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feat(worker): per-seed discovery contributions so score sums across chunks/sweeps

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Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Jonathan
2026-07-13 15:18:30 +02:00
parent bdd8fe72e9
commit de052a4ef7
2 changed files with 149 additions and 34 deletions
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worker/lyra_worker/discovery.py
+72 -26
View File
@@ -143,15 +143,18 @@ def _derive_albums(conn: psycopg.Connection, browser: MbBrowser, surfaced: list[
return albums
def run_discovery(conn: psycopg.Connection, sources: list[SimilaritySource],
browser: MbBrowser, cfg: DiscoveryConfig) -> DiscoveryResult:
"""Aggregate similar artists across the library's seed artists; upsert suggestions."""
seeds = _seed_artists(conn, cfg)
followed = _followed_mbids(conn)
live = _healthy_sources(sources)
def _record_contributions(conn: psycopg.Connection, seed_mbid: str, live, followed: set[str],
cfg: DiscoveryConfig) -> set[str]:
"""Replace seed_mbid's contribution rows with its current similar-artist scores.
Returns the set of candidate mbids whose aggregate may have changed (old contributors
of this seed ∪ newly inserted), so callers recompute drop-offs too."""
with conn.cursor() as cur:
cur.execute('SELECT "candidateMbid" FROM "DiscoverySeedContribution" WHERE "seedMbid" = %s',
(seed_mbid,))
affected = {r[0] for r in cur.fetchall()}
cur.execute('DELETE FROM "DiscoverySeedContribution" WHERE "seedMbid" = %s', (seed_mbid,))
agg: dict[str, dict] = {}
for seed_mbid, _seed_name in seeds:
per_cand: dict[str, dict] = {}
for src in live:
try:
similar = src.similar_artists(seed_mbid)
@@ -161,29 +164,72 @@ def run_discovery(conn: psycopg.Connection, sources: list[SimilaritySource],
for sa in sorted(similar, key=lambda a: a.score, reverse=True)[: cfg.similar_per_seed]:
if sa.mbid in followed:
continue
c = agg.setdefault(sa.mbid, {"mbid": sa.mbid, "name": sa.name,
"score": 0.0, "seeds": set(), "sources": set()})
c = per_cand.setdefault(sa.mbid, {"name": sa.name, "score": 0.0, "sources": set()})
c["score"] += sa.score
c["seeds"].add(seed_mbid)
c["sources"].add(src.name)
if sa.name and not c["name"]:
c["name"] = sa.name
artists = 0
surfaced: list[dict] = []
for c in sorted(agg.values(), key=lambda c: c["score"], reverse=True):
if c["score"] < cfg.min_score:
continue
cand = {"mbid": c["mbid"], "name": c["name"], "score": c["score"],
"seed_count": len(c["seeds"]), "sources": c["sources"]}
inserted = _upsert_artist(conn, cand)
artists += inserted
# Only derive albums for candidates that produced a live pending suggestion.
# A no-op upsert (rowcount 0) means the artist was already dismissed/wanted —
# don't surface their albums against a dismissal.
if inserted:
surfaced.append(cand)
with conn.cursor() as cur:
for mbid, c in per_cand.items():
cur.execute(
'INSERT INTO "DiscoverySeedContribution" (id, "candidateMbid", "candidateName", '
'"seedMbid", score, sources, "updatedAt") '
'VALUES (gen_random_uuid()::text, %s, %s, %s, %s, %s, now())',
(mbid, c["name"], seed_mbid, c["score"], sorted(c["sources"])),
)
affected.add(mbid)
return affected
def _recompute_suggestions(conn: psycopg.Connection, affected: set[str],
cfg: DiscoveryConfig) -> list[dict]:
"""Recompute each affected candidate's DiscoverySuggestion from the sum of its
contributions. Returns the candidates that produced a live pending suggestion."""
if not affected:
return []
with conn.cursor() as cur:
cur.execute(
'SELECT "candidateMbid", "candidateName", score, "seedMbid", sources '
'FROM "DiscoverySeedContribution" WHERE "candidateMbid" = ANY(%s)',
(list(affected),))
rows = cur.fetchall()
agg: dict[str, dict] = {}
for mbid, name, score, seed, sources in rows:
a = agg.setdefault(mbid, {"mbid": mbid, "name": name, "score": 0.0,
"seeds": set(), "sources": set()})
a["score"] += score
a["seeds"].add(seed)
a["sources"].update(sources or [])
if name and not a["name"]:
a["name"] = name
surfaced: list[dict] = []
for a in sorted(agg.values(), key=lambda a: a["score"], reverse=True):
if a["score"] < cfg.min_score:
continue
cand = {"mbid": a["mbid"], "name": a["name"], "score": a["score"],
"seed_count": len(a["seeds"]), "sources": a["sources"]}
if _upsert_artist(conn, cand): # rowcount 1 => a live pending suggestion
surfaced.append(cand)
return surfaced
def run_discovery(conn: psycopg.Connection, sources: list[SimilaritySource],
browser: MbBrowser, cfg: DiscoveryConfig) -> DiscoveryResult:
"""Record each seed's similar-artist contributions, then recompute affected
suggestions from the summed contributions — so a candidate's score is the sum of
every seed's latest contribution, independent of chunk and sweep boundaries."""
seeds = _seed_artists(conn, cfg)
followed = _followed_mbids(conn)
live = _healthy_sources(sources)
affected: set[str] = set()
for seed_mbid, _seed_name in seeds:
affected |= _record_contributions(conn, seed_mbid, live, followed, cfg)
surfaced = _recompute_suggestions(conn, affected, cfg)
albums = _derive_albums(conn, browser, surfaced, cfg)
with conn.cursor() as cur:
@@ -191,4 +237,4 @@ def run_discovery(conn: psycopg.Connection, sources: list[SimilaritySource],
cur.execute('UPDATE "WatchedArtist" SET "lastDiscoveredAt" = now() WHERE mbid = %s',
(seed_mbid,))
conn.commit()
return DiscoveryResult(artists=artists, albums=albums, seeds=len(seeds))
return DiscoveryResult(artists=len(surfaced), albums=albums, seeds=len(seeds))
worker/tests/test_discovery.py
+69
View File
@@ -96,6 +96,75 @@ def test_config_from_config_parses_and_defaults():
d.albums_per_artist, d.min_score) == (False, 168, 5, 20, 1, 0.0)
def _artist_score(conn, mbid):
with conn.cursor() as cur:
cur.execute('SELECT score, "seedCount" FROM "DiscoverySuggestion" WHERE "artistMbid" = %s', (mbid,))
return cur.fetchone()
def test_score_sums_across_separate_chunks(conn):
# Two seeds, both eligible, forced into separate one-seed chunks by chunk_size=1.
insert_watched_artist(conn, mbid="s1", name="Seed One",
last_discovered_sql="now() - interval '200 hours'")
insert_watched_artist(conn, mbid="s2", name="Seed Two",
last_discovered_sql="now() - interval '190 hours'")
src = FakeSimilaritySource(similar={
"s1": [SimilarArtist("c1", "Shared", 0.6)],
"s2": [SimilarArtist("c1", "Shared", 0.5)],
})
cfg = DiscoveryConfig(chunk_size=1)
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)
def test_partial_sweep_preserves_other_seeds_contribution(conn):
# Full sweep: c1 gets s1(0.6)+s2(0.5)=1.1. Then only s1 is due again with a new
# score; s2's contribution must survive untouched.
insert_watched_artist(conn, mbid="s1", name="Seed One",
last_discovered_sql="now() - interval '200 hours'")
insert_watched_artist(conn, mbid="s2", name="Seed Two",
last_discovered_sql="now() - interval '200 hours'")
src = FakeSimilaritySource(similar={
"s1": [SimilarArtist("c1", "Shared", 0.6)],
"s2": [SimilarArtist("c1", "Shared", 0.5)],
})
run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig()) # both -> 1.1/2
assert _artist_score(conn, "c1") == (1.1, 2)
# s1 due again (older than interval), s2 fresh; s1 now scores c1 at 0.7.
with conn.cursor() as cur:
cur.execute('UPDATE "WatchedArtist" SET "lastDiscoveredAt" = now() - interval \'200 hours\' WHERE mbid = %s', ("s1",))
cur.execute('UPDATE "WatchedArtist" SET "lastDiscoveredAt" = now() WHERE mbid = %s', ("s2",))
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)
def test_seed_dropoff_lowers_seedcount(conn):
insert_watched_artist(conn, mbid="s1", name="Seed One",
last_discovered_sql="now() - interval '200 hours'")
insert_watched_artist(conn, mbid="s2", name="Seed Two",
last_discovered_sql="now() - interval '200 hours'")
src = FakeSimilaritySource(similar={
"s1": [SimilarArtist("c1", "Shared", 0.6)],
"s2": [SimilarArtist("c1", "Shared", 0.5)],
})
run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig())
assert _artist_score(conn, "c1") == (1.1, 2)
# s1 re-swept but no longer finds c1 -> its contribution is removed, c1 recomputed.
with conn.cursor() as cur:
cur.execute('UPDATE "WatchedArtist" SET "lastDiscoveredAt" = now() - interval \'200 hours\' WHERE mbid = %s', ("s1",))
cur.execute('UPDATE "WatchedArtist" SET "lastDiscoveredAt" = now() WHERE mbid = %s', ("s2",))
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
def test_run_discovery_processes_at_most_chunk_size_seeds(conn):
from lyra_worker.discovery import DiscoveryConfig, run_discovery
from lyra_worker.adapters.fakes import FakeMbBrowser, FakeSimilaritySource