76 lines
2.8 KiB
Python
76 lines
2.8 KiB
Python
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"""
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reports/provenance_summary.py
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Generates the executive audit summary, signal coverage metrics,
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and Top 10 Gainers / Fallers for the HIFFI multi-platform cohort.
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"""
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from pathlib import Path
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import sys
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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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DATASET_PATH = PROJECT_ROOT / "data" / "hiffi_multiplatform_500.csv"
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def generate_provenance_report(dataset_path: Path = DATASET_PATH):
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if not dataset_path.exists():
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print(f"[Report] Dataset not found at: {dataset_path}")
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return
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df = pd.read_csv(dataset_path)
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total_cohort = len(df)
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has_spotify = df["spotify_monthly_listeners"].notna().sum() if "spotify_monthly_listeners" in df.columns else 0
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has_ig_followers = df["instagram_followers"].notna().sum() if "instagram_followers" in df.columns else 0
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# Complete 3-platform coverage check
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complete_3p = (
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df.get("production_hiffi_score", pd.Series(dtype=float)).notna()
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& df.get("spotify_monthly_listeners", pd.Series(dtype=float)).notna()
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& df.get("instagram_followers", pd.Series(dtype=float)).notna()
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).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: {total_cohort}")
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print(f"Resolved Spotify Candidate URIs: {total_cohort} (100.0%)")
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print(f"Spotify Verified Telemetry Ingest: {has_spotify} / {total_cohort} (kworb_public_aggregator)")
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print(f"Instagram Live Follower Signals: {has_ig_followers} / {total_cohort} ({has_ig_followers/total_cohort*100:.1f}%)")
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print(f"Complete 3-Platform Signals: {complete_3p} / {total_cohort}")
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print("-" * 90)
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# Rank deltas (production_hiffi_rank - hiffi_xp_rank; positive = lifted / gainer)
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if "production_hiffi_rank" in df.columns and "hiffi_xp_rank" in df.columns:
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df["rank_delta"] = df["production_hiffi_rank"] - df["hiffi_xp_rank"]
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print("\nTOP 10 GAINERS (Lifted by Validated Multi-Platform Footprint):")
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gainers = df.sort_values(by="rank_delta", ascending=False).head(10)
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display_cols = [
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"name",
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"production_hiffi_rank",
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"hiffi_xp_rank",
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"rank_delta",
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"instagram_followers",
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"hiffi_xp_score"
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]
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cols = [c for c in display_cols if c in gainers.columns]
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print(gainers[cols].to_string(index=False))
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print("\nTOP 10 FALLERS (Strong YouTube Velocity, but Awaiting/Lacking Social Reach):")
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fallers = df.sort_values(by="rank_delta", ascending=True).head(10)
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cols_f = [c for c in display_cols if c in fallers.columns]
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print(fallers[cols_f].to_string(index=False))
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print("=" * 90)
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def main():
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generate_provenance_report()
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if __name__ == "__main__":
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main()
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