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feat(discover): max-normalize similarity scores across sources

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_record_contributions now normalizes each source's top-N to [0,1] by its own
top score before summing across sources. ListenBrainz co-occurrence counts
(thousands) no longer drown Last.fm's 0–1 match scores, so a small-scale
source contributes real ranking signal instead of only adding candidates.

Discovery score assertions updated to the normalized scale (single-candidate
fakes normalize to 1.0); added a cross-source test showing a small-scale
source's candidate ranks equal to a large-scale one. min_score is now on the
[0, n_sources] scale (default 0.0 → unaffected). worker 231 tests.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Jonathan
2026-07-14 21:43:02 +02:00
parent 0f64e3c83a
commit 1943fe2bdf
2 changed files with 39 additions and 11 deletions
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worker/tests/test_discovery.py
+30 -9
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@@ -21,12 +21,33 @@ def test_aggregates_scores_across_seeds(conn):
result = run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig())
assert result.artists == 2
rows = _artist_rows(conn)
# c1 recommended by both seeds: score 1.1, seedCount 2; c2 by one seed
assert rows[0][0] == "c1" and abs(rows[0][2] - 1.1) < 1e-6 and rows[0][3] == 2
# c1 recommended by both seeds; each seed max-normalizes its top score to 1.0, so
# c1 = 1.0 + 1.0 = 2.0, seedCount 2; c2 (0.4/0.6 within s1) by one seed
assert rows[0][0] == "c1" and abs(rows[0][2] - 2.0) < 1e-6 and rows[0][3] == 2
assert rows[0][4] == ["fake"] and rows[0][5] == "pending"
assert rows[1][0] == "c2" and rows[1][3] == 1
def test_normalizes_across_sources_of_different_scales(conn):
# ListenBrainz emits co-occurrence counts in the thousands; Last.fm emits 0–1 match
# scores. Max-normalizing each source before summing keeps the small-scale source's
# candidates from being drowned.
insert_watched_artist(conn, mbid="s1", name="Seed")
lb = FakeSimilaritySource(similar={"s1": [
SimilarArtist("c_lb", "LB Only", 5000.0), SimilarArtist("c_shared", "Shared", 4000.0)]})
lb.name = "listenbrainz"
lfm = FakeSimilaritySource(similar={"s1": [
SimilarArtist("c_lfm", "LFM Only", 0.9), SimilarArtist("c_shared", "Shared", 0.5)]})
lfm.name = "lastfm"
run_discovery(conn, [lb, lfm], FakeMbBrowser(), DiscoveryConfig())
lb_only = _artist_score(conn, "c_lb")[0] # 5000/5000 = 1.0
lfm_only = _artist_score(conn, "c_lfm")[0] # 0.9/0.9 = 1.0
shared = _artist_score(conn, "c_shared")[0] # 4000/5000 + 0.5/0.9 = 0.8 + 0.556
assert abs(lb_only - lfm_only) < 1e-6 # small-scale source not drowned — ranks equal
assert shared > lb_only # co-sourced candidate outranks a single-source one
def test_skips_already_followed_artists(conn):
insert_watched_artist(conn, mbid="s1", name="Seed")
insert_watched_artist(conn, mbid="c1", name="Already Followed")
@@ -116,7 +137,7 @@ def test_score_sums_across_separate_chunks(conn):
run_discovery(conn, [src], FakeMbBrowser(), cfg) # processes s1 (older)
run_discovery(conn, [src], FakeMbBrowser(), cfg) # processes s2
# replace-not-accumulate would leave 0.5/seedCount1; contributions give the sum.
assert _artist_score(conn, "c1") == (1.1, 2)
assert _artist_score(conn, "c1") == (2.0, 2) # each seed max-normalizes to 1.0
def test_partial_sweep_preserves_other_seeds_contribution(conn):
@@ -131,7 +152,7 @@ def test_partial_sweep_preserves_other_seeds_contribution(conn):
"s2": [SimilarArtist("c1", "Shared", 0.5)],
})
run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig()) # both -> 1.1/2
assert _artist_score(conn, "c1") == (1.1, 2)
assert _artist_score(conn, "c1") == (2.0, 2) # each seed max-normalizes to 1.0
# s1 due again (older than interval), s2 fresh; s1 now scores c1 at 0.7.
with conn.cursor() as cur:
@@ -140,7 +161,7 @@ def test_partial_sweep_preserves_other_seeds_contribution(conn):
conn.commit()
src2 = FakeSimilaritySource(similar={"s1": [SimilarArtist("c1", "Shared", 0.7)]})
run_discovery(conn, [src2], FakeMbBrowser(), DiscoveryConfig())
assert _artist_score(conn, "c1") == (1.2, 2) # 0.7 (new s1) + 0.5 (kept s2)
assert _artist_score(conn, "c1") == (2.0, 2) # 1.0 (new s1) + 1.0 (kept s2), max-normalized
def test_seed_dropoff_lowers_seedcount(conn):
@@ -153,7 +174,7 @@ def test_seed_dropoff_lowers_seedcount(conn):
"s2": [SimilarArtist("c1", "Shared", 0.5)],
})
run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig())
assert _artist_score(conn, "c1") == (1.1, 2)
assert _artist_score(conn, "c1") == (2.0, 2) # each seed max-normalizes to 1.0
# s1 re-swept but no longer finds c1 -> its contribution is removed, c1 recomputed.
with conn.cursor() as cur:
@@ -162,7 +183,7 @@ def test_seed_dropoff_lowers_seedcount(conn):
conn.commit()
src2 = FakeSimilaritySource(similar={"s1": [SimilarArtist("c9", "Other", 0.9)]})
run_discovery(conn, [src2], FakeMbBrowser(), DiscoveryConfig())
assert _artist_score(conn, "c1") == (0.5, 1) # only s2 remains
assert _artist_score(conn, "c1") == (1.0, 1) # only s2 remains (max-normalized to 1.0)
def test_evicts_contributions_from_removed_seed(conn):
@@ -179,7 +200,7 @@ def test_evicts_contributions_from_removed_seed(conn):
"s2": [SimilarArtist("c1", "Shared", 0.5)],
})
run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig())
assert _artist_score(conn, "c1") == (1.1, 2)
assert _artist_score(conn, "c1") == (2.0, 2) # each seed max-normalizes to 1.0
# Unfollow s1 (no FK cascade on DiscoverySeedContribution).
with conn.cursor() as cur:
@@ -193,7 +214,7 @@ def test_evicts_contributions_from_removed_seed(conn):
src2 = FakeSimilaritySource(similar={"s2": [SimilarArtist("c1", "Shared", 0.5)]})
run_discovery(conn, [src2], FakeMbBrowser(), DiscoveryConfig())
assert _artist_score(conn, "c1") == (0.5, 1) # s1's stale 0.6 evicted, not summed
assert _artist_score(conn, "c1") == (1.0, 1) # s1's stale contribution evicted; s2 alone -> 1.0
with conn.cursor() as cur:
cur.execute('SELECT count(*) FROM "DiscoverySeedContribution" WHERE "seedMbid" = %s', ("s1",))
assert cur.fetchone()[0] == 0