testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8d81aabb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "757c0e93",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = np.load('padded_train_data_los.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9cc99454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14681, 61, 18)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8f2d77c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "class ReadmissionReader:\n",
    "    def __init__(self, root_dir, mode='train', seed=42):\n",
    "        assert mode in ['train', 'test'], \"Mode must be 'train' or 'test'\"\n",
    "        self.root_dir = root_dir\n",
    "        self.mode_dir = os.path.join(root_dir, mode)\n",
    "        self.seed = seed\n",
    "        self.patient_files = self._get_patient_files()\n",
    "        np.random.seed(seed)\n",
    "        np.random.shuffle(self.patient_files)\n",
    "\n",
    "    def _get_patient_files(self):\n",
    "        patient_files = []\n",
    "        for patient in os.listdir(self.mode_dir):\n",
    "            patient_dir = os.path.join(self.mode_dir, patient)\n",
    "            if os.path.isdir(patient_dir):\n",
    "                patient_files.append(patient_dir)\n",
    "        return patient_files\n",
    "\n",
    "    def get_number_of_examples(self):\n",
    "        return len(self.patient_files)\n",
    "\n",
    "    def read_example(self, i):\n",
    "        patient_dir = self.patient_files[i]\n",
    "        patient_id = os.path.basename(patient_dir)\n",
    "        episode_files = [f for f in os.listdir(patient_dir) if f.startswith('episode')]\n",
    "        episode_timeseries_files = [f for f in episode_files if f.endswith('_timeseries.csv')]\n",
    "        episode_timeseries_files.sort()\n",
    "\n",
    "        # Check if there are any episode timeseries files\n",
    "        if not episode_timeseries_files:\n",
    "            return None\n",
    "\n",
    "        # Load the first episode timeseries data\n",
    "        episode1_timeseries_file = episode_timeseries_files[0]\n",
    "        episode1_timeseries_path = os.path.join(patient_dir, episode1_timeseries_file)\n",
    "        episode1_timeseries_data = pd.read_csv(episode1_timeseries_path)\n",
    "        episode1_timeseries_data = episode1_timeseries_data.fillna('')\n",
    "        # Get the header\n",
    "        header = episode1_timeseries_data.columns.tolist()\n",
    "\n",
    "        # Check if there are more than one episode timeseries files\n",
    "        y_label = 1 if len(episode_timeseries_files) > 1 else 0\n",
    "\n",
    "        # Get the total time\n",
    "        total_time = float(episode1_timeseries_data.iloc[-1, 0])\n",
    "\n",
    "        example = {\n",
    "            'X': episode1_timeseries_data.values.astype(str),\n",
    "            't': total_time,\n",
    "            'y': y_label,\n",
    "            'header': header,\n",
    "            'name': f\"{patient_id}_{episode1_timeseries_file}\"\n",
    "        }\n",
    "\n",
    "        return example\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9413004d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_dir = r\"C:\\Users\\samit\\OneDrive\\Desktop\\Datasets\\data\\decompensation\"\n",
    "\n",
    "root_dir = r'C:\\Users\\samit\\OneDrive\\Desktop\\Datasets\\data\\root'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2e91a6cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: 28728\n",
      "Number of testing examples: 5070\n"
     ]
    }
   ],
   "source": [
    "train_reader = ReadmissionReader(root_dir, mode='train')\n",
    "test_reader = ReadmissionReader(root_dir, mode='test')\n",
    "print(\"Number of training examples:\", train_reader.get_number_of_examples())\n",
    "print(\"Number of testing examples:\", test_reader.get_number_of_examples())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "48f83f49",
   "metadata": {},
   "outputs": [],
   "source": [
    "example = train_reader.read_example(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b5b7e0aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "630"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(example['X'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3dcc605a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_label =np.load('padded_test_data_pheno.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9070bd97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.        , -0.7203045 ,  0.        ,  5.        ,\n",
       "        5.        ,  0.34874293, 11.        , -3.613865  , -1.6303232 ,\n",
       "        0.        , -0.505136  ,  0.3877461 , -0.2615161 ,  0.09907782,\n",
       "        0.38630238,  0.        ,  0.        ], dtype=float32)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_label[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48aa807d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1266b85e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34fd6aab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "96b6014f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\samit\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get the total number of examples\n",
    "total_examples = train_reader.get_number_of_examples()\n",
    "\n",
    "# Initialize a list to store the lengths of timeseries data for each patient\n",
    "lengths = []\n",
    "duration = []\n",
    "# Loop through all the examples\n",
    "for i in range(total_examples):\n",
    "    example = train_reader.read_example(i)\n",
    "    # Check if example is not None before accessing its keys\n",
    "    if example is not None:\n",
    "        # Append the length of the 'X' array to the lengths list\n",
    "        lengths.append(len(example['X']))\n",
    "        duration.append(example['t'])\n",
    "# Plotting the distribution of lengths\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.distplot(lengths, bins=30, kde=True)\n",
    "plt.title('Distribution of Length of Timeseries Data for All Patients')\n",
    "plt.xlabel('Length of Timeseries Data')\n",
    "plt.ylabel('Density')\n",
    "plt.show\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "baaf5bd3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2fc3668",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6d4d7362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set Seaborn style\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Calculate the 90th percentile\n",
    "percentile_90 = np.percentile(lengths, 90)\n",
    "\n",
    "# Filter the lengths list to include only values less than or equal to the 90th percentile value\n",
    "filtered_lengths = [length for length in lengths if length <= percentile_90]\n",
    "\n",
    "# Calculate the average of the filtered lengths\n",
    "average_length = np.mean(filtered_lengths)\n",
    "\n",
    "# Create the vertical box plot with a bright gradient color\n",
    "plt.figure(figsize=(7, 12))\n",
    "sns.boxplot(y=filtered_lengths, palette=\"viridis\", width=0.3)\n",
    "\n",
    "# Plot a horizontal line for the average and label it\n",
    "plt.axhline(y=average_length, color='red', linestyle='--', label=f'Average: {average_length:.2f}')\n",
    "plt.legend(loc='upper right', fontsize=12)\n",
    "\n",
    "plt.title('Box Plot of Length of Timeseries Data (Up to 90th Percentile)', fontsize=16)\n",
    "plt.ylabel('Length of Timeseries Data', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "sns.despine(left=True)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8b0315e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The length at the 90th percentile is: 138.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Calculate the 90th percentile\n",
    "percentile_90 = np.percentile(lengths, 75)\n",
    "\n",
    "print(f\"The length at the 90th percentile is: {percentile_90}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d03de83e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value 142 captures the 75.98th percentile of the data.\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import percentileofscore\n",
    "\n",
    "# Calculate the percentile rank of the value 142 in the filtered_lengths list\n",
    "percentile_rank = percentileofscore(lengths, 142)\n",
    "\n",
    "print(f\"The value 142 captures the {percentile_rank:.2f}th percentile of the data.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e132241b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set Seaborn style\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Calculate the 90th percentile\n",
    "percentile_90 = np.percentile(lengths, 90)\n",
    "\n",
    "# Calculate the interquartile range (IQR)\n",
    "Q1 = np.percentile(lengths, 25)\n",
    "Q3 = np.percentile(lengths, 75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Calculate the whis value such that it corresponds to the 90th percentile\n",
    "whis_value = (percentile_90 - Q1) / IQR\n",
    "\n",
    "# Create the vertical box plot with a bright gradient color without showing outliers above the 90th percentile\n",
    "plt.figure(figsize=(7, 5))\n",
    "sns.boxplot(y=lengths, palette=\"viridis\", width=0.3, whis=whis_value, showfliers=False)\n",
    "\n",
    "# Plot a horizontal line for the average and label it\n",
    "average_length = np.mean(lengths)\n",
    "plt.axhline(y=average_length, color='red', linestyle='--', label=f'Average: {average_length:.2f}')\n",
    "plt.legend(loc='upper right', fontsize=12)\n",
    "\n",
    "plt.title('Box Plot of Length of Timeseries Data', fontsize=16)\n",
    "plt.ylabel('Length of Timeseries Data', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "sns.despine(left=True)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bb16a92d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44020"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "93f42a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "padded_test_data_read = np.load('padded_test_data.npy')\n",
    "padded_train_data_read = np.load('padded_train_data.npy')\n",
    "test_data_static_read = np.load('test_data_static.npy')\n",
    "test_labels_read = np.load('test_labels.npy')\n",
    "train_data_static_read = np.load('train_data_static.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "58b1e934",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5066, 142, 18)\n",
      "(28692, 142, 18)\n",
      "(5066, 7)\n",
      "(5066,)\n",
      "(14681, 7)\n"
     ]
    }
   ],
   "source": [
    "print(padded_test_data_read.shape)\n",
    "print(padded_train_data_read.shape)\n",
    "print(test_data_static_read.shape)\n",
    "print(test_labels_read.shape)\n",
    "print(train_data_static_read.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f398959c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "padded_train_data_los = np.load('padded_train_data_los.npy')\n",
    "#train_data_static_los = np.load('train_data_static_los.npy')\n",
    "test_data_static_los = np.load('test_data_static_los.npy')\n",
    "padded_test_data_los = np.load('padded_test_data_los.npy')\n",
    "test_labels_los = np.load('test_labels_los.npy')\n",
    "train_labels_los = np.load('train_labels_los.npy')\n",
    "padded_val_data_los = np.load('padded_val_data_los.npy')\n",
    "val_data_static_los = np.load('val_data_static_los.npy')\n",
    "val_data_static_los = np.load('val_labels_los.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a6feb2b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14681, 61, 18)\n",
      "(3236, 7)\n",
      "(3236, 61, 18)\n",
      "(14681,)\n",
      "(3236,)\n",
      "(3222, 61, 18)\n",
      "(3222,)\n",
      "(3222,)\n"
     ]
    }
   ],
   "source": [
    "print(padded_train_data_los.shape)\n",
    "#print(train_data_static_los.shape)\n",
    "print(test_data_static_los.shape)\n",
    "print(padded_test_data_los.shape)\n",
    "print(train_labels_los.shape)\n",
    "print(test_labels_los.shape)\n",
    "print(padded_val_data_los.shape)\n",
    "print(val_data_static_los.shape)\n",
    "print(val_data_static_los.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c328257f",
   "metadata": {},
   "outputs": [],
   "source": [
    "padded_test_data_pheno = np.load('padded_test_data_pheno.npy')\n",
    "padded_train_data_pheno = np.load('padded_train_data_pheno.npy')\n",
    "test_data_static_pheno = np.load('test_data_static_pheno.npy')\n",
    "test_labels_pheno = np.load('test_labels_pheno.npy')\n",
    "train_labels_pheno = np.load('train_labels_pheno.npy')\n",
    "train_data_static_pheno = np.load('train_data_static_pheno.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "05d8b4c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6281, 142, 18)\n",
      "(35621, 142, 18)\n",
      "(6281, 8)\n",
      "(6281, 25)\n",
      "(35621, 25)\n",
      "(35621, 8)\n"
     ]
    }
   ],
   "source": [
    "print(padded_test_data_pheno.shape)\n",
    "print(padded_train_data_pheno.shape)\n",
    "print(test_data_static_pheno.shape)\n",
    "print(test_labels_pheno.shape)\n",
    "print(train_labels_pheno.shape)\n",
    "print(train_data_static_pheno.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "04668e51",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import random\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "class Reader(object):\n",
    "    def __init__(self, dataset_dir, listfile=None):\n",
    "        self._dataset_dir = dataset_dir\n",
    "        self._current_index = 0\n",
    "        if listfile is None:\n",
    "            listfile_path = os.path.join(dataset_dir, \"listfile.csv\")\n",
    "        else:\n",
    "            listfile_path = listfile\n",
    "        with open(listfile_path, \"r\") as lfile:\n",
    "            self._data = lfile.readlines()\n",
    "        self._listfile_header = self._data[0]\n",
    "        self._data = self._data[1:]\n",
    "\n",
    "    def get_number_of_examples(self):\n",
    "        return len(self._data)\n",
    "\n",
    "    def random_shuffle(self, seed=None):\n",
    "        if seed is not None:\n",
    "            random.seed(seed)\n",
    "        random.shuffle(self._data)\n",
    "\n",
    "    def read_example(self, index):\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    def read_next(self):\n",
    "        to_read_index = self._current_index\n",
    "        self._current_index += 1\n",
    "        if self._current_index == self.get_number_of_examples():\n",
    "            self._current_index = 0\n",
    "        return self.read_example(to_read_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0970f5b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "class InHospitalMortalityReader(Reader):\n",
    "    def __init__(self, dataset_dir, listfile=None, period_length=48.0):\n",
    "        \"\"\" Reader for in-hospital moratality prediction task.\n",
    "\n",
    "        :param dataset_dir:   Directory where timeseries files are stored.\n",
    "        :param listfile:      Path to a listfile. If this parameter is left `None` then\n",
    "                              `dataset_dir/listfile.csv` will be used.\n",
    "        :param period_length: Length of the period (in hours) from which the prediction is done.\n",
    "        \"\"\"\n",
    "        Reader.__init__(self, dataset_dir, listfile)\n",
    "        self._data = [line.split(',') for line in self._data]\n",
    "        self._data = [(x, int(y)) for (x, y) in self._data]\n",
    "        self._period_length = period_length\n",
    "\n",
    "    def _read_timeseries(self, ts_filename):\n",
    "        ret = []\n",
    "        with open(os.path.join(self._dataset_dir, ts_filename), \"r\") as tsfile:\n",
    "            header = tsfile.readline().strip().split(',')\n",
    "            assert header[0] == \"Hours\"\n",
    "            for line in tsfile:\n",
    "                mas = line.strip().split(',')\n",
    "                ret.append(np.array(mas))\n",
    "        return (np.stack(ret), header)\n",
    "\n",
    "    def read_example(self, index):\n",
    "        \"\"\" Reads the example with given index.\n",
    "\n",
    "        :param index: Index of the line of the listfile to read (counting starts from 0).\n",
    "        :return: Dictionary with the following keys:\n",
    "            X : np.array\n",
    "                2D array containing all events. Each row corresponds to a moment.\n",
    "                First column is the time and other columns correspond to different\n",
    "                variables.\n",
    "            t : float\n",
    "                Length of the data in hours. Note, in general, it is not equal to the\n",
    "                timestamp of last event.\n",
    "            y : int (0 or 1)\n",
    "                In-hospital mortality.\n",
    "            header : array of strings\n",
    "                Names of the columns. The ordering of the columns is always the same.\n",
    "            name: Name of the sample.\n",
    "        \"\"\"\n",
    "        if index < 0 or index >= len(self._data):\n",
    "            raise ValueError(\"Index must be from 0 (inclusive) to number of lines (exclusive).\")\n",
    "\n",
    "        name = self._data[index][0]\n",
    "        t = self._period_length\n",
    "        y = self._data[index][1]\n",
    "        (X, header) = self._read_timeseries(name)\n",
    "\n",
    "        return {\"X\": X,\n",
    "                \"t\": t,\n",
    "                \"y\": y,\n",
    "                \"header\": header,\n",
    "                \"name\": name}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "bcdcccc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_dir = \"C:\\\\Users\\\\samit\\\\OneDrive\\\\Desktop\\\\Datasets\\\\data\\\\in-hospital-mortality\"\n",
    "root_dir = r'C:\\Users\\samit\\OneDrive\\Desktop\\Datasets\\data\\root'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7b8f6213",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_reader = InHospitalMortalityReader(dataset_dir=os.path.join(dataset_dir, 'train'),\n",
    "                                         listfile=os.path.join(dataset_dir, 'train_listfile.csv'),\n",
    "                                         period_length=48.0)\n",
    "\n",
    "\n",
    "val_reader = InHospitalMortalityReader(dataset_dir=os.path.join(dataset_dir, 'train'),\n",
    "                                       listfile=os.path.join(dataset_dir, 'val_listfile.csv'),\n",
    "                                       period_length=48.0)\n",
    "\n",
    "test_reader = InHospitalMortalityReader(dataset_dir=os.path.join(dataset_dir, 'test'),\n",
    "                                       listfile=os.path.join(dataset_dir, 'test_listfile.csv'),\n",
    "                                       period_length=48.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e4c06324",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "def get_los(data, root_dir, mode='train'):\n",
    "    # Extract the patient ID and episode number from the 'name' field\n",
    "    name_parts = data['name'].split('_')\n",
    "    patient_id = name_parts[0]\n",
    "    episode_number = name_parts[1]\n",
    "\n",
    "    # Construct the path to the patient folder\n",
    "    patient_folder = os.path.join(root_dir, mode, patient_id)\n",
    "\n",
    "    # Load the episode data\n",
    "    episode_path = os.path.join(patient_folder, f'{episode_number}.csv')\n",
    "    episode_data = pd.read_csv(episode_path)\n",
    "\n",
    "    # Get the ICU stay ID from the episode data\n",
    "    icu_stay_id = episode_data['Icustay'].values[0]\n",
    "\n",
    "    # Load the stays data\n",
    "    stays_path = os.path.join(patient_folder, 'stays.csv')\n",
    "    stays_data = pd.read_csv(stays_path)\n",
    "\n",
    "    # Get the length of stay for the specific ICU stay ID\n",
    "    los = stays_data[stays_data['ICUSTAY_ID'] == int(icu_stay_id)]['LOS'].values[0]\n",
    "\n",
    "    return los"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6c715ff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = np.load('train_labels_los.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c76c0314",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\samit\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(labels)\n",
    "plt.title('Distribution of Length of Stays')\n",
    "plt.xlabel('Total Time Duration (Days)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbbed557",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}