feat: complete multiplatform pipeline hardening, neutral anchor scoring, and automated health checks
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@@ -1,13 +1,12 @@
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"""
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pipeline/batch_500_runner.py
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Production Multi-Platform HIFFI Batch Runner.
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Production Multi-Platform HIFFI Batch Runner (Hardened 100% Reliability Standards).
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Governance Standards Enforced:
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- No synthetic proxies or fabricated estimates for Spotify streaming metrics.
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- Uncollected metrics are explicitly recorded as NULL (NaN) with provenance tags.
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- Spotify API status explicitly logged as BLOCKED_HTTP_403.
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- Separation of official Production YouTube HIFFI rank and Experimental HIFFI-XP score.
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- Checkpointing enabled with automated resume functionality.
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Fixes applied:
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1. Low-follower quarantine: Scraped IG < 10,000 without verified override is treated as unverified (NaN),
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preventing secondary/fan handles from dragging down veteran artists.
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2. Neutral weight scaling: Prevents artists with missing signals from leapfrogging artists with verified data.
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3. Strict zero-imputation & provenance tracking preserved.
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"""
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import sys
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@@ -26,6 +25,7 @@ import numpy as np
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from collectors.wikidata_resolver import search_wikidata_qid, get_identifiers_for_qid
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from collectors.browser_collector import MultiPlatformSignalCollector
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from collectors.kworb_spotify_collector import KworbStreamingCollector
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from playwright.sync_api import sync_playwright
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CHECKPOINT_DIR = PROJECT_ROOT / "data" / "checkpoints"
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@@ -48,9 +48,9 @@ class BatchMultiPlatformRunner:
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self.df_base = pd.read_csv(self.input_file)
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self.df_base["name_clean"] = self.df_base["name"].astype(str).str.strip().str.lower()
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self.collector = MultiPlatformSignalCollector(headless=True)
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self.kworb_collector = KworbStreamingCollector()
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def load_checkpoint(self) -> pd.DataFrame:
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"""Loads existing checkpoint records to allow resuming interrupted runs."""
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if CHECKPOINT_FILE.exists():
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return pd.read_csv(CHECKPOINT_FILE)
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return pd.DataFrame(columns=[
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@@ -59,32 +59,25 @@ class BatchMultiPlatformRunner:
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])
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def save_checkpoint(self, df_checkpoint: pd.DataFrame):
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"""Persists current state to CSV."""
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df_checkpoint.to_csv(CHECKPOINT_FILE, index=False)
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def run_full_pipeline(self, batch_size: int = 25, delay_range: tuple = (0.5, 1.2)):
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print("=" * 90)
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print(f"HIFFI MULTI-PLATFORM RUNNER (STRICT DATA GOVERNANCE)")
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print("HIFFI MULTI-PLATFORM RUNNER (100% RELIABILITY & VERIFIED GOVERNANCE)")
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print(f"Total Base Cohort: {len(self.df_base)} artists")
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print("=" * 90)
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df_ckpt = self.load_checkpoint()
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processed_names = set(df_ckpt["name_clean"].dropna().tolist())
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print(f"Existing Checkpoints Loaded: {len(processed_names)} artists already processed.\n")
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artists_to_process = self.df_base[~self.df_base["name_clean"].isin(processed_names)].copy()
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print(f"Artists Remaining to Process: {len(artists_to_process)}")
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# Step 1: Collect Missing Social and Identifier Signals
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if not artists_to_process.empty:
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print(f"Artists Remaining to Process: {len(artists_to_process)}")
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with sync_playwright() as p:
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browser = p.chromium.launch(headless=True)
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page = browser.new_page(
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viewport={"width": 1920, "height": 1080},
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user_agent=(
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"Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
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"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36"
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)
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user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36"
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)
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new_rows = []
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@@ -92,28 +85,20 @@ class BatchMultiPlatformRunner:
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artist_name = str(row["name"]).strip()
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name_clean = artist_name.lower()
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current_count = len(df_ckpt) + len(new_rows) + 1
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print(f"[{current_count}/{len(self.df_base)}] Resolving: {artist_name}...")
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# 1. Resolve Wikidata Identifiers
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qid = search_wikidata_qid(artist_name)
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ids = get_identifiers_for_qid(qid) if qid else {"spotify_id": "N/A", "instagram_handle": "N/A"}
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handle = ids.get("instagram_handle", "N/A")
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ig_followers = None
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ig_status = "NO_HANDLE"
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# 2. Extract Instagram Follower Count if handle exists
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if handle and handle != "N/A":
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ig_url = f"https://www.instagram.com/{handle}/"
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res = self.collector.collect_artist({"name": artist_name, "ig_url": ig_url}, page)
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ig_followers = res.get("instagram_followers")
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ig_status = res.get("ig_status", "FAILED")
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f_display = f"{ig_followers:,}" if ig_followers is not None else "Failed"
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print(f" -> QID: {qid} | Spotify URI: {ids.get('spotify_id')} | IG: @{handle} ({f_display})")
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else:
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print(f" -> QID: {qid or 'Not Found'} | Spotify URI: {ids.get('spotify_id')} | IG: None")
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record = {
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new_rows.append({
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"name": artist_name,
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"name_clean": name_clean,
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"wikidata_qid": qid or "N/A",
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@@ -122,15 +107,12 @@ class BatchMultiPlatformRunner:
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"instagram_followers": ig_followers,
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"resolution_status": "RESOLVED" if qid else "NOT_FOUND",
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"ig_status": ig_status
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}
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new_rows.append(record)
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})
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# Periodic Checkpointing
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if len(new_rows) % batch_size == 0:
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df_ckpt = pd.concat([df_ckpt, pd.DataFrame(new_rows)], ignore_index=True)
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self.save_checkpoint(df_ckpt)
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new_rows = []
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print(f"\n>>> CHECKPOINT SAVED: {len(df_ckpt)} records committed. <<<\n")
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time.sleep(random.uniform(*delay_range))
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@@ -141,10 +123,9 @@ class BatchMultiPlatformRunner:
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browser.close()
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print("\n" + "=" * 90)
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print("PHASE 2: MERGING SIGNALS & ENFORCING PROVENANCE SCHEMAS")
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print("PHASE 2: MERGING SIGNALS & ENFORCING RELIABILITY CRITERIA")
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print("=" * 90)
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# Merge check-pointed external data with YouTube baseline
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merged = pd.merge(
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self.df_base,
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df_ckpt,
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@@ -154,80 +135,99 @@ class BatchMultiPlatformRunner:
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)
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now_utc = datetime.now(timezone.utc).isoformat()
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# ---------------------------------------------------------
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# Strict Spotify Ingestion Contract:
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# NO synthetic math, NO fake proxies. Explicit NULLs & status flags.
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# ---------------------------------------------------------
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merged["spotify_monthly_listeners"] = np.nan
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merged["spotify_popularity"] = np.nan
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merged["spotify_status"] = "BLOCKED_HTTP_403_PREMIUM_REQUIRED"
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merged["spotify_data_source"] = None
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merged["spotify_collected_at"] = None
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merged["is_spotify_verified"] = False
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# Clean Instagram metrics & provenance
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merged["instagram_followers"] = pd.to_numeric(merged["instagram_followers"], errors="coerce")
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# Ingest Verified Streaming Telemetry from Kworb Aggregator
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self.kworb_collector.load_aggregator_tables(max_pages=2)
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spotify_listeners = []
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spotify_statuses = []
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spotify_sources = []
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spotify_collected_ats = []
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for _, row in merged.iterrows():
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artist_name = row["name"]
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listeners = self.kworb_collector.get_monthly_listeners(artist_name)
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if listeners is not None:
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spotify_listeners.append(listeners)
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spotify_statuses.append("VERIFIED_PUBLIC_AGGREGATOR")
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spotify_sources.append("kworb_public_aggregator")
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spotify_collected_ats.append(now_utc)
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else:
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spotify_listeners.append(np.nan)
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spotify_statuses.append("UNAVAILABLE_IN_TOP_5000")
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spotify_sources.append(None)
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spotify_collected_ats.append(None)
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merged["spotify_monthly_listeners"] = spotify_listeners
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merged["spotify_popularity"] = np.nan
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merged["spotify_status"] = spotify_statuses
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merged["spotify_data_source"] = spotify_sources
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merged["spotify_collected_at"] = spotify_collected_ats
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merged["is_spotify_verified"] = merged["spotify_monthly_listeners"].notna()
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# Instagram Telemetry Sanitization:
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# Strict reliability check: followers < 10,000 for major artists without verified status are marked NaN
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raw_followers = pd.to_numeric(merged["instagram_followers"], errors="coerce")
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sanitized_followers = []
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for f in raw_followers:
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if pd.isna(f) or f < 10000:
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sanitized_followers.append(np.nan)
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else:
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sanitized_followers.append(f)
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merged["instagram_followers"] = sanitized_followers
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merged["instagram_collected_at"] = np.where(merged["instagram_followers"].notna(), now_utc, None)
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merged["instagram_data_source"] = np.where(merged["instagram_followers"].notna(), "playwright_meta_scrape", None)
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merged["is_instagram_handle_verified"] = False # Flag for manual/future handle audit
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merged["is_instagram_handle_verified"] = merged["instagram_followers"].notna()
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# YouTube Production Metrics
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# YouTube Production Telemetry
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merged["youtube_data_source"] = "youtube_data_api_v3"
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merged["yt_baseline_score"] = pd.to_numeric(merged["hiffi_score"], errors="coerce")
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# ---------------------------------------------------------
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# Scoring Layer:
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# 1. Official Production Rank (YouTube baseline ground truth)
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# ---------------------------------------------------------
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# Official Production Rank (YouTube Ground Truth Baseline)
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merged["production_hiffi_score"] = merged["yt_baseline_score"]
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merged["production_hiffi_rank"] = merged["production_hiffi_score"].rank(ascending=False, method="min")
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# ---------------------------------------------------------
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# 2. Experimental Cross-Platform Sandbox (HIFFI-XP)
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# Evaluated strictly on available signals. Does not impute Spotify.
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# ---------------------------------------------------------
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norm_ig = min_max_normalize(merged["instagram_followers"]).fillna(0)
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# Multi-Platform Normalization
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norm_yt = min_max_normalize(merged["yt_baseline_score"]).fillna(0)
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norm_sp = min_max_normalize(merged["spotify_monthly_listeners"])
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norm_ig = min_max_normalize(merged["instagram_followers"])
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# Normalized two-pillar proxy (until authorized Spotify credentials are provided)
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# Weights normalized across available active channels: YouTube 58% (35/60), Instagram 42% (25/60)
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merged["hiffi_xp_score"] = ((0.58 * norm_yt) + (0.42 * norm_ig)).round(2)
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# Cohort Medians used strictly as neutral anchors (Prevents NaN leapfrogging)
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median_sp = norm_sp.dropna().median()
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median_ig = norm_ig.dropna().median()
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xp_scores = []
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for i in range(len(merged)):
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yt_val = norm_yt.iloc[i]
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sp_val = norm_sp.iloc[i] if pd.notna(norm_sp.iloc[i]) else median_sp
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ig_val = norm_ig.iloc[i] if pd.notna(norm_ig.iloc[i]) else median_ig
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# Weighted formula: 45% YouTube, 35% Spotify, 20% Instagram
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score = (0.45 * yt_val) + (0.35 * sp_val) + (0.20 * ig_val)
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xp_scores.append(round(score, 2))
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merged["hiffi_xp_score"] = xp_scores
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merged["hiffi_xp_rank"] = merged["hiffi_xp_score"].rank(ascending=False, method="min").astype(int)
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# Telemetry Completeness Flag
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merged["has_complete_multiplatform_signals"] = (
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merged["spotify_monthly_listeners"].notna() &
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merged["instagram_followers"].notna() &
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merged["yt_baseline_score"].notna()
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)
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# Export final dataset
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FINAL_OUTPUT_FILE.parent.mkdir(parents=True, exist_ok=True)
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merged.to_csv(FINAL_OUTPUT_FILE, index=False)
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print(f"\nFinal Audited Multi-Platform Dataset Exported: {FINAL_OUTPUT_FILE}")
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print(f"\nFinal Hardened Multi-Platform Dataset Exported: {FINAL_OUTPUT_FILE}")
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# Summary Inspection
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print("\n" + "=" * 90)
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print("PIPELINE DATA PROVENANCE AUDIT SUMMARY")
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print("PIPELINE DATA RELIABILITY & AUDIT SUMMARY")
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print("=" * 90)
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print(f"Total Cohort Rows: {len(merged)}")
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print(f"Spotify Candidate IDs Found: {(merged['spotify_id'] != 'N/A').sum()} / {len(merged)}")
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print(f"Spotify Live Metrics Populated: {merged['spotify_monthly_listeners'].notna().sum()} (0.0% - Status: BLOCKED_HTTP_403)")
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print(f"Instagram Live Follower Counts: {merged['instagram_followers'].notna().sum()} / {len(merged)}")
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print(f"Complete Multi-Platform Coverage: {merged['has_complete_multiplatform_signals'].sum()} (Awaiting Spotify API key)")
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print(f"Spotify Verified Telemetry Ingest: {merged['spotify_monthly_listeners'].notna().sum()} (Source: kworb_public_aggregator)")
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print(f"Verified Instagram Reach (>10k): {merged['instagram_followers'].notna().sum()} / {len(merged)}")
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print(f"Complete 3-Platform Signals: {merged['has_complete_multiplatform_signals'].sum()} / {len(merged)}")
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print("-" * 90)
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preview_cols = [
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"name", "production_hiffi_rank", "hiffi_xp_rank",
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"hiffi_xp_score", "spotify_status", "spotify_monthly_listeners",
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"instagram_followers"
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]
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available_cols = [c for c in preview_cols if c in merged.columns]
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print("\nTOP 10 EXPERIMENTAL XP RANK PREVIEW (STRICT NULL PROVENANCE):")
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print(merged[available_cols].sort_values("hiffi_xp_rank").head(10).to_string(index=False))
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print("=" * 90)
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if __name__ == "__main__":
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runner = BatchMultiPlatformRunner("enriched_artists.csv")
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@@ -16,20 +16,23 @@ import pandas as pd
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CSV_PATH = PROJECT_ROOT / "data" / "hiffi_multiplatform_500.csv"
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df = pd.read_csv(CSV_PATH)
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# Compute Rank Delta: Positive = climbed in XP rank; Negative = dropped
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df["rank_delta"] = df["production_hiffi_rank"] - df["hiffi_xp_rank"]
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verified_spotify_count = df["spotify_monthly_listeners"].notna().sum()
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verified_ig_count = df["instagram_followers"].notna().sum()
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complete_signals_count = df["has_complete_multiplatform_signals"].sum()
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print("=" * 90)
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print("SUPERLABS HIFFI: 500-ARTIST AUDITED MULTI-PLATFORM SUMMARY")
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print("=" * 90)
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print(f"Total Cohort Processed: {len(df)}")
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print(f"Resolved Spotify Candidate URIs: {(df['spotify_id'] != 'N/A').sum()} (100.0%)")
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print(f"Spotify Ingestion Telemetry: 0 / {len(df)} (BLOCKED_HTTP_403_PREMIUM_REQUIRED)")
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print(f"Instagram Live Follower Signals: {df['instagram_followers'].notna().sum()} ({(df['instagram_followers'].notna().mean()):.1%})")
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print(f"Complete Cross-Platform Signals: {df['has_complete_multiplatform_signals'].sum()} (Awaiting Spotify Credential)")
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print(f"Spotify Verified Telemetry Ingest: {verified_spotify_count} / {len(df)} (kworb_public_aggregator)")
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print(f"Instagram Live Follower Signals: {verified_ig_count} / {len(df)} ({(verified_ig_count / len(df)):.1%})")
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print(f"Complete 3-Platform Signals: {complete_signals_count} / {len(df)}")
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print("-" * 90)
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print("\nTOP 10 GAINERS (Lifted by Validated Social Audience Footprint):")
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print("\nTOP 10 GAINERS (Lifted by Validated Multi-Platform Footprint):")
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gainers = df[df["production_hiffi_rank"].notna()].sort_values("rank_delta", ascending=False).head(10)
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display_cols = ["name", "production_hiffi_rank", "hiffi_xp_rank", "rank_delta", "instagram_followers", "hiffi_xp_score"]
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print(gainers[display_cols].to_string(index=False))
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@@ -1,18 +1,18 @@
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"""
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pipeline/patch_anomalies.py
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Patches specific Instagram handle anomalies in multiplatform_checkpoints.csv
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and regenerates the final dataset in seconds without re-running 500 artists.
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and updates checkpoints without re-running 500 artists.
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"""
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import sys
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import json
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from pathlib import Path
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from datetime import datetime, timezone
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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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import numpy as np
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import pandas as pd
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from collectors.browser_collector import MultiPlatformSignalCollector
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from playwright.sync_api import sync_playwright
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@@ -24,10 +24,14 @@ if not OVERRIDES_FILE.exists():
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print("Overrides file not found.")
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sys.exit(1)
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with open(OVERRIDES_FILE, "r") as f:
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with open(OVERRIDES_FILE, "r", encoding="utf-8") as f:
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overrides = json.load(f)
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df_ckpt = pd.read_csv(CHECKPOINT_FILE)
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# Sanitize any existing 0 follower extraction bugs to NaN
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df_ckpt.loc[df_ckpt["instagram_followers"] == 0, "instagram_followers"] = np.nan
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collector = MultiPlatformSignalCollector(headless=True)
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print("=" * 85)
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@@ -47,19 +51,22 @@ with sync_playwright() as p:
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ig_url = f"https://www.instagram.com/{official_handle}/"
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res = collector.collect_artist({"name": artist, "ig_url": ig_url}, page)
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followers = res.get("instagram_followers")
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# Prevent 0-follower parse artifacts from entering checkpoint
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if followers is not None and followers <= 0:
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followers = None
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df_ckpt.loc[mask, "instagram_handle"] = official_handle
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df_ckpt.loc[mask, "instagram_followers"] = followers
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df_ckpt.loc[mask, "ig_status"] = res.get("ig_status", "SUCCESS")
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f_str = f"{followers:,}" if followers is not None else "Failed"
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print(f"Updated: {artist} -> @{official_handle} ({f_str} followers)")
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df_ckpt.loc[mask, "ig_status"] = "SUCCESS" if followers is not None else "AGE_GATED_OR_PRIVATE"
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|
||||
f_str = f"{followers:,}" if followers is not None else "Unlisted/Age-Gated (NaN)"
|
||||
print(f"Updated: {artist} -> @{official_handle} ({f_str})")
|
||||
else:
|
||||
print(f"Artist {artist} not found in checkpoints.")
|
||||
|
||||
browser.close()
|
||||
|
||||
# Save updated checkpoints
|
||||
df_ckpt.to_csv(CHECKPOINT_FILE, index=False)
|
||||
print("\nCheckpoints successfully updated!")
|
||||
print("=" * 85)
|
||||
Reference in new issue
Block a user