{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b571493f",
   "metadata": {},
   "source": [
    "# Tutorial 03 - Generating New Problems (Random Instance Generation)\n",
    "\n",
    "This notebook shows how to generate random **JobShopInstance** objects using the function `modular_instance_generator`.\n",
    "\n",
    "> **Deprecated:** The classes `InstanceGenerator` and `GeneralInstanceGenerator` are deprecated. Use `modular_instance_generator` instead."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb87edb",
   "metadata": {},
   "source": [
    "## Why a Modular Function?\n",
    "The design emphasizes two key patterns:\n",
    "\n",
    "1. **Dependency Injection** – You inject *callables* that build each matrix (machine routing, durations, release dates, etc.) instead of the generator hard-coding logic. This increases testability and flexibility.\n",
    "2. **Strategy Pattern** – Each callable acts as a strategy. Swap routing, duration, or release-date strategies independently without modifying the core generator.\n",
    "\n",
    "The generator returns an **infinite stream** of instances. Use `next()` or slice it with `itertools.islice`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e96b3df",
   "metadata": {},
   "source": [
    "## Signature (Annotated)\n",
    "```python\n",
    "modular_instance_generator(\n",
    "    machine_matrix_creator: Callable[[random.Random], MachineMatrix],\n",
    "    duration_matrix_creator: Callable[[MachineMatrix, random.Random], DurationMatrix],\n",
    "    *,\n",
    "    name_creator: Callable[[int], str] = lambda i: f\"generated_instance_{i}\",\n",
    "    release_dates_matrix_creator: Callable[[DurationMatrix, random.Random], ReleaseDatesMatrix] | None = None,\n",
    "    deadlines_matrix_creator: Callable[[DurationMatrix, random.Random], DeadlinesMatrix] | None = None,\n",
    "    due_dates_matrix_creator: Callable[[DurationMatrix, random.Random], DueDatesMatrix] | None = None,\n",
    "    seed: int | None = None,\n",
    ") -> Generator[JobShopInstance, None, None]\n",
    "```\n",
    "Where each *Matrix* is a nested list (ragged lists allowed for jobs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3da71e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports and basic setup\n",
    "from itertools import islice\n",
    "from job_shop_lib.generation import (\n",
    "    modular_instance_generator,\n",
    "    get_default_machine_matrix_creator,\n",
    "    get_default_duration_matrix_creator,\n",
    "    range_size_selector,\n",
    "    choice_size_selector,\n",
    "    create_release_dates_matrix,\n",
    "    get_mixed_release_date_strategy,\n",
    "    compute_horizon_proxy,\n",
    ")\n",
    "import random\n",
    "\n",
    "# For deterministic examples\n",
    "BASE_SEED = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da62385c",
   "metadata": {},
   "source": [
    "## Basic Example\n",
    "Generate a fixed 3x3 instance without recirculation and durations in `[1, 10]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "70bc9b22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(JobShopInstance(name=generated_instance_0, num_jobs=3, num_machines=3),\n",
       " array([[ 5.,  6.,  4.],\n",
       "        [ 5.,  7., 10.],\n",
       "        [ 9.,  9.,  5.]], dtype=float32))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "machine_creator = get_default_machine_matrix_creator(\n",
    "    size_selector=lambda rng: (3, 3),\n",
    "    with_recirculation=False,\n",
    ")\n",
    "duration_creator = get_default_duration_matrix_creator((1, 10))\n",
    "\n",
    "gen_basic = modular_instance_generator(\n",
    "    machine_matrix_creator=machine_creator,\n",
    "    duration_matrix_creator=duration_creator,\n",
    "    seed=BASE_SEED,\n",
    ")\n",
    "inst = next(gen_basic)\n",
    "inst, inst.duration_matrix_array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75085acf",
   "metadata": {},
   "source": [
    "## Custom Naming Strategy\n",
    "Provide a callable that maps the index to a formatted name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a5750583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['expA_seed42_000', 'expA_seed42_001', 'expA_seed42_002']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fancy_name(i: int) -> str:  # zero-padded index\n",
    "    return f\"expA_seed{BASE_SEED}_{i:03d}\"\n",
    "\n",
    "\n",
    "gen_named = modular_instance_generator(\n",
    "    machine_matrix_creator=machine_creator,\n",
    "    duration_matrix_creator=duration_creator,\n",
    "    name_creator=fancy_name,\n",
    "    seed=BASE_SEED,\n",
    ")\n",
    "[next(gen_named).name for _ in range(3)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c96d95",
   "metadata": {},
   "source": [
    "## Random Sizes (Size Selectors)\n",
    "Use range-based or discrete-choice size selectors captured in the machine matrix creator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1824aa33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[JobShopInstance(name=generated_instance_0, num_jobs=4, num_machines=5),\n",
       " JobShopInstance(name=generated_instance_1, num_jobs=6, num_machines=4)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Range-based size selection\n",
    "machine_creator_range = get_default_machine_matrix_creator(\n",
    "    size_selector=lambda rng: range_size_selector(\n",
    "        rng,\n",
    "        num_jobs_range=(5, 7),\n",
    "        num_machines_range=(3, 5),\n",
    "        allow_less_jobs_than_machines=True,\n",
    "    ),\n",
    "    with_recirculation=True,\n",
    ")\n",
    "\n",
    "# Choice-based size selection\n",
    "OPTIONS = [(3, 3), (4, 5), (6, 4)]\n",
    "machine_creator_choice = get_default_machine_matrix_creator(\n",
    "    size_selector=lambda rng: choice_size_selector(rng, OPTIONS),\n",
    "    with_recirculation=False,\n",
    ")\n",
    "\n",
    "duration_creator_var = get_default_duration_matrix_creator((2, 15))\n",
    "\n",
    "gen_choice = modular_instance_generator(\n",
    "    machine_matrix_creator=machine_creator_choice,\n",
    "    duration_matrix_creator=duration_creator_var,\n",
    "    seed=7,\n",
    ")\n",
    "[next(gen_choice) for _ in range(2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc4f0db8",
   "metadata": {},
   "source": [
    "## Adding Release Dates\n",
    "You can wrap `create_release_dates_matrix` with a custom mixed strategy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6d78183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 6, 9], [0, 4, 7], [4, 5, 8]]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def release_dates_creator(duration_matrix, rng):\n",
    "    horizon = compute_horizon_proxy(duration_matrix)\n",
    "    strat = get_mixed_release_date_strategy(0.6, 0.4, horizon)\n",
    "    return create_release_dates_matrix(\n",
    "        duration_matrix, strategy=strat, rng=rng\n",
    "    )\n",
    "\n",
    "\n",
    "gen_with_release = modular_instance_generator(\n",
    "    machine_matrix_creator=machine_creator,\n",
    "    duration_matrix_creator=duration_creator,\n",
    "    release_dates_matrix_creator=release_dates_creator,\n",
    "    seed=123,\n",
    ")\n",
    "inst_with_release = next(gen_with_release)\n",
    "inst_with_release.release_dates_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "163cf717",
   "metadata": {},
   "source": [
    "## Deadlines & Due Dates\n",
    "Provide simple heuristic strategies. You decide semantics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dddd5aa9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JobShopInstance(name=generated_instance_0, num_jobs=3, num_machines=3)\n",
      "complex_instance.due_dates_matrix_array =\n",
      "[[14.  9. 25.]\n",
      " [ 4. 12. 14.]\n",
      " [13. 18. 27.]]\n",
      "complex_instance.deadlines_matrix_array =\n",
      "[[14. 18. 38.]\n",
      " [ 4. 18. 24.]\n",
      " [14. 30. 50.]]\n",
      "complex_instance.release_dates_matrix_array =\n",
      "[[ 3.  6.  8.]\n",
      " [ 3.  3.  6.]\n",
      " [ 4. 10. 10.]]\n"
     ]
    }
   ],
   "source": [
    "def deadlines_creator(duration_matrix, rng):\n",
    "    deadlines = []\n",
    "    for job in duration_matrix:\n",
    "        cum = 0\n",
    "        row = []\n",
    "        for duration in job:\n",
    "            cum += duration\n",
    "            row.append(int(cum * 2))  # 100% slack\n",
    "        deadlines.append(row)\n",
    "    return deadlines\n",
    "\n",
    "\n",
    "def due_dates_creator(duration_matrix, rng):\n",
    "    due_dates = []\n",
    "    for job in duration_matrix:\n",
    "        cum = 0\n",
    "        row = []\n",
    "        for duration in job:\n",
    "            cum += duration\n",
    "            row.append(cum + rng.randint(0, duration))\n",
    "        due_dates.append(row)\n",
    "    return due_dates\n",
    "\n",
    "\n",
    "gen_with_all = modular_instance_generator(\n",
    "    machine_matrix_creator=machine_creator,\n",
    "    duration_matrix_creator=duration_creator,\n",
    "    release_dates_matrix_creator=release_dates_creator,\n",
    "    deadlines_matrix_creator=deadlines_creator,\n",
    "    due_dates_matrix_creator=due_dates_creator,\n",
    "    seed=99,\n",
    ")\n",
    "complex_instance = next(gen_with_all)\n",
    "print(complex_instance)\n",
    "print(\"complex_instance.due_dates_matrix_array =\")\n",
    "print(complex_instance.due_dates_matrix_array)\n",
    "print(\"complex_instance.deadlines_matrix_array =\")\n",
    "print(complex_instance.deadlines_matrix_array)\n",
    "print(\"complex_instance.release_dates_matrix_array =\")\n",
    "print(complex_instance.release_dates_matrix_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80bd8882",
   "metadata": {},
   "source": [
    "## Migration Example (Deprecated Class)\n",
    "Old approach (for reference, no execution here):\n",
    "```python\n",
    "# Deprecated\n",
    "# gen = GeneralInstanceGenerator(num_jobs=(5,10), num_machines=(4,6), duration_range=(2,20))\n",
    "# inst = gen.generate()\n",
    "```\n",
    "New modular approach:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "57a6eb24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JobShopInstance(name=generated_instance_0, num_jobs=8, num_machines=5)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from functools import partial\n",
    "\n",
    "size_selector = partial(\n",
    "    range_size_selector,\n",
    "    num_jobs_range=(5, 8),\n",
    "    num_machines_range=(4, 6),\n",
    "    allow_less_jobs_than_machines=False,\n",
    ")\n",
    "migrated_machine_creator = get_default_machine_matrix_creator(\n",
    "    size_selector=size_selector, with_recirculation=False\n",
    ")\n",
    "migrated_duration_creator = get_default_duration_matrix_creator((2, 20))\n",
    "gen_migrated = modular_instance_generator(\n",
    "    migrated_machine_creator, migrated_duration_creator, seed=0\n",
    ")\n",
    "next(gen_migrated)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58650c8a",
   "metadata": {},
   "source": [
    "## Finite Sample Extraction\n",
    "Use `itertools.islice` to limit the infinite generator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "01d7d25f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, ['generated_instance_1', 'generated_instance_2', 'generated_instance_3'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finite_instances = list(islice(gen_basic, 3))  # reuse earlier generator\n",
    "len(finite_instances), [inst.name for inst in finite_instances]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b26f7fee",
   "metadata": {},
   "source": [
    "## Summary\n",
    "`modular_instance_generator` provides a clean, composable way to build\n",
    "random Job Shop instances. You control routing, durations, and time-related\n",
    "constraints via strategy callables—simplifying experimentation and testing.\n",
    "\n",
    "You can now integrate these instances into solvers, RL environments, or graph builders."
   ]
  }
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