extract_activity_features.ipynb

{
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  {
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   "outputs": [
    {
     "ename": "PermissionError",
     "evalue": "[Errno 13] Permission denied: '/content'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 23\u001b[0m\n\u001b[1;32m     20\u001b[0m output_dir \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/content/preprocessed_data\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(output_dir):\n\u001b[0;32m---> 23\u001b[0m     \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     25\u001b[0m all_features \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame()  \n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m filepath \u001b[38;5;129;01min\u001b[39;00m glob\u001b[38;5;241m.\u001b[39mglob(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(input_dir, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)):\n",
      "File \u001b[0;32m~/anaconda3/envs/adhd/lib/python3.8/os.py:213\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m    211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mand\u001b[39;00m tail \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39mexists(head):\n\u001b[1;32m    212\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 213\u001b[0m         \u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexist_ok\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    214\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n\u001b[1;32m    215\u001b[0m         \u001b[38;5;66;03m# Defeats race condition when another thread created the path\u001b[39;00m\n\u001b[1;32m    216\u001b[0m         \u001b[38;5;28;01mpass\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/adhd/lib/python3.8/os.py:223\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    222\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 223\u001b[0m     \u001b[43mmkdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    224\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[1;32m    225\u001b[0m     \u001b[38;5;66;03m# Cannot rely on checking for EEXIST, since the operating system\u001b[39;00m\n\u001b[1;32m    226\u001b[0m     \u001b[38;5;66;03m# could give priority to other errors like EACCES or EROFS\u001b[39;00m\n\u001b[1;32m    227\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m exist_ok \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39misdir(name):\n",
      "\u001b[0;31mPermissionError\u001b[0m: [Errno 13] Permission denied: '/content'"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import csv\n",
    "import glob\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from tsfresh import extract_features\n",
    "\n",
    "def read_activity_file(filepath, patient_id):\n",
    "    data = []\n",
    "    with open(filepath) as f:\n",
    "        csv_reader = csv.reader(f, delimiter=\";\")\n",
    "        next(csv_reader)\n",
    "        for line in csv_reader:\n",
    "            data.append([datetime.strptime(line[0], \"%m-%d-%Y %H:%M\").timestamp(), int(line[1].split(\" \")[0])])\n",
    "    data = pd.DataFrame(data, columns=[\"TIME\", \"ACC\"])\n",
    "    data[\"ID\"] = patient_id\n",
    "    return data\n",
    "\n",
    "input_dir = \"/content/activity_data\"\n",
    "output_dir = os.path.join(\"/preprocessed_data\")\n",
    "\n",
    "if not os.path.exists(output_dir):\n",
    "    os.makedirs(output_dir)\n",
    "\n",
    "all_features = pd.DataFrame()  \n",
    "\n",
    "for filepath in glob.glob(os.path.join(input_dir, \"*.csv\")):\n",
    "    print(\"Reading %s\" % filepath)\n",
    "    patient_id = os.path.splitext(os.path.basename(filepath))[0]\n",
    "    patient_id = patient_id.split(\"_\")[-1]\n",
    "\n",
    "    #print(patient_id)\n",
    "    data = read_activity_file(filepath, patient_id)\n",
    "    features = extract_features(data, column_id=\"ID\", column_value=\"ACC\", column_sort=\"TIME\", n_jobs=0, show_warnings=False)\n",
    "\n",
    "    if \"ID\" not in features.columns:\n",
    "        features[\"ID\"] = patient_id\n",
    "\n",
    "    all_features = pd.concat([all_features, features])\n",
    "\n",
    "all_features.set_index(\"ID\", inplace=True)\n",
    "\n",
    "\n",
    "all_features.to_csv(os.path.join(output_dir, \"activity_features.csv\"), sep=\";\")"
   ]
  }
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