""" Carmignac Data Challenge — AUM Branching / Repair ================================================== Takes as input: - The original AUM file (pre-repair) - The mapping CSV produced by carmignac_repair.py - (Optionally) the surgery log, for audit annotation Produces: - A repaired AUM file where every Registrar Account ID is replaced by its canonical identity (reg_orig) as determined by the pipeline. Core logic ---------- The mapping table encodes, for every (date, reg_orig) pair, which physical code (reg_used) was actually present in the data at that date. reg_orig = the stable canonical identity (output label) reg_used = the code that appeared in the raw data at that date For rows where reg_used != reg_orig (changed=True), the raw code is a historical alias that the surgery pass identified as belonging to reg_orig. The repair simply relabels those rows. For accounts not in the repair universe (below the AUM threshold, or excluded categories), rows are passed through unchanged. Self-mapped surgeries (reg_from == reg_to in the surgery log) do not require any relabelling — they signal a data quality issue on that month, not a code change. Usage ----- python carmignac_branch.py # default paths python carmignac_branch.py \\ --aum raw_AUM.csv \\ --mapping carmignac_mapping.csv \\ --surgery carmignac_surgery_log.csv \\ --out AUM_repaired.csv """ import argparse import os import sys import s3fs import pandas as pd # ───────────────────────────────────────────────────────────── # 1. LOAD # ───────────────────────────────────────────────────────────── def load_inputs(mapping_path, surgery_path): fs = s3fs.S3FileSystem( client_kwargs={'endpoint_url': 'https://'+'minio-simple.lab.groupe-genes.fr'}, key = os.environ["AWS_ACCESS_KEY_ID"], secret = os.environ["AWS_SECRET_ACCESS_KEY"], token = os.environ["AWS_SESSION_TOKEN"]) with fs.open('s3://projet-bdc-data/carmignac/AUM ENSAE V2 -20251105.csv', 'rb') as f: aum = pd.read_csv(f, sep=";") mapping = pd.read_csv(mapping_path, parse_dates=["date"]) surgery = pd.read_csv(surgery_path, parse_dates=["date"]) if surgery_path else pd.DataFrame() # Normalise ID columns to string aum["Registrar Account - ID"] = aum["Registrar Account - ID"].astype(str) mapping["reg_orig"] = mapping["reg_orig"].astype(str) mapping["reg_used"] = mapping["reg_used"].astype(str) if not surgery.empty: surgery["reg_orig"] = surgery["reg_orig"].astype(str) surgery["reg_from"] = surgery["reg_from"].astype(str) surgery["reg_to"] = surgery["reg_to"].astype(str) return aum, mapping, surgery # ───────────────────────────────────────────────────────────── # 2. BUILD RENAME LOOKUP # ───────────────────────────────────────────────────────────── def build_rename_lookup(mapping): """ Returns a dict {(date, reg_used) -> reg_orig} restricted to rows where reg_used != reg_orig (actual changes). For self-mapped surgeries or stable accounts, no entry is needed. """ changed = mapping[mapping["changed"] & (mapping["reg_orig"] != mapping["reg_used"])] lookup = {} for _, row in changed.iterrows(): key = (row["date"], row["reg_used"]) if key in lookup and lookup[key] != row["reg_orig"]: # Conflict: two different reg_origs claim the same (date, reg_used) # Should never happen with a well-formed mapping, but warn if it does print(f" [WARN] Conflicting mapping at {row['date'].date()} " f"reg_used={row['reg_used']}: " f"{lookup[key]} vs {row['reg_orig']} — keeping first") else: lookup[key] = row["reg_orig"] return lookup # ───────────────────────────────────────────────────────────── # 3. APPLY BRANCHING # ───────────────────────────────────────────────────────────── def apply_branching(aum, lookup): """ Renames Registrar Account - ID in the AUM dataframe according to the lookup {(date, reg_used) -> reg_orig}. Rows not in the lookup are left untouched. Returns: - repaired : the full AUM dataframe with corrected IDs - audit : a subset showing only the renamed rows, with both the original and canonical IDs for verification """ aum = aum.copy() aum["Centralisation Date"] = pd.to_datetime(aum["Centralisation Date"]) aum["_date_key"] = aum["Centralisation Date"] aum["_reg_key"] = aum["Registrar Account - ID"].astype(str) # Vectorised lookup via merge lookup_df = pd.DataFrame( [(d, reg_used, reg_orig) for (d, reg_used), reg_orig in lookup.items()], columns=["_date_key", "_reg_key", "_canonical_id"] ) merged = aum.merge(lookup_df, on=["_date_key", "_reg_key"], how="left") # Audit: rows that were actually renamed renamed_mask = merged["_canonical_id"].notna() audit = merged[renamed_mask].copy() audit["original_reg_id"] = audit["_reg_key"] audit["canonical_reg_id"] = audit["_canonical_id"] audit = audit[["Centralisation Date", "original_reg_id", "canonical_reg_id", "Product - Isin", "Quantity - AUM", "Value - AUM €"]] # Apply rename merged.loc[renamed_mask, "Registrar Account - ID"] = merged.loc[renamed_mask, "_canonical_id"] # Drop helper columns repaired = merged.drop(columns=["_date_key", "_reg_key", "_canonical_id"]) return repaired, audit # ───────────────────────────────────────────────────────────── # 4. CONSISTENCY CHECK # ───────────────────────────────────────────────────────────── def consistency_check(original, repaired, mapping, surgery): """ Sanity checks after branching: 1. Row count preserved 2. No reg_used alias remains in the repaired file (for changed entries) 3. For each (reg_orig, isin, date) there is at most one row (branching should not create duplicates) 4. Summary of surgery operations applied """ print("\n[Consistency checks]") # 1. Row count if len(original) == len(repaired): print(f" ✓ Row count preserved : {len(repaired)}") else: print(f" ✗ Row count changed : {len(original)} → {len(repaired)}") # 2. Aliases eliminated changed = mapping[mapping["changed"] & (mapping["reg_orig"] != mapping["reg_used"])] aliases = set(changed["reg_used"].unique()) still_present = set(repaired["Registrar Account - ID"].astype(str)) & aliases if not still_present: print(f" ✓ All {len(aliases)} aliased code(s) successfully relabelled") else: print(f" ✗ {len(still_present)} aliased code(s) still present: {still_present}") # 3. Duplicates key_cols = ["Registrar Account - ID", "Product - Isin", "Centralisation Date"] dup_count = repaired.duplicated(subset=key_cols).sum() if dup_count == 0: print(f" ✓ No duplicate (reg_id, isin, date) keys") else: print(f" ✗ {dup_count} duplicate (reg_id, isin, date) rows found — inspect manually") print(repaired[repaired.duplicated(subset=key_cols, keep=False)] [key_cols + ["Quantity - AUM"]].head(10).to_string(index=False)) # 4. Surgery summary if not surgery.empty: print(f"\n[Surgery operations applied]") for _, op in surgery.sort_values("date").iterrows(): self_map = " [self-map — data quality flag, no rename]" \ if op["reg_from"] == op["reg_to"] else "" print(f" {op['date'].date()} | {op['reg_orig']} : " f"{op['reg_from']} → {op['reg_to']}" f" (Jaccard={op['jaccard_composite']:.4f}, " f"gain={op['gain_vs_no_surgery']:.6f}){self_map}") # ───────────────────────────────────────────────────────────── # 5. EXPORT PATHS (branched accounts only) # ───────────────────────────────────────────────────────────── def export_paths(aum, mapping, surgery, repaired): """ Builds a condensed AUM file for ALL accounts in the repair universe (i.e. every reg_orig present in the mapping). - Stable accounts (no surgery): single leg where reg_used == reg_orig throughout, pulled directly from the repaired AUM. - Branched accounts (at least one genuine surgery): multiple legs, reg_used shows which physical code was active at each date. The output makes every account's full path explicit: reg_orig | reg_used | date | isin | qty_aum | ... ─────────┼───────────────┼────────────┼──────┼─────────┼─── REG_001 | REG_001 | 2020-01-31 | ... | ... | <- stable REG_002 | REG_002_OLD | 2020-01-31 | ... | ... | <- leg 1 REG_002 | REG_002 | 2022-07-31 | ... | ... | <- leg 2 Self-mapped surgeries (reg_from == reg_to) are noted in the summary but do not add extra legs — the account kept its code. Returns the paths DataFrame (never None if mapping is non-empty). """ # All canonical accounts in the universe all_accounts = sorted(mapping["reg_orig"].astype(str).unique()) # Branched accounts (genuine code changes only) branched_accounts = set() if not surgery.empty: genuine = surgery[surgery["reg_from"] != surgery["reg_to"]] branched_accounts = set(genuine["reg_orig"].astype(str).unique()) print(f"\n[Paths] {len(all_accounts)} account(s) in universe, " f"{len(branched_accounts)} branched: " f"{sorted(branched_accounts) or 'none'}") # Build (date, reg_orig) → reg_used lookup from mapping map_df = mapping[["date", "reg_orig", "reg_used"]].copy() map_df["date"] = pd.to_datetime(map_df["date"]) map_df["reg_orig"] = map_df["reg_orig"].astype(str) map_df["reg_used"] = map_df["reg_used"].astype(str) map_df = map_df.rename(columns={"date": "_date_key", "reg_orig": "_reg_key"}) # Pull all universe rows from the repaired AUM aum_universe = repaired[ repaired["Registrar Account - ID"].astype(str).isin(all_accounts) ].copy() aum_universe["Centralisation Date"] = pd.to_datetime(aum_universe["Centralisation Date"]) aum_universe["_date_key"] = aum_universe["Centralisation Date"] aum_universe["_reg_key"] = aum_universe["Registrar Account - ID"].astype(str) # Join reg_used from mapping paths = aum_universe.merge( map_df[["_date_key", "_reg_key", "reg_used"]], on=["_date_key", "_reg_key"], how="left", ).drop(columns=["_date_key", "_reg_key"]) # For stable accounts, mapping may not cover every AUM date (e.g. sparse # months) — fall back to reg_orig (= Registrar Account - ID) for those. paths["reg_used"] = paths["reg_used"].fillna( paths["Registrar Account - ID"].astype(str) ) # Rename canonical column paths = paths.rename(columns={"Registrar Account - ID": "reg_orig"}) # Column order other_cols = [c for c in paths.columns if c not in ("reg_orig", "reg_used")] paths = paths[["reg_orig", "reg_used"] + other_cols] paths = paths.sort_values(["reg_orig", "Centralisation Date", "Product - Isin"]) paths = paths.reset_index(drop=True) # Summary for acc in all_accounts: sub = paths[paths["reg_orig"] == acc] legs = list(sub["reg_used"].unique()) tag = " [branched]" if acc in branched_accounts else " [stable]" print(f" {acc}: {len(sub)} rows, legs = {legs}{tag}") return paths # ───────────────────────────────────────────────────────────── # 6. MAIN # ───────────────────────────────────────────────────────────── def main(): parser = argparse.ArgumentParser( description="Apply Carmignac repair mapping to the raw AUM file" ) parser.add_argument("--mapping", default="repair_results/carmignac_mapping.csv", help="Path to mapping CSV from carmignac_repair.py") parser.add_argument("--surgery", default="repair_results/carmignac_surgery_log.csv", help="Path to surgery log CSV (optional, for audit)") parser.add_argument("--out", default="AUM_repaired.csv", help="Output path for repaired AUM CSV") parser.add_argument("--audit", default="AUM_repair_audit.csv", help="Output path for audit CSV (renamed rows only)") parser.add_argument("--paths", default="AUM_paths.csv", help="Output path for condensed paths CSV (branched accounts only)") args = parser.parse_args() def resolve(p, required=True): if os.path.exists(p): return p alt = os.path.join(os.path.dirname(os.path.abspath(__file__)), p) if os.path.exists(alt): return alt if required: sys.exit(f"[ERROR] File not found: {p}") return None mapping_path = resolve(args.mapping) surgery_path = resolve(args.surgery, required=False) print("=" * 60) print("CARMIGNAC — AUM Branching / Repair") print("=" * 60) print(f" Mapping : {mapping_path}") print(f" Surgery : {surgery_path or '(not provided)'}") # Load aum, mapping, surgery = load_inputs(mapping_path, surgery_path) print(f"\n Raw AUM rows : {len(aum)}") print(f" Mapping rows : {len(mapping)}") print(f" Mapping changed rows : {mapping['changed'].sum()}") print(f" Surgery operations : {len(surgery)}") # Build lookup lookup = build_rename_lookup(mapping) print(f"\n Rename operations : {len(lookup)}") if lookup: sample = list(lookup.items())[:3] for (d, used), orig in sample: print(f" ({d.date()}, {used}) → {orig}") if len(lookup) > 3: print(f" ... and {len(lookup) - 3} more") # Apply repaired, audit = apply_branching(aum, lookup) print(f"\n Rows renamed : {len(audit)}") # Checks consistency_check(aum, repaired, mapping, surgery) # Save out_dir = os.path.dirname(os.path.abspath(args.out)) os.makedirs(out_dir, exist_ok=True) repaired.to_csv(args.out, index=False) print(f"\n ✓ Repaired AUM → {args.out}") if len(audit) > 0: audit.to_csv(args.audit, index=False) print(f" ✓ Audit log → {args.audit}") else: print(f"(No rows renamed — audit log not written)") # Paths: condensed AUM for branched accounts df_paths = export_paths(aum, mapping, surgery, repaired) if df_paths is not None: df_paths.to_csv(args.paths, index=False) print(f" ✓ Paths file → {args.paths}") # Print renamed reg_ids summary if len(audit) > 0: print("\n[Renamed identifiers]") summary = (audit.groupby(["original_reg_id", "canonical_reg_id"]) .size() .reset_index(name="n_rows")) for _, row in summary.iterrows(): print(f" {row['original_reg_id']:20s} → {row['canonical_reg_id']:20s} " f"({row['n_rows']} rows)") print("\nDone.") if __name__ == "__main__": main()