{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model import *\n",
    "from similarity import *\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_similarity_matrix(filename, key):\n",
    "    with open(filename) as file:\n",
    "        similarity_matrix = {}\n",
    "\n",
    "        for line in csv.DictReader(file, skipinitialspace=True):\n",
    "            for k, v in line.items():\n",
    "                if k == key:\n",
    "                    key_v = v\n",
    "                    similarity_matrix[key_v] = {}\n",
    "                else:\n",
    "                    similarity_matrix[key_v][k] = float(v)\n",
    "\n",
    "        return similarity_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CaseBase(cases=499, fields=[('v', 'v_left', 'v_front', 'd_left', 'd_front', 'type_left', 'type_front', 'radius_curve(m)', 'slope_street', 'street_type', 'time', 'weather', 'type_vehicle', 'speed_limit(km/h)'), 'action'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Case(problem={'v_left': 36.5, 'v_front': 23.0, 'd_left': -56.0, 'd_front': 45.0, 'type_left': 'sportscar', 'type_front': 'truck', 'radius_curve(m)': 2237.0, 'slope_street': 'flat', 'street_type': 'country_road (separated)', 'time': 'night', 'weather': 'dry', 'type_vehicle': 'car', 'speed_limit(km/h)': 100.0}, solution={'action': 'continue'}),\n",
       " Case(problem={'v_left': 32.0, 'v_front': 28.0, 'd_left': -114.0, 'd_front': 44.0, 'type_left': 'motorcycle', 'type_front': 'car', 'radius_curve(m)': 3891.0, 'slope_street': 'ascending', 'street_type': 'autobahn', 'time': 'night', 'weather': 'dry', 'type_vehicle': 'truck', 'speed_limit(km/h)': 250.0}, solution={'action': 'continue'}),\n",
       " Case(problem={'v_left': 43.0, 'v_front': 31.5, 'd_left': -98.0, 'd_front': 60.0, 'type_left': 'truck', 'type_front': 'car', 'radius_curve(m)': 1720.0, 'slope_street': 'flat', 'street_type': 'autobahn', 'time': 'dusk', 'weather': 'rain', 'type_vehicle': 'car', 'speed_limit(km/h)': 130.0}, solution={'action': 'continue'})]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case_base = CaseBase.from_csv(\n",
    "    \"data/SIM_001.csv\",\n",
    "    problem_fields = (\"v\", \"v_left\", \"v_front\", \"d_left\", \"d_front\", \"type_left\", \"type_front\", \"radius_curve(m)\", \"slope_street\", \"street_type\", \"time\", \"weather\", \"type_vehicle\", \"speed_limit(km/h)\"),\n",
    "    solution_fields = (\"action\"),\n",
    "    encoding = \"utf-8\",\n",
    "    delimiter = \";\",\n",
    "    set_int = True\n",
    ")\n",
    "print(case_base)\n",
    "case_base[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "case_base.add_symbolic_sim(\n",
    "    field = \"type_left\",\n",
    "    similarity_matrix = create_similarity_matrix(\"data/vehicle_type_sim.csv\", \"type_vehicle\")\n",
    ")\n",
    "\n",
    "case_base.add_symbolic_sim(\n",
    "    field = \"type_front\",\n",
    "    similarity_matrix = create_similarity_matrix(\"data/vehicle_type_sim.csv\", \"type_vehicle\")\n",
    ")\n",
    "\n",
    "case_base.add_symbolic_sim(\n",
    "    field = \"type_vehicle\",\n",
    "    similarity_matrix = create_similarity_matrix(\"data/vehicle_type_sim.csv\", \"type_vehicle\")\n",
    ")\n",
    "\n",
    "case_base.add_symbolic_sim(\n",
    "    field = \"slope_street\",\n",
    "    similarity_matrix = create_similarity_matrix(\"data/street_slope_sim.csv\", \"type_street_slope\")\n",
    ")\n",
    "\n",
    "case_base.add_symbolic_sim(\n",
    "    field = \"time\",\n",
    "    similarity_matrix = create_similarity_matrix(\"data/time_type_sim.csv\", \"type_time\")\n",
    ")\n",
    "\n",
    "case_base.add_symbolic_sim(\n",
    "    field = \"weather\",\n",
    "    similarity_matrix = create_similarity_matrix(\"data/weather_type_sim.csv\", \"type_weather\")\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your Query:\n",
      "  - v = 28\n",
      "  - v_left = 37\n",
      "  - v_front = 22.5\n",
      "  - d_left = -20\n",
      "  - d_front = 51\n",
      "  - radius_curve = 2000\n",
      "  - speed_limit = 200\n",
      "  - type_vehicle = motorcycle\n",
      "  - type_left = motorcycle\n",
      "  - type_front = motorcycle\n",
      "\n",
      "I recommend you this car:\n",
      "Accelerated_lane_change\n",
      "\n",
      "Explanation:\n",
      "  - v_left = 37.0 (similarity: 1.00)\n",
      "  - v_front = 22.5 (similarity: 1.00)\n",
      "  - d_left = -17.0 (similarity: 0.25)\n",
      "  - d_front = 51.0 (similarity: 1.00)\n",
      "  - radius_curve(m) = 3020.0 (similarity: 0.00)\n",
      "  - speed_limit(km/h) = 120.0 (similarity: 0.01)\n",
      "  - type_vehicle = car (similarity: 0.50)\n",
      "  - type_left = truck (similarity: 0.50)\n",
      "  - type_front = truck (similarity: 0.50)\n"
     ]
    }
   ],
   "source": [
    "query = Query.from_problems(\n",
    "    v = 28,\n",
    "    v_left = 37,\n",
    "    v_front = 22.5,\n",
    "    d_left = -20,\n",
    "    d_front = 51,\n",
    "    radius_curve = 2000,\n",
    "    speed_limit = 200,\n",
    "    type_vehicle = \"motorcycle\",\n",
    "    type_left = \"motorcycle\",\n",
    "    type_front = \"motorcycle\",\n",
    ")\n",
    "\n",
    "# sim_funcs: manhattan_sim, euclid_sim\n",
    "retrieved = case_base.retrieve(\n",
    "    query,\n",
    "    v_left = euclid_sim,\n",
    "    v_front = euclid_sim,\n",
    "    d_left = euclid_sim,\n",
    "    d_front = euclid_sim,\n",
    "    radius_curve = manhattan_sim,\n",
    "    speed_limit = manhattan_sim,\n",
    "    type_vehicle = symbolic_sim,\n",
    "    type_left = symbolic_sim,\n",
    "    type_front = symbolic_sim,\n",
    ")\n",
    "\n",
    "print(\"Your Query:\")\n",
    "for k, v in query.problem.items():\n",
    "    print(f\"  - {k} = {v}\")\n",
    "print()\n",
    "print(\"I recommend you this car:\")\n",
    "print(\" \".join(retrieved.solution.values()).capitalize())\n",
    "print()\n",
    "print(\"Explanation:\")\n",
    "for field, sim_val in retrieved.sim_per_field.items():\n",
    "    print(f\"  - {field} =\", retrieved.problem[field], f\"(similarity: {sim_val:.2f})\")"
   ]
  }
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