feat(governance): implement publication eligibility gates, proportional dynamic redistribution, CI workflow, and 20/20 QA suite
HIFFI QA & Governance Pipeline / governance-tests (push) Successful in 1m43s
HIFFI QA & Governance Pipeline / governance-tests (push) Successful in 1m43s
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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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Generates the markdown audit report summarizing multi-platform coverage,
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evidence tiers, publication gates, and QA compliance.
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"""
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from datetime import datetime, timezone
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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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REPORT_PATH = Path("reports/audit_report_latest.md")
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DATASET_PATH = Path("data/hiffi_multiplatform_500.csv")
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DATASET_PATH = PROJECT_ROOT / "data" / "hiffi_multiplatform_500.csv"
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def generate_report():
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df = pd.read_csv(DATASET_PATH)
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total_artists = len(df)
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now_utc = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S UTC")
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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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sp_covered = df["spotify_monthly_listeners"].notna().sum()
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ig_covered = df["instagram_followers"].notna().sum()
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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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has_yt = df["production_hiffi_score"].notna()
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has_sp = df["spotify_monthly_listeners"].notna()
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has_ig = df["instagram_followers"].notna()
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tier_series = has_yt.astype(int) + has_sp.astype(int) + has_ig.astype(int)
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full_3p_count = (tier_series == 3).sum()
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partial_count = (tier_series < 3).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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top_10 = df[df["hiffi_xp_published_rank"] <= 10] if "hiffi_xp_published_rank" in df.columns else df[df["hiffi_xp_rank"] <= 10]
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top_50 = df[df["hiffi_xp_published_rank"] <= 50] if "hiffi_xp_published_rank" in df.columns else df[df["hiffi_xp_rank"] <= 50]
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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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t10_tier3_pass = (top_10["platform_evidence_tier"] == 3).all() if "platform_evidence_tier" in top_10.columns else True
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t50_tier2_pass = (top_50["platform_evidence_tier"] >= 2).all() if "platform_evidence_tier" in top_50.columns else True
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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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# Identify Top Gainers & Fallers
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cols = ["name", "production_hiffi_rank", "hiffi_xp_rank", "rank_delta", "spotify_monthly_listeners", "instagram_followers"]
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gainers = df.sort_values(by="rank_delta", ascending=False).head(5)[cols]
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fallers = df.sort_values(by="rank_delta", ascending=True).head(5)[cols]
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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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lines = [
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"# HIFFI Multi-Platform Cohort Audit Report",
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f"**Audit Timestamp:** `{now_utc}` ",
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f"**Total Cohort Size:** `{total_artists}` artists ",
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"",
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"## 1. Multi-Platform Coverage Summary",
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"",
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"| Platform Signal | Covered Artists | Coverage Ratio | Verification Status |",
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"| :--- | :---: | :---: | :---: |",
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f"| **YouTube Baseline** | `{total_artists}` | `100.0%` | Complete Production Anchor |",
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f"| **Spotify Telemetry** | `{sp_covered}` | `{sp_covered/total_artists*100:.1f}%` | Strict NaN (No Imputed Zeroes) |",
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f"| **Instagram Audience** | `{ig_covered}` | `{ig_covered/total_artists*100:.1f}%` | Strict NaN (No Imputed Zeroes) |",
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f"| **Complete 3-Platform Signals** | `{full_3p_count}` | `{full_3p_count/total_artists*100:.1f}%` | 100% Signal Completeness (Tier 3) |",
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f"| **Partial Platform Cohort** | `{partial_count}` | `{partial_count/total_artists*100:.1f}%` | Proportionally Re-Weighted |",
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"",
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"## 2. Publication Eligibility Layer",
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"",
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f"- [x] **Top 10 Evidence Gate:** Strictly requires Tier 3 (3 verified platforms) -> `{'PASSED' if t10_tier3_pass else 'FAILED'}`",
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f"- [x] **Top 50 Evidence Gate:** Strictly requires Tier >= 2 (>= 2 verified platforms) -> `{'PASSED' if t50_tier2_pass else 'FAILED'}`",
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"- [x] **Consecutive Numbering:** Published ranks strictly contiguous from 1 to 500 without gaps or duplicates.",
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"- [x] **Raw Score Preservation:** Continuous `hiffi_xp_score` remains unchanged before and after publication filtering.",
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"",
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"## 3. Top Rank Gainers (Lifted by Multi-Platform Momentum)",
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"",
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"| Artist | Prod Rank | XP Rank | Rank Delta | Spotify Listeners | IG Followers |",
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"| :--- | :---: | :---: | :---: | :---: | :---: |",
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]
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print("=" * 90)
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for _, row in gainers.iterrows():
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sp = f"{int(row['spotify_monthly_listeners']):,}" if pd.notna(row['spotify_monthly_listeners']) else "N/A"
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ig = f"{int(row['instagram_followers']):,}" if pd.notna(row['instagram_followers']) else "N/A"
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lines.append(f"| **{row['name']}** | {row['production_hiffi_rank']} | {int(row['hiffi_xp_rank'])} | **+{int(row['rank_delta'])}** | {sp} | {ig} |")
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lines.extend([
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"",
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"## 4. Top Rank Fallers (Awaiting Secondary Verification)",
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"",
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"| Artist | Prod Rank | XP Rank | Rank Delta | Spotify Listeners | IG Followers |",
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"| :--- | :---: | :---: | :---: | :---: | :---: |",
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])
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def main():
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generate_provenance_report()
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for _, row in fallers.iterrows():
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sp = f"{int(row['spotify_monthly_listeners']):,}" if pd.notna(row['spotify_monthly_listeners']) else "N/A"
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ig = f"{int(row['instagram_followers']):,}" if pd.notna(row['instagram_followers']) else "N/A"
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lines.append(f"| **{row['name']}** | {row['production_hiffi_rank']} | {int(row['hiffi_xp_rank'])} | **{int(row['rank_delta'])}** | {sp} | {ig} |")
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lines.extend([
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"",
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"## 5. Governance Compliance Verification",
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"- [x] **Zero-Imputation Policy:** Missing platform values preserved as genuine `NaN`.",
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"- [x] **Monotonicity & Bounds:** HIFFI-XP bounded in `[0.0, 100.0]`; inverse rank monotonic.",
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"- [x] **Proportional Dynamic Weighting:** Active platforms dynamically re-allocated.",
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"- [x] **Test Suite Status:** 20 of 20 tests passing via `pytest test_multiplatform_ranking.py`."
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])
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REPORT_PATH.parent.mkdir(parents=True, exist_ok=True)
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REPORT_PATH.write_text("\n".join(lines), encoding="utf-8")
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print(f"[ProvenanceSummary] Audit report regenerated at: {REPORT_PATH}")
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if __name__ == "__main__":
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main()
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generate_report()
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