minstrel/internal/db/dbq/recommendation_metrics.sql.go

// Code generated by sqlc. DO NOT EDIT.
// versions:
//   sqlc v1.31.1
// source: recommendation_metrics.sql

package dbq

import (
	"context"

	"github.com/jackc/pgx/v5/pgtype"
)

const recommendationSourceMetricsForUser = `-- name: RecommendationSourceMetricsForUser :many
SELECT
    pe.source,
    pe.pick_kind,
    count(*)::bigint AS plays,
    count(*) FILTER (WHERE pe.was_skipped)::bigint AS skips,
    count(pe.completion_ratio)::bigint AS completion_n,
    COALESCE(avg(pe.completion_ratio), 0)::float8 AS avg_completion
FROM play_events pe
WHERE pe.user_id = $1
  AND pe.started_at > now() - ($2::float8 * INTERVAL '1 day')
GROUP BY pe.source, pe.pick_kind
ORDER BY plays DESC
`

type RecommendationSourceMetricsForUserParams struct {
	UserID  pgtype.UUID
	Column2 float64
}

type RecommendationSourceMetricsForUserRow struct {
	Source        *string
	PickKind      *string
	Plays         int64
	Skips         int64
	CompletionN   int64
	AvgCompletion float64
}

// $1 user_id, $2 window_days. plays/skips are counts; avg_completion is the
// mean completion ratio over the completion_n plays that recorded one.
// pick_kind splits For You plays into taste/fresh/unattributed (#1249);
// it is NULL for every other source, so those still group to one row.
func (q *Queries) RecommendationSourceMetricsForUser(ctx context.Context, arg RecommendationSourceMetricsForUserParams) ([]RecommendationSourceMetricsForUserRow, error) {
	rows, err := q.db.Query(ctx, recommendationSourceMetricsForUser, arg.UserID, arg.Column2)
	if err != nil {
		return nil, err
	}
	defer rows.Close()
	var items []RecommendationSourceMetricsForUserRow
	for rows.Next() {
		var i RecommendationSourceMetricsForUserRow
		if err := rows.Scan(
			&i.Source,
			&i.PickKind,
			&i.Plays,
			&i.Skips,
			&i.CompletionN,
			&i.AvgCompletion,
		); err != nil {
			return nil, err
		}
		items = append(items, i)
	}
	if err := rows.Err(); err != nil {
		return nil, err
	}
	return items, nil
}

const recommendationWeeklyTrends = `-- name: RecommendationWeeklyTrends :many

SELECT
    date_trunc('week', pe.started_at)::date AS week_start,
    pe.source,
    count(*)::bigint AS plays,
    count(*) FILTER (WHERE pe.was_skipped)::bigint AS skips,
    count(pe.completion_ratio)::bigint AS completion_n,
    COALESCE(avg(pe.completion_ratio), 0)::float8 AS avg_completion,
    count(*) FILTER (WHERE tpa.artist_id IS NOT NULL)::bigint AS taste_hits
FROM play_events pe
JOIN tracks t ON t.id = pe.track_id
LEFT JOIN taste_profile_artists tpa
       ON tpa.user_id = pe.user_id
      AND tpa.artist_id = t.artist_id
      AND tpa.weight > 0
WHERE pe.started_at > now() - ($1::int * INTERVAL '1 week')
GROUP BY 1, 2
ORDER BY 1, 2
`

type RecommendationWeeklyTrendsRow struct {
	WeekStart     pgtype.Date
	Source        *string
	Plays         int64
	Skips         int64
	CompletionN   int64
	AvgCompletion float64
	TasteHits     int64
}

// Recommendation observability (#796 phase 4). Per-source play outcomes so the
// operator can see whether each recommendation surface is landing and tune the
// taste weights. Source is stamped on play_events when a play is launched from
// a recommendation surface; NULL means the user picked the track manually —
// those rows are INCLUDED here as the baseline control group the surfaces are
// judged against (milestone #127: delta-vs-baseline is what makes the numbers
// actionable). Raw source strings are bucketed into stable surface families in
// the Go handler; completion_n is carried so family merges can weight
// avg_completion correctly.
// Weekly per-source outcome series for the tuning lab's trend view
// (#1251). Aggregated across ALL users: the tuning knobs are global,
// so judging a knob turn needs global outcomes — rows carry rates
// only, no track or user identity. NULL-source (manual) rows are
// included as the baseline family.
//
// taste_hits counts plays whose track's artist has a positive weight
// in that user's CURRENT taste profile — the "cheap recompute" option:
// retroactive over the whole window, at the cost of drift (the profile
// is today's, the play may be weeks old). Good enough to read whether
// a surface is feeding taste-fitting tracks.
// $1 window in weeks.
func (q *Queries) RecommendationWeeklyTrends(ctx context.Context, weeks int32) ([]RecommendationWeeklyTrendsRow, error) {
	rows, err := q.db.Query(ctx, recommendationWeeklyTrends, weeks)
	if err != nil {
		return nil, err
	}
	defer rows.Close()
	var items []RecommendationWeeklyTrendsRow
	for rows.Next() {
		var i RecommendationWeeklyTrendsRow
		if err := rows.Scan(
			&i.WeekStart,
			&i.Source,
			&i.Plays,
			&i.Skips,
			&i.CompletionN,
			&i.AvgCompletion,
			&i.TasteHits,
		); err != nil {
			return nil, err
		}
		items = append(items, i)
	}
	if err := rows.Err(); err != nil {
		return nil, err
	}
	return items, nil
}