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"""
Regime-Conditioned Forecasting Analysis
======================================
Reproduces all decision-grade evidence used in the consulting slides:
1. Demand structure segmentation
2. Model performance by structure
3. Portfolio-level mean loss
4. Tail-risk (P90) analysis
5. SKU win-rate vs portfolio loss disconnect
6. Chronos2 positioning (descriptive, not causal)
Inputs (from sandbox):
- combined_metrics.csv
- best_by_sku.csv
No assumptions beyond the data.
"""
import pandas as pd
import numpy as np
# ---------------------------------------------------------------------
# CONFIG
# ---------------------------------------------------------------------
DATA_DIR = "metrics" # adjust if needed
COMBINED_PATH = f"{DATA_DIR}/combined_metrics.csv"
BEST_BY_SKU_PATH = f"{DATA_DIR}/best_by_sku.csv"
SCORE_COL = "score"
BIAS_COL = "bias"
MODEL_COL = "model"
BEST_MODEL_COL = "best_model"
SKU_COL = "id"
# optional if present
STRUCTURE_COLS = ["cv_bin", "adi_bin", "regime"]
# ---------------------------------------------------------------------
# LOAD DATA
# ---------------------------------------------------------------------
combined = pd.read_csv(COMBINED_PATH)
best_by_sku = pd.read_csv(BEST_BY_SKU_PATH)
# sanity
assert SCORE_COL in combined.columns
assert MODEL_COL in combined.columns
print("\nLoaded data:")
print(f" combined_metrics rows: {len(combined):,}")
print(f" best_by_sku rows: {len(best_by_sku):,}")
print(best_by_sku.head())
print(best_by_sku.columns)
# ---------------------------------------------------------------------
# 1. PORTFOLIO-LEVEL PERFORMANCE
# ---------------------------------------------------------------------
portfolio_summary = (
combined
.groupby(MODEL_COL)
.agg(
mean_score=(SCORE_COL, "mean"),
p90_score=(SCORE_COL, lambda x: np.percentile(x, 90)),
mean_abs_bias=(BIAS_COL, lambda x: np.mean(np.abs(x))),
std_score=(SCORE_COL, "std")
)
.sort_values("mean_score")
)
print("\n=== Portfolio-Level Performance ===")
print(portfolio_summary.round(2))
# ---------------------------------------------------------------------
# 2. WIN-RATE (BEST-MODEL SHARE)
# ---------------------------------------------------------------------
win_rate = (
best_by_sku
.groupby(BEST_MODEL_COL)
.size()
.rename("sku_wins")
.to_frame()
)
win_rate["win_share"] = win_rate["sku_wins"] / win_rate["sku_wins"].sum()
print("\n=== SKU Win Share ===")
print(win_rate.sort_values("win_share", ascending=False).round(3))
# ---------------------------------------------------------------------
# 3. WIN-RATE vs PORTFOLIO LOSS RELATIONSHIP
# ---------------------------------------------------------------------
win_vs_loss = (
portfolio_summary
.merge(win_rate, left_index=True, right_index=True, how="left")
.fillna(0)
)
correlation = win_vs_loss["win_share"].corr(win_vs_loss["mean_score"])
print("\n=== Win-Rate vs Mean Score ===")
print(win_vs_loss[["mean_score", "win_share"]].round(3))
print(f"\nCorrelation (win_share vs mean_score): {correlation:.3f}")
# ---------------------------------------------------------------------
# 4. STRUCTURE-CONDITIONAL PERFORMANCE
# ---------------------------------------------------------------------
structure_cols = [c for c in STRUCTURE_COLS if c in combined.columns]
if structure_cols:
print("\n=== Structure-Conditional Analysis ===")
for col in structure_cols:
print(f"\n-- Performance by {col} --")
table = (
combined
.groupby([col, MODEL_COL])[SCORE_COL]
.mean()
.unstack()
)
print(table.round(2))
else:
print("\nNo explicit structure columns found (cv_bin / regime).")
# ---------------------------------------------------------------------
# 5. TAIL-RISK DOMINANCE
# ---------------------------------------------------------------------
tail_ratio = (
portfolio_summary["p90_score"] /
portfolio_summary["mean_score"]
).rename("p90_to_mean_ratio")
print("\n=== Tail-Risk Ratio (P90 / Mean) ===")
print(tail_ratio.sort_values())
# ---------------------------------------------------------------------
# 6. CHRONOS2 DESCRIPTIVE POSITIONING
# ---------------------------------------------------------------------
if "chronos2" in portfolio_summary.index:
chronos_row = portfolio_summary.loc["chronos2"]
chronos_win = win_rate.loc["chronos2"] if "chronos2" in win_rate.index else None
print("\n=== Chronos2 Positioning ===")
print(chronos_row.round(2))
if chronos_win is not None:
print("\nChronos2 win share:")
print(chronos_win.round(3))
else:
print("\nChronos2 not found in model list.")
# ---------------------------------------------------------------------
# 7. OPTIONAL: EXPORT TABLES FOR SLIDES
# ---------------------------------------------------------------------
portfolio_summary.round(3).to_csv(f"{DATA_DIR}/analysis_portfolio_summary.csv")
win_vs_loss.round(3).to_csv(f"{DATA_DIR}/analysis_win_vs_loss.csv")
print("\nAnalysis tables exported:")
print(" analysis_portfolio_summary.csv")
print(" analysis_win_vs_loss.csv")
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