benchmark_infomax_multi.ipynb

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     "text": [
      "Loading database at ../navbench/datasets/rc_car/Stanmer_park_dataset/0511/unwrapped_dataset1...\nDatabase contains 411 images\nLoading database at ../navbench/datasets/rc_car/Stanmer_park_dataset/0511/unwrapped_dataset2...\nDatabase contains 367 images\nLoading database at ../navbench/datasets/rc_car/Stanmer_park_dataset/0511/unwrapped_dataset3...\nDatabase contains 402 images\n"
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "NAVBENCH_PATH = '../navbench'\n",
    "sys.path.append(NAVBENCH_PATH)\n",
    "\n",
    "import json\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import navbench as nb\n",
    "import numpy as np\n",
    "\n",
    "def read_json(filepath):\n",
    "    file = open(filepath, \"r\")\n",
    "    data = json.load(file)[\"data\"]\n",
    "    file.close()\n",
    "    return data\n",
    "\n",
    "data = read_json('benchmark_infomax_multi.json')\n",
    "\n",
    "DB_ROOT = os.path.join(NAVBENCH_PATH, \"datasets/rc_car/Stanmer_park_dataset\")\n",
    "\n",
    "def get_training_data(routes):\n",
    "    x_train = []\n",
    "    y_train = []\n",
    "    heads_train = []\n",
    "    for dbname in routes:\n",
    "        db = nb.Database(os.path.join(DB_ROOT, dbname))\n",
    "        offset = db.calculate_heading_offset(0.25)\n",
    "\n",
    "        for x in db.x:\n",
    "            x_train.append(x)\n",
    "        for y in db.y:\n",
    "            y_train.append(y)\n",
    "        \n",
    "        cur_heads = np.deg2rad(db.heading) + offset\n",
    "        for head in cur_heads:\n",
    "            heads_train.append(head)\n",
    "    \n",
    "    return np.array(x_train), np.array(y_train), np.array(heads_train)\n",
    "\n",
    "x_train, y_train, heads_train = get_training_data(data['experiments'][0]['training']['routes'])\n",
    "\n",
    "db_test = nb.Database(os.path.join(DB_ROOT, data['experiments'][0]['testing']['routes'][0]))\n",
    "offset = db_test.calculate_heading_offset(0.25)\n",
    "\n",
    "def get_test_data(db_test, x_train, y_train, heads_train, expt, offset):\n",
    "    heads = offset + np.deg2rad(expt['testing']['headings_deg'])\n",
    "    errors = []\n",
    "    for entry, (x, y, head) in enumerate(zip(db_test.x, db_test.y, heads)):\n",
    "        distances = np.abs(np.hypot(x - x_train, y - y_train))\n",
    "        best_match = np.argmin(distances)\n",
    "        \n",
    "        error = np.rad2deg(head - heads_train[best_match])\n",
    "        while error > 180:\n",
    "            error -= 360\n",
    "        while error <= -180:\n",
    "            error += 360\n",
    "        error = np.abs(error)\n",
    "        errors.append(error)\n",
    "\n",
    "    return errors\n",
    "\n",
    "\n",
    "errors = np.empty((len(db_test), 1 + len(data['experiments'])))\n",
    "data_pm = read_json('benchmark_pm.json')\n",
    "errors[:, 0] = get_test_data(db_test, x_train, y_train, heads_train, data_pm['experiments'][0], offset)\n",
    "for expt_idx, expt in enumerate(data['experiments']):\n",
    "    errors[:, 1+expt_idx] = get_test_data(db_test, x_train, y_train, heads_train, expt, offset)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(errors, whis=(5, 95))\n",
    "med = np.median(errors[:, 0])\n",
    "ax.hlines(med, *ax.get_xlim(), 'k', '--', alpha=0.75, zorder=100)\n",
    "ax.set_xticklabels(['(perfect memory)', *data['num_hiddens']])\n",
    "ax.set_xlabel('Number of hidden units')\n",
    "ax.set_ylabel('Error (°)')\n",
    "\n",
    "fig.savefig('figures/num_hidden_boxplot.svg', bbox_inches='tight');"
   ]
  }
 ]
}