Lab: Produce an Evidence-Backed Decision Memo

option,completed_deliveries,attempted_deliveries,total_cost_pkr,median_support_hours,terms_verified
Option A,88,100,30800,5,true
Option B,81,90,27000,2,false

from pathlib import Path
import json
import pandas as pd

df = pd.read_csv("options.csv")
required = {
    "option", "completed_deliveries", "attempted_deliveries",
    "total_cost_pkr", "median_support_hours", "terms_verified"
}
missing = required - set(df.columns)
if missing:
    raise ValueError(f"Missing columns: {sorted(missing)}")

if (df["attempted_deliveries"] <= 0).any():
    raise ValueError("attempted_deliveries must be positive")
if (df["completed_deliveries"] <= 0).any():
    raise ValueError("completed_deliveries must be positive before calculating cost per completion")
if (df["completed_deliveries"] > df["attempted_deliveries"]).any():
    raise ValueError("completed cannot exceed attempted")
if (df["total_cost_pkr"] < 0).any():
    raise ValueError("cost cannot be negative in this fixture")

df["completion_rate"] = df["completed_deliveries"] / df["attempted_deliveries"]
df["cost_per_completed_pkr"] = df["total_cost_pkr"] / df["completed_deliveries"]

# Normalize only within this comparison. Lower cost/support is better.
def benefit(series):
    span = series.max() - series.min()
    return pd.Series([1.0] * len(series), index=series.index) if span == 0 else (series - series.min()) / span

def inverse_benefit(series):
    span = series.max() - series.min()
    return pd.Series([1.0] * len(series), index=series.index) if span == 0 else (series.max() - series) / span

weights = {"reliability": 0.50, "cost": 0.30, "support": 0.20}
df["score"] = (
    weights["reliability"] * benefit(df["completion_rate"])
    + weights["cost"] * inverse_benefit(df["cost_per_completed_pkr"])
    + weights["support"] * inverse_benefit(df["median_support_hours"])
)

# A missing verified term is a gate, not a hidden scoring penalty.
df["decision_status"] = df["terms_verified"].map(
    {True: "eligible_for_pilot", False: "blocked_pending_terms"}
)

columns = [
    "option", "completion_rate", "cost_per_completed_pkr",
    "median_support_hours", "score", "decision_status"
]
result = df[columns].round(4)
Path("calculated_options.csv").write_text(result.to_csv(index=False), encoding="utf-8")
Path("calculation_record.json").write_text(
    json.dumps({"weights": weights, "rows": result.to_dict(orient="records")}, indent=2),
    encoding="utf-8",
)
print(result.to_string(index=False))