From 1943fe2bdf693787e88d5c36fab90be70522c2cc Mon Sep 17 00:00:00 2001 From: Jonathan Date: Tue, 14 Jul 2026 21:43:02 +0200 Subject: [PATCH] feat(discover): max-normalize similarity scores across sources MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit _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) --- worker/lyra_worker/discovery.py | 11 ++++++++-- worker/tests/test_discovery.py | 39 +++++++++++++++++++++++++-------- 2 files changed, 39 insertions(+), 11 deletions(-) diff --git a/worker/lyra_worker/discovery.py b/worker/lyra_worker/discovery.py index befa215..3370224 100644 --- a/worker/lyra_worker/discovery.py +++ b/worker/lyra_worker/discovery.py @@ -181,11 +181,18 @@ def _record_contributions(conn: psycopg.Connection, seed_mbid: str, live, follow except Exception as e: # a bad source/seed must not abort the sweep print(f"worker: discovery source {src.name} failed for {seed_mbid}: {e}", flush=True) continue - for sa in sorted(similar, key=lambda a: a.score, reverse=True)[: cfg.similar_per_seed]: + top = sorted(similar, key=lambda a: a.score, reverse=True)[: cfg.similar_per_seed] + # Max-normalize each source to [0, 1] by its own top score before summing across + # sources, so sources on wildly different scales contribute comparably (Last.fm's + # `match` is 0–1; ListenBrainz co-occurrence counts run to the thousands — raw sums + # let ListenBrainz drown Last.fm's ranking signal). + max_score = max((sa.score for sa in top), default=0.0) + for sa in top: if sa.mbid in followed: continue + norm = sa.score / max_score if max_score > 0 else 0.0 c = per_cand.setdefault(sa.mbid, {"name": sa.name, "score": 0.0, "sources": set()}) - c["score"] += sa.score + c["score"] += norm c["sources"].add(src.name) if sa.name and not c["name"]: c["name"] = sa.name diff --git a/worker/tests/test_discovery.py b/worker/tests/test_discovery.py index 82b36f9..445e5e2 100644 --- a/worker/tests/test_discovery.py +++ b/worker/tests/test_discovery.py @@ -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