test_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import numpy as np\n",
    "import json\n",
    "\n",
    "from stochastic_arrival_times.fjsp_env.debug_gantt_chart import draw_gantt_chart, get_machine_op_durations\n",
    "from evaluations.standard.boxplot.gnn import new_model\n",
    "from validate import validate_and_get_environment\n",
    "from uniform_instance_gen import datasetConfigs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. Set problem size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change this, either 1 to 10 based on Brandimarte problems (MK01 to MK10)\n",
    "PROBLEM_SIZE = 1\n",
    "\n",
    "if PROBLEM_SIZE > 10 or PROBLEM_SIZE < 1:\n",
    "    raise Exception('PROBLEM_SIZE must be between 1 and 10')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Load model and load benchmark problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Load weight\n",
    "i = PROBLEM_SIZE - 1\n",
    "weights = json.load(open('saved_weights.json'))['stochastic']\n",
    "model = new_model(problem_size=i+1, weight_id=weights['MK{:02d}'.format(i+1)])\n",
    "\n",
    "\n",
    "# 2. Load benchmark problem\n",
    "problem = np.load('./evaluations/standard/brandimarte/brandimarte_dataset_numpy/MK{:02d}.fjs.npy'.format(i+1))\n",
    "ub_ops = datasetConfigs[\"MK{:02d}\".format(i+1)].highest_num_of_operations_per_job"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Get Makespan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "makespan, env = validate_and_get_environment(validation=problem, model=model, ub_num_of_operations_per_job=ub_ops, release_times=None)\n",
    "makespan *= -1\n",
    "\n",
    "\n",
    "makespan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Visualize schedule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "horizon = makespan * 1.05\n",
    "machine_op_durations = get_machine_op_durations(\n",
    "    env.machine_op_ids,\n",
    "    env.jobs,\n",
    "    env.op_id_to_job_info\n",
    ")\n",
    "\n",
    "draw_gantt_chart(\n",
    "    horizon,\n",
    "    env.machine_start_times,\n",
    "    machine_op_durations,\n",
    "    env.machine_op_ids,\n",
    "    env.op_id_to_job_info,\n",
    "    env.number_of_jobs\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('fjsp-rl')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "9af82945d3fcc9f5a9e513a177df704e75e4a91e795d1082ff4bcabadd9bc4e8"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}