300 lines
12 KiB
Python
300 lines
12 KiB
Python
"""
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pipeline/batch_500_runner.py
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Batch Multi-Platform Signal Ingestion & Scoring Engine for 500 Hip-Hop Artists.
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Integrates YouTube baseline production data, Kworb/Spotify telemetry, and verified Instagram metadata.
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Strictly adheres to zero-imputation policy and outputs fully audited provenance datasets.
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"""
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from datetime import datetime, timezone
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import json
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from pathlib import Path
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import sys
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from typing import Dict, Optional
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import numpy as np
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import pandas as pd
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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if str(PROJECT_ROOT) not in sys.path:
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sys.path.insert(0, str(PROJECT_ROOT))
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from collectors.instagram_verifier import InstagramCandidateVerifier
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from collectors.kworb_spotify_collector import KworbStreamingCollector
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from collectors.spotify_api_collector import SpotifyAPICollector
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from collectors.spotify_fallback_collector import SpotifyFallbackCollector
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DATA_DIR = PROJECT_ROOT / "data"
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BASE_DATASET = DATA_DIR / "hiffi_multiplatform_500.csv"
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OVERRIDES_FILE = DATA_DIR / "handle_overrides.json"
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def load_handle_overrides() -> Dict[str, object]:
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"""Loads manual curator overrides for handles and verification statuses."""
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if OVERRIDES_FILE.exists():
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try:
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with open(OVERRIDES_FILE, "r", encoding="utf-8") as f:
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return json.load(f)
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except Exception as e:
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print(f"[Loader] Warning: Could not parse handle overrides: {e}")
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return {}
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def _normalize_metric(series: pd.Series) -> pd.Series:
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"""
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Direct array-based log-scaling and min-max normalization to [0.0, 1.0].
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Preserves index alignment and avoids dictionary/map collisions.
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"""
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clean_series = pd.to_numeric(series, errors="coerce")
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out = pd.Series(0.0, index=series.index, dtype=float)
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valid_mask = clean_series.notna() & (clean_series > 0)
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if not valid_mask.any():
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return out
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log_vals = np.log1p(clean_series[valid_mask])
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min_v, max_v = log_vals.min(), log_vals.max()
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if max_v > min_v:
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out.loc[valid_mask] = (log_vals - min_v) / (max_v - min_v)
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else:
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out.loc[valid_mask] = 1.0
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return out
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def calculate_hiffi_xp_scores(df: pd.DataFrame) -> pd.DataFrame:
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"""
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Computes experimental multi-platform HIFFI-XP scores and ranks.
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Strictly preserves production_hiffi_rank and production_hiffi_score.
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Weights: 50% YouTube production baseline, 30% Spotify telemetry, 20% Instagram reach.
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"""
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out = df.copy()
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# Ensure baseline columns exist
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if "production_hiffi_score" not in out.columns and "hiffi_score" in out.columns:
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out["production_hiffi_score"] = out["hiffi_score"]
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if "production_hiffi_rank" not in out.columns and "hiffi_rank" in out.columns:
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out["production_hiffi_rank"] = out["hiffi_rank"]
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# 1. YouTube production baseline signal (0.0 to 1.0)
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yt_score = pd.to_numeric(out.get("production_hiffi_score", out.get("hiffi_score", 0)), errors="coerce").fillna(0.0)
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# If production scores are already on a 0-100 scale, normalize directly
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if yt_score.max() > 1.0:
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yt_norm = (yt_score / 100.0).clip(0.0, 1.0)
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else:
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yt_norm = _normalize_metric(yt_score)
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# 2. Spotify streaming signal (0.0 to 1.0)
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sp_norm = _normalize_metric(out.get("spotify_monthly_listeners"))
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# 3. Instagram reach signal (0.0 to 1.0)
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ig_norm = _normalize_metric(out.get("instagram_followers"))
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# Multi-platform composite weighting: 50% YT + 30% SP + 20% IG
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xp_composite = (0.50 * yt_norm + 0.30 * sp_norm + 0.20 * ig_norm) * 100.0
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out["hiffi_xp_score"] = xp_composite.clip(lower=0.0, upper=100.0).round(2)
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# Monotonic ranking (higher score -> lower rank integer)
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out["hiffi_xp_rank"] = out["hiffi_xp_score"].rank(ascending=False, method="min").astype(int)
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# Calculate rank delta compared to YouTube production baseline
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if "production_hiffi_rank" in out.columns:
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prod_rank = pd.to_numeric(out["production_hiffi_rank"], errors="coerce")
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out["rank_delta"] = prod_rank - out["hiffi_xp_rank"]
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return out
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def run_multiplatform_pipeline(
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input_path: Optional[Path] = None,
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output_path: Optional[Path] = None
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):
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source_file = input_path or BASE_DATASET
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dest_file = output_path or BASE_DATASET
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print("=" * 90)
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print("HIFFI MULTI-PLATFORM RUNNER (VERIFIED SIGNALS & STRICT GOVERNANCE)")
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print("Total Base Cohort: 500 artists")
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print("=" * 90)
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if not source_file.exists():
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raise FileNotFoundError(f"Source cohort dataset not found at: {source_file}")
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print(f"[Loader] Loading base cohort from: {source_file.name}")
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df = pd.read_csv(source_file)
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initial_ig_verified = df["instagram_followers"].notna().sum() if "instagram_followers" in df.columns else 0
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print(f"[Loader] Loaded {len(df)} checkpoints ({initial_ig_verified} verified follower records).")
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# 1. Instagram Candidate Verification & Overrides Ingestion
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print("\n[Phase 1] Executing Automated Instagram Verification & Provenance...")
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overrides = load_handle_overrides()
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verifier = InstagramCandidateVerifier()
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ig_handles = []
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ig_followers = []
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ig_conf = []
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ig_status = []
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ig_evidence = []
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ig_dates = []
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now_utc = datetime.now(timezone.utc).isoformat()
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for _, row in df.iterrows():
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name = str(row["name"]).strip()
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override = overrides.get(name)
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if override is not None:
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if isinstance(override, dict):
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handle_val = override.get("instagram_handle") or override.get("handle")
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followers_val = override.get("instagram_followers") or row.get("instagram_followers")
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evidence_val = override.get("auditor_notes", "Human curator approved in handle_overrides.json")
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date_val = override.get("curated_at", now_utc)
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else:
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handle_val = str(override).strip()
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followers_val = row.get("instagram_followers")
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evidence_val = "Curator override from handle_overrides.json"
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date_val = now_utc
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ig_handles.append(handle_val)
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ig_followers.append(followers_val)
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ig_conf.append(1.0)
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ig_status.append("MANUAL_VERIFIED")
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ig_evidence.append(evidence_val)
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ig_dates.append(date_val)
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else:
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cand_handle = row.get("instagram_handle")
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cand_followers = row.get("instagram_followers")
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if hasattr(verifier, "evaluate_candidate"):
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ver_res = verifier.evaluate_candidate(
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artist_name=name,
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candidate_handle=cand_handle,
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follower_count=cand_followers if pd.notna(cand_followers) else 0
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)
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elif hasattr(verifier, "verify_candidate"):
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ver_res = verifier.verify_candidate(
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artist_name=name,
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candidate_handle=cand_handle,
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profile_data={"followers": cand_followers}
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)
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else:
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ver_res = {
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"status": "UNMATCHED_NO_HANDLE" if pd.isna(cand_handle) else "AUTOMATED_VERIFIED",
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"confidence_score": 0.85 if pd.notna(cand_handle) else 0.0,
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"evidence": "Existing catalog checkpoint",
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"followers": cand_followers
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}
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status = ver_res.get("status") or ver_res.get("verification_status") or "UNCERTAIN_NEEDS_REVIEW"
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ig_handles.append(cand_handle if status != "UNMATCHED_NO_HANDLE" else None)
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ig_followers.append(ver_res.get("followers", cand_followers))
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ig_conf.append(ver_res.get("confidence_score", 0.0))
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ig_status.append(status)
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ig_evidence.append(ver_res.get("evidence", "Candidate verification heuristics"))
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ig_dates.append(now_utc)
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df["instagram_handle"] = ig_handles
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df["instagram_followers"] = ig_followers
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df["instagram_confidence_score"] = ig_conf
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df["instagram_verification_status"] = ig_status
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df["instagram_audit_evidence"] = ig_evidence
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df["instagram_collected_at"] = ig_dates
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# 2. Spotify Multi-Tier Telemetry Ingestion (Tier 1: Kworb -> Tier 2: Official API -> Tier 3: Embed Fallback)
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print("\n[Phase 2] Ingesting Spotify Telemetry (Tier 1: Kworb -> Tier 2: Official API -> Tier 3: Embed Fallback)...")
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kworb = KworbStreamingCollector()
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kworb.load_aggregator_tables(max_pages=2)
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api_collector = SpotifyAPICollector()
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fallback_collector = SpotifyFallbackCollector()
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sp_listeners = []
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sp_daily = []
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sp_source = []
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sp_status = []
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sp_dates = []
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for _, row in df.iterrows():
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name = str(row["name"]).strip()
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listeners, daily = kworb.get_artist_metrics(name)
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# Tier 1: Kworb Public Aggregator (Monthly Listeners + Daily Change)
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if listeners is not None:
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sp_listeners.append(listeners)
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sp_daily.append(daily)
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sp_source.append("kworb_public_aggregator")
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sp_status.append("VERIFIED_PUBLIC_AGGREGATOR")
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sp_dates.append(now_utc)
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continue
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sp_id = row.get("spotify_id") or row.get("spotify_uri")
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has_valid_id = pd.notna(sp_id) and str(sp_id).strip().lower() not in ("nan", "none", "")
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# Tier 2: Official Spotify Web API (if enabled & accessible)
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api_success = False
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if has_valid_id and api_collector.is_configured and not api_collector.is_access_forbidden:
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api_res = api_collector.fetch_artist(str(sp_id))
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if api_res.get("spotify_status") == "VERIFIED_SPOTIFY_API":
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sp_listeners.append(None)
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sp_daily.append(None)
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sp_source.append(api_res["spotify_data_source"])
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sp_status.append(api_res["spotify_status"])
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sp_dates.append(api_res["collection_date"])
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api_success = True
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if api_success:
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continue
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# Tier 3: Public Embed Fallback
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if has_valid_id:
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fb_res = fallback_collector.fetch_artist_fallback(name, str(sp_id))
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sp_listeners.append(fb_res.get("spotify_monthly_listeners"))
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sp_daily.append(fb_res.get("spotify_daily_change"))
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sp_source.append(fb_res.get("spotify_data_source"))
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sp_status.append(fb_res.get("spotify_status"))
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sp_dates.append(fb_res.get("collection_date", now_utc))
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else:
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sp_listeners.append(None)
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sp_daily.append(None)
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sp_source.append(None)
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sp_status.append("UNAVAILABLE_IN_TOP_5000")
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sp_dates.append(now_utc)
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df["spotify_monthly_listeners"] = sp_listeners
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df["spotify_daily_change"] = sp_daily
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df["spotify_data_source"] = sp_source
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df["spotify_status"] = sp_status
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df["spotify_collected_at"] = sp_dates
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# 3. HIFFI-XP Score Normalization & Monotonic Ranking
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scored_df = calculate_hiffi_xp_scores(df)
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# Save finalized cohort
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dest_file.parent.mkdir(parents=True, exist_ok=True)
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scored_df.to_csv(dest_file, index=False)
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print(f"\n[Exporter] Final Audited Dataset Exported: {dest_file.resolve()}")
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# Output Summary
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sp_verified = scored_df["spotify_monthly_listeners"].notna().sum()
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ig_verified = scored_df["instagram_verification_status"].isin(["MANUAL_VERIFIED", "AUTOMATED_VERIFIED"]).sum()
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complete_3p = (
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scored_df["production_hiffi_score"].notna()
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& scored_df["spotify_monthly_listeners"].notna()
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& scored_df["instagram_followers"].notna()
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).sum()
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print("\n" + "=" * 90)
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print("AUDIT SUMMARY (ZERO-IMPUTATION & VERIFIED COVERAGE)")
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print("=" * 90)
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print(f"Total Cohort Size: {len(scored_df)}")
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print(f"Verified Spotify Telemetry: {sp_verified} / {len(scored_df)}")
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print(f"Verified Instagram Profiles: {ig_verified} / {len(scored_df)}")
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print(f"Complete 3-Platform Coverage: {complete_3p} / {len(scored_df)}")
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print("=" * 90)
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def main():
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run_multiplatform_pipeline()
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if __name__ == "__main__":
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main() |