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