptable",
"mode": "function_match",
},
{
"pair_id": "missed",
"target_cas": "1",
"substitute_cas": "3",
"grade": "acceptable",
"mode": "function_match",
},
]
result = evaluate_retriever(rows, tiny_profiles(), top_k=1)
assert result["n_eval_rows"] == 2
assert result["n_excluded"] == 1
assert result["n_evaluable"] == 1
assert result["n_missed"] == 1
assert result["n_hit"] == 0
def test_assessment_interfaces_do_not_emit_safety_authority():
material = evaluate_material("1", tiny_profiles())
formula = evaluate_formula([{"cas": "1", "weight_fraction": 0.4}], tiny_profiles())
assert material["pimt_version"] == "v11"
assert "IFRA/allergen/use-level" in material["enrichment_pending"]
assert formula["substitution_risks"] == ["safety/use-level flags enrichment-pending; no safety claims emitted"]
def test_missing_features_are_unobserved_not_neutral_positive_evidence():
scored = score_candidate(tiny_profiles()[0], {"cas": "4", "name": "Empty"})
assert scored["score"] == 0.0
assert scored["evidence_coverage"] == 0.0
assert all(value is None for value in scored["reasons"].values())
def test_answer_resolving_to_target_is_invalid_not_a_miss():
rows = [{
"pair_id": "self-answer", "target_cas": "1", "substitute_cas": "1",
"grade": "acceptable", "mode": "function_match",
}]
result = evaluate_retriever(rows, tiny_profiles())
assert result["n_evaluable"] == 0
assert result["excluded"] == [{"pair_id": "self-answer", "reason": "answer_resolves_to_target"}]
def test_unimplemented_mode_is_reported_unsupported_not_as_a_miss():
rows = [{
"pair_id": "cost-without-cost-data", "target_cas": "1", "substitute_cas": "2",
"grade": "acceptable", "mode": "cost_match",
}]
result = evaluate_retriever(rows, tiny_profiles())
assert result["n_evaluable"] == 0
assert result["excluded"][0]["reason"] == "mode_features_unsupported"
def test_cost_mode_uses_observed_cost_axis_when_available():
target = tiny_profiles()[0] | {"cost_tier": "low"}
same = tiny_profiles()[1] | {"cost_tier": "low"}
different = tiny_profiles()[2] | {"cost_tier": "high"}
assert score_candidate(target, same, mode="cost_match")["reasons"]["constraints"] == 1.0
assert score_candidate(target, different, mode="cost_match")["reasons"]["constraints"] == 0.0
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