1038 lines
36 KiB
Python
1038 lines
36 KiB
Python
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"""
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Carmignac Data Challenge — Pipeline Results Analysis
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=====================================================
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Analyses the CSV outputs produced by carmignac_repair.py:
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- carmignac_scores.csv (post-surgery score history)
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- carmignac_mapping.csv (reg_id mapping history)
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- carmignac_surgery_log.csv (surgery operations)
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Produces a self-contained HTML report with interactive charts.
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Usage:
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python carmignac_analysis.py
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python carmignac_analysis.py --scores path/to/scores.csv \
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--mapping path/to/mapping.csv \
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--surgery path/to/surgery_log.csv \
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--out report.html
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"""
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import argparse
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import json
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import os
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import sys
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import numpy as np
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import pandas as pd
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# ─────────────────────────────────────────────────────────────
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# 1. LOAD & VALIDATE
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# ─────────────────────────────────────────────────────────────
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def load_outputs(scores_path, mapping_path, surgery_path):
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scores = pd.read_csv(scores_path, parse_dates=["date"])
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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"])
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# Normalise dtypes
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scores["reg_id"] = scores["reg_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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mapping["changed"] = mapping["changed"].astype(bool)
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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 scores, mapping, surgery
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# ─────────────────────────────────────────────────────────────
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# 2. COMPUTE ANALYTICS
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# ─────────────────────────────────────────────────────────────
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def compute_analytics(scores, mapping, surgery):
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dates = sorted(scores["date"].unique())
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# ── 2.1 Sum of scores per date (post-surgery) ──────────────
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sum_post = (scores.groupby("date")["score"]
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.sum()
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.reindex(dates)
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.rename("sum_post"))
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# ── 2.2 Reconstruct pre-surgery (counterfactual) ───────────
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# Without surgery, every reg_id that had a hard break would score 0
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# from that date backwards. We propagate the surgery "gain" as a
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# cumulative deficit going back in time.
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gain_by_date = surgery.groupby("date")["gain_vs_no_surgery"].sum()
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# cumulative deficit = sum of gains for all surgeries at or after date t
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cumulative_deficit = pd.Series(0.0, index=dates)
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for d in dates:
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cumulative_deficit[d] = gain_by_date[gain_by_date.index >= d].sum()
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sum_pre = (sum_post - cumulative_deficit).clip(lower=0).rename("sum_pre")
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timeline = pd.DataFrame({"sum_post": sum_post, "sum_pre": sum_pre})
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timeline.index = pd.to_datetime(timeline.index)
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timeline["recovery_pct"] = np.where(
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sum_pre < sum_post,
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(sum_post - sum_pre) / sum_post.clip(lower=1e-9) * 100,
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0.0,
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)
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# ── 2.3 Per-date surgery stats ─────────────────────────────
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surgery_stats = (
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surgery.groupby("date")
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.agg(
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n_surgeries = ("reg_orig", "count"),
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total_gain = ("gain_vs_no_surgery", "sum"),
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avg_gain = ("gain_vs_no_surgery", "mean"),
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avg_jaccard = ("jaccard_composite", "mean"),
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avg_score_before = ("score_before", "mean"),
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avg_score_after = ("score_after", "mean"),
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)
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.reindex(dates, fill_value=0)
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)
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# ── 2.4 Score distribution over time ───────────────────────
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# Wide format: rows=dates, cols=reg_ids
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pivot = scores.pivot_table(index="date", columns="reg_id",
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values="score", aggfunc="last")
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pivot = pivot.reindex(dates)
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pivot.index = pd.to_datetime(pivot.index)
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# ── 2.5 Mapping churn ──────────────────────────────────────
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# For each date, how many reg_ids are remapped (not using their original code)?
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churn = (mapping.groupby("date")["changed"]
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.sum()
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.reindex(dates, fill_value=0)
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.rename("n_remapped"))
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# ── 2.6 Score entropy (distribution spread) ────────────────
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def entropy(row):
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p = row.dropna()
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p = p[p > 0]
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if len(p) == 0:
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return np.nan
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p = p / p.sum()
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return -(p * np.log(p)).sum()
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timeline["entropy"] = pivot.apply(entropy, axis=1).values
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# ── 2.7 Individual score trajectories ──────────────────────
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# Identify which reg_ids were ever remapped
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ever_remapped = set(mapping.loc[mapping["changed"], "reg_orig"].unique())
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# ── 2.8 Surgery detail table ───────────────────────────────
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surgery_detail = surgery.copy()
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surgery_detail["gain_pct_of_score"] = (
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surgery_detail["gain_vs_no_surgery"]
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/ surgery_detail["score_before"].clip(lower=1e-9) * 100
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).round(2)
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return {
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"timeline": timeline,
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"surgery_stats": surgery_stats,
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"pivot": pivot,
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"churn": churn,
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"ever_remapped": ever_remapped,
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"surgery_detail": surgery_detail,
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"dates": [d.strftime("%Y-%m-%d") for d in dates],
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}
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# ─────────────────────────────────────────────────────────────
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# 3. PRINT CONSOLE SUMMARY
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# ─────────────────────────────────────────────────────────────
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def print_summary(analytics, surgery):
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tl = analytics["timeline"]
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ss = analytics["surgery_stats"]
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print("\n" + "=" * 65)
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print(" CARMIGNAC PIPELINE — RESULTS SUMMARY")
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print("=" * 65)
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print(f"\n Date range : {tl.index.min().date()} → {tl.index.max().date()}")
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print(f" Total months : {len(tl)}")
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print(f" Reg IDs : {analytics['pivot'].shape[1]}")
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print(f"\n ── Score (Σ) ──────────────────────────────────────────")
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print(f" At t_ref (latest) : {tl['sum_post'].iloc[-1]:.6f}")
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print(f" At t_min (earliest): {tl['sum_post'].iloc[0]:.6f}")
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print(f" Min (post-surgery) : {tl['sum_post'].min():.6f} "
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f"({tl['sum_post'].idxmin().date()})")
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print(f" Min (pre-surgery) : {tl['sum_pre'].min():.6f} "
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f"({tl['sum_pre'].idxmin().date()})")
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print(f" Max recovery (pct) : {tl['recovery_pct'].max():.2f}%")
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print(f"\n ── Surgeries ─────────────────────────────────────────")
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if len(surgery) == 0:
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print(" No surgeries performed.")
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else:
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print(f" Total operations : {len(surgery)}")
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print(f" Total score gained : {surgery['gain_vs_no_surgery'].sum():.6f}")
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print(f" Avg Jaccard : {surgery['jaccard_composite'].mean():.4f}")
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print(f" Avg gain / surgery : {surgery['gain_vs_no_surgery'].mean():.6f}")
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print()
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print(f" {'Date':12s} {'Reg orig':12s} {'From':15s} {'To':15s} "
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f"{'Jaccard':>8s} {'Gain':>10s}")
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print(" " + "-" * 78)
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for _, row in surgery.sort_values("date").iterrows():
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print(f" {str(row['date'].date()):12s} {row['reg_orig']:12s} "
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f"{row['reg_from']:15s} {row['reg_to']:15s} "
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f"{row['jaccard_composite']:8.4f} {row['gain_vs_no_surgery']:10.6f}")
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print(f"\n ── Mapping churn ─────────────────────────────────────")
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ch = analytics["churn"]
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print(f" Max remapped at one date : {int(ch.max())} ({ch.idxmax().date() if ch.max()>0 else 'N/A'})")
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print(f" Reg IDs ever remapped : {len(analytics['ever_remapped'])}")
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print(f"\n ── Score entropy (distribution spread) ───────────────")
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ent = analytics["timeline"]["entropy"]
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print(f" Mean entropy : {ent.mean():.4f}")
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print(f" Std entropy : {ent.std():.4f}")
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print()
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# ─────────────────────────────────────────────────────────────
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# 4. BUILD HTML REPORT
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# ─────────────────────────────────────────────────────────────
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def build_html(analytics, surgery, scores, mapping):
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tl = analytics["timeline"]
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ss = analytics["surgery_stats"]
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piv = analytics["pivot"]
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ch = analytics["churn"]
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dates_str = analytics["dates"]
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# ── helpers to serialise for JS ─────────────────────────────
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def jf(arr, decimals=6):
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return json.dumps([round(float(v), decimals) if not np.isnan(v) else None
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for v in arr])
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def js(arr):
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return json.dumps(list(arr))
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# ── colour palette ───────────────────────────────────────────
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REG_COLORS = [
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"#2563eb","#16a34a","#dc2626","#d97706","#7c3aed",
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"#0891b2","#db2777","#65a30d","#ea580c","#6366f1",
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"#059669","#b45309","#9333ea","#0284c7","#e11d48",
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]
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# ── 4.1 Surgery sparkline data ──────────────────────────────
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surg_dates = [d.strftime("%Y-%m-%d") for d in ss.index]
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n_surg = jf(ss["n_surgeries"].values, 0)
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total_gain = jf(ss["total_gain"].values)
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avg_gain = jf(ss["avg_gain"].values)
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avg_jaccard = jf(ss["avg_jaccard"].values)
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# ── 4.2 Individual trajectories ────────────────────────────
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reg_ids = list(piv.columns)
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traj_datasets = []
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for idx, rid in enumerate(reg_ids):
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col = analytics["ever_remapped"]
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dashed = rid in col
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traj_datasets.append({
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"label": rid,
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"data": [round(float(v), 6) if not np.isnan(v) else None
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for v in piv[rid].values],
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"borderColor": REG_COLORS[idx % len(REG_COLORS)],
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"backgroundColor": REG_COLORS[idx % len(REG_COLORS)] + "22",
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"borderWidth": 2 if not dashed else 2,
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"borderDash": [] if not dashed else [6, 3],
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"pointRadius": 0,
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"tension": 0.3,
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"fill": False,
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})
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traj_json = json.dumps(traj_datasets)
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# ── 4.3 Surgery detail table rows ──────────────────────────
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sd = analytics["surgery_detail"].sort_values("date")
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surg_rows_html = ""
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if len(sd) == 0:
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surg_rows_html = "<tr><td colspan='8' style='text-align:center;color:#888'>No surgeries performed</td></tr>"
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else:
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for _, r in sd.iterrows():
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gain_class = "gain-high" if r["gain_vs_no_surgery"] > 0.05 else "gain-low"
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surg_rows_html += f"""
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<tr>
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<td>{r['date'].date()}</td>
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<td><span class="reg-badge">{r['reg_orig']}</span></td>
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<td class="code-cell">{r['reg_from']}</td>
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<td>→</td>
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<td class="code-cell">{r['reg_to']}</td>
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<td>{r['jaccard_composite']:.4f}</td>
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<td class="{gain_class}">+{r['gain_vs_no_surgery']:.6f}</td>
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<td>{r['gain_pct_of_score']:.1f}%</td>
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</tr>"""
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# ── 4.4 Top accounts table ──────────────────────────────────
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last_date = piv.index.max()
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top_accounts = piv.loc[last_date].dropna().sort_values(ascending=False)
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top_rows_html = ""
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for rank, (rid, sc) in enumerate(top_accounts.items(), 1):
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remapped = "✓" if rid in analytics["ever_remapped"] else ""
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bar_w = int(sc / top_accounts.max() * 100)
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color = REG_COLORS[(rank - 1) % len(REG_COLORS)]
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top_rows_html += f"""
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<tr>
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<td class="rank">#{rank}</td>
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<td><span class="reg-badge" style="background:{color}22;border-color:{color}">{rid}</span></td>
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<td class="score-val">{sc:.6f}</td>
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<td class="bar-cell">
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<div class="score-bar" style="width:{bar_w}%;background:{color}"></div>
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</td>
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<td class="center">{remapped}</td>
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</tr>"""
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# ─────────────────────────────────────────────────────────────
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# HTML TEMPLATE
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# ─────────────────────────────────────────────────────────────
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html = f"""<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width,initial-scale=1">
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<title>Carmignac Pipeline — Analysis Report</title>
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<script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
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<style>
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@import url('https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600&family=IBM+Plex+Sans:wght@300;400;600;700&display=swap');
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:root {{
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--bg: #0d0f12;
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--surface: #151820;
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--border: #252a35;
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--accent: #3b82f6;
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--accent2: #10b981;
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--warn: #f59e0b;
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--danger: #ef4444;
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--text: #e2e8f0;
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--muted: #64748b;
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--mono: 'IBM Plex Mono', monospace;
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|
|
--sans: 'IBM Plex Sans', sans-serif;
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
*, *::before, *::after {{ box-sizing: border-box; margin: 0; padding: 0; }}
|
|||
|
|
|
|||
|
|
body {{
|
|||
|
|
font-family: var(--sans);
|
|||
|
|
background: var(--bg);
|
|||
|
|
color: var(--text);
|
|||
|
|
min-height: 100vh;
|
|||
|
|
padding: 0 0 60px;
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
/* ── Header ── */
|
|||
|
|
.header {{
|
|||
|
|
background: linear-gradient(135deg, #0d1117 0%, #111827 50%, #0d1f3c 100%);
|
|||
|
|
border-bottom: 1px solid var(--border);
|
|||
|
|
padding: 40px 48px 36px;
|
|||
|
|
position: relative;
|
|||
|
|
overflow: hidden;
|
|||
|
|
}}
|
|||
|
|
.header::before {{
|
|||
|
|
content: '';
|
|||
|
|
position: absolute;
|
|||
|
|
inset: 0;
|
|||
|
|
background: radial-gradient(ellipse 70% 80% at 80% 50%, #1e40af18, transparent);
|
|||
|
|
pointer-events: none;
|
|||
|
|
}}
|
|||
|
|
.header-eyebrow {{
|
|||
|
|
font-family: var(--mono);
|
|||
|
|
font-size: 11px;
|
|||
|
|
letter-spacing: 0.15em;
|
|||
|
|
color: var(--accent);
|
|||
|
|
text-transform: uppercase;
|
|||
|
|
margin-bottom: 10px;
|
|||
|
|
}}
|
|||
|
|
.header h1 {{
|
|||
|
|
font-size: 2rem;
|
|||
|
|
font-weight: 700;
|
|||
|
|
letter-spacing: -0.02em;
|
|||
|
|
line-height: 1.1;
|
|||
|
|
margin-bottom: 8px;
|
|||
|
|
}}
|
|||
|
|
.header-sub {{
|
|||
|
|
font-size: 0.85rem;
|
|||
|
|
color: var(--muted);
|
|||
|
|
font-family: var(--mono);
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
/* ── KPI strip ── */
|
|||
|
|
.kpi-strip {{
|
|||
|
|
display: grid;
|
|||
|
|
grid-template-columns: repeat(auto-fit, minmax(160px, 1fr));
|
|||
|
|
gap: 1px;
|
|||
|
|
background: var(--border);
|
|||
|
|
border-bottom: 1px solid var(--border);
|
|||
|
|
}}
|
|||
|
|
.kpi {{
|
|||
|
|
background: var(--surface);
|
|||
|
|
padding: 22px 28px;
|
|||
|
|
display: flex;
|
|||
|
|
flex-direction: column;
|
|||
|
|
gap: 4px;
|
|||
|
|
}}
|
|||
|
|
.kpi-label {{
|
|||
|
|
font-size: 0.7rem;
|
|||
|
|
letter-spacing: 0.1em;
|
|||
|
|
text-transform: uppercase;
|
|||
|
|
color: var(--muted);
|
|||
|
|
font-family: var(--mono);
|
|||
|
|
}}
|
|||
|
|
.kpi-value {{
|
|||
|
|
font-size: 1.6rem;
|
|||
|
|
font-weight: 700;
|
|||
|
|
font-family: var(--mono);
|
|||
|
|
color: var(--text);
|
|||
|
|
line-height: 1;
|
|||
|
|
}}
|
|||
|
|
.kpi-value.accent {{ color: var(--accent); }}
|
|||
|
|
.kpi-value.success {{ color: var(--accent2); }}
|
|||
|
|
.kpi-value.warn {{ color: var(--warn); }}
|
|||
|
|
.kpi-sub {{
|
|||
|
|
font-size: 0.7rem;
|
|||
|
|
color: var(--muted);
|
|||
|
|
font-family: var(--mono);
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
/* ── Main layout ── */
|
|||
|
|
.main {{
|
|||
|
|
max-width: 1400px;
|
|||
|
|
margin: 0 auto;
|
|||
|
|
padding: 36px 48px;
|
|||
|
|
display: flex;
|
|||
|
|
flex-direction: column;
|
|||
|
|
gap: 32px;
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
/* ── Cards ── */
|
|||
|
|
.card {{
|
|||
|
|
background: var(--surface);
|
|||
|
|
border: 1px solid var(--border);
|
|||
|
|
border-radius: 8px;
|
|||
|
|
overflow: hidden;
|
|||
|
|
}}
|
|||
|
|
.card-header {{
|
|||
|
|
padding: 18px 24px 14px;
|
|||
|
|
border-bottom: 1px solid var(--border);
|
|||
|
|
display: flex;
|
|||
|
|
align-items: baseline;
|
|||
|
|
gap: 12px;
|
|||
|
|
}}
|
|||
|
|
.card-title {{
|
|||
|
|
font-size: 0.8rem;
|
|||
|
|
font-weight: 600;
|
|||
|
|
letter-spacing: 0.1em;
|
|||
|
|
text-transform: uppercase;
|
|||
|
|
color: var(--muted);
|
|||
|
|
font-family: var(--mono);
|
|||
|
|
}}
|
|||
|
|
.card-desc {{
|
|||
|
|
font-size: 0.78rem;
|
|||
|
|
color: #475569;
|
|||
|
|
}}
|
|||
|
|
.card-body {{
|
|||
|
|
padding: 24px;
|
|||
|
|
}}
|
|||
|
|
.chart-wrap {{
|
|||
|
|
position: relative;
|
|||
|
|
height: 280px;
|
|||
|
|
}}
|
|||
|
|
.chart-wrap-tall {{
|
|||
|
|
position: relative;
|
|||
|
|
height: 340px;
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
/* ── Two-column grid ── */
|
|||
|
|
.grid-2 {{
|
|||
|
|
display: grid;
|
|||
|
|
grid-template-columns: 1fr 1fr;
|
|||
|
|
gap: 24px;
|
|||
|
|
}}
|
|||
|
|
@media (max-width: 900px) {{
|
|||
|
|
.grid-2 {{ grid-template-columns: 1fr; }}
|
|||
|
|
.main {{ padding: 24px 20px; }}
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
/* ── Section label ── */
|
|||
|
|
.section-label {{
|
|||
|
|
font-family: var(--mono);
|
|||
|
|
font-size: 0.68rem;
|
|||
|
|
letter-spacing: 0.15em;
|
|||
|
|
text-transform: uppercase;
|
|||
|
|
color: var(--muted);
|
|||
|
|
padding: 0 4px;
|
|||
|
|
border-left: 3px solid var(--accent);
|
|||
|
|
padding-left: 10px;
|
|||
|
|
margin-bottom: -8px;
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
/* ── Tables ── */
|
|||
|
|
table {{
|
|||
|
|
width: 100%;
|
|||
|
|
border-collapse: collapse;
|
|||
|
|
font-size: 0.82rem;
|
|||
|
|
}}
|
|||
|
|
th {{
|
|||
|
|
font-family: var(--mono);
|
|||
|
|
font-size: 0.68rem;
|
|||
|
|
letter-spacing: 0.08em;
|
|||
|
|
text-transform: uppercase;
|
|||
|
|
color: var(--muted);
|
|||
|
|
padding: 10px 14px;
|
|||
|
|
text-align: left;
|
|||
|
|
border-bottom: 1px solid var(--border);
|
|||
|
|
background: #0f1218;
|
|||
|
|
}}
|
|||
|
|
td {{
|
|||
|
|
padding: 10px 14px;
|
|||
|
|
border-bottom: 1px solid #1a1f2a;
|
|||
|
|
vertical-align: middle;
|
|||
|
|
}}
|
|||
|
|
tr:last-child td {{ border-bottom: none; }}
|
|||
|
|
tr:hover td {{ background: #181e2b; }}
|
|||
|
|
|
|||
|
|
.rank {{ color: var(--muted); font-family: var(--mono); font-size: 0.75rem; }}
|
|||
|
|
.score-val {{ font-family: var(--mono); color: var(--accent2); }}
|
|||
|
|
.code-cell {{ font-family: var(--mono); font-size: 0.78rem; color: #94a3b8; }}
|
|||
|
|
.center {{ text-align: center; color: var(--accent2); }}
|
|||
|
|
.gain-high {{ font-family: var(--mono); color: var(--accent2); font-weight: 600; }}
|
|||
|
|
.gain-low {{ font-family: var(--mono); color: var(--warn); }}
|
|||
|
|
|
|||
|
|
.bar-cell {{ width: 120px; }}
|
|||
|
|
.score-bar {{
|
|||
|
|
height: 6px;
|
|||
|
|
border-radius: 3px;
|
|||
|
|
min-width: 2px;
|
|||
|
|
transition: width 0.3s;
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
.reg-badge {{
|
|||
|
|
display: inline-block;
|
|||
|
|
padding: 2px 8px;
|
|||
|
|
border-radius: 4px;
|
|||
|
|
background: #1e2a3a;
|
|||
|
|
border: 1px solid #2d3f54;
|
|||
|
|
font-family: var(--mono);
|
|||
|
|
font-size: 0.75rem;
|
|||
|
|
color: var(--accent);
|
|||
|
|
white-space: nowrap;
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
/* ── Legend patch ── */
|
|||
|
|
.legend-patch {{
|
|||
|
|
display: inline-block;
|
|||
|
|
width: 12px; height: 12px;
|
|||
|
|
border-radius: 2px;
|
|||
|
|
margin-right: 4px;
|
|||
|
|
vertical-align: middle;
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
/* ── No-surgery notice ── */
|
|||
|
|
.no-surg {{
|
|||
|
|
padding: 32px;
|
|||
|
|
text-align: center;
|
|||
|
|
color: var(--muted);
|
|||
|
|
font-family: var(--mono);
|
|||
|
|
font-size: 0.82rem;
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
/* ── Footer ── */
|
|||
|
|
.footer {{
|
|||
|
|
text-align: center;
|
|||
|
|
font-family: var(--mono);
|
|||
|
|
font-size: 0.68rem;
|
|||
|
|
color: #334155;
|
|||
|
|
margin-top: 16px;
|
|||
|
|
letter-spacing: 0.05em;
|
|||
|
|
}}
|
|||
|
|
</style>
|
|||
|
|
</head>
|
|||
|
|
<body>
|
|||
|
|
|
|||
|
|
<!-- ═══════════════════════════════════════════ HEADER -->
|
|||
|
|
<div class="header">
|
|||
|
|
<div class="header-eyebrow">Carmignac × ENSAE · Data Challenge 2025</div>
|
|||
|
|
<h1>Pipeline Results — Analysis Report</h1>
|
|||
|
|
<div class="header-sub">Registrar ID repair · Score propagation · Surgery audit</div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<!-- ═══════════════════════════════════════════ KPI STRIP -->
|
|||
|
|
<div class="kpi-strip">
|
|||
|
|
<div class="kpi">
|
|||
|
|
<span class="kpi-label">Σ score at t_ref</span>
|
|||
|
|
<span class="kpi-value success">{tl['sum_post'].iloc[-1]:.4f}</span>
|
|||
|
|
<span class="kpi-sub">post-surgery</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="kpi">
|
|||
|
|
<span class="kpi-label">Σ score at t_min</span>
|
|||
|
|
<span class="kpi-value accent">{tl['sum_post'].iloc[0]:.4f}</span>
|
|||
|
|
<span class="kpi-sub">post-surgery</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="kpi">
|
|||
|
|
<span class="kpi-label">Max recovery</span>
|
|||
|
|
<span class="kpi-value warn">{tl['recovery_pct'].max():.1f}%</span>
|
|||
|
|
<span class="kpi-sub">score rescued by surgery</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="kpi">
|
|||
|
|
<span class="kpi-label">Total surgeries</span>
|
|||
|
|
<span class="kpi-value">{len(surgery)}</span>
|
|||
|
|
<span class="kpi-sub">operations performed</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="kpi">
|
|||
|
|
<span class="kpi-label">Reg IDs universe</span>
|
|||
|
|
<span class="kpi-value">{piv.shape[1]}</span>
|
|||
|
|
<span class="kpi-sub">at reference date</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="kpi">
|
|||
|
|
<span class="kpi-label">Ever remapped</span>
|
|||
|
|
<span class="kpi-value warn">{len(analytics['ever_remapped'])}</span>
|
|||
|
|
<span class="kpi-sub">reg IDs w/ code change</span>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<!-- ═══════════════════════════════════════════ MAIN -->
|
|||
|
|
<div class="main">
|
|||
|
|
|
|||
|
|
<div class="section-label">01 · Score Integrity Over Time</div>
|
|||
|
|
|
|||
|
|
<!-- Chart 1: Σ score with vs without surgery -->
|
|||
|
|
<div class="card">
|
|||
|
|
<div class="card-header">
|
|||
|
|
<span class="card-title">Sum of scores — pre vs post surgery</span>
|
|||
|
|
<span class="card-desc">
|
|||
|
|
Post-surgery (solid) shows the corrected score after code repairs.
|
|||
|
|
Pre-surgery (dashed) is the counterfactual without any remapping.
|
|||
|
|
Gap = score rescued.
|
|||
|
|
</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="card-body">
|
|||
|
|
<div class="chart-wrap-tall">
|
|||
|
|
<canvas id="chartSigma"></canvas>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<!-- Chart 2: Score drop (pre) -->
|
|||
|
|
<div class="grid-2">
|
|||
|
|
<div class="card">
|
|||
|
|
<div class="card-header">
|
|||
|
|
<span class="card-title">Score recovered by surgery</span>
|
|||
|
|
<span class="card-desc">Difference post − pre at each date</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="card-body">
|
|||
|
|
<div class="chart-wrap">
|
|||
|
|
<canvas id="chartRecovery"></canvas>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<div class="card">
|
|||
|
|
<div class="card-header">
|
|||
|
|
<span class="card-title">Portfolio concentration (entropy)</span>
|
|||
|
|
<span class="card-desc">Shannon entropy of score distribution — higher = more spread</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="card-body">
|
|||
|
|
<div class="chart-wrap">
|
|||
|
|
<canvas id="chartEntropy"></canvas>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<div class="section-label">02 · Individual Score Trajectories</div>
|
|||
|
|
|
|||
|
|
<div class="card">
|
|||
|
|
<div class="card-header">
|
|||
|
|
<span class="card-title">Score per Registrar Account — full history</span>
|
|||
|
|
<span class="card-desc">
|
|||
|
|
Dashed lines = accounts that were remapped at some point (surgery applied).
|
|||
|
|
Solid lines = stable codes throughout.
|
|||
|
|
</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="card-body">
|
|||
|
|
<div class="chart-wrap-tall" style="height:360px">
|
|||
|
|
<canvas id="chartTraj"></canvas>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<div class="section-label">03 · Surgery Operations</div>
|
|||
|
|
|
|||
|
|
<div class="grid-2">
|
|||
|
|
<div class="card">
|
|||
|
|
<div class="card-header">
|
|||
|
|
<span class="card-title">Surgeries per time step</span>
|
|||
|
|
<span class="card-desc">Number of code remappings performed at each month</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="card-body">
|
|||
|
|
<div class="chart-wrap">
|
|||
|
|
<canvas id="chartNSurg"></canvas>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<div class="card">
|
|||
|
|
<div class="card-header">
|
|||
|
|
<span class="card-title">Score gain per surgery</span>
|
|||
|
|
<span class="card-desc">Average gain in Σ score from surgery at each month</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="card-body">
|
|||
|
|
<div class="chart-wrap">
|
|||
|
|
<canvas id="chartGain"></canvas>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<div class="card">
|
|||
|
|
<div class="card-header">
|
|||
|
|
<span class="card-title">Jaccard similarity of surgery matches</span>
|
|||
|
|
<span class="card-desc">
|
|||
|
|
Composite Jaccard score of the matched code pair — closer to 1.0 = stronger portfolio overlap.
|
|||
|
|
Low values may indicate uncertain matches.
|
|||
|
|
</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="card-body">
|
|||
|
|
<div class="chart-wrap">
|
|||
|
|
<canvas id="chartJaccard"></canvas>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<div class="section-label">04 · Surgery Detail Log</div>
|
|||
|
|
|
|||
|
|
<div class="card">
|
|||
|
|
<div class="card-header">
|
|||
|
|
<span class="card-title">All surgery operations</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="card-body" style="padding:0">
|
|||
|
|
{'<div class="no-surg">No surgeries were performed on this dataset.</div>' if len(surgery) == 0 else f"""
|
|||
|
|
<table>
|
|||
|
|
<thead>
|
|||
|
|
<tr>
|
|||
|
|
<th>Date</th>
|
|||
|
|
<th>Reg orig</th>
|
|||
|
|
<th>Code from</th>
|
|||
|
|
<th></th>
|
|||
|
|
<th>Code to</th>
|
|||
|
|
<th>Jaccard</th>
|
|||
|
|
<th>Score gain</th>
|
|||
|
|
<th>% of score</th>
|
|||
|
|
</tr>
|
|||
|
|
</thead>
|
|||
|
|
<tbody>{surg_rows_html}</tbody>
|
|||
|
|
</table>"""}
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<div class="section-label">05 · Score Ranking at t_ref</div>
|
|||
|
|
|
|||
|
|
<div class="card">
|
|||
|
|
<div class="card-header">
|
|||
|
|
<span class="card-title">Accounts ranked by weight at reference date</span>
|
|||
|
|
<span class="card-desc">✓ in last column = account was remapped at some point in history</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="card-body" style="padding:0">
|
|||
|
|
<table>
|
|||
|
|
<thead>
|
|||
|
|
<tr>
|
|||
|
|
<th>Rank</th>
|
|||
|
|
<th>Registrar ID</th>
|
|||
|
|
<th>Score (weight)</th>
|
|||
|
|
<th style="width:140px">Relative size</th>
|
|||
|
|
<th>Remapped</th>
|
|||
|
|
</tr>
|
|||
|
|
</thead>
|
|||
|
|
<tbody>{top_rows_html}</tbody>
|
|||
|
|
</table>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
</div><!-- /main -->
|
|||
|
|
|
|||
|
|
<div class="footer">Generated by carmignac_analysis.py · Carmignac × ENSAE Data Challenge 2025</div>
|
|||
|
|
|
|||
|
|
<!-- ═══════════════════════════════════════════ CHARTS JS -->
|
|||
|
|
<script>
|
|||
|
|
Chart.defaults.color = '#64748b';
|
|||
|
|
Chart.defaults.borderColor = '#1e2535';
|
|||
|
|
Chart.defaults.font.family = "'IBM Plex Mono', monospace";
|
|||
|
|
Chart.defaults.font.size = 11;
|
|||
|
|
|
|||
|
|
const DATES = {js(dates_str)};
|
|||
|
|
const SUM_POST = {jf(tl['sum_post'].values)};
|
|||
|
|
const SUM_PRE = {jf(tl['sum_pre'].values)};
|
|||
|
|
const RECOVERY = {jf(tl['recovery_pct'].values, 4)};
|
|||
|
|
const ENTROPY = {jf(tl['entropy'].values, 4)};
|
|||
|
|
const SURG_DATES = {js(surg_dates)};
|
|||
|
|
const N_SURG = {n_surg};
|
|||
|
|
const TOTAL_GAIN = {total_gain};
|
|||
|
|
const AVG_GAIN = {avg_gain};
|
|||
|
|
const AVG_JACCARD = {avg_jaccard};
|
|||
|
|
const TRAJ = {traj_json};
|
|||
|
|
|
|||
|
|
// ── Shared options helpers ────────────────────────────────────
|
|||
|
|
function timeAxis(label) {{
|
|||
|
|
return {{
|
|||
|
|
type: 'category',
|
|||
|
|
ticks: {{ maxTicksLimit: 10, maxRotation: 0 }},
|
|||
|
|
grid: {{ color: '#1a2030' }},
|
|||
|
|
title: {{ display: !!label, text: label, color: '#475569' }},
|
|||
|
|
}};
|
|||
|
|
}}
|
|||
|
|
function yAxis(label, opts={{}}) {{
|
|||
|
|
return {{
|
|||
|
|
grid: {{ color: '#1a2030' }},
|
|||
|
|
title: {{ display: !!label, text: label, color: '#475569' }},
|
|||
|
|
...opts,
|
|||
|
|
}};
|
|||
|
|
}}
|
|||
|
|
function tooltip() {{
|
|||
|
|
return {{
|
|||
|
|
backgroundColor: '#0d1117',
|
|||
|
|
borderColor: '#252a35',
|
|||
|
|
borderWidth: 1,
|
|||
|
|
titleFont: {{ family: "'IBM Plex Mono'" }},
|
|||
|
|
bodyFont: {{ family: "'IBM Plex Mono'" }},
|
|||
|
|
padding: 10,
|
|||
|
|
}};
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
// ── 1. Sigma pre/post ─────────────────────────────────────────
|
|||
|
|
new Chart(document.getElementById('chartSigma'), {{
|
|||
|
|
type: 'line',
|
|||
|
|
data: {{
|
|||
|
|
labels: DATES,
|
|||
|
|
datasets: [
|
|||
|
|
{{
|
|||
|
|
label: 'Σ score (post-surgery)',
|
|||
|
|
data: SUM_POST,
|
|||
|
|
borderColor: '#10b981',
|
|||
|
|
backgroundColor: '#10b98115',
|
|||
|
|
borderWidth: 2.5,
|
|||
|
|
pointRadius: 0,
|
|||
|
|
fill: false,
|
|||
|
|
tension: 0.2,
|
|||
|
|
}},
|
|||
|
|
{{
|
|||
|
|
label: 'Σ score (pre-surgery / counterfactual)',
|
|||
|
|
data: SUM_PRE,
|
|||
|
|
borderColor: '#ef4444',
|
|||
|
|
borderDash: [6, 4],
|
|||
|
|
borderWidth: 1.5,
|
|||
|
|
pointRadius: 0,
|
|||
|
|
fill: false,
|
|||
|
|
tension: 0.2,
|
|||
|
|
backgroundColor: 'transparent',
|
|||
|
|
}},
|
|||
|
|
],
|
|||
|
|
}},
|
|||
|
|
options: {{
|
|||
|
|
responsive: true, maintainAspectRatio: false,
|
|||
|
|
interaction: {{ mode: 'index', intersect: false }},
|
|||
|
|
plugins: {{
|
|||
|
|
legend: {{ position: 'top', labels: {{ boxWidth: 12, padding: 16 }} }},
|
|||
|
|
tooltip: tooltip(),
|
|||
|
|
}},
|
|||
|
|
scales: {{
|
|||
|
|
x: timeAxis(),
|
|||
|
|
y: yAxis('Σ scores', {{ min: 0, max: 1.05, ticks: {{ stepSize: 0.1 }} }}),
|
|||
|
|
}},
|
|||
|
|
}},
|
|||
|
|
}});
|
|||
|
|
|
|||
|
|
// ── 2. Recovery ───────────────────────────────────────────────
|
|||
|
|
new Chart(document.getElementById('chartRecovery'), {{
|
|||
|
|
type: 'bar',
|
|||
|
|
data: {{
|
|||
|
|
labels: DATES,
|
|||
|
|
datasets: [{{
|
|||
|
|
label: 'Score recovered (%)',
|
|||
|
|
data: RECOVERY,
|
|||
|
|
backgroundColor: '#3b82f6aa',
|
|||
|
|
borderColor: '#3b82f6',
|
|||
|
|
borderWidth: 1,
|
|||
|
|
borderRadius: 2,
|
|||
|
|
}}],
|
|||
|
|
}},
|
|||
|
|
options: {{
|
|||
|
|
responsive: true, maintainAspectRatio: false,
|
|||
|
|
plugins: {{ legend: {{ display: false }}, tooltip: tooltip() }},
|
|||
|
|
scales: {{
|
|||
|
|
x: timeAxis(),
|
|||
|
|
y: yAxis('Recovery (% of Σ)', {{ min: 0 }}),
|
|||
|
|
}},
|
|||
|
|
}},
|
|||
|
|
}});
|
|||
|
|
|
|||
|
|
// ── 3. Entropy ────────────────────────────────────────────────
|
|||
|
|
new Chart(document.getElementById('chartEntropy'), {{
|
|||
|
|
type: 'line',
|
|||
|
|
data: {{
|
|||
|
|
labels: DATES,
|
|||
|
|
datasets: [{{
|
|||
|
|
label: 'Shannon entropy',
|
|||
|
|
data: ENTROPY,
|
|||
|
|
borderColor: '#d97706',
|
|||
|
|
backgroundColor: '#d9770622',
|
|||
|
|
borderWidth: 2,
|
|||
|
|
pointRadius: 0,
|
|||
|
|
fill: true,
|
|||
|
|
tension: 0.3,
|
|||
|
|
}}],
|
|||
|
|
}},
|
|||
|
|
options: {{
|
|||
|
|
responsive: true, maintainAspectRatio: false,
|
|||
|
|
plugins: {{ legend: {{ display: false }}, tooltip: tooltip() }},
|
|||
|
|
scales: {{ x: timeAxis(), y: yAxis('Entropy (nats)') }},
|
|||
|
|
}},
|
|||
|
|
}});
|
|||
|
|
|
|||
|
|
// ── 4. Trajectories ───────────────────────────────────────────
|
|||
|
|
new Chart(document.getElementById('chartTraj'), {{
|
|||
|
|
type: 'line',
|
|||
|
|
data: {{ labels: DATES, datasets: TRAJ }},
|
|||
|
|
options: {{
|
|||
|
|
responsive: true, maintainAspectRatio: false,
|
|||
|
|
interaction: {{ mode: 'index', intersect: false }},
|
|||
|
|
plugins: {{
|
|||
|
|
legend: {{ position: 'right', labels: {{ boxWidth: 10, padding: 10, font: {{ size: 10 }} }} }},
|
|||
|
|
tooltip: {{ ...tooltip(), itemSort: (a,b) => b.raw - a.raw }},
|
|||
|
|
}},
|
|||
|
|
scales: {{ x: timeAxis(), y: yAxis('Score (weight)') }},
|
|||
|
|
}},
|
|||
|
|
}});
|
|||
|
|
|
|||
|
|
// ── 5. N surgeries ────────────────────────────────────────────
|
|||
|
|
new Chart(document.getElementById('chartNSurg'), {{
|
|||
|
|
type: 'bar',
|
|||
|
|
data: {{
|
|||
|
|
labels: SURG_DATES,
|
|||
|
|
datasets: [{{
|
|||
|
|
label: 'Surgeries',
|
|||
|
|
data: N_SURG,
|
|||
|
|
backgroundColor: '#7c3aed99',
|
|||
|
|
borderColor: '#7c3aed',
|
|||
|
|
borderWidth: 1,
|
|||
|
|
borderRadius: 3,
|
|||
|
|
}}],
|
|||
|
|
}},
|
|||
|
|
options: {{
|
|||
|
|
responsive: true, maintainAspectRatio: false,
|
|||
|
|
plugins: {{ legend: {{ display: false }}, tooltip: tooltip() }},
|
|||
|
|
scales: {{
|
|||
|
|
x: timeAxis('Month'),
|
|||
|
|
y: yAxis('# operations', {{ min: 0, ticks: {{ stepSize: 1 }} }}),
|
|||
|
|
}},
|
|||
|
|
}},
|
|||
|
|
}});
|
|||
|
|
|
|||
|
|
// ── 6. Avg gain ───────────────────────────────────────────────
|
|||
|
|
new Chart(document.getElementById('chartGain'), {{
|
|||
|
|
type: 'bar',
|
|||
|
|
data: {{
|
|||
|
|
labels: SURG_DATES,
|
|||
|
|
datasets: [
|
|||
|
|
{{
|
|||
|
|
label: 'Total gain',
|
|||
|
|
data: TOTAL_GAIN,
|
|||
|
|
backgroundColor: '#10b98199',
|
|||
|
|
borderColor: '#10b981',
|
|||
|
|
borderWidth: 1,
|
|||
|
|
borderRadius: 3,
|
|||
|
|
}},
|
|||
|
|
{{
|
|||
|
|
label: 'Avg gain / surgery',
|
|||
|
|
data: AVG_GAIN,
|
|||
|
|
backgroundColor: '#06b6d455',
|
|||
|
|
borderColor: '#06b6d4',
|
|||
|
|
borderWidth: 1,
|
|||
|
|
borderRadius: 3,
|
|||
|
|
}},
|
|||
|
|
],
|
|||
|
|
}},
|
|||
|
|
options: {{
|
|||
|
|
responsive: true, maintainAspectRatio: false,
|
|||
|
|
interaction: {{ mode: 'index', intersect: false }},
|
|||
|
|
plugins: {{ legend: {{ position: 'top', labels: {{ boxWidth: 10 }} }}, tooltip: tooltip() }},
|
|||
|
|
scales: {{ x: timeAxis('Month'), y: yAxis('Score gain') }},
|
|||
|
|
}},
|
|||
|
|
}});
|
|||
|
|
|
|||
|
|
// ── 7. Jaccard ────────────────────────────────────────────────
|
|||
|
|
new Chart(document.getElementById('chartJaccard'), {{
|
|||
|
|
type: 'bar',
|
|||
|
|
data: {{
|
|||
|
|
labels: SURG_DATES,
|
|||
|
|
datasets: [{{
|
|||
|
|
label: 'Avg Jaccard composite',
|
|||
|
|
data: AVG_JACCARD,
|
|||
|
|
backgroundColor: '#f59e0b88',
|
|||
|
|
borderColor: '#f59e0b',
|
|||
|
|
borderWidth: 1,
|
|||
|
|
borderRadius: 3,
|
|||
|
|
}}],
|
|||
|
|
}},
|
|||
|
|
options: {{
|
|||
|
|
responsive: true, maintainAspectRatio: false,
|
|||
|
|
plugins: {{ legend: {{ display: false }}, tooltip: tooltip() }},
|
|||
|
|
scales: {{
|
|||
|
|
x: timeAxis('Month'),
|
|||
|
|
y: yAxis('Jaccard composite', {{ min: 0, max: 1.05 }}),
|
|||
|
|
}},
|
|||
|
|
}},
|
|||
|
|
}});
|
|||
|
|
</script>
|
|||
|
|
</body>
|
|||
|
|
</html>"""
|
|||
|
|
|
|||
|
|
return html
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ─────────────────────────────────────────────────────────────
|
|||
|
|
# 5. MAIN
|
|||
|
|
# ─────────────────────────────────────────────────────────────
|
|||
|
|
|
|||
|
|
def main():
|
|||
|
|
parser = argparse.ArgumentParser(description="Carmignac pipeline results analyser")
|
|||
|
|
parser.add_argument("--scores", default="repair_results/carmignac_scores.csv")
|
|||
|
|
parser.add_argument("--mapping", default="repair_results/carmignac_mapping.csv")
|
|||
|
|
parser.add_argument("--surgery", default="repair_results/carmignac_surgery_log.csv")
|
|||
|
|
parser.add_argument("--out", default="repair_results/carmignac_report.html")
|
|||
|
|
args = parser.parse_args()
|
|||
|
|
|
|||
|
|
# Resolve paths relative to this script's directory if files not found
|
|||
|
|
base = os.path.dirname(os.path.abspath(__file__))
|
|||
|
|
def resolve(p):
|
|||
|
|
if os.path.exists(p):
|
|||
|
|
return p
|
|||
|
|
alt = os.path.join(base, p)
|
|||
|
|
if os.path.exists(alt):
|
|||
|
|
return alt
|
|||
|
|
sys.exit(f"[ERROR] File not found: {p}")
|
|||
|
|
|
|||
|
|
scores_path = resolve(args.scores)
|
|||
|
|
mapping_path = resolve(args.mapping)
|
|||
|
|
surgery_path = resolve(args.surgery)
|
|||
|
|
|
|||
|
|
print(f"[Load] scores : {scores_path}")
|
|||
|
|
print(f"[Load] mapping : {mapping_path}")
|
|||
|
|
print(f"[Load] surgery : {surgery_path}")
|
|||
|
|
|
|||
|
|
scores, mapping, surgery = load_outputs(scores_path, mapping_path, surgery_path)
|
|||
|
|
analytics = compute_analytics(scores, mapping, surgery)
|
|||
|
|
|
|||
|
|
print_summary(analytics, surgery)
|
|||
|
|
|
|||
|
|
html = build_html(analytics, surgery, scores, mapping)
|
|||
|
|
|
|||
|
|
out_path = args.out
|
|||
|
|
with open(out_path, "w", encoding="utf-8") as f:
|
|||
|
|
f.write(html)
|
|||
|
|
print(f"\n[Report] Written to → {out_path}")
|
|||
|
|
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if __name__ == "__main__":
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main()
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