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220 lines
9.2 KiB
Python
220 lines
9.2 KiB
Python
"""SQL рекомендателя на настоящей схеме (CI-job `ci / db`; схему заранее накатывают миграции api).
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Локально: TEST_DATABASE_URL=postgresql://… uv run pytest tests/test_db_integration.py
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"""
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import json
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import os
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import time
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import uuid
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from typing import Any
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import numpy as np
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import psycopg
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import pytest
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from pipu_rec.analysis.features import Features
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from pipu_rec.analysis.worker import Worker
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from pipu_rec.train.pipeline import load_interactions, train_all
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from pipu_rec.wave.engine import Profile, Session, build_batch
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from pipu_rec.wave.store import load_index, load_profile
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DSN = os.environ.get("TEST_DATABASE_URL", "")
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pytestmark = pytest.mark.skipif(not DSN, reason="TEST_DATABASE_URL is not set")
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def uid() -> str:
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return str(uuid.uuid4())
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@pytest.fixture()
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def world() -> dict[str, Any]:
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"""Хранилище, 2 исполнителя × 10 треков, 6 слушателей с событиями и реакциями."""
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with psycopg.connect(DSN, autocommit=True) as conn:
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src = uid()
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conn.execute(
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"INSERT INTO library_sources (id, name, type, local_path, status) VALUES (%s, 'it', 'local', '', 'mounted')",
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(src,),
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)
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tracks: list[str] = []
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for a in range(2):
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artist = uid()
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conn.execute(
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"INSERT INTO artists (id, name, sort_name, name_norm) VALUES (%s, %s, %s, %s)",
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(artist, f"A{a}", f"A{a}", f"a{a}-{artist[:6]}"),
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)
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album = uid()
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conn.execute(
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"INSERT INTO albums (id, title, title_norm, album_artist_id) VALUES (%s, %s, %s, %s)",
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(album, f"Al{a}", f"al{a}", artist),
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)
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for i in range(10):
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t, f = uid(), uid()
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conn.execute(
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"INSERT INTO tracks (id, title, title_norm, album_id, duration_ms) VALUES (%s, %s, %s, %s, 200000)",
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(t, f"T{a}{i}", f"t{a}{i}", album),
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)
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conn.execute(
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"INSERT INTO media_files (id, source_id, rel_path, size_bytes, mtime, format, duration_ms, track_id) "
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"VALUES (%s, %s, %s, 1, now(), 'flac', 200000, %s)",
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(f, src, f"{a}/{i}.flac", t),
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)
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conn.execute("UPDATE tracks SET primary_file_id = %s WHERE id = %s", (f, t))
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conn.execute(
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"INSERT INTO track_artists (track_id, artist_id, role, position) VALUES (%s, %s, 'main', 0)",
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(t, artist),
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)
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tracks.append(t)
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users = []
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for u in range(6):
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user = uid()
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conn.execute(
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"INSERT INTO users (id, username, password_hash) VALUES (%s, %s, 'x')",
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(user, f"u{u}-{user[:6]}"),
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)
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users.append(user)
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group = tracks[:10] if u % 2 == 0 else tracks[10:]
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for k, t in enumerate(group):
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kind = "play_end" if k < 8 else "skip"
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conn.execute(
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"INSERT INTO play_events (id, user_id, track_id, type, occurred_at, played_ms, client_type) "
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"VALUES (%s, %s, %s, %s, now() - interval '20 days', %s, 'web')",
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(uid(), user, t, kind, 200000 if kind == "play_end" else 3000),
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)
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conn.execute(
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"INSERT INTO user_track_stats (user_id, track_id, play_count, complete_count, last_played_at) "
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"VALUES (%s, %s, 1, %s, now() - interval '20 days')",
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(user, t, 1 if kind == "play_end" else 0),
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)
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conn.execute(
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"INSERT INTO track_reactions (user_id, track_id, reaction) VALUES (%s, %s, 'like')",
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(user, group[0]),
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)
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return {"tracks": tracks, "users": users}
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def test_analysis_worker_saves_features(world: dict[str, Any]) -> None:
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w = Worker(DSN, "/library", "/library-host", 1)
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emb = np.zeros(256, dtype=np.float32)
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emb[0] = 1.0
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feats = Features(
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bpm=120.0,
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bpm_confidence=0.9,
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key="C",
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scale="major",
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loudness_lufs=-12.0,
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energy=0.6,
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danceability=0.5,
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valence=0.5,
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acousticness=0.2,
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intro_ms=1000,
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embedding=emb,
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)
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with psycopg.connect(DSN, autocommit=True) as conn:
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jobs = w.claim(conn, 5)
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assert jobs, "every new track is queued for analysis by the trigger"
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w.save(conn, jobs[0].track_id, feats)
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w.fail(conn, jobs[1].track_id, "boom")
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w.requeue_stale(conn)
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row = conn.execute(
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"SELECT bpm FROM track_features WHERE track_id = %s", (jobs[0].track_id,)
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).fetchone()
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assert row is not None and row[0] == pytest.approx(120.0)
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def test_training_and_wave_queries(world: dict[str, Any]) -> None:
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with psycopg.connect(DSN) as conn:
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assert len(load_interactions(conn)) >= 60
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result = train_all(DSN, now=time.time())
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assert result.get("activated") is True, result
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with psycopg.connect(DSN) as conn:
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index = load_index(conn)
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assert set(world["tracks"]) <= set(index.ids)
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assert index.has_cf.sum() >= 20
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profile: Profile = load_profile(conn, world["users"][0])
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assert profile.taste_cf is not None
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assert profile.recent_likes
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active = conn.execute(
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"SELECT count(*) FROM model_versions WHERE kind = 'als' AND is_active"
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).fetchone()
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assert active is not None and active[0] == 1
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picks = build_batch(index, profile, Session(), [], n=5, rng=np.random.default_rng(0))
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assert len(picks) == 5
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def test_onboarding_seeds_taste(world: dict[str, Any]) -> None:
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user = uid()
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with psycopg.connect(DSN, autocommit=True) as conn:
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conn.execute(
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"INSERT INTO users (id, username, password_hash) VALUES (%s, %s, 'x')", (user, f"new-{user[:6]}")
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)
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artist = conn.execute(
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"SELECT artist_id::text FROM track_artists WHERE track_id = %s", (world["tracks"][0],)
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).fetchone()
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assert artist is not None
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conn.execute(
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"INSERT INTO user_profiles_rec (user_id, onboarding) VALUES (%s, %s)",
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(user, json.dumps({"artist_ids": [artist[0]], "genres": []})),
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)
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profile = load_profile(conn, user)
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assert len(profile.scores) >= 10, "chosen artist's tracks become the initial taste"
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assert not profile.played
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def test_impression_features_roundtrip(world: dict[str, Any]) -> None:
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from pipu_rec.train.ranker import load_rows, train_and_save
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from pipu_rec.wave.engine import FEATURE_NAMES
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session, user, track = uid(), world["users"][0], world["tracks"][0]
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with psycopg.connect(DSN, autocommit=True) as conn:
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conn.execute("INSERT INTO wave_sessions (id, user_id) VALUES (%s, %s)", (session, user))
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conn.execute(
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"INSERT INTO wave_impressions (wave_session_id, position, track_id, candidate_source, outcome) "
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"VALUES (%s, 0, %s, 'audio_sim', 'complete')",
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(session, track),
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)
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conn.execute(
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"INSERT INTO rec_impression_features (wave_session_id, track_id, features) VALUES (%s, %s, %s)",
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(session, track, [0.5] * len(FEATURE_NAMES)),
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)
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rows = load_rows(conn)
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assert any(r[0] == session and r[2] == "complete" for r in rows)
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result = train_and_save(conn, time.time())
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assert "skipped" in result["ranker"], "too few impressions — v1 stays"
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def test_demographic_prior_advances_age_and_preserves_own_feedback(world: dict[str, Any]) -> None:
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user = uid()
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with psycopg.connect(DSN, autocommit=True) as conn:
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conn.execute(
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"INSERT INTO users (id, username, password_hash) VALUES (%s, %s, 'x')",
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(user, f"cohort-{user[:6]}"),
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)
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# Target selected 13–19 twenty years ago; matching listeners are now 30–39.
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conn.execute(
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"INSERT INTO user_profiles_rec (user_id, onboarding) VALUES (%s, "
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"jsonb_build_object('age_group', '13_19', 'gender', 'male', "
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"'age_group_year', EXTRACT(YEAR FROM CURRENT_DATE)::int - 20))",
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(user,),
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)
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for peer in world["users"][:3]:
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conn.execute(
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"INSERT INTO user_profiles_rec (user_id, onboarding) VALUES (%s, %s) "
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"ON CONFLICT (user_id) DO UPDATE SET onboarding = EXCLUDED.onboarding",
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(peer, json.dumps({"gender": "male", "age_group": "30_39"})),
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)
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profile = load_profile(conn, user)
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track = world["tracks"][0] # two matching listeners liked it
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assert 0 < profile.scores[track] <= 0.25
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assert not profile.played
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conn.execute(
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"INSERT INTO track_reactions (user_id, track_id, reaction) VALUES (%s, %s, 'dislike')",
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(user, track),
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)
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assert track not in load_profile(conn, user).scores
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conn.execute(
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"UPDATE user_profiles_rec SET onboarding = %s WHERE user_id = %s",
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(json.dumps({"gender": "female"}), user),
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)
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assert not load_profile(conn, user).scores, "no invented preferences when the cohort has no evidence"
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