added decision engine
This commit is contained in:
@@ -1,444 +1,113 @@
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#currently a consecpt
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#!/bin/bash
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# ==============================================================================================
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# ================================== DECISION ENGINE ===========================================
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# =============================== LIDARR DISCOVERY =============================================
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# ==============================================================================================
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# Central behavioral decision engine for the media ecosystem.
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# Lidarr discovery orchestrator.
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#
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# This engine transforms recent user activity into weighted decision output used by
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# downstream automation systems such as Lidarr, Sonarr, Radarr, and future services.
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# Consumes:
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# Kernel/decision_engine.sh
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#
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# The engine is:
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# Time-aware — recent activity matters more than historical ownership
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# Behavior-driven — decisions are based on actual usage patterns
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# Family-aware — balances influence across active users dynamically
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# Domain-agnostic — policies can adapt behavior per media type
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# Constraint-based — no single user can dominate system-wide discovery
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# Purpose:
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# Discover high-quality artists/albums for acquisition using
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# family-aware and behavior-driven scoring logic.
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#
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# ── CORE PHILOSOPHY ────────────────────────────────────────────────────────────────────────────
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# This script is intentionally selective.
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#
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# This is NOT:
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# • a recommendation engine
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# • a media AI
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# • a centralized content manager
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# Music discovery is treated differently than TV/movies:
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#
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# This IS:
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# • a decision layer
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# • a weighting system
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# • a behavioral context engine
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# Higher strictness
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# Stronger quality bias
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# Lower tolerance for trend chasing
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# Longer behavioral memory
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#
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# Recent activity defines current context.
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#
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# Discovery influence is earned through participation, not ownership.
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# Users who actively engage with media contribute more strongly to discovery weighting,
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# while configurable caps prevent any single user from overwhelming the ecosystem.
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#
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# The result is a continuously adapting media environment that reflects the current
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# behavioral state of the household rather than static long-term bias.
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#
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# ── RESPONSIBILITIES ───────────────────────────────────────────────────────────────────────────
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#
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# • Ingest recent activity from media systems and APIs
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# • Normalize activity across users and domains
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# • Apply time-decay and recency weighting
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# • Enforce per-user influence caps
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# • Generate ranked discovery candidates
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# • Return structured decision output to consumer scripts
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#
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# ── NON-RESPONSIBILITIES ───────────────────────────────────────────────────────────────────────
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#
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# The decision engine NEVER:
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# • Executes downloads
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# • Calls arr APIs directly
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# • Starts/stops containers
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# • Performs filesystem operations
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# • Handles infrastructure orchestration
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#
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# Infrastructure execution belongs to:
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# • adapter scripts
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# • orchestrators
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# • common.sh shared runtime functions
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#
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# ── ARCHITECTURE ROLE ─────────────────────────────────────────────────────────────────────────
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#
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# Ecosystem Layers:
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#
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# Config Layer
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# master.conf + host configs
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# ↓
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# Runtime Layer
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# common.sh
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# ↓
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# Decision Layer
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# decision_engine.sh ← THIS FILE
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# ↓
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# Domain Adapters
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# lidarr_discovery.sh
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# sonarr_discovery.sh
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# radarr_discovery.sh
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# transcoding_manager.sh
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#
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# This separation keeps decision logic centralized while allowing execution systems
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# to evolve independently.
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#
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# ── FUTURE EXPANSION ──────────────────────────────────────────────────────────────────────────
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#
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# Planned consumers:
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# • Lidarr music discovery
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# • Sonarr TV discovery balancing
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# • Radarr movie discovery weighting
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# • Transcoding priority orchestration
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# • Queue scheduling systems
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# • Resource-aware automation policies
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#
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# Shared decision primitives may eventually include:
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# • Recency decay models
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# • User weighting models
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# • Fairness constraints
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# • Diversity scoring
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# • Load-sensitive prioritization
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#
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# ── DESIGN RULES ──────────────────────────────────────────────────────────────────────────────
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#
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# • Engines decide — adapters execute
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# • Policies tune behavior — engines apply logic
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# • Shared infrastructure belongs in common.sh
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# • Domain logic belongs in policy modules
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# • No infrastructure execution inside the engine
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#
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# ── VERSION ───────────────────────────────────────────────────────────────────────────────────
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# v1.0 — Initial decision engine architecture
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# ==============================================================================================
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import requests
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from collections import defaultdict
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from dataclasses import dataclass
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from datetime import datetime
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# =========================================================
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# CONFIG
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# =========================================================
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LASTFM_API_KEY = "YOUR_LASTFM_KEY"
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LASTFM_USER = "YOUR_USERNAME"
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EMBY_URL = "http://YOUR_EMBY:8096"
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EMBY_API_KEY = "YOUR_EMBY_API_KEY"
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# influence constraints
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MAX_USER_WEIGHT = 0.35 # no single user > 35%
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MIN_USER_ACTIVITY = 5 # ignore near-zero listeners
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# scoring thresholds
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CORE_THRESHOLD = 90
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CONTEXT_THRESHOLD = 80
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FINAL_SELECTION_LIMIT = 3
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# =========================================================
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# PLAYLIST CONTEXT (intentional listening structure)
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# =========================================================
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PLAYLIST_CONTEXTS = [
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{"name": "90s Alternative", "tags": ["alternative", "rock", "90s"], "weight": 1.3},
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{"name": "Favorites Mix", "tags": ["all-time", "mixed"], "weight": 1.5},
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{"name": "ICP / Aggressive", "tags": ["hardcore", "rap", "aggressive"], "weight": 1.2},
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{"name": "Country Party", "tags": ["country", "party"], "weight": 1.3},
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{"name": "2000s Rock", "tags": ["rock", "2000s"], "weight": 1.25},
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]
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# =========================================================
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# DATA MODEL
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# =========================================================
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@dataclass
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class ArtistCandidate:
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name: str
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score: float = 0.0
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tier: str = "rotation" # core | context | rotation
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# =========================================================
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# EMBY: PLAYBACK ACTIVITY (TRUTH SOURCE)
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# =========================================================
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def get_emby_recent_events(days=30):
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"""
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Pull recent playback activity from Emby.
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Each event should include: user + artist
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"""
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url = f"{EMBY_URL}/Users/{EMBY_API_KEY}/Items/Latest"
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headers = {"X-Emby-Token": EMBY_API_KEY}
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try:
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r = requests.get(url, headers=headers)
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data = r.json()
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except Exception:
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return []
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events = []
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for item in data:
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if "ArtistName" in item and "UserId" in item:
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events.append({
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"user": item["UserId"],
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"artist": item["ArtistName"]
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})
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return events
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# =========================================================
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# ACTIVE USER DETECTION (TIME-BOUND CONTEXT)
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# =========================================================
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def get_active_users(events):
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user_activity = defaultdict(int)
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for e in events:
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user_activity[e["user"]] += 1
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# filter low activity users
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filtered = {
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u: c for u, c in user_activity.items()
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if c >= MIN_USER_ACTIVITY
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}
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if not filtered:
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return {}
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# normalize weights
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max_count = max(filtered.values())
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user_weights = {}
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for u, c in filtered.items():
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weight = c / max_count
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user_weights[u] = min(weight, MAX_USER_WEIGHT)
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return user_weights
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# =========================================================
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# LAST.FM SIGNALS (GLOBAL TASTE GRAPH)
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# =========================================================
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def lastfm_similar(artist):
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url = "http://ws.audioscrobbler.com/2.0/"
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params = {
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"method": "artist.getsimilar",
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"artist": artist,
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"api_key": LASTFM_API_KEY,
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"format": "json",
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"limit": 20
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}
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try:
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r = requests.get(url, params=params)
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data = r.json()
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except Exception:
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return []
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results = []
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try:
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for a in data["similarartists"]["artist"]:
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results.append({
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"name": a["name"],
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"match": float(a["match"])
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})
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except Exception:
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pass
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return results
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def lastfm_top_artists():
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url = "http://ws.audioscrobbler.com/2.0/"
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params = {
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"method": "user.gettopartists",
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"user": LASTFM_USER,
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"api_key": LASTFM_API_KEY,
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"format": "json",
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"limit": 50
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}
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try:
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r = requests.get(url, params=params)
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data = r.json()
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except Exception:
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return []
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results = []
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try:
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for a in data["topartists"]["artist"]:
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results.append((a["name"], int(a["playcount"])))
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except Exception:
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pass
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return results
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# =========================================================
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# CONTEXT VECTOR (PLAYLIST BLENDING)
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# =========================================================
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def build_context_vector():
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ctx = defaultdict(float)
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for p in PLAYLIST_CONTEXTS:
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for tag in p["tags"]:
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ctx[tag] += p["weight"]
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return ctx
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# =========================================================
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# SCORING ENGINE (TIME-BOUND + MULTI-SIGNAL)
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# =========================================================
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def build_scores():
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scores = defaultdict(float)
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context_vector = build_context_vector()
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# -----------------------------------------------------
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# 1. EMBY: playback events (current taste context)
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# -----------------------------------------------------
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events = get_emby_recent_events()
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active_users = get_active_users(events)
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user_artist_counts = defaultdict(lambda: defaultdict(int))
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for e in events:
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if e["user"] in active_users:
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user_artist_counts[e["user"]][e["artist"]] += 1
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# weighted user contribution
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for user, artists in user_artist_counts.items():
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weight = active_users[user]
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for artist, count in artists.items():
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scores[artist] += count * 50 * weight
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# -----------------------------------------------------
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# 2. LAST.FM: long-term identity
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# -----------------------------------------------------
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top = lastfm_top_artists()
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for artist, plays in top:
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scores[artist] += min(plays * 0.25, 80)
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# -----------------------------------------------------
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# 3. GRAPH EXPANSION
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# -----------------------------------------------------
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seeds = list(scores.keys())[:15]
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for seed in seeds:
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for s in lastfm_similar(seed):
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scores[s["name"]] += s["match"] * 60
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# -----------------------------------------------------
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# 4. CONTEXT BLENDING (playlist influence)
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# -----------------------------------------------------
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for artist in list(scores.keys()):
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for tag, weight in context_vector.items():
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if tag.lower() in artist.lower():
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scores[artist] += weight * 8
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return scores
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# =========================================================
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# TIER CLASSIFICATION
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# =========================================================
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def classify_tier(score):
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if score >= CORE_THRESHOLD:
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return "core"
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elif score >= CONTEXT_THRESHOLD:
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return "context"
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return "rotation"
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# =========================================================
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# SURVIVAL FILTER
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# =========================================================
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def filter_candidates(scores):
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candidates = []
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for name, score in scores.items():
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candidates.append(ArtistCandidate(
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name=name,
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score=score,
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tier=classify_tier(score)
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))
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candidates = [c for c in candidates if c.score >= CONTEXT_THRESHOLD]
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candidates.sort(key=lambda x: x.score, reverse=True)
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return candidates
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# =========================================================
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# FINAL SELECTION (balanced survival)
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# =========================================================
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def select_final(candidates):
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if not candidates:
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return []
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final = []
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cores = [c for c in candidates if c.tier == "core"]
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contexts = [c for c in candidates if c.tier == "context"]
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final.extend(cores[:2])
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final.extend(contexts[:1])
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return final[:FINAL_SELECTION_LIMIT]
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# =========================================================
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# OUTPUT (LIDARR HOOK)
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# =========================================================
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def send_to_lidarr(artists):
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for a in artists:
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print(f"[{a.tier.upper()}] {a.name} ({a.score:.2f})")
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# =========================================================
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# MAIN CYCLE
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# =========================================================
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def run():
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print("\n=== DISCOVERY CYCLE START ===")
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print(f"Time: {datetime.now()}")
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scores = build_scores()
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candidates = filter_candidates(scores)
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final = select_final(candidates)
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if not final:
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print("\nNo artists survived this cycle.")
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return
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print("\nSurvivors:")
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for f in final:
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print(f"- {f.name} [{f.tier}] ({f.score:.2f})")
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send_to_lidarr(final)
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print("\n=== CYCLE COMPLETE ===")
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if __name__ == "__main__":
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run()
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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ROOT_DIR="$(dirname "$(dirname "$SCRIPT_DIR")")"
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source "$ROOT_DIR/load_config.sh"
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source "$ROOT_DIR/Kernel/decision_engine.sh"
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# ==============================================================================================
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# ── CONFIG ────────────────────────────────────────────────────────────────────────────────────
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# ==============================================================================================
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DISCOVERY_THRESHOLD=70
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# ==============================================================================================
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# ── EXAMPLE CANDIDATE ─────────────────────────────────────────────────────────────────────────
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# ==============================================================================================
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#
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# Real implementation would pull:
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#
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# Last.fm
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# Trakt-style behavior history
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# Lidarr metadata
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# User weighting
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# Genre affinity
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# Temporal activity
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#
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# ==============================================================================================
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ARTIST_NAME="Example Artist"
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USER_SCORE=35
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POPULARITY_SCORE=15
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RECENCY_SCORE=10
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QUALITY_SCORE=20
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# ==============================================================================================
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# ── SCORING ───────────────────────────────────────────────────────────────────────────────────
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# ==============================================================================================
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TOTAL_SCORE=$(score_candidate \
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"$USER_SCORE" \
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"$POPULARITY_SCORE" \
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"$RECENCY_SCORE" \
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"$QUALITY_SCORE"
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)
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# ==============================================================================================
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# ── DECISION ──────────────────────────────────────────────────────────────────────────────────
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# ==============================================================================================
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DECISION=$(make_decision "$TOTAL_SCORE" "$DISCOVERY_THRESHOLD")
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# ==============================================================================================
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# ── OUTPUT ────────────────────────────────────────────────────────────────────────────────────
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# ==============================================================================================
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||||
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echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
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echo "🎵 Lidarr Discovery Candidate"
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echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
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echo "Artist: $ARTIST_NAME"
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echo "Score : $TOTAL_SCORE"
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echo "Result: $DECISION"
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echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
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||||
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||||
# ==============================================================================================
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||||
# ── ACTION ────────────────────────────────────────────────────────────────────────────────────
|
||||
# ==============================================================================================
|
||||
#
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||||
# Real implementation would:
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||||
#
|
||||
# Add artist to Lidarr
|
||||
# Queue search
|
||||
# Log decision
|
||||
# Record scoring metadata
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||||
# Update history state
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||||
#
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||||
# ==============================================================================================
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||||
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||||
if [[ "$DECISION" == "ACCEPT" ]]; then
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||||
|
||||
echo "Adding artist to Lidarr..."
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||||
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||||
# future:
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||||
# curl -X POST "$LIDARR_URL/api/v1/artist"
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||||
|
||||
else
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||||
|
||||
echo "Candidate rejected."
|
||||
|
||||
fi
|
||||
Reference in New Issue
Block a user