troubleshooting-batch-runner.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Troubleshooting batchrunner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mesa import Agent, Model\n",
    "from mesa.time import RandomActivation\n",
    "from mesa.space import MultiGrid\n",
    "from mesa.datacollection import DataCollector\n",
    "\n",
    "class MyAgent(Agent):\n",
    "    def __init__(self, name, model):\n",
    "        super().__init__(name, model)\n",
    "        self.name = name\n",
    "\n",
    "    def step(self):\n",
    "        pass\n",
    "\n",
    "class MyModel(Model):\n",
    "    def __init__(self, n_agents):\n",
    "        super().__init__()\n",
    "        self.schedule = RandomActivation(self)\n",
    "        self.grid = MultiGrid(10, 10, torus=True)\n",
    "        for i in range(n_agents):\n",
    "            a = MyAgent(i, self)\n",
    "            self.schedule.add(a)\n",
    "            coords = (self.random.randrange(0, 10), self.random.randrange(0, 10))\n",
    "            self.grid.place_agent(a, coords)\n",
    "        self.dc = DataCollector(\n",
    "            model_reporters={\n",
    "                \"agent_count\": lambda m: m.schedule.get_agent_count()\n",
    "            },\n",
    "            agent_reporters={\"name\": lambda a: a.name}\n",
    "        )\n",
    "\n",
    "    def step(self):\n",
    "        self.schedule.step()\n",
    "        self.dc.collect(self)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "4it [00:00, 1078.92it/s]\n"
     ]
    }
   ],
   "source": [
    "from mesa.batchrunner import BatchRunner\n",
    "\n",
    "parameters = {\"n_agents\": range(1, 5)}\n",
    "batch_run = BatchRunner(\n",
    "    MyModel, \n",
    "    parameters, \n",
    "    max_steps=10,\n",
    "    model_reporters={\n",
    "        \"n_agents\": lambda m: m.schedule.get_agent_count()\n",
    "    }\n",
    ")\n",
    "batch_run.run_all()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_agents</th>\n",
       "      <th>Run</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_agents  Run\n",
       "0         1    0\n",
       "1         2    1\n",
       "2         3    2\n",
       "3         4    3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_run.get_model_vars_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_This is incorrect, there should be an index for time step; each model should be run for 10 steps, so I should have 40 rows._\n",
    "\n",
    "Additionally, re-declaring the model reporters seems to be repetitive, especially when it's already declared in the Model class definition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "4it [00:00, 1453.96it/s]\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'BatchRunner' object has no attribute 'model_vars'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-0be3ef67a8a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m )\n\u001b[1;32m      9\u001b[0m \u001b[0mbatch_run\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mbatch_run\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_model_vars_dataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda/envs/prog_forecast/lib/python3.8/site-packages/mesa/batchrunner.py\u001b[0m in \u001b[0;36mget_model_vars_dataframe\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m         \"\"\"\n\u001b[0;32m--> 213\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_report_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_vars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_agent_vars_dataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'BatchRunner' object has no attribute 'model_vars'"
     ]
    }
   ],
   "source": [
    "from mesa.batchrunner import BatchRunner\n",
    "\n",
    "parameters = {\"n_agents\": range(1, 5)}\n",
    "batch_run = BatchRunner(\n",
    "    MyModel, \n",
    "    parameters, \n",
    "    max_steps=10,\n",
    ")\n",
    "batch_run.run_all()\n",
    "batch_run.get_model_vars_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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