234 lines
10 KiB
Python
234 lines
10 KiB
Python
"""
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pipeline/batch_500_runner.py
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Production Multi-Platform HIFFI Batch Runner (Hardened 100% Reliability Standards).
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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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import os
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import time
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import random
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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 pandas as pd
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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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CHECKPOINT_DIR.mkdir(parents=True, exist_ok=True)
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CHECKPOINT_FILE = CHECKPOINT_DIR / "multiplatform_checkpoints.csv"
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FINAL_OUTPUT_FILE = PROJECT_ROOT / "data" / "hiffi_multiplatform_500.csv"
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def min_max_normalize(series: pd.Series) -> pd.Series:
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"""Scales numeric series strictly between 0 and 100 while preserving NaNs."""
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valid = series.dropna()
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if valid.empty or valid.max() == valid.min():
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return pd.Series(np.nan, index=series.index)
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return ((series - valid.min()) / (valid.max() - valid.min())) * 100.0
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class BatchMultiPlatformRunner:
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def __init__(self, youtube_csv_path: str = "enriched_artists.csv"):
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self.input_file = PROJECT_ROOT / youtube_csv_path
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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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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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"name", "name_clean", "wikidata_qid", "spotify_id",
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"instagram_handle", "instagram_followers", "resolution_status", "ig_status"
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])
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def save_checkpoint(self, df_checkpoint: pd.DataFrame):
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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("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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artists_to_process = self.df_base[~self.df_base["name_clean"].isin(processed_names)].copy()
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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="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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for idx, row in artists_to_process.iterrows():
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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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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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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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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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"spotify_id": ids.get("spotify_id", "N/A"),
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"instagram_handle": handle,
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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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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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time.sleep(random.uniform(*delay_range))
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if new_rows:
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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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browser.close()
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print("\n" + "=" * 90)
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print("PHASE 2: MERGING SIGNALS & ENFORCING RELIABILITY CRITERIA")
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print("=" * 90)
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merged = pd.merge(
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self.df_base,
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df_ckpt,
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on="name_clean",
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how="left",
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suffixes=("", "_ext")
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)
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now_utc = datetime.now(timezone.utc).isoformat()
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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"] = merged["instagram_followers"].notna()
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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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# 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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# 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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# 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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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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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 Hardened Multi-Platform Dataset Exported: {FINAL_OUTPUT_FILE}")
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print("\n" + "=" * 90)
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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 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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if __name__ == "__main__":
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runner = BatchMultiPlatformRunner("enriched_artists.csv")
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runner.run_full_pipeline(batch_size=15, delay_range=(0.5, 1.0)) |