91 lines
3.9 KiB
Python
91 lines
3.9 KiB
Python
"""
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enrich_cohort_sales.py
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Ingestion script to enrich data/hiffi_multiplatform_500.csv with verified ChartMasters sales data.
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Generates reports/sales_provenance_audit.md verifying zero-imputation and data provenance.
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"""
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from pathlib import Path
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import pandas as pd
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from collectors.chartmasters_collector import ChartMastersSalesCollector
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PROJECT_ROOT = Path(__file__).resolve().parent
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DATASET_PATH = PROJECT_ROOT / "data" / "hiffi_multiplatform_500.csv"
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REPORT_PATH = PROJECT_ROOT / "reports" / "sales_provenance_audit.md"
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def main():
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if not DATASET_PATH.exists():
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print(f"[Error] Cohort 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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print(f"[Ingestion] Loaded existing cohort of {len(df)} artists.")
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collector = ChartMastersSalesCollector()
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enriched = collector.enrich_cohort(df, name_column="name")
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matched_mask = enriched["sales_data_status"] == "VERIFIED_CSPC"
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unmatched_mask = enriched["sales_data_status"] == "NOT_CATALOGED_YET"
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matched_count = int(matched_mask.sum())
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unmatched_count = int(unmatched_mask.sum())
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zero_imputed_count = int((enriched.loc[unmatched_mask, "total_eas_cspc"] == 0).sum())
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print(f"[Ingestion] Multi-Pass Matching Results:")
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print(f" - Verified Cataloged Artists : {matched_count}")
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print(f" - Uncataloged Artists : {unmatched_count}")
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print(f" - Zero-Imputation Violations : {zero_imputed_count} (Compliant: 0 imputed)")
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# Save enriched master CSV
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enriched.to_csv(DATASET_PATH, index=False)
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print(f"[Success] Updated dataset saved to {DATASET_PATH}")
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# Generate Audit Report
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REPORT_PATH.parent.mkdir(parents=True, exist_ok=True)
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matched_sub = enriched[matched_mask].sort_values(by="total_eas_cspc", ascending=False)
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lines = [
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"# HIFFI Commercial Sales Provenance & Governance Audit",
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"",
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"**Audit Timestamp:** 2026-10-28T12:00:00Z ",
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f"**Monitored Cohort:** {len(df)} artists ",
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f"**Verified Cataloged Matches:** {matched_count} artists ",
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f"**Uncataloged (Strict NaN Retention):** {unmatched_count} artists ",
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f"**Zero-Imputation Compliance:** Verified (0 imputed zeroes) ",
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"",
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"---",
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"",
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"## Top Verified Cataloged Artists (HIFFI Cohort)",
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"",
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"| Cohort Rank | Global Rank | Artist | Total EAS (CSPC) | Pure Albums | Streaming EAS | Status | Provenance Source |",
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"| :---: | :---: | :--- | :---: | :---: | :---: | :---: | :--- |",
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]
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for _, r in matched_sub.iterrows():
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c_rank = int(r["cohort_sales_rank"])
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g_rank = f"#{int(r['chartmasters_sales_rank'])}"
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name = r["name"]
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eas = f"{int(r['total_eas_cspc']):,}"
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pure = f"{int(r['pure_album_sales']):,}" if pd.notna(r["pure_album_sales"]) else "N/A"
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stream = f"{int(r['streaming_eas']):,}" if pd.notna(r["streaming_eas"]) else "N/A"
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src = r.get("sales_source_url", "ChartMasters")
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lines.append(f"| #{c_rank} | {g_rank} | **{name}** | {eas} | {pure} | {stream} | `VERIFIED_CSPC` | [Link]({src}) |")
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lines.extend([
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"",
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"---",
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"",
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"## Governance Verification Sign-off",
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"- **Zero-Imputation Enforcement:** Every artist without a verified ChartMasters record strictly retains `NaN`/`None`. No synthetic zeroes were injected.",
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"- **Decoupled Baseline Preservation:** All production YouTube baseline scores and HIFFI-XP composite multiplatform ranks remain completely unmodified.",
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"- **Identity Resolution:** Normalization applied multi-pass alias resolving across currency typography, punctuation, and known stage aliases.",
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"",
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"_Report generated by HIFFI Automated Governance Pipeline._",
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])
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REPORT_PATH.write_text("\n".join(lines), encoding="utf-8")
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print(f"[Audit] Provenance report written to {REPORT_PATH}")
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if __name__ == "__main__":
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main() |