66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
"""
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scripts/audit_weights.py
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Audits dynamic weight allocation, platform presence tiers, and high-rank evidence.
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"""
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from pathlib import Path
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import pandas as pd
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import numpy as np
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DATASET_PATH = Path("data/hiffi_multiplatform_500.csv")
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if not DATASET_PATH.exists():
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raise FileNotFoundError(f"Master cohort not found at {DATASET_PATH}")
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df = pd.read_csv(DATASET_PATH)
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# Explicitly define platform presence (YouTube, Spotify presence, Instagram presence)
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df["has_youtube"] = df["production_hiffi_score"].notna()
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df["has_spotify"] = df["spotify_monthly_listeners"].notna()
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df["has_instagram"] = df["instagram_followers"].notna()
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df["verified_platform_count"] = (
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df["has_youtube"].astype(int) +
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df["has_spotify"].astype(int) +
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df["has_instagram"].astype(int)
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)
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print("=" * 70)
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print("1. VERIFIED PLATFORM PRESENCE TIERS")
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print("=" * 70)
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tier_summary = df.groupby("verified_platform_count")["hiffi_xp_score"].agg(
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count="count",
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mean="mean",
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std="std",
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min="min",
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max="max"
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).round(2)
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print(tier_summary)
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print("\n" + "=" * 70)
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print("2. TOP 50 PLATFORM EVIDENCE DISTRIBUTION")
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print("=" * 70)
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top_50 = df[df["hiffi_xp_rank"] <= 50]
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top_50_dist = top_50["verified_platform_count"].value_counts().sort_index(ascending=False)
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for tier, count in top_50_dist.items():
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print(f"Tier {tier} Platforms Verified: {count:2d} artists ({count / len(top_50) * 100:4.1f}%)")
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print("\n" + "=" * 70)
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print("3. TOP 10 PLATFORM EVIDENCE DISTRIBUTION")
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print("=" * 70)
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top_10 = df[df["hiffi_xp_rank"] <= 10]
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top_10_dist = top_10["verified_platform_count"].value_counts().sort_index(ascending=False)
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for tier, count in top_10_dist.items():
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print(f"Tier {tier} Platforms Verified: {count:2d} artists ({count / len(top_10) * 100:4.1f}%)")
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print("\n" + "=" * 70)
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print("4. RANK DELTA EXTREMES & AUDIT TRAIL")
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print("=" * 70)
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cols = ["name", "production_hiffi_rank", "hiffi_xp_rank", "rank_delta", "verified_platform_count"]
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top_gainers = df.sort_values(by="rank_delta", ascending=False).head(3)[cols]
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top_fallers = df.sort_values(by="rank_delta", ascending=True).head(3)[cols]
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print("Top 3 Gainers:")
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print(top_gainers.to_string(index=False))
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print("\nTop 3 Fallers:")
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print(top_fallers.to_string(index=False))
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print("=" * 70) |