Value-based price from a value buildup

The exact vocabulary the automated check enforces: keys, tokens, entry points, and worked examples. Generated from the verification source.

{
 "constants": {},
 "entryName": "compute_value_based_price",
 "expectNote": "expect is the comparison-space value (your return value goes through normalize first when one is published)",
 "hiddenCaseCount": 4,
 "hiddenCaseNames": [
  "modest",
  "greenfield_quarter_split",
  "negative_incremental_caps_at_value",
  "price_capped_at_total_value"
 ],
 "inputKeys": [
  "next_best_alt_price",
  "next_best_alt_value",
  "value_components",
  "value_split_fraction"
 ],
 "normalizeSource": "def _value_price_normalize(r):\n    try:\n        return {k: round(float(r[k]), 2) for k in\n                (\"total_value\", \"incremental_value\", \"recommended_price\", \"customer_surplus\")}\n    except Exception:\n        return repr(r)\n",
 "returnShapes": [
  [
   "customer_surplus",
   "incremental_value",
   "recommended_price",
   "total_value"
  ]
 ],
 "signature": "def compute_value_based_price(inp):",
 "submission": "python exposing the entry function; inp is one input object; graded on held-out cases",
 "tier": "calculator",
 "visibleCases": [
  {
   "expect": {
    "customer_surplus": 500,
    "incremental_value": 500,
    "recommended_price": 1000,
    "total_value": 1500
   },
   "input": {
    "next_best_alt_price": 750,
    "next_best_alt_value": 1000,
    "value_components": [
     1000,
     100,
     200,
     200
    ],
    "value_split_fraction": 0.5
   },
   "name": "book_example"
  }
 ],
 "vocabulary": [
  "-?\\d+(?:\\.\\d+)?",
  "customer_surplus",
  "incremental_value",
  "next_best_alt_price",
  "next_best_alt_value",
  "recommended_price",
  "total_value",
  "value_components",
  "value_split_fraction"
 ]
}