82 lines
2.9 KiB
Python
82 lines
2.9 KiB
Python
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#!/usr/bin/env python3.12
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from difflib import SequenceMatcher
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from typing import List, Optional, Tuple
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import re
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class TrackMatcher:
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"""Track Matcher"""
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def __init__(self, threshold: float = 0.85):
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"""
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Initialize the TrackMatcher with a similarity threshold.
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Args:
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threshold (float): Minimum similarity score to consider a match valid
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(between 0 and 1, default 0.85)
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"""
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self.threshold = threshold
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def find_best_match(self, input_track: str, candidate_tracks: List[str]) -> Optional[Tuple[str, float]]:
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"""
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Find the best matching track from the candidate list.
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Args:
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input_track (str): Input track in "ARTIST - SONG" format
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candidate_tracks (List[str]): List of candidate tracks in same format
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Returns:
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Optional[Tuple[str, float]]: Tuple of (best matching track, similarity score)
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or None if no good match found
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"""
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if not input_track or not candidate_tracks:
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return None
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# Normalize input track
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input_track = self._normalize_string(input_track)
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best_match = None
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best_score = 0
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for candidate in candidate_tracks:
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normalized_candidate = self._normalize_string(candidate)
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# Calculate various similarity scores
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exact_score = 1.0 if input_track == normalized_candidate else 0.0
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sequence_score = SequenceMatcher(None, input_track, normalized_candidate).ratio()
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token_score = self._calculate_token_similarity(input_track, normalized_candidate)
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# Take the maximum of the different scoring methods
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final_score = max(exact_score, sequence_score, token_score)
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if final_score > best_score:
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best_score = final_score
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best_match = candidate
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# Return the match only if it meets the threshold
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return (best_match, best_score) if best_score >= self.threshold else None
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def _normalize_string(self, text: str) -> str:
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"""
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Normalize string for comparison by removing special characters,
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extra spaces, and converting to lowercase.
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"""
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# Remove special characters and convert to lowercase
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text = re.sub(r'[^\w\s-]', '', text.lower())
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# Normalize spaces
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text = ' '.join(text.split())
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return text
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def _calculate_token_similarity(self, str1: str, str2: str) -> float:
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"""
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Calculate similarity based on matching tokens (words).
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"""
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tokens1 = set(str1.split())
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tokens2 = set(str2.split())
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if not tokens1 or not tokens2:
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return 0.0
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intersection = tokens1.intersection(tokens2)
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union = tokens1.union(tokens2)
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return len(intersection) / len(union)
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