43 lines
1.9 KiB
Python
43 lines
1.9 KiB
Python
"""
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pipeline/generate_report.py
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Generates executive audit summaries and identifies movers between the
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verified Production YouTube rank and the Experimental HIFFI-XP model.
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"""
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import sys
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from pathlib import Path
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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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CSV_PATH = PROJECT_ROOT / "data" / "hiffi_multiplatform_500.csv"
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df = pd.read_csv(CSV_PATH)
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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 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 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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print("\nTOP 10 FALLERS (Strong YouTube Velocity, but Awaiting/Lacking Social Reach):")
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fallers = df[df["production_hiffi_rank"].notna()].sort_values("rank_delta", ascending=True).head(10)
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print(fallers[display_cols].to_string(index=False))
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print("=" * 90) |