CaseBasedReasoning/explore.ipynb

{
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  {
   "cell_type": "code",
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   "source": [
    "from model import *\n",
    "from similarity import *\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "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 = \"street_type\",\n",
    "    similarity_matrix = create_similarity_matrix(\"data/street_type_sim.csv\", \"type_street\")\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = Query.from_problems(\n",
    "    v = 28.5,\n",
    "    v_left = 42.5,\n",
    "    v_front = 5,\n",
    "    d_left = -137,\n",
    "    d_front = 54,\n",
    "    type_left = \"motorcycle\",\n",
    "    type_front = \"truck\",\n",
    "    radius_curve = 2391,\n",
    "    slope_street = \"flat\",\n",
    "    street_type = \"country_road (separated)\",\n",
    "    time = \"day\",\n",
    "    weather = \"dry\",\n",
    "    type_vehicle = \"car\",\n",
    "    speed_limit = 100,\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",
    "    type_left = symbolic_sim,\n",
    "    type_front = symbolic_sim,\n",
    "    radius_curve = euclid_sim,\n",
    "    slope_street = symbolic_sim,\n",
    "    street_type = symbolic_sim,\n",
    "    time = symbolic_sim,\n",
    "    weather = symbolic_sim,\n",
    "    type_vehicle = symbolic_sim,\n",
    "    speed_limit = euclid_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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