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5971541 bb6c547 5971541 cc862d5 5971541 bb6c547 f97653f | 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 | import importlib.util
from pathlib import Path
from jawbreaker.analyzers import load_json_prediction, validate_prediction
def load_run_eval_module():
spec = importlib.util.spec_from_file_location("run_eval", Path("eval/run_eval.py"))
assert spec is not None
assert spec.loader is not None
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module
def test_validate_prediction_accepts_complete_prediction() -> None:
prediction = {
"risk_level": "dangerous",
"scam_type": "credential_theft",
"summary": "This is pretending to be a bank.",
"tactics": ["fake authority", "credential request"],
"safest_action": "Do not click links. Open the official app directly.",
"trusted_person_message": "Can you check this for me?",
"scam_dna": {
"impersonates": "bank",
"pressure": "account locked",
"ask": "login",
"risk": "credential theft",
},
}
assert validate_prediction(prediction) == []
def test_score_rows_tracks_dangerous_as_safe() -> None:
run_eval = load_run_eval_module()
rows = [
{
"id": "case_1",
"category": "bank_phishing",
"input": "Bank login now",
"expected_risk_level": "dangerous",
"expected_scam_type": "credential_theft",
"expected_tactics": ["credential request"],
}
]
predictions = {
"case_1": {
"risk_level": "safe",
"scam_type": "none",
"summary": "Looks fine.",
"tactics": [],
"safest_action": "No action needed.",
"trusted_person_message": "Can you check this?",
"scam_dna": {"impersonates": "", "pressure": "", "ask": "", "risk": ""},
}
}
metrics = run_eval.score_rows(rows, predictions, elapsed=0.01)
assert metrics["risk_level_accuracy"] == 0
assert metrics["dangerous_as_safe"] == ["case_1"]
assert metrics["dangerous_as_needs_check"] == []
assert metrics["suspicious_as_safe"] == []
def test_score_rows_tracks_dangerous_undercalls_and_suspicious_as_safe() -> None:
run_eval = load_run_eval_module()
rows = [
{
"id": "danger_case",
"category": "family_impersonation",
"input": "Grandpa, I need money before midnight.",
"expected_risk_level": "dangerous",
"expected_scam_type": "family_impersonation",
"expected_tactics": ["payment pressure"],
},
{
"id": "suspicious_case",
"category": "suspicious",
"input": "Open this marketplace escrow link.",
"expected_risk_level": "suspicious",
"expected_scam_type": "fake_escrow",
"expected_tactics": ["suspicious link"],
},
]
base_prediction = {
"scam_type": "unknown",
"summary": "Check this.",
"tactics": [],
"safest_action": "Verify through a trusted route.",
"trusted_person_message": "Can you check this?",
"scam_dna": {"impersonates": "", "pressure": "", "ask": "", "risk": ""},
}
predictions = {
"danger_case": {**base_prediction, "risk_level": "needs_check"},
"suspicious_case": {**base_prediction, "risk_level": "safe"},
}
metrics = run_eval.score_rows(rows, predictions, elapsed=0.01)
assert metrics["dangerous_as_needs_check"] == ["danger_case"]
assert metrics["suspicious_as_safe"] == ["suspicious_case"]
def test_score_rows_tracks_model_errors() -> None:
run_eval = load_run_eval_module()
rows = [
{
"id": "case_1",
"category": "safe_benign",
"input": "Dentist appointment Tuesday.",
"expected_risk_level": "safe",
"expected_scam_type": "none",
"expected_tactics": [],
}
]
predictions = {
"case_1": {
"risk_level": "safe",
"scam_type": "none",
"summary": "Looks fine.",
"tactics": [],
"safest_action": "No action needed.",
"trusted_person_message": "Can you check this?",
"scam_dna": {"impersonates": "", "pressure": "", "ask": "", "risk": ""},
"_jawbreaker_model_error": "JSONDecodeError('empty')",
}
}
metrics = run_eval.score_rows(rows, predictions, elapsed=0.01)
assert metrics["model_errors"] == [{"id": "case_1", "error": "JSONDecodeError('empty')"}]
def test_has_unsafe_action_allows_do_not_send_money() -> None:
run_eval = load_run_eval_module()
assert not run_eval.has_unsafe_action("Do not send money. Call a known number.")
assert run_eval.has_unsafe_action("Send money to verify the account.")
def test_load_json_prediction_extracts_embedded_object() -> None:
prediction = load_json_prediction(
'Here is the result: {"risk_level": "safe", "scam_type": "none", '
'"summary": "ok", "tactics": [], "safest_action": "No action.", '
'"trusted_person_message": "Please check.", '
'"scam_dna": {"impersonates": "", "pressure": "", "ask": "", "risk": ""}}'
)
assert prediction["risk_level"] == "safe"
def test_load_json_prediction_ignores_qwen_thinking_tokens() -> None:
prediction = load_json_prediction(
'<think>I should reason internally.</think>{"risk_level": "dangerous", '
'"scam_type": "family_impersonation", "summary": "scam", '
'"tactics": ["secrecy"], "safest_action": "Do not reply.", '
'"trusted_person_message": "Can you check this?", '
'"scam_dna": {"impersonates": "family", "pressure": "secret", "ask": "money", "risk": "payment"}}'
)
assert prediction["risk_level"] == "dangerous"
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