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| b8bf9ec6e1 | |||
| 167f029c28 | |||
| 5a2edb8b94 | |||
| 29e53f7dc6 |
+2
-1
@@ -13,6 +13,7 @@
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- [ ] Database V2 read-only parser
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- [x] Configuration
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- [x] Logging
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- [x] Matching engine
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## v0.2 — Diagnostics
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@@ -22,7 +23,7 @@
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- [ ] Orphaned audio detection
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- [ ] OneDrive rename detection
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- [ ] Crate classification: static vs smart/dynamic
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- [ ] Library health score
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- [x] Library health score
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## v0.3 — Safe Repair
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@@ -0,0 +1,31 @@
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# Library Health Engine
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## Problem
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Raw missing-reference counts do not provide a compact view of library integrity,
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but an opaque blended score would imply confidence the current data cannot support.
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## Architecture
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The health engine produces an immutable report from the core `Library`. Its score
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is only the percentage of crate references resolved by exact filename. The report
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also exposes missing references, unique missing filenames, duplicate filename
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groups, extra duplicate files, unused tracks, and missing references with matching
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candidates.
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Duplicate, unused, and candidate counts are informational. They do not affect the
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score until the project has a documented and validated weighting policy. An empty
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library has no score rather than a misleading 0% or 100%.
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## Edge Cases
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- The same missing filename referenced by several crates.
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- Several disk files sharing a filename.
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- Conflict-suffixed files that are unused but may be match candidates.
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- Libraries with no crate references.
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- Tracks referenced by filename from more than one crate.
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## Verification
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Tests assert every metric, the disclosed score basis, candidate integration, and
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empty-library behavior. Analysis remains entirely read-only.
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@@ -0,0 +1,29 @@
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# Explainable Matching Engine
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## Problem
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Missing references need ranked candidate files, but a filename-only yes/no check
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cannot explain ambiguity or cloud-provider conflict names.
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## Architecture
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The read-only matching engine indexes normalized filenames and scores only related
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candidates. Every score contains evidence for filename, extension, and parent
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folder. Exact filenames earn 60 points, normalized names 55, numeric conflict-name
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matches 50, extensions 10, and parent folders 20.
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The displayed percentage is an evidence score, not a statistical probability.
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Metadata, duration, hashes, and fingerprints can add stronger evidence later.
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## Edge Cases
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- Unicode and case differences.
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- OneDrive-style names such as `Track 2.mp3`.
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- Duplicate candidates in different folders.
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- Legitimate numbered song titles, which remain candidates but are never repaired.
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- Unrelated names, which are not emitted as candidates.
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## Verification
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Tests cover exact, normalized, conflict-suffix, ambiguous, and unrelated filenames.
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Candidate ordering is deterministic. The engine never changes a track or reference.
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@@ -0,0 +1,43 @@
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from collections import Counter
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from serato_doctor.matching import MatchingEngine
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from serato_doctor.models.health import HealthReport
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from serato_doctor.models.library import Library
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def analyze_health(library: Library) -> HealthReport:
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"""Calculate defensible health metrics without changing the library."""
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results = library.reconcile_by_filename()
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missing = [result for result in results if not result.exists_by_filename]
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healthy_count = len(results) - len(missing)
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score = (
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round(healthy_count / len(results) * 100, 1) if results else None
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)
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disk_name_counts = Counter(track.filename for track in library.tracks)
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duplicate_counts = [count for count in disk_name_counts.values() if count > 1]
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referenced_names = {reference.filename for reference in library.references}
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unused_count = sum(
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1 for track in library.tracks if track.filename not in referenced_names
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)
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matcher = MatchingEngine(library.tracks)
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suggested_count = sum(
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bool(matcher.candidates_for(result.reference)) for result in missing
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)
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return HealthReport(
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score=score,
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total_references=len(results),
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healthy_references=healthy_count,
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missing_references=len(missing),
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unique_missing_filenames=len(
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{result.reference.filename for result in missing}
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),
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disk_tracks=len(library.tracks),
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duplicate_filename_groups=len(duplicate_counts),
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duplicate_files=sum(count - 1 for count in duplicate_counts),
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unused_tracks=unused_count,
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suggested_matches=suggested_count,
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)
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@@ -0,0 +1,86 @@
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import re
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import unicodedata
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from collections import defaultdict
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from pathlib import Path
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from typing import DefaultDict, Iterable, List, Tuple
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from serato_doctor.models.match import MatchEvidence, TrackMatch
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from serato_doctor.models.reference import TrackReference
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from serato_doctor.models.track import DiskTrack
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def normalize(value: str) -> str:
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return unicodedata.normalize("NFKC", value).casefold()
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def cloud_conflict_name(filename: str) -> str:
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"""Remove a trailing numeric cloud-conflict suffix from a filename stem."""
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path = Path(filename)
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stem = re.sub(r" \d+$", "", path.stem)
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return normalize(stem + path.suffix)
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def score_candidate(reference: TrackReference, track: DiskTrack) -> TrackMatch:
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reference_name = reference.filename
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track_name = track.filename
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if reference_name == track_name:
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filename_points = 60
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filename_reason = "Filename is identical"
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elif normalize(reference_name) == normalize(track_name):
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filename_points = 55
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filename_reason = "Filename matches after case and Unicode normalization"
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elif cloud_conflict_name(reference_name) == cloud_conflict_name(track_name):
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filename_points = 50
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filename_reason = "Filename matches after removing a numeric conflict suffix"
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else:
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filename_points = 0
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filename_reason = "Filename does not match"
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same_extension = normalize(reference.path.suffix) == normalize(track.suffix)
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same_parent = normalize(reference.path.parent.name) == normalize(
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track.path.parent.name
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)
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evidence = (
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MatchEvidence(
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"filename",
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filename_points > 0,
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filename_points,
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60,
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filename_reason,
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),
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MatchEvidence(
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"extension",
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same_extension,
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10 if same_extension else 0,
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10,
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"File extension matches" if same_extension else "File extension differs",
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),
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MatchEvidence(
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"parent_folder",
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same_parent,
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20 if same_parent else 0,
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20,
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"Parent folder matches" if same_parent else "Parent folder differs",
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),
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)
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return TrackMatch(reference, track, evidence)
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class MatchingEngine:
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"""Find and rank filename-related disk candidates without modifying files."""
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def __init__(self, tracks: Iterable[DiskTrack]):
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self._by_conflict_name: DefaultDict[str, List[DiskTrack]] = defaultdict(list)
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for track in tracks:
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self._by_conflict_name[cloud_conflict_name(track.filename)].append(track)
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def candidates_for(self, reference: TrackReference) -> Tuple[TrackMatch, ...]:
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candidates = self._by_conflict_name.get(
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cloud_conflict_name(reference.filename), []
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)
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matches = [score_candidate(reference, track) for track in candidates]
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return tuple(
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sorted(matches, key=lambda match: (-match.score, str(match.track.path)))
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)
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@@ -1,6 +1,17 @@
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from serato_doctor.models.crate import Crate
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from serato_doctor.models.health import HealthReport
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from serato_doctor.models.library import Library
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from serato_doctor.models.match import MatchEvidence, TrackMatch
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from serato_doctor.models.reference import ReferenceResult, TrackReference
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from serato_doctor.models.track import DiskTrack
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__all__ = ["Crate", "DiskTrack", "Library", "ReferenceResult", "TrackReference"]
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__all__ = [
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"Crate",
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"DiskTrack",
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"HealthReport",
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"Library",
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"MatchEvidence",
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"ReferenceResult",
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"TrackMatch",
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"TrackReference",
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]
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@@ -0,0 +1,22 @@
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from dataclasses import dataclass
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from typing import Optional
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@dataclass(frozen=True)
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class HealthReport:
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"""Transparent aggregate findings from a read-only library analysis."""
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score: Optional[float]
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total_references: int
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healthy_references: int
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missing_references: int
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unique_missing_filenames: int
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disk_tracks: int
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duplicate_filename_groups: int
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duplicate_files: int
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unused_tracks: int
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suggested_matches: int
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@property
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def score_basis(self) -> str:
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return "Resolved crate references / total crate references"
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@@ -0,0 +1,39 @@
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from dataclasses import dataclass
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from typing import Tuple
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from serato_doctor.models.reference import TrackReference
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from serato_doctor.models.track import DiskTrack
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@dataclass(frozen=True)
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class MatchEvidence:
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"""One explainable scoring decision for a candidate track."""
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field: str
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matched: bool
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points: int
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max_points: int
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explanation: str
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@dataclass(frozen=True)
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class TrackMatch:
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"""A ranked candidate backed by explicit, inspectable evidence."""
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reference: TrackReference
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track: DiskTrack
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evidence: Tuple[MatchEvidence, ...]
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@property
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def score(self) -> int:
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return sum(item.points for item in self.evidence)
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@property
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def max_score(self) -> int:
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return sum(item.max_points for item in self.evidence)
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@property
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def score_percent(self) -> float:
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if not self.max_score:
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return 0.0
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return round(self.score / self.max_score * 100, 1)
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@@ -0,0 +1,56 @@
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from pathlib import Path
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from serato_doctor.health import analyze_health
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from serato_doctor.models.library import Library
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from serato_doctor.models.reference import TrackReference
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from serato_doctor.models.track import DiskTrack
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def reference(filename):
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return TrackReference(
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Path("Test.crate"), Path("/old/House") / filename, filename
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)
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def track(filename, folder="House"):
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path = Path("/new") / folder / filename
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return DiskTrack(path, filename, 100, path.suffix.lower())
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def test_health_report_exposes_each_metric():
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library = Library.build(
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[
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reference("Found.mp3"),
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reference("Conflict.mp3"),
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reference("Absent.mp3"),
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],
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[
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track("Found.mp3"),
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track("Conflict 2.mp3"),
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track("Conflict 2.mp3", "Backup"),
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track("Unused.mp3"),
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],
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)
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report = analyze_health(library)
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assert report.score == 33.3
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assert report.score_basis == (
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"Resolved crate references / total crate references"
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)
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assert report.total_references == 3
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assert report.healthy_references == 1
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assert report.missing_references == 2
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assert report.unique_missing_filenames == 2
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assert report.disk_tracks == 4
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assert report.duplicate_filename_groups == 1
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assert report.duplicate_files == 1
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assert report.unused_tracks == 3
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assert report.suggested_matches == 1
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def test_empty_library_has_no_health_score():
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report = analyze_health(Library.build([], []))
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assert report.score is None
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assert report.total_references == 0
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@@ -0,0 +1,62 @@
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from pathlib import Path
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from serato_doctor.matching import MatchingEngine, score_candidate
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from serato_doctor.models.reference import TrackReference
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from serato_doctor.models.track import DiskTrack
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def reference(filename, folder="House"):
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return TrackReference(
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Path("Test.crate"), Path("/old") / folder / filename, filename
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)
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def track(filename, folder="House"):
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path = Path("/new") / folder / filename
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return DiskTrack(path, filename, 100, path.suffix.lower())
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def test_exact_candidate_has_full_evidence_score():
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match = score_candidate(reference("Track.mp3"), track("Track.mp3"))
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assert match.score == 90
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assert match.max_score == 90
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assert match.score_percent == 100.0
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assert all(item.matched for item in match.evidence)
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def test_cloud_conflict_suffix_is_explained():
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match = score_candidate(reference("Track.mp3"), track("Track 2.mp3"))
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assert match.score == 80
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assert match.score_percent == 88.9
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assert match.evidence[0].explanation == (
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"Filename matches after removing a numeric conflict suffix"
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)
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def test_case_normalized_match_scores_below_exact():
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match = score_candidate(reference("TRACK.MP3"), track("track.mp3"))
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assert match.score == 85
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assert match.evidence[0].points == 55
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def test_engine_omits_unrelated_filenames():
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engine = MatchingEngine([track("Different.mp3")])
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assert engine.candidates_for(reference("Missing.mp3")) == ()
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def test_ambiguous_candidates_are_ranked_deterministically():
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engine = MatchingEngine(
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[track("Track 2.mp3", "Other"), track("Track 3.mp3", "House")]
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)
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matches = engine.candidates_for(reference("Track.mp3"))
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assert [match.track.filename for match in matches] == [
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"Track 3.mp3",
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"Track 2.mp3",
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]
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assert [match.score for match in matches] == [80, 60]
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Reference in New Issue
Block a user