GenerateGaussianData.ipynb

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      "name": "python"
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    "gpuClass": "standard"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Boiler Plate"
      ],
      "metadata": {
        "id": "WwRsd6J1IGd3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z6Zt9FhSgq5S",
        "outputId": "d190b9c2-1ef5-49ff-f8c1-a14cd1506e85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pickle\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import gc"
      ],
      "metadata": {
        "id": "sKgKBg6fhDS1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Seed value\n",
        "# Apparently you may use different seed values at each stage\n",
        "seed_value= 0\n",
        "\n",
        "# 1. Set the `PYTHONHASHSEED` environment variable at a fixed value\n",
        "import os\n",
        "os.environ['PYTHONHASHSEED']=str(seed_value)\n",
        "\n",
        "# 2. Set the `python` built-in pseudo-random generator at a fixed value\n",
        "import random\n",
        "random.seed(seed_value)\n",
        "\n",
        "# 3. Set the `numpy` pseudo-random generator at a fixed value\n",
        "np.random.seed(seed_value)\n",
        "\n",
        "# 4. Set the `tensorflow` pseudo-random generator at a fixed value\n",
        "import tensorflow as tf\n",
        "tf.random.set_seed(seed_value)\n",
        "# for later versions: \n",
        "# tf.compat.v1.set_random_seed(seed_value)\n",
        "\n",
        "# 5. Configure a new global `tensorflow` session\n",
        "from keras import backend as K\n",
        "# session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)\n",
        "# sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)\n",
        "# K.set_session(sess)\n",
        "# for later versions:\n",
        "session_conf = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)\n",
        "sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=session_conf)\n",
        "tf.compat.v1.keras.backend.set_session(sess)"
      ],
      "metadata": {
        "id": "ubePkDG1utZW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Load Orginal Data"
      ],
      "metadata": {
        "id": "KjRGu-CkINbX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Load in the clinical data for each of the deidentified patients\n",
        "patientData = pd.read_excel( \"/content/drive/My Drive/ECMOProj/patientDataCombo.xlsx\", engine='openpyxl', sheet_name = 0)\n",
        "\n",
        "patientData = patientData[patientData[\"sent\"] == \"y\"]\n",
        "patientData.index = patientData[\"record_id\"]\n"
      ],
      "metadata": {
        "id": "aFT2UAToBjgk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#All deidentified patient perfusion data. \n",
        "o = open(\"/content/drive/My Drive/ECMOProj/AllNorm.pkl\", \"rb\")\n",
        "totalNormed = pickle.load(o)"
      ],
      "metadata": {
        "id": "jDqR8l_LBjlP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Create time-based pairs"
      ],
      "metadata": {
        "id": "OsGXztyvIdTb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aliveIDs = list(set(patientData[patientData[\"ecmo_successful\"]==1][\"record_id\"]))\n",
        "decIDs = list(set(patientData[patientData[\"ecmo_successful\"]==0][\"record_id\"]))\n",
        "\n",
        "alive = pd.DataFrame(np.zeros([91,2]), columns = ['Id', 'Val'])\n",
        "dec = pd.DataFrame(np.zeros([29,2]), columns = ['Id', 'Val'])\n"
      ],
      "metadata": {
        "id": "6MN_LgQvCaKI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i,x in enumerate(aliveIDs):\n",
        "  x = int(x)\n",
        "  if str(x) not in totalNormed.keys():\n",
        "    continue\n",
        "  alive.iloc[i] = [x,totalNormed[str(x)][\"start_time_seconds\"].ffill().iloc[-1]]\n",
        "\n",
        "alive = alive[alive[\"Id\"]!=0]\n",
        "alive.sort_values(\"Val\",inplace=True)\n",
        "alive.index = np.arange(len(alive))\n",
        "alive[\"Pairs\"] = (alive.index/3).astype(int)\n",
        "\n",
        "for i,x in enumerate(decIDs):\n",
        "  x = int(x)\n",
        "  if str(x) not in totalNormed.keys():\n",
        "    continue\n",
        "  dec.iloc[i] = [x,totalNormed[str(x)][\"start_time_seconds\"].ffill().iloc[-1]]\n",
        "\n",
        "dec = dec[dec[\"Id\"]!=0]\n",
        "dec.sort_values(\"Val\",inplace=True)\n",
        "dec.index = np.arange(len(dec))\n",
        "dec[\"Pairs\"] = (dec.index/3).astype(int)"
      ],
      "metadata": {
        "id": "QR-5MzFqEnfp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "alive ## 30 Alive triplets\n",
        "dec ## 9 Dec triplets\n",
        "##So in total there should be 39*8 = 312 Gaussian Process Models\n",
        "## Dictionary with keys alive0, alive1, ..., alive44, dec0, dec1,..., dec13. Each keys points to a list of eight Gaussian process models, one for each feature"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Id           Val  Pairs\n",
              "0   2007.0  1.704800e+04      0\n",
              "1   2076.0  1.905600e+05      0\n",
              "2   1042.0  2.467800e+05      0\n",
              "3   2060.0  2.481480e+05      1\n",
              "4   2001.0  2.784968e+05      1\n",
              "5   2056.0  3.737475e+05      1\n",
              "6   1016.0  6.592200e+05      2\n",
              "7   1038.0  7.555710e+05      2\n",
              "8   2035.0  9.878689e+05      2\n",
              "9   2029.0  1.090007e+06      3\n",
              "10  2051.0  1.101232e+06      3\n",
              "11  1024.0  1.185812e+06      3\n",
              "12  1035.0  1.340440e+06      4\n",
              "13  1036.0  1.723529e+06      4\n",
              "14  2061.0  1.730250e+06      4\n",
              "15  1029.0  1.985643e+06      5\n",
              "16  2022.0  2.234859e+06      5\n",
              "17  1028.0  2.477205e+06      5\n",
              "18  2081.0  2.644750e+06      6\n",
              "19  1010.0  2.843595e+06      6\n",
              "20  2015.0  2.983396e+06      6\n",
              "21  2024.0  3.325765e+06      7\n",
              "22  1012.0  3.598869e+06      7\n",
              "23  2009.0  3.818769e+06      7\n",
              "24  1030.0  3.879075e+06      8\n",
              "25  1019.0  4.363756e+06      8\n",
              "26  1011.0  6.642090e+06      8\n",
              "27  1032.0  2.702541e+07      9"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Val</th>\n",
              "      <th>Pairs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2007.0</td>\n",
              "      <td>1.704800e+04</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2076.0</td>\n",
              "      <td>1.905600e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1042.0</td>\n",
              "      <td>2.467800e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2060.0</td>\n",
              "      <td>2.481480e+05</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2001.0</td>\n",
              "      <td>2.784968e+05</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2056.0</td>\n",
              "      <td>3.737475e+05</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1016.0</td>\n",
              "      <td>6.592200e+05</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1038.0</td>\n",
              "      <td>7.555710e+05</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2035.0</td>\n",
              "      <td>9.878689e+05</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2029.0</td>\n",
              "      <td>1.090007e+06</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2051.0</td>\n",
              "      <td>1.101232e+06</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1024.0</td>\n",
              "      <td>1.185812e+06</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1035.0</td>\n",
              "      <td>1.340440e+06</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1036.0</td>\n",
              "      <td>1.723529e+06</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2061.0</td>\n",
              "      <td>1.730250e+06</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1029.0</td>\n",
              "      <td>1.985643e+06</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2022.0</td>\n",
              "      <td>2.234859e+06</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1028.0</td>\n",
              "      <td>2.477205e+06</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2081.0</td>\n",
              "      <td>2.644750e+06</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1010.0</td>\n",
              "      <td>2.843595e+06</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2015.0</td>\n",
              "      <td>2.983396e+06</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2024.0</td>\n",
              "      <td>3.325765e+06</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1012.0</td>\n",
              "      <td>3.598869e+06</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2009.0</td>\n",
              "      <td>3.818769e+06</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1030.0</td>\n",
              "      <td>3.879075e+06</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1019.0</td>\n",
              "      <td>4.363756e+06</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1011.0</td>\n",
              "      <td>6.642090e+06</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1032.0</td>\n",
              "      <td>2.702541e+07</td>\n",
              "      <td>9</td>\n",
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          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Create Clinical Data"
      ],
      "metadata": {
        "id": "4rG86UHfInSz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "patColsNum = [\"ecmo_successful\", 'age', 'sex','bmi'] \n",
        "finalPat = patientData[patColsNum]"
      ],
      "metadata": {
        "id": "3-AxmiGuICfY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Create Gaussian Models"
      ],
      "metadata": {
        "id": "kcO_bqpyRP6D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "colList = list(finalPat.columns)\n",
        "colList.remove('ecmo_successful')\n",
        "XClin = finalPat[colList]\n",
        "XPerf = np.arange(2054).reshape(-1,1)"
      ],
      "metadata": {
        "id": "iHZjLuNSCaOy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "XClinScaled = pd.DataFrame(StandardScaler().fit_transform(XClin), index = XClin.index.astype(int))\n",
        "XPerfScaled = StandardScaler().fit_transform(XPerf)"
      ],
      "metadata": {
        "id": "32KuhZcHY0gc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.gaussian_process import GaussianProcessRegressor\n",
        "from sklearn.gaussian_process.kernels import RBF, WhiteKernel, ConstantKernel, DotProduct\n",
        "from copy import deepcopy"
      ],
      "metadata": {
        "id": "xT2lUdclcr5-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "columns = ['Delta P', 'PVen', 'Pump Flow', 'RPM', 'Sweep', 'flow_normalized', 'start_time_seconds', 'sweepflow_normalized']\n",
        "kernelReg = RBF(length_scale_bounds=\"fixed\",length_scale = 1)\n",
        "kernelLarge = RBF(length_scale_bounds=\"fixed\",length_scale = 100)"
      ],
      "metadata": {
        "id": "6imf7AzcTLJY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for x in range(alive[\"Pairs\"].max()+1):\n",
        "  triplet = list(alive[alive[\"Pairs\"]==x][\"Id\"].astype(int))\n",
        "  patientOne = totalNormed[str(triplet[0])]\n",
        "  patientTwo = totalNormed[str(triplet[1])]\n",
        "  patientThree = totalNormed[str(triplet[2])]\n",
        "  GaussianInputOne = np.append(np.repeat([XClinScaled.loc[triplet[0]]], 2054, axis = 0), XPerfScaled, axis = 1)\n",
        "  GaussianInputTwo = np.append(np.repeat([XClinScaled.loc[triplet[1]]], 2054, axis = 0), XPerfScaled, axis = 1)\n",
        "  GaussianInputThree = np.append(np.repeat([XClinScaled.loc[triplet[2]]], 2054, axis = 0), XPerfScaled, axis = 1)\n",
        "  X = np.append(np.append(GaussianInputOne, GaussianInputTwo, axis = 0), GaussianInputThree, axis = 0)\n",
        "\n",
        "  tempGaussReg = []\n",
        "  tempGaussLarge = []\n",
        "  for j in columns:\n",
        "    print(\"Triplet: \"+str(x)+\" \"+ \" Column: \" + j)\n",
        "    GaussianOutputOne = patientOne[j].ffill().fillna(value = 0)\n",
        "    GaussianOutputTwo = patientTwo[j].ffill().fillna(value = 0)\n",
        "    GaussianOutputThree = patientThree[j].ffill().fillna(value = 0)\n",
        "    Y = np.append(np.append(GaussianOutputOne, GaussianOutputTwo),GaussianOutputThree).reshape(-1,1)\n",
        "\n",
        "    gprReg = GaussianProcessRegressor(kernel = kernelReg, random_state=0, normalize_y = True).fit(X,Y)\n",
        "    gprLarge = GaussianProcessRegressor(kernel = kernelLarge, random_state=0, normalize_y = True).fit(X,Y)\n",
        "    tempGaussReg.append(deepcopy(gprReg))\n",
        "    tempGaussLarge.append(deepcopy(gprLarge))\n",
        "  f = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/alive{}.pkl\".format(x),\"wb\")\n",
        "  pickle.dump(tempGaussReg,f)\n",
        "  f.close()\n",
        "  f = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/LargeAlive{}.pkl\".format(x),\"wb\")\n",
        "  pickle.dump(tempGaussLarge,f)\n",
        "  f.close()\n",
        "  del tempGaussReg\n",
        "  del tempGaussLarge\n",
        "  while(gc.collect()>0):\n",
        "    continue\n",
        "\n"
      ],
      "metadata": {
        "id": "JTjH43_FcTPW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c02dceb-069d-462a-a56e-c8ac8ea1281b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Triplet: 0  Column: Delta P\n",
            "Triplet: 0  Column: PVen\n",
            "Triplet: 0  Column: Pump Flow\n",
            "Triplet: 0  Column: RPM\n",
            "Triplet: 0  Column: Sweep\n",
            "Triplet: 0  Column: flow_normalized\n",
            "Triplet: 0  Column: start_time_seconds\n",
            "Triplet: 0  Column: sweepflow_normalized\n",
            "Triplet: 1  Column: Delta P\n",
            "Triplet: 1  Column: PVen\n",
            "Triplet: 1  Column: Pump Flow\n",
            "Triplet: 1  Column: RPM\n",
            "Triplet: 1  Column: Sweep\n",
            "Triplet: 1  Column: flow_normalized\n",
            "Triplet: 1  Column: start_time_seconds\n",
            "Triplet: 1  Column: sweepflow_normalized\n",
            "Triplet: 2  Column: Delta P\n",
            "Triplet: 2  Column: PVen\n",
            "Triplet: 2  Column: Pump Flow\n",
            "Triplet: 2  Column: RPM\n",
            "Triplet: 2  Column: Sweep\n",
            "Triplet: 2  Column: flow_normalized\n",
            "Triplet: 2  Column: start_time_seconds\n",
            "Triplet: 2  Column: sweepflow_normalized\n",
            "Triplet: 3  Column: Delta P\n",
            "Triplet: 3  Column: PVen\n",
            "Triplet: 3  Column: Pump Flow\n",
            "Triplet: 3  Column: RPM\n",
            "Triplet: 3  Column: Sweep\n",
            "Triplet: 3  Column: flow_normalized\n",
            "Triplet: 3  Column: start_time_seconds\n",
            "Triplet: 3  Column: sweepflow_normalized\n",
            "Triplet: 4  Column: Delta P\n",
            "Triplet: 4  Column: PVen\n",
            "Triplet: 4  Column: Pump Flow\n",
            "Triplet: 4  Column: RPM\n",
            "Triplet: 4  Column: Sweep\n",
            "Triplet: 4  Column: flow_normalized\n",
            "Triplet: 4  Column: start_time_seconds\n",
            "Triplet: 4  Column: sweepflow_normalized\n",
            "Triplet: 5  Column: Delta P\n",
            "Triplet: 5  Column: PVen\n",
            "Triplet: 5  Column: Pump Flow\n",
            "Triplet: 5  Column: RPM\n",
            "Triplet: 5  Column: Sweep\n",
            "Triplet: 5  Column: flow_normalized\n",
            "Triplet: 5  Column: start_time_seconds\n",
            "Triplet: 5  Column: sweepflow_normalized\n",
            "Triplet: 6  Column: Delta P\n",
            "Triplet: 6  Column: PVen\n",
            "Triplet: 6  Column: Pump Flow\n",
            "Triplet: 6  Column: RPM\n",
            "Triplet: 6  Column: Sweep\n",
            "Triplet: 6  Column: flow_normalized\n",
            "Triplet: 6  Column: start_time_seconds\n",
            "Triplet: 6  Column: sweepflow_normalized\n",
            "Triplet: 7  Column: Delta P\n",
            "Triplet: 7  Column: PVen\n",
            "Triplet: 7  Column: Pump Flow\n",
            "Triplet: 7  Column: RPM\n",
            "Triplet: 7  Column: Sweep\n",
            "Triplet: 7  Column: flow_normalized\n",
            "Triplet: 7  Column: start_time_seconds\n",
            "Triplet: 7  Column: sweepflow_normalized\n",
            "Triplet: 8  Column: Delta P\n",
            "Triplet: 8  Column: PVen\n",
            "Triplet: 8  Column: Pump Flow\n",
            "Triplet: 8  Column: RPM\n",
            "Triplet: 8  Column: Sweep\n",
            "Triplet: 8  Column: flow_normalized\n",
            "Triplet: 8  Column: start_time_seconds\n",
            "Triplet: 8  Column: sweepflow_normalized\n",
            "Triplet: 9  Column: Delta P\n",
            "Triplet: 9  Column: PVen\n",
            "Triplet: 9  Column: Pump Flow\n",
            "Triplet: 9  Column: RPM\n",
            "Triplet: 9  Column: Sweep\n",
            "Triplet: 9  Column: flow_normalized\n",
            "Triplet: 9  Column: start_time_seconds\n",
            "Triplet: 9  Column: sweepflow_normalized\n",
            "Triplet: 10  Column: Delta P\n",
            "Triplet: 10  Column: PVen\n",
            "Triplet: 10  Column: Pump Flow\n",
            "Triplet: 10  Column: RPM\n",
            "Triplet: 10  Column: Sweep\n",
            "Triplet: 10  Column: flow_normalized\n",
            "Triplet: 10  Column: start_time_seconds\n",
            "Triplet: 10  Column: sweepflow_normalized\n",
            "Triplet: 11  Column: Delta P\n",
            "Triplet: 11  Column: PVen\n",
            "Triplet: 11  Column: Pump Flow\n",
            "Triplet: 11  Column: RPM\n",
            "Triplet: 11  Column: Sweep\n",
            "Triplet: 11  Column: flow_normalized\n",
            "Triplet: 11  Column: start_time_seconds\n",
            "Triplet: 11  Column: sweepflow_normalized\n",
            "Triplet: 12  Column: Delta P\n",
            "Triplet: 12  Column: PVen\n",
            "Triplet: 12  Column: Pump Flow\n",
            "Triplet: 12  Column: RPM\n",
            "Triplet: 12  Column: Sweep\n",
            "Triplet: 12  Column: flow_normalized\n",
            "Triplet: 12  Column: start_time_seconds\n",
            "Triplet: 12  Column: sweepflow_normalized\n",
            "Triplet: 13  Column: Delta P\n",
            "Triplet: 13  Column: PVen\n",
            "Triplet: 13  Column: Pump Flow\n",
            "Triplet: 13  Column: RPM\n",
            "Triplet: 13  Column: Sweep\n",
            "Triplet: 13  Column: flow_normalized\n",
            "Triplet: 13  Column: start_time_seconds\n",
            "Triplet: 13  Column: sweepflow_normalized\n",
            "Triplet: 14  Column: Delta P\n",
            "Triplet: 14  Column: PVen\n",
            "Triplet: 14  Column: Pump Flow\n",
            "Triplet: 14  Column: RPM\n",
            "Triplet: 14  Column: Sweep\n",
            "Triplet: 14  Column: flow_normalized\n",
            "Triplet: 14  Column: start_time_seconds\n",
            "Triplet: 14  Column: sweepflow_normalized\n",
            "Triplet: 15  Column: Delta P\n",
            "Triplet: 15  Column: PVen\n",
            "Triplet: 15  Column: Pump Flow\n",
            "Triplet: 15  Column: RPM\n",
            "Triplet: 15  Column: Sweep\n",
            "Triplet: 15  Column: flow_normalized\n",
            "Triplet: 15  Column: start_time_seconds\n",
            "Triplet: 15  Column: sweepflow_normalized\n",
            "Triplet: 16  Column: Delta P\n",
            "Triplet: 16  Column: PVen\n",
            "Triplet: 16  Column: Pump Flow\n",
            "Triplet: 16  Column: RPM\n",
            "Triplet: 16  Column: Sweep\n",
            "Triplet: 16  Column: flow_normalized\n",
            "Triplet: 16  Column: start_time_seconds\n",
            "Triplet: 16  Column: sweepflow_normalized\n",
            "Triplet: 17  Column: Delta P\n",
            "Triplet: 17  Column: PVen\n",
            "Triplet: 17  Column: Pump Flow\n",
            "Triplet: 17  Column: RPM\n",
            "Triplet: 17  Column: Sweep\n",
            "Triplet: 17  Column: flow_normalized\n",
            "Triplet: 17  Column: start_time_seconds\n",
            "Triplet: 17  Column: sweepflow_normalized\n",
            "Triplet: 18  Column: Delta P\n",
            "Triplet: 18  Column: PVen\n",
            "Triplet: 18  Column: Pump Flow\n",
            "Triplet: 18  Column: RPM\n",
            "Triplet: 18  Column: Sweep\n",
            "Triplet: 18  Column: flow_normalized\n",
            "Triplet: 18  Column: start_time_seconds\n",
            "Triplet: 18  Column: sweepflow_normalized\n",
            "Triplet: 19  Column: Delta P\n",
            "Triplet: 19  Column: PVen\n",
            "Triplet: 19  Column: Pump Flow\n",
            "Triplet: 19  Column: RPM\n",
            "Triplet: 19  Column: Sweep\n",
            "Triplet: 19  Column: flow_normalized\n",
            "Triplet: 19  Column: start_time_seconds\n",
            "Triplet: 19  Column: sweepflow_normalized\n",
            "Triplet: 20  Column: Delta P\n",
            "Triplet: 20  Column: PVen\n",
            "Triplet: 20  Column: Pump Flow\n",
            "Triplet: 20  Column: RPM\n",
            "Triplet: 20  Column: Sweep\n",
            "Triplet: 20  Column: flow_normalized\n",
            "Triplet: 20  Column: start_time_seconds\n",
            "Triplet: 20  Column: sweepflow_normalized\n",
            "Triplet: 21  Column: Delta P\n",
            "Triplet: 21  Column: PVen\n",
            "Triplet: 21  Column: Pump Flow\n",
            "Triplet: 21  Column: RPM\n",
            "Triplet: 21  Column: Sweep\n",
            "Triplet: 21  Column: flow_normalized\n",
            "Triplet: 21  Column: start_time_seconds\n",
            "Triplet: 21  Column: sweepflow_normalized\n",
            "Triplet: 22  Column: Delta P\n",
            "Triplet: 22  Column: PVen\n",
            "Triplet: 22  Column: Pump Flow\n",
            "Triplet: 22  Column: RPM\n",
            "Triplet: 22  Column: Sweep\n",
            "Triplet: 22  Column: flow_normalized\n",
            "Triplet: 22  Column: start_time_seconds\n",
            "Triplet: 22  Column: sweepflow_normalized\n",
            "Triplet: 23  Column: Delta P\n",
            "Triplet: 23  Column: PVen\n",
            "Triplet: 23  Column: Pump Flow\n",
            "Triplet: 23  Column: RPM\n",
            "Triplet: 23  Column: Sweep\n",
            "Triplet: 23  Column: flow_normalized\n",
            "Triplet: 23  Column: start_time_seconds\n",
            "Triplet: 23  Column: sweepflow_normalized\n",
            "Triplet: 24  Column: Delta P\n",
            "Triplet: 24  Column: PVen\n",
            "Triplet: 24  Column: Pump Flow\n",
            "Triplet: 24  Column: RPM\n",
            "Triplet: 24  Column: Sweep\n",
            "Triplet: 24  Column: flow_normalized\n",
            "Triplet: 24  Column: start_time_seconds\n",
            "Triplet: 24  Column: sweepflow_normalized\n",
            "Triplet: 25  Column: Delta P\n",
            "Triplet: 25  Column: PVen\n",
            "Triplet: 25  Column: Pump Flow\n",
            "Triplet: 25  Column: RPM\n",
            "Triplet: 25  Column: Sweep\n",
            "Triplet: 25  Column: flow_normalized\n",
            "Triplet: 25  Column: start_time_seconds\n",
            "Triplet: 25  Column: sweepflow_normalized\n",
            "Triplet: 26  Column: Delta P\n",
            "Triplet: 26  Column: PVen\n",
            "Triplet: 26  Column: Pump Flow\n",
            "Triplet: 26  Column: RPM\n",
            "Triplet: 26  Column: Sweep\n",
            "Triplet: 26  Column: flow_normalized\n",
            "Triplet: 26  Column: start_time_seconds\n",
            "Triplet: 26  Column: sweepflow_normalized\n",
            "Triplet: 27  Column: Delta P\n",
            "Triplet: 27  Column: PVen\n",
            "Triplet: 27  Column: Pump Flow\n",
            "Triplet: 27  Column: RPM\n",
            "Triplet: 27  Column: Sweep\n",
            "Triplet: 27  Column: flow_normalized\n",
            "Triplet: 27  Column: start_time_seconds\n",
            "Triplet: 27  Column: sweepflow_normalized\n",
            "Triplet: 28  Column: Delta P\n",
            "Triplet: 28  Column: PVen\n",
            "Triplet: 28  Column: Pump Flow\n",
            "Triplet: 28  Column: RPM\n",
            "Triplet: 28  Column: Sweep\n",
            "Triplet: 28  Column: flow_normalized\n",
            "Triplet: 28  Column: start_time_seconds\n",
            "Triplet: 28  Column: sweepflow_normalized\n",
            "Triplet: 29  Column: Delta P\n",
            "Triplet: 29  Column: PVen\n",
            "Triplet: 29  Column: Pump Flow\n",
            "Triplet: 29  Column: RPM\n",
            "Triplet: 29  Column: Sweep\n",
            "Triplet: 29  Column: flow_normalized\n",
            "Triplet: 29  Column: start_time_seconds\n",
            "Triplet: 29  Column: sweepflow_normalized\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for x in range(dec[\"Pairs\"].max()):\n",
        "  triplet = list(dec[dec[\"Pairs\"]==x][\"Id\"].astype(int))\n",
        "  patientOne = totalNormed[str(triplet[0])]\n",
        "  patientTwo = totalNormed[str(triplet[1])]\n",
        "  patientThree = totalNormed[str(triplet[2])]\n",
        "  GaussianInputOne = np.append(np.repeat([XClinScaled.loc[triplet[0]]], 2054, axis = 0), XPerfScaled, axis = 1)\n",
        "  GaussianInputTwo = np.append(np.repeat([XClinScaled.loc[triplet[1]]], 2054, axis = 0), XPerfScaled, axis = 1)\n",
        "  GaussianInputThree = np.append(np.repeat([XClinScaled.loc[triplet[2]]], 2054, axis = 0), XPerfScaled, axis = 1)\n",
        "  X = np.append(np.append(GaussianInputOne, GaussianInputTwo, axis = 0), GaussianInputThree, axis = 0)\n",
        "  \n",
        "  tempGaussReg = []\n",
        "  tempGaussLarge = []\n",
        "  for j in columns:\n",
        "    print(\"Triplet: \"+str(x)+\" \"+ \" Column: \" + j)\n",
        "    GaussianOutputOne = patientOne[j].ffill().fillna(value = 0)\n",
        "    GaussianOutputTwo = patientTwo[j].ffill().fillna(value = 0)\n",
        "    GaussianOutputThree = patientThree[j].ffill().fillna(value = 0)\n",
        "    Y = np.append(np.append(GaussianOutputOne, GaussianOutputTwo),GaussianOutputThree).reshape(-1,1)\n",
        "\n",
        "    gprReg = GaussianProcessRegressor(kernel = kernelReg, random_state=0, normalize_y = True).fit(X,Y)\n",
        "    gprLarge = GaussianProcessRegressor(kernel = kernelLarge, random_state=0, normalize_y = True).fit(X,Y)\n",
        "    tempGaussReg.append(deepcopy(gprReg))\n",
        "    tempGaussLarge.append(deepcopy(gprLarge))\n",
        "  f = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/dec{}.pkl\".format(x),\"wb\")\n",
        "  pickle.dump(tempGaussReg,f)\n",
        "  f.close()\n",
        "  f = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/LargeDec{}.pkl\".format(x),\"wb\")\n",
        "  pickle.dump(tempGaussLarge,f)\n",
        "  f.close()\n",
        "  del tempGaussReg\n",
        "  del tempGaussLarge\n",
        "  while(gc.collect()>0):\n",
        "    continue\n"
      ],
      "metadata": {
        "id": "53n463vBR75z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed45acf4-6ed4-416c-c6b3-7342965fd6f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Triplet: 0  Column: Delta P\n",
            "Triplet: 0  Column: PVen\n",
            "Triplet: 0  Column: Pump Flow\n",
            "Triplet: 0  Column: RPM\n",
            "Triplet: 0  Column: Sweep\n",
            "Triplet: 0  Column: flow_normalized\n",
            "Triplet: 0  Column: start_time_seconds\n",
            "Triplet: 0  Column: sweepflow_normalized\n",
            "Triplet: 1  Column: Delta P\n",
            "Triplet: 1  Column: PVen\n",
            "Triplet: 1  Column: Pump Flow\n",
            "Triplet: 1  Column: RPM\n",
            "Triplet: 1  Column: Sweep\n",
            "Triplet: 1  Column: flow_normalized\n",
            "Triplet: 1  Column: start_time_seconds\n",
            "Triplet: 1  Column: sweepflow_normalized\n",
            "Triplet: 2  Column: Delta P\n",
            "Triplet: 2  Column: PVen\n",
            "Triplet: 2  Column: Pump Flow\n",
            "Triplet: 2  Column: RPM\n",
            "Triplet: 2  Column: Sweep\n",
            "Triplet: 2  Column: flow_normalized\n",
            "Triplet: 2  Column: start_time_seconds\n",
            "Triplet: 2  Column: sweepflow_normalized\n",
            "Triplet: 3  Column: Delta P\n",
            "Triplet: 3  Column: PVen\n",
            "Triplet: 3  Column: Pump Flow\n",
            "Triplet: 3  Column: RPM\n",
            "Triplet: 3  Column: Sweep\n",
            "Triplet: 3  Column: flow_normalized\n",
            "Triplet: 3  Column: start_time_seconds\n",
            "Triplet: 3  Column: sweepflow_normalized\n",
            "Triplet: 4  Column: Delta P\n",
            "Triplet: 4  Column: PVen\n",
            "Triplet: 4  Column: Pump Flow\n",
            "Triplet: 4  Column: RPM\n",
            "Triplet: 4  Column: Sweep\n",
            "Triplet: 4  Column: flow_normalized\n",
            "Triplet: 4  Column: start_time_seconds\n",
            "Triplet: 4  Column: sweepflow_normalized\n",
            "Triplet: 5  Column: Delta P\n",
            "Triplet: 5  Column: PVen\n",
            "Triplet: 5  Column: Pump Flow\n",
            "Triplet: 5  Column: RPM\n",
            "Triplet: 5  Column: Sweep\n",
            "Triplet: 5  Column: flow_normalized\n",
            "Triplet: 5  Column: start_time_seconds\n",
            "Triplet: 5  Column: sweepflow_normalized\n",
            "Triplet: 6  Column: Delta P\n",
            "Triplet: 6  Column: PVen\n",
            "Triplet: 6  Column: Pump Flow\n",
            "Triplet: 6  Column: RPM\n",
            "Triplet: 6  Column: Sweep\n",
            "Triplet: 6  Column: flow_normalized\n",
            "Triplet: 6  Column: start_time_seconds\n",
            "Triplet: 6  Column: sweepflow_normalized\n",
            "Triplet: 7  Column: Delta P\n",
            "Triplet: 7  Column: PVen\n",
            "Triplet: 7  Column: Pump Flow\n",
            "Triplet: 7  Column: RPM\n",
            "Triplet: 7  Column: Sweep\n",
            "Triplet: 7  Column: flow_normalized\n",
            "Triplet: 7  Column: start_time_seconds\n",
            "Triplet: 7  Column: sweepflow_normalized\n",
            "Triplet: 8  Column: Delta P\n",
            "Triplet: 8  Column: PVen\n",
            "Triplet: 8  Column: Pump Flow\n",
            "Triplet: 8  Column: RPM\n",
            "Triplet: 8  Column: Sweep\n",
            "Triplet: 8  Column: flow_normalized\n",
            "Triplet: 8  Column: start_time_seconds\n",
            "Triplet: 8  Column: sweepflow_normalized\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Create Data from Gaussian Models"
      ],
      "metadata": {
        "id": "58mxfLRrcvPo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "X-KvAs6YeXTy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#There are 30 triplets of alive patients and 8 triplets of deceased patients. \n",
        "#For the training/val/test split, this can be divided into 18/6/6 and 3/1/1\n",
        "#For the training data set we can sample 3 each from 18 and 18 from 3 for a total of 108\n",
        "#For the validation/test data set we can sample 3 from 6 and 18 from 1 for a total of 36 \n",
        "#Which triplets go in each group can be randomized \n",
        "trainingTriplets = np.zeros([12,5,21])\n",
        "validationTriplets = np.zeros([12,5,7])\n",
        "testTriplets = np.zeros([12,5,7])\n",
        "\n",
        "trainingClinical = np.zeros([12,5,108, 3])\n",
        "validationClinical = np.zeros([12,5,36, 3])\n",
        "testClinical = np.zeros([12,5,36, 3])\n",
        "\n",
        "trainingPerfusion = np.zeros([12,5,108,2054, 8])\n",
        "validationPerfusion = np.zeros([12,5,36,2054, 8])\n",
        "testPerfusion = np.zeros([12,5,36,2054, 8])\n",
        "\n",
        "trainingPerfusionLarge = np.zeros([12,5,108,2054, 8])\n",
        "validationPerfusionLarge = np.zeros([12,5,36,2054, 8])\n",
        "testPerfusionLarge = np.zeros([12,5,36,2054, 8])"
      ],
      "metadata": {
        "id": "3-ed9eBXfdxT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Create the arrays of triplet IDs for each crossvalidation\n",
        "\n",
        "for x in range(12):\n",
        "  np.random.seed(x) #By using x as the seed, this makes it repeatable\n",
        "  aliveIndex = np.arange(30)\n",
        "  decIndex = np.arange(8)+30\n",
        "  np.random.shuffle(aliveIndex)\n",
        "  np.random.shuffle(decIndex)\n",
        "\n",
        "  #Truncate indices into a length divisible by 5 in order to makes the folds works\n",
        "  aliveIndex = aliveIndex[:int(len(aliveIndex)/5)*5]\n",
        "  decIndex = decIndex[:int(len(decIndex)/5)*5]\n",
        "\n",
        "  #Split the indices into five groups\n",
        "  aliveFolds = np.array(np.split(aliveIndex, 5))\n",
        "  decFolds = np.array(np.split(decIndex, 5))\n",
        "\n",
        "  for j in range(5):\n",
        "    testTriplets[x,j] = np.append(aliveFolds[j], decFolds[j])\n",
        "    validationTriplets[x,j] = np.append(aliveFolds[(j+1)%5], decFolds[(j+1)%5])\n",
        "    \n",
        "    leftOver = np.setdiff1d(np.setdiff1d(np.arange(5), j), (j+1)%5)\n",
        "    trainingTriplets[x,j] = np.append(aliveFolds[leftOver], decFolds[leftOver])\n",
        "\n"
      ],
      "metadata": {
        "id": "e06a5SzInFxb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Create the arrays of clinicalInfo for each crossvalidation\n",
        "\n",
        "np.random.seed(0)\n",
        "# clinicalInfo = np.append(np.random.normal(size = 30*3*3).reshape(90,3), np.random.normal(size = 8*18*3).reshape(144,3), axis =0)\n",
        "aliveNoise = XClinScaled.loc[alive[\"Id\"]].to_numpy() + np.random.normal(size = 30*3*3, scale = 0.01).reshape(90,3)\n",
        "decNoise = np.repeat(XClinScaled.loc[dec[dec[\"Pairs\"]<8][\"Id\"]].to_numpy(), 6, axis = 0) + np.random.normal(size = 18*8*3, scale = 0.01).reshape(144,3)\n",
        "clinicalInfo = np.append(aliveNoise, decNoise, axis = 0)\n",
        "\n",
        "for x in range(12):\n",
        "  for j in range(5):\n",
        "    triplePatient = (np.repeat([trainingTriplets[x,j,:18]*3], 3, axis = 0).T + np.repeat([np.arange(3)],18, axis = 0)).reshape(-1)\n",
        "    eighteenPatient = (np.repeat([(trainingTriplets[x,j,18:]-30)*18], 18, axis = 0).T + np.repeat([np.arange(18)],3, axis = 0) +90).reshape(-1)\n",
        "    allPatients = np.append(triplePatient,eighteenPatient)\n",
        "    trainingClinical[x,j] = clinicalInfo[allPatients.astype(int)]\n",
        "\n",
        "    triplePatient = (np.repeat([validationTriplets[x,j,:6]*3], 3, axis = 0).T + np.repeat([np.arange(3)],6, axis = 0)).reshape(-1)\n",
        "    eighteenPatient = (np.repeat([(validationTriplets[x,j,6:]-30)*18], 18, axis = 0).T + np.repeat([np.arange(18)],1, axis = 0) +90).reshape(-1)\n",
        "    allPatients = np.append(triplePatient,eighteenPatient)\n",
        "    validationClinical[x,j] = clinicalInfo[allPatients.astype(int)]\n",
        "\n",
        "    triplePatient = (np.repeat([testTriplets[x,j,:6]*3], 3, axis = 0).T + np.repeat([np.arange(3)],6, axis = 0)).reshape(-1)\n",
        "    eighteenPatient = (np.repeat([(testTriplets[x,j,6:]-30)*18], 18, axis = 0).T + np.repeat([np.arange(18)],1, axis = 0) +90).reshape(-1)\n",
        "    allPatients = np.append(triplePatient,eighteenPatient)\n",
        "    testClinical[x,j] = clinicalInfo[allPatients.astype(int)]\n",
        "\n",
        "    "
      ],
      "metadata": {
        "id": "R0EeEN46wqTz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "perfusionXInput = np.arange(2054).reshape(-1,1)\n",
        "perfusionXInput = StandardScaler().fit_transform(perfusionXInput)"
      ],
      "metadata": {
        "id": "TBr84f-Sd3pO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "allPatientPerfusion = np.zeros([30*3 + 8*18, 2054, 8])\n",
        "allPatientPerfusionLarge = np.zeros([30*3 + 8*18, 2054, 8])\n",
        "allPatientPerfusionSmall = np.zeros([30*3 + 8*18, 2054, 8])\n",
        "for x in range(30):\n",
        "  print(\"alive: \" + str(x))\n",
        "  o = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/alive{}.pkl\".format(x), \"rb\")\n",
        "  aliveGaussArray = pickle.load(o)\n",
        "  o = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/LargeAlive{}.pkl\".format(x), \"rb\")\n",
        "  aliveGaussArrayLarge = pickle.load(o)\n",
        "  for j in range(3):\n",
        "    clinicalInfoIndiv = clinicalInfo[x*3+j]\n",
        "    X = np.append(np.repeat([clinicalInfoIndiv], 2054, axis =0), perfusionXInput, axis = 1)\n",
        "    for k in range(8):\n",
        "      sample = aliveGaussArray[k].predict(X)\n",
        "      sampleLarge = aliveGaussArrayLarge[k].predict(X)\n",
        "      allPatientPerfusion[x*3+j,:,k] = sample.reshape(-1)\n",
        "      allPatientPerfusionLarge[x*3+j,:,k] = sampleLarge.reshape(-1)\n",
        "  del aliveGaussArray\n",
        "  del aliveGaussArrayLarge\n",
        "# \n",
        "for x in range(8):\n",
        "  print(\"dec: \" + str(x))\n",
        "  o = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/dec{}.pkl\".format(x), \"rb\")\n",
        "  decGaussArray = pickle.load(o)\n",
        "  o = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/LargeDec{}.pkl\".format(x), \"rb\")\n",
        "  decGaussArrayLarge = pickle.load(o)\n",
        "  for j in range(18):\n",
        "    clinicalInfoIndiv = clinicalInfo[90+x*18+j]\n",
        "    X = np.append(np.repeat([clinicalInfoIndiv], 2054, axis =0), perfusionXInput, axis = 1)\n",
        "    for k in range(8):\n",
        "      sample = decGaussArray[k].predict(X)\n",
        "      sampleLarge = decGaussArrayLarge[k].predict(X)\n",
        "      allPatientPerfusion[90+x*18+j,:,k] = sample.reshape(-1)\n",
        "      allPatientPerfusionLarge[90+x*18+j,:,k] = sampleLarge.reshape(-1)\n",
        "  del decGaussArray\n",
        "  del decGaussArrayLarge\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "_HjeAjAxc__g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb9f99a6-b1f4-4154-a79b-8a77f349b557"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alive: 0\n",
            "alive: 1\n",
            "alive: 2\n",
            "alive: 3\n",
            "alive: 4\n",
            "alive: 5\n",
            "alive: 6\n",
            "alive: 7\n",
            "alive: 8\n",
            "alive: 9\n",
            "alive: 10\n",
            "alive: 11\n",
            "alive: 12\n",
            "alive: 13\n",
            "alive: 14\n",
            "alive: 15\n",
            "alive: 16\n",
            "alive: 17\n",
            "alive: 18\n",
            "alive: 19\n",
            "alive: 20\n",
            "alive: 21\n",
            "alive: 22\n",
            "alive: 23\n",
            "alive: 24\n",
            "alive: 25\n",
            "alive: 26\n",
            "alive: 27\n",
            "alive: 28\n",
            "alive: 29\n",
            "dec: 0\n",
            "dec: 1\n",
            "dec: 2\n",
            "dec: 3\n",
            "dec: 4\n",
            "dec: 5\n",
            "dec: 6\n",
            "dec: 7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# f = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/allPatientPerfusionCloseClin.pkl\",\"wb\")\n",
        "# pickle.dump(allPatientPerfusion,f)\n",
        "# f.close()\n",
        "# f = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/allPatientPerfusionLargeCloseClin.pkl\",\"wb\")\n",
        "# pickle.dump(allPatientPerfusionLarge,f)\n",
        "# f.close()"
      ],
      "metadata": {
        "id": "OtX_38FNL8Sr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Can be found here: https://anonymous.4open.science/status/CHILData-4164"
      ],
      "metadata": {
        "id": "WltQ6cPiu_Lq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "o = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/allPatientPerfusionCloseClin.pkl\", \"rb\")\n",
        "allPatientPerfusion = pickle.load(o)\n",
        "o = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/allPatientPerfusionLargeCloseClin.pkl\", \"rb\")\n",
        "allPatientPerfusionLarge = pickle.load(o)"
      ],
      "metadata": {
        "id": "JxiaPpJqGG_8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for x in range(12):\n",
        "  for j in range(5):\n",
        "    triplePatient = (np.repeat([trainingTriplets[x,j,:18]*3], 3, axis = 0).T + np.repeat([np.arange(3)],18, axis = 0)).reshape(-1)\n",
        "    eighteenPatient = (np.repeat([(trainingTriplets[x,j,18:]-30)*18], 18, axis = 0).T + np.repeat([np.arange(18)],3, axis = 0) +90).reshape(-1)\n",
        "    allPatients = np.append(triplePatient,eighteenPatient)\n",
        "    trainingPerfusion[x,j] = allPatientPerfusion[allPatients.astype(int)]\n",
        "    trainingPerfusionLarge[x,j] = allPatientPerfusionLarge[allPatients.astype(int)]\n",
        "\n",
        "    triplePatient = (np.repeat([validationTriplets[x,j,:6]*3], 3, axis = 0).T + np.repeat([np.arange(3)],6, axis = 0)).reshape(-1)\n",
        "    eighteenPatient = (np.repeat([(validationTriplets[x,j,6:]-30)*18], 18, axis = 0).T + np.repeat([np.arange(18)],1, axis = 0) +90).reshape(-1)\n",
        "    allPatients = np.append(triplePatient,eighteenPatient)\n",
        "    validationPerfusion[x,j] = allPatientPerfusion[allPatients.astype(int)]\n",
        "    validationPerfusionLarge[x,j] = allPatientPerfusionLarge[allPatients.astype(int)]\n",
        "\n",
        "    triplePatient = (np.repeat([testTriplets[x,j,:6]*3], 3, axis = 0).T + np.repeat([np.arange(3)],6, axis = 0)).reshape(-1)\n",
        "    eighteenPatient = (np.repeat([(testTriplets[x,j,6:]-30)*18], 18, axis = 0).T + np.repeat([np.arange(18)],1, axis = 0) +90).reshape(-1)\n",
        "    allPatients = np.append(triplePatient,eighteenPatient)\n",
        "    testPerfusion[x,j] = allPatientPerfusion[allPatients.astype(int)]\n",
        "    testPerfusionLarge[x,j] = allPatientPerfusionLarge[allPatients.astype(int)]\n",
        "    "
      ],
      "metadata": {
        "id": "70Leb1OSeMoG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "#Standardize Data\n",
        "for x in range(12):\n",
        "  for j in range(5):\n",
        "    scal = MinMaxScaler()\n",
        "    trainingPerfusion[x,j] = scal.fit_transform(trainingPerfusion[x,j].reshape(-1,8)).reshape(trainingPerfusion[x,j].shape)\n",
        "    validationPerfusion[x,j] = scal.transform(validationPerfusion[x,j].reshape(-1,8)).reshape(validationPerfusion[x,j].shape)\n",
        "    testPerfusion[x,j] = scal.transform(testPerfusion[x,j].reshape(-1,8)).reshape(testPerfusion[x,j].shape)\n",
        "\n",
        "    scalLarge = MinMaxScaler()\n",
        "    trainingPerfusionLarge[x,j] = scalLarge.fit_transform(trainingPerfusionLarge[x,j].reshape(-1,8)).reshape(trainingPerfusionLarge[x,j].shape)\n",
        "    validationPerfusionLarge[x,j] = scalLarge.transform(validationPerfusionLarge[x,j].reshape(-1,8)).reshape(validationPerfusionLarge[x,j].shape)\n",
        "    testPerfusionLarge[x,j] = scalLarge.transform(testPerfusionLarge[x,j].reshape(-1,8)).reshape(testPerfusionLarge[x,j].shape)\n"
      ],
      "metadata": {
        "id": "XWwN3ZDQwT3t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# f = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/trainingPerfusionCloseClin.pkl\",\"wb\")\n",
        "# pickle.dump(trainingPerfusion,f)\n",
        "# f.close()\n",
        "# f = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/validationPerfusionCloseClin.pkl\",\"wb\")\n",
        "# pickle.dump(validationPerfusion,f)\n",
        "# f.close()\n",
        "# f = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/testPerfusionCloseClin.pkl\",\"wb\")\n",
        "# pickle.dump(testPerfusion,f)\n",
        "# f.close()\n",
        "\n",
        "# f = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/trainingPerfusionLargeCloseClin.pkl\",\"wb\")\n",
        "# pickle.dump(trainingPerfusionLarge,f)\n",
        "# f.close()\n",
        "# f = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/validationPerfusionLargeCloseClin.pkl\",\"wb\")\n",
        "# pickle.dump(validationPerfusionLarge,f)\n",
        "# f.close()\n",
        "# f = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/testPerfusionLargeCloseClin.pkl\",\"wb\")\n",
        "# pickle.dump(testPerfusionLarge,f)\n",
        "# f.close()\n"
      ],
      "metadata": {
        "id": "_a7Kf_drhYSV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# f = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/trainingClinicalCloseClin.pkl\",\"wb\")\n",
        "# pickle.dump(trainingClinical,f)\n",
        "# f.close()\n",
        "# f = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/validationClinicalCloseClin.pkl\",\"wb\")\n",
        "# pickle.dump(validationClinical,f)\n",
        "# f.close()\n",
        "# f = open(\"/content/drive/My Drive/ECMOProj/GaussianProcessForArtData/testClinicalCloseClin.pkl\",\"wb\")\n",
        "# pickle.dump(testClinical,f)\n",
        "# f.close()"
      ],
      "metadata": {
        "id": "QKcxOlQnjjhh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#TSNE"
      ],
      "metadata": {
        "id": "JkDF6cvNcxFq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "allPatientPerfusion.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ni03rJyDb_G8",
        "outputId": "3a911691-0f96-46c7-da61-fa283845bfc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(234, 2054, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "original = np.zeros([0,2054,8])\n",
        "for x in totalNormed:\n",
        "  original = np.append(original,[totalNormed[x].ffill().fillna(0)], axis = 0 )\n",
        "original.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CJQIoKi8b_Jj",
        "outputId": "547ccf89-cc93-4bf2-9d1c-281379a938e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(121, 2054, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.manifold import TSNE\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler"
      ],
      "metadata": {
        "id": "Ar2SpZK0I0A7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scal = MinMaxScaler()\n",
        "originalScal = scal.fit_transform(original.reshape(-1,8)).reshape(original.shape)\n",
        "allPatientPerfusionScal = scal.transform(allPatientPerfusion.reshape(-1,8)).reshape(allPatientPerfusion.shape)\n",
        "allPatientPerfusionLargeScal = scal.transform(allPatientPerfusionLarge.reshape(-1,8)).reshape(allPatientPerfusionLarge.shape)"
      ],
      "metadata": {
        "id": "rMTADNLJyrSO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "semiSetWithOrig = np.append(np.append(allPatientPerfusionScal.reshape(-1,2054*8), allPatientPerfusionLargeScal.reshape(-1,2054*8), axis = 0), originalScal.reshape(-1, 2054*8), axis = 0)\n",
        "semiSet = np.append(allPatientPerfusionScal.reshape(-1,2054*8), allPatientPerfusionLargeScal.reshape(-1,2054*8), axis = 0)\n"
      ],
      "metadata": {
        "id": "Ybh_DECWF9A5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "columns = ['Delta Pressure',\n",
        " 'Venous Pressure',\n",
        " 'Pump Flow',\n",
        " 'RPM',\n",
        " 'Sweep',\n",
        " 'Pump Flow Normalized to BMI',\n",
        " 'Seconds on ECMO',\n",
        " 'Sweep Normalized to Flow']"
      ],
      "metadata": {
        "id": "2HPfWvl8HMbK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "fig, ax = plt.subplots(nrows=2, ncols=4, figsize = (14,6))\n",
        "fig.suptitle('Comparison of Synthetic Data', fontsize = 16)\n",
        "fig.subplots_adjust(hspace=0.25, wspace = 0.25)\n",
        "for x in range(8):\n",
        "  ax[int(x/4)][x%4].plot(allPatientPerfusionLargeScal[1,:,x], label = \"L=100\")\n",
        "  ax[int(x/4)][x%4].plot(allPatientPerfusionScal[1,:,x], label = \"L=1\")\n",
        "  ax[int(x/4)][x%4].set_title(columns[x])\n",
        "  if(x%4 == 0):\n",
        "    ax[int(x/4)][x%4].set_ylabel(\"Scaled Val\")\n",
        "  if(int(x/4)==2):\n",
        "    ax[int(x/4)][x%4].set_xlabel(\"Time Step\")\n",
        "  if(x==0):\n",
        "    ax[int(x/4)][x%4].legend()\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "zKwW9niBDn34",
        "outputId": "d6d6af08-74fa-41f7-ad4f-d93316177106"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(nrows=2, ncols=4, figsize = (14,6))\n",
        "fig.suptitle('Original Data', fontsize = 16)\n",
        "fig.subplots_adjust(hspace=0.25, wspace = 0.25)\n",
        "for x in range(8):\n",
        "  ax[int(x/4)][x%4].plot(originalScal[0,:,x], label = \"Original\")\n",
        "  ax[int(x/4)][x%4].plot(allPatientPerfusionLargeScal[2,:,x], label = \"L=100\")\n",
        "  ax[int(x/4)][x%4].plot(allPatientPerfusionScal[2,:,x], label = \"L=1\")\n",
        "  ax[int(x/4)][x%4].set_title(columns[x])\n",
        "  if(x%4 == 0):\n",
        "    ax[int(x/4)][x%4].set_ylabel(\"Scaled Val\")\n",
        "  if(int(x/4)==2):\n",
        "    ax[int(x/4)][x%4].set_xlabel(\"Time Step\")\n",
        "  if(x==0):\n",
        "    ax[int(x/4)][x%4].legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "jZSWZmzuMSjB",
        "outputId": "f2cb395a-eb46-41e1-d925-830f8778e656"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols = totalNormed[\"1001\"].columns"
      ],
      "metadata": {
        "id": "VVYV6mAPqNrN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(nrows=2, ncols=4, figsize = (14,6))\n",
        "fig.suptitle('Original Data', fontsize = 18)\n",
        "fig.subplots_adjust(hspace=0.25)\n",
        "for x in range(8):\n",
        "  ax[int(x/4)][x%4].plot(totalNormed[\"1001\"][cols[x]], label = \"Five Successful\", c = \"green\")\n",
        "  ax[int(x/4)][x%4].plot(totalNormed[\"1003\"][cols[x]], c = \"green\")\n",
        "  ax[int(x/4)][x%4].plot(totalNormed[\"1005\"][cols[x]], c = \"green\")\n",
        "  ax[int(x/4)][x%4].plot(totalNormed[\"1007\"][cols[x]], c = \"green\")\n",
        "  ax[int(x/4)][x%4].plot(totalNormed[\"1008\"][cols[x]], c = \"green\")\n",
        "\n",
        "  ax[int(x/4)][x%4].plot(totalNormed[\"1019\"][cols[x]], label = \"Five Unsuccessful\", c = \"red\")\n",
        "  ax[int(x/4)][x%4].plot(totalNormed[\"1010\"][cols[x]], c = \"red\")\n",
        "  ax[int(x/4)][x%4].plot(totalNormed[\"1011\"][cols[x]], c = \"red\")\n",
        "  ax[int(x/4)][x%4].plot(totalNormed[\"1012\"][cols[x]], c = \"red\")\n",
        "  ax[int(x/4)][x%4].plot(totalNormed[\"1016\"][cols[x]], c = \"red\")\n",
        "  ax[int(x/4)][x%4].set_title(columns[x], fontsize = 14)\n",
        "  if(x%4 == 0):\n",
        "    ax[int(x/4)][x%4].set_ylabel(\"Val\")\n",
        "  if(int(x/4)==2):\n",
        "    ax[int(x/4)][x%4].set_xlabel(\"Time Step\")\n",
        "  if(x==0):\n",
        "    ax[int(x/4)][x%4].legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "ZU1E2lf6X26B",
        "outputId": "fa3ff40e-8419-452e-a766-218340926937"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TSNESynth = TSNE(random_state = 0).fit_transform(semiSetWithOrig)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RXH1JnFEzthz",
        "outputId": "94001f92-2d6b-498b-8bb4-46362f90b6f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/manifold/_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "allPatientPerfusionScal.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q7v6-2gEuTJw",
        "outputId": "99708b79-291d-4cdb-8765-deffce19f6b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(234, 2054, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TSNESynth.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Qy_nTPOuZG-",
        "outputId": "7b7975e3-c617-49f7-e87b-2322079ebc3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(589, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "colorScheme = ['red']*234\n",
        "colorSchemeGreen = ['green']*234\n",
        "colorSchemeBlue = ['blue']*121\n",
        "colorScheme = colorScheme+colorSchemeGreen+ colorSchemeBlue"
      ],
      "metadata": {
        "id": "0uUg6eoAuvlz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(nrows=1, ncols=4, figsize = (8,6))\n",
        "fig.suptitle('TSNE of Synthetic Data')\n",
        "fig.subplots_adjust(wspace=0)\n",
        "\n",
        "ax[1].scatter(TSNESynth[:234,0], TSNESynth[:234,1], c = \"red\", label = \"L=1\")\n",
        "ax[1].set_title(\"L=1 Data\")\n",
        "ax[1].set_xlabel(\"x1\")\n",
        "ax[2].scatter(TSNESynth[234:468,0], TSNESynth[234:468,1], c = \"green\", label = \"L=100\")\n",
        "ax[2].set_title(\"L=100 Data\")\n",
        "ax[2].set_xlabel(\"x1\")\n",
        "ax[3].scatter(TSNESynth[468:,0], TSNESynth[468:,1], c = \"blue\", label = \"Original\")\n",
        "ax[3].set_title(\"Original Data\")\n",
        "ax[3].set_xlabel(\"x1\")\n",
        "\n",
        "np.random.seed(0)\n",
        "randomIndices = np.arange(589).astype(int)\n",
        "np.random.shuffle(randomIndices)\n",
        "ax[0].scatter(TSNESynth[randomIndices,0], TSNESynth[randomIndices,1], c = [colorScheme[i] for i in randomIndices])\n",
        "ax[0].set_title(\"All Data\")\n",
        "ax[0].set_ylabel(\"x2\")\n",
        "ax[0].set_xlabel(\"x1\")\n",
        "\n",
        "\n",
        "for ax in fig.get_axes():\n",
        "    ax.label_outer()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "R1Wg6k4m2tMm",
        "outputId": "638a0d9b-b610-4d57-e962-e2d4aae6a50b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 4 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}