File size: 3,904 Bytes
23d337e | 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 | """Unit tests for confidence/engine.py."""
from __future__ import annotations
from dataclasses import dataclass
from confidence.engine import ConfidenceEngine
@dataclass
class FakeBox:
detector: str
confidence: float
@dataclass
class FakeMatch:
query_face_index: int
best_match: str
distance: float
recognizer: str
@dataclass
class FakeAnalysis:
provider: str
quality_score: float
@dataclass
class FakeMetadata:
provider: str
format: str
@dataclass
class FakeForensics:
provider: str
integrity_score: float
class TestConfidenceEngine:
def test_score_detection_returns_score_in_range(self):
engine = ConfidenceEngine()
box = FakeBox(detector="haar", confidence=0.9)
score = engine.score_detection(box, {})
assert 0.0 <= score.overall <= 1.0
assert "source_reliability" in score.components
assert "cross_provider_consensus" in score.components
assert score.method == "weighted_average"
assert "haar" in score.explanation
def test_score_detection_higher_confidence_yields_higher_score(self):
engine = ConfidenceEngine()
box_low = FakeBox(detector="haar", confidence=0.1)
box_high = FakeBox(detector="haar", confidence=1.0)
score_low = engine.score_detection(box_low, {})
score_high = engine.score_detection(box_high, {})
assert score_high.overall > score_low.overall
def test_score_match_returns_score_in_range(self):
engine = ConfidenceEngine()
m = FakeMatch(query_face_index=0, best_match="alice", distance=0.3, recognizer="face_recognition")
score = engine.score_match(m)
assert 0.0 <= score.overall <= 1.0
assert "face_recognition" in score.explanation
def test_score_match_smaller_distance_yields_higher_score(self):
engine = ConfidenceEngine()
m_close = FakeMatch(query_face_index=0, best_match="alice", distance=0.1, recognizer="face_recognition")
m_far = FakeMatch(query_face_index=0, best_match="alice", distance=0.9, recognizer="face_recognition")
assert engine.score_match(m_close).overall > engine.score_match(m_far).overall
def test_score_image_analysis(self):
engine = ConfidenceEngine()
a = FakeAnalysis(provider="image_quality", quality_score=0.8)
score = engine.score_image_analysis(a)
assert 0.0 <= score.overall <= 1.0
assert "image_quality" in score.explanation
def test_score_metadata_with_format(self):
engine = ConfidenceEngine()
m = FakeMetadata(provider="exif", format="JPEG")
score = engine.score_metadata(m)
assert 0.0 <= score.overall <= 1.0
assert "JPEG" in score.explanation
def test_score_metadata_without_format(self):
engine = ConfidenceEngine()
m = FakeMetadata(provider="exif", format=None)
score = engine.score_metadata(m)
# Without format, extraction_conf drops to 0.5 — should be lower than with format
m_with = FakeMetadata(provider="exif", format="JPEG")
score_with = engine.score_metadata(m_with)
assert score.overall < score_with.overall
def test_score_forensics(self):
engine = ConfidenceEngine()
f = FakeForensics(provider="image_integrity", integrity_score=0.9)
score = engine.score_forensics(f)
assert 0.0 <= score.overall <= 1.0
def test_score_overall_with_no_results(self):
engine = ConfidenceEngine()
score = engine.score_overall()
assert score.overall == 0.0
assert "No results" in score.explanation
def test_reliability_overrides(self):
engine = ConfidenceEngine(reliability_overrides={"haar": 1.0})
assert engine._reliability["haar"] == 1.0
# Default reliabilities should still be present
assert "dnn" in engine._reliability
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