| """Unit tests for confidence/engine.py.""" |
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| from __future__ import annotations |
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| from dataclasses import dataclass |
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| from confidence.engine import ConfidenceEngine |
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| @dataclass |
| class FakeBox: |
| detector: str |
| confidence: float |
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| @dataclass |
| class FakeMatch: |
| query_face_index: int |
| best_match: str |
| distance: float |
| recognizer: str |
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| @dataclass |
| class FakeAnalysis: |
| provider: str |
| quality_score: float |
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| @dataclass |
| class FakeMetadata: |
| provider: str |
| format: str |
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| @dataclass |
| class FakeForensics: |
| provider: str |
| integrity_score: float |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| def test_score_metadata_without_format(self): |
| engine = ConfidenceEngine() |
| m = FakeMetadata(provider="exif", format=None) |
| score = engine.score_metadata(m) |
| |
| m_with = FakeMetadata(provider="exif", format="JPEG") |
| score_with = engine.score_metadata(m_with) |
| assert score.overall < score_with.overall |
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| 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 |
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| 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 |
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| def test_reliability_overrides(self): |
| engine = ConfidenceEngine(reliability_overrides={"haar": 1.0}) |
| assert engine._reliability["haar"] == 1.0 |
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| assert "dnn" in engine._reliability |
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