Jonathan
82 lines
4.2 KiB
Python
82 lines
4.2 KiB
Python
from lyra_worker.adapters.fakes import FakeMbBrowser, FakeSimilaritySource
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from lyra_worker.discovery import DiscoveryConfig, run_discovery
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from lyra_worker.similarity.base import SimilarArtist
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from tests.conftest import insert_discovery_suggestion, insert_watched_artist
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def _artist_rows(conn):
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with conn.cursor() as cur:
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cur.execute('SELECT "artistMbid", "artistName", score, "seedCount", sources, status::text '
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'FROM "DiscoverySuggestion" WHERE kind = \'artist\' ORDER BY score DESC')
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return cur.fetchall()
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def test_aggregates_scores_across_seeds(conn):
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insert_watched_artist(conn, mbid="s1", name="Seed One")
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insert_watched_artist(conn, mbid="s2", name="Seed Two")
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src = FakeSimilaritySource(similar={
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"s1": [SimilarArtist("c1", "Shared", 0.6), SimilarArtist("c2", "Only1", 0.4)],
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"s2": [SimilarArtist("c1", "Shared", 0.5)],
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})
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result = run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig())
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assert result.artists == 2
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rows = _artist_rows(conn)
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# c1 recommended by both seeds: score 1.1, seedCount 2; c2 by one seed
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assert rows[0][0] == "c1" and abs(rows[0][2] - 1.1) < 1e-6 and rows[0][3] == 2
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assert rows[0][4] == ["fake"] and rows[0][5] == "pending"
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assert rows[1][0] == "c2" and rows[1][3] == 1
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def test_skips_already_followed_artists(conn):
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insert_watched_artist(conn, mbid="s1", name="Seed")
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insert_watched_artist(conn, mbid="c1", name="Already Followed")
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src = FakeSimilaritySource(similar={"s1": [SimilarArtist("c1", "X", 0.9),
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SimilarArtist("c2", "New", 0.8)]})
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run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig())
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assert [r[0] for r in _artist_rows(conn)] == ["c2"] # c1 filtered (followed)
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def test_min_score_filters_weak_candidates(conn):
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insert_watched_artist(conn, mbid="s1", name="Seed")
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src = FakeSimilaritySource(similar={"s1": [SimilarArtist("c1", "Strong", 0.9),
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SimilarArtist("c2", "Weak", 0.1)]})
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run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig(min_score=0.5))
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assert [r[0] for r in _artist_rows(conn)] == ["c1"]
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def test_dismissed_suggestion_is_never_revived(conn):
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insert_watched_artist(conn, mbid="s1", name="Seed")
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insert_discovery_suggestion(conn, kind="artist", artist_mbid="c1", status="dismissed", score=0.0)
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src = FakeSimilaritySource(similar={"s1": [SimilarArtist("c1", "X", 0.9)]})
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run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig())
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with conn.cursor() as cur:
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cur.execute('SELECT status::text, score FROM "DiscoverySuggestion" WHERE "artistMbid" = %s', ("c1",))
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assert cur.fetchone() == ("dismissed", 0.0) # untouched — WHERE status='pending' blocked the update
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def test_stamps_last_discovered_and_throttles(conn):
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insert_watched_artist(conn, mbid="s1", name="Seed")
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src = FakeSimilaritySource(similar={"s1": [SimilarArtist("c1", "X", 0.9)]})
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assert run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig()).artists == 1
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with conn.cursor() as cur:
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cur.execute('SELECT "lastDiscoveredAt" IS NOT NULL FROM "WatchedArtist" WHERE mbid = %s', ("s1",))
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assert cur.fetchone()[0] is True
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# second run: seed not due (lastDiscoveredAt recent) -> no seeds -> nothing new
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assert run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig()).artists == 0
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def test_unhealthy_source_contributes_nothing(conn):
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insert_watched_artist(conn, mbid="s1", name="Seed")
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src = FakeSimilaritySource(similar={"s1": [SimilarArtist("c1", "X", 0.9)]}, healthy=False)
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assert run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig()).artists == 0
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assert _artist_rows(conn) == []
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def test_config_from_config_parses_and_defaults():
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cfg = DiscoveryConfig.from_config({"discover.enabled": "true", "discover.maxSeeds": "10",
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"discover.minScore": "0.3"})
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assert cfg.enabled is True and cfg.max_seeds == 10 and cfg.min_score == 0.3
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d = DiscoveryConfig.from_config({})
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assert (d.enabled, d.interval_hours, d.max_seeds, d.similar_per_seed,
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d.albums_per_artist, d.min_score) == (False, 168, 50, 20, 1, 0.0)
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