def compare_to_release(self, release, weights):
"""
Compare metadata to a MusicBrainz release. Produces a probability as a
linear combination of weights that the metadata matches a certain album.
"""
parts = self.compare_to_release_parts(release, weights)
sim = linear_combination_of_weights(parts) * get_score(release)
return SimMatchRelease(similarity=sim, release=release)
def compare_to_track(self, track, weights):
parts = []
if 'title' in self:
a = self['title']
b = track.get('title', '')
parts.append((similarity2(a, b), weights["title"]))
if 'artist' in self:
a = self['artist']
artist_credits = track.get('artist-credit', [])
b = artist_credit_from_node(artist_credits)[0]
parts.append((similarity2(a, b), weights["artist"]))
a = self.length
if a > 0 and 'length' in track:
b = track['length']
score = self.length_score(a, b)
parts.append((score, weights["length"]))
releases = []
if "releases" in track:
releases = track['releases']
search_score = get_score(track)
if not releases:
sim = linear_combination_of_weights(parts) * search_score
return SimMatchTrack(similarity=sim,
releasegroup=None,
release=None,
track=track)
if 'isvideo' in weights:
metadata_is_video = self['~video'] == '1'
track_is_video = track.get('video', False)
score = 1 if metadata_is_video == track_is_video else 0
parts.append((score, weights['isvideo']))
result = SimMatchTrack(similarity=-1,
releasegroup=None,
release=None,
track=None)
for release in releases:
release_parts = self.compare_to_release_parts(release, weights)
sim = linear_combination_of_weights(parts +
release_parts) * search_score
if sim > result.similarity:
rg = release[
'release-group'] if "release-group" in release else None
result = SimMatchTrack(similarity=sim,
releasegroup=rg,
release=release,
track=track)
return result
def test_get_score_no_score(self):
self.assertEqual(1.0, get_score({}))
def test_get_score(self):
for score, expected in ((42, 0.42), ('100', 1.0), (0, 0.0), (None, 1.0), ('', 1.0)):
self.assertEqual(expected, get_score({'score': score}))
