ventilator_gb_rescaling_eda_V2.ipynb

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    "kernelspec": {
      "language": "python",
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python",
      "version": "3.7.10",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "colab": {
      "name": "ventilator-gb-rescaling-eda-V2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bobbercheng/ventilator-pressure-prediction/blob/master/ventilator_gb_rescaling_eda_V2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yh6Zg50KJQBQ"
      },
      "source": [
        "V1: Keep R/C, improve\n",
        "\n",
        "V2: Add R**2, no improve\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uslq2jL49nGk"
      },
      "source": [
        "# Update pandas version for Colab TPU\n",
        "#!pip install pandas==1.3.2"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RBJlpUNI9com"
      },
      "source": [
        "## Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXzLriPA9coq"
      },
      "source": [
        "try:\n",
        "  import google.colab\n",
        "  IN_COLAB = True\n",
        "except:\n",
        "  IN_COLAB = False"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9esF__aG9cor",
        "outputId": "0f8f609d-ad56-434f-fe7f-74833f0e75ed"
      },
      "source": [
        "if IN_COLAB:\n",
        "  from google.colab import drive\n",
        "  drive.mount(\"/gdrive\", force_remount=True)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZ_f3Z2M9cos",
        "outputId": "a82b1994-cab6-494d-fac4-c0737002f6be"
      },
      "source": [
        "if IN_COLAB:\n",
        "  !pip install kaggle\n",
        "  !mkdir /root/.kaggle\n",
        "  !cp /gdrive/MyDrive/ventilator-pressure-prediction/kaggle.json /root/.kaggle\n",
        "  !kaggle competitions download -c ventilator-pressure-prediction\n",
        "  !mkdir -p ../input/ventilator-pressure-prediction\n",
        "  !unzip '*.zip' -d ../input/ventilator-pressure-prediction\n",
        "  !ls ../input/ventilator-pressure-prediction"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (5.0.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.62.3)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2021.5.30)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n",
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "test.csv.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "sample_submission.csv.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "train.csv.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "Archive:  train.csv.zip\n",
            "replace ../input/ventilator-pressure-prediction/train.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n",
            "\n",
            "Archive:  sample_submission.csv.zip\n",
            "\n",
            "Archive:  test.csv.zip\n",
            "\n",
            "3 archives were successfully processed.\n",
            "sample_submission.csv  test.csv  train.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-21T17:30:55.782617Z",
          "iopub.execute_input": "2021-10-21T17:30:55.783998Z",
          "iopub.status.idle": "2021-10-21T17:30:55.793669Z",
          "shell.execute_reply.started": "2021-10-21T17:30:55.783937Z",
          "shell.execute_reply": "2021-10-21T17:30:55.792667Z"
        },
        "trusted": true,
        "id": "1TfjZidC9cos"
      },
      "source": [
        "import gc\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random\n",
        "import os\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
        "from tensorflow.keras.layers import Bidirectional, LSTM\n",
        "from tensorflow.keras.layers import Dense, Dropout, Input\n",
        "from tensorflow.keras.layers import Concatenate, Add, GRU\n",
        "\n",
        "# Random Seed Initialize\n",
        "RANDOM_SEED = 42\n",
        "\n",
        "def seed_everything(seed=RANDOM_SEED):\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "    random.seed(seed)\n",
        "    tf.random.set_seed(seed)\n",
        "    \n",
        "seed_everything()"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XycyrO7g9cot"
      },
      "source": [
        "## Load datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-21T17:24:55.352925Z",
          "iopub.execute_input": "2021-10-21T17:24:55.353455Z",
          "iopub.status.idle": "2021-10-21T17:25:06.101910Z",
          "shell.execute_reply.started": "2021-10-21T17:24:55.353418Z",
          "shell.execute_reply": "2021-10-21T17:25:06.100969Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "p_vUMe_39cot",
        "outputId": "d2bdcb90-59dc-4ad4-820d-e6d633f1b921"
      },
      "source": [
        "train_df = pd.read_csv('../input/ventilator-pressure-prediction/train.csv')\n",
        "print(f\"train_df: {train_df.shape}\")\n",
        "train_df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train_df: (6036000, 8)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>breath_id</th>\n",
              "      <th>R</th>\n",
              "      <th>C</th>\n",
              "      <th>time_step</th>\n",
              "      <th>u_in</th>\n",
              "      <th>u_out</th>\n",
              "      <th>pressure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "   id  breath_id   R   C  time_step       u_in  u_out   pressure\n",
              "0   1          1  20  50   0.000000   0.083334      0   5.837492\n",
              "1   2          1  20  50   0.033652  18.383041      0   5.907794\n",
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              "4   5          1  20  50   0.135756  25.355850      0  12.234987"
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          "metadata": {},
          "execution_count": 6
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    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-21T17:25:11.213742Z",
          "iopub.execute_input": "2021-10-21T17:25:11.214675Z",
          "iopub.status.idle": "2021-10-21T17:25:11.297152Z",
          "shell.execute_reply.started": "2021-10-21T17:25:11.214617Z",
          "shell.execute_reply": "2021-10-21T17:25:11.295985Z"
        },
        "trusted": true,
        "id": "R5oBMjYJ9cov"
      },
      "source": [
        "all_pressure = np.sort(train_df['pressure'].unique())\n",
        "pressure_min =  all_pressure[0].item()\n",
        "pressure_max = all_pressure[-1].item()\n",
        "pressure_step = (all_pressure[1] - all_pressure[0]).item()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80hIcGg09cou"
      },
      "source": [
        "DEBUG = True\n",
        "\n",
        "if DEBUG:\n",
        "    train_df = train_df[:80*10000]"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-21T17:25:06.103441Z",
          "iopub.execute_input": "2021-10-21T17:25:06.104426Z",
          "iopub.status.idle": "2021-10-21T17:25:11.211412Z",
          "shell.execute_reply.started": "2021-10-21T17:25:06.104376Z",
          "shell.execute_reply": "2021-10-21T17:25:11.209506Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "y-0XhQ3a9cov",
        "outputId": "2cdea2b8-c74e-4586-d0b9-3d7243fb4464"
      },
      "source": [
        "test_df = pd.read_csv('../input/ventilator-pressure-prediction/test.csv')\n",
        "print(f\"test_df: {test_df.shape}\")\n",
        "test_df.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test_df: (4024000, 7)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>breath_id</th>\n",
              "      <th>R</th>\n",
              "      <th>C</th>\n",
              "      <th>time_step</th>\n",
              "      <th>u_in</th>\n",
              "      <th>u_out</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
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              "      <td>5</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
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              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  breath_id  R   C  time_step       u_in  u_out\n",
              "0   1          0  5  20   0.000000   0.000000      0\n",
              "1   2          0  5  20   0.031904   7.515046      0\n",
              "2   3          0  5  20   0.063827  14.651675      0\n",
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          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVPWgQDL9cow"
      },
      "source": [
        "## Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-21T17:25:11.298767Z",
          "iopub.execute_input": "2021-10-21T17:25:11.299164Z",
          "iopub.status.idle": "2021-10-21T17:27:48.006762Z",
          "shell.execute_reply.started": "2021-10-21T17:25:11.299125Z",
          "shell.execute_reply": "2021-10-21T17:27:48.005658Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1NHNQSZp9cow",
        "outputId": "56c91ffb-f992-4bd4-f3d8-c438c1613e7f"
      },
      "source": [
        "def add_features(df):\n",
        "    df['cross']= df['u_in'] * df['u_out']\n",
        "    df['cross2']= df['time_step'] * df['u_out']\n",
        "    df['area'] = df['time_step'] * df['u_in']\n",
        "    df['area'] = df.groupby('breath_id')['area'].cumsum()\n",
        "    df['time_step_cumsum'] = df.groupby(['breath_id'])['time_step'].cumsum()\n",
        "    df['u_in_cumsum'] = (df['u_in']).groupby(df['breath_id']).cumsum()\n",
        "    print(\"Step-1...Completed\")\n",
        "    \n",
        "    df['u_in_lag1'] = df.groupby('breath_id')['u_in'].shift(1)\n",
        "    df['u_out_lag1'] = df.groupby('breath_id')['u_out'].shift(1)\n",
        "    df['u_in_lag_back1'] = df.groupby('breath_id')['u_in'].shift(-1)\n",
        "    df['u_out_lag_back1'] = df.groupby('breath_id')['u_out'].shift(-1)\n",
        "    df['u_in_lag2'] = df.groupby('breath_id')['u_in'].shift(2)\n",
        "    df['u_out_lag2'] = df.groupby('breath_id')['u_out'].shift(2)\n",
        "    df['u_in_lag_back2'] = df.groupby('breath_id')['u_in'].shift(-2)\n",
        "    df['u_out_lag_back2'] = df.groupby('breath_id')['u_out'].shift(-2)\n",
        "    df['u_in_lag3'] = df.groupby('breath_id')['u_in'].shift(3)\n",
        "    df['u_out_lag3'] = df.groupby('breath_id')['u_out'].shift(3)\n",
        "    df['u_in_lag_back3'] = df.groupby('breath_id')['u_in'].shift(-3)\n",
        "    df['u_out_lag_back3'] = df.groupby('breath_id')['u_out'].shift(-3)\n",
        "    df['u_in_lag4'] = df.groupby('breath_id')['u_in'].shift(4)\n",
        "    df['u_out_lag4'] = df.groupby('breath_id')['u_out'].shift(4)\n",
        "    df['u_in_lag_back4'] = df.groupby('breath_id')['u_in'].shift(-4)\n",
        "    df['u_out_lag_back4'] = df.groupby('breath_id')['u_out'].shift(-4)\n",
        "    df = df.fillna(0)\n",
        "    print(\"Step-2...Completed\")\n",
        "    \n",
        "    df['breath_id__u_in__max'] = df.groupby(['breath_id'])['u_in'].transform('max')\n",
        "    df['breath_id__u_out__max'] = df.groupby(['breath_id'])['u_out'].transform('max')\n",
        "    df['breath_id__u_in__diffmax'] = df.groupby(['breath_id'])['u_in'].transform('max') - df['u_in']\n",
        "    df['breath_id__u_in__diffmean'] = df.groupby(['breath_id'])['u_in'].transform('mean') - df['u_in']\n",
        "    df['breath_id__u_in__diffmax'] = df.groupby(['breath_id'])['u_in'].transform('max') - df['u_in']\n",
        "    df['breath_id__u_in__diffmean'] = df.groupby(['breath_id'])['u_in'].transform('mean') - df['u_in']\n",
        "    print(\"Step-3...Completed\")\n",
        "    \n",
        "    df['u_in_diff1'] = df['u_in'] - df['u_in_lag1']\n",
        "    df['u_out_diff1'] = df['u_out'] - df['u_out_lag1']\n",
        "    df['u_in_diff2'] = df['u_in'] - df['u_in_lag2']\n",
        "    df['u_out_diff2'] = df['u_out'] - df['u_out_lag2']\n",
        "    df['u_in_diff3'] = df['u_in'] - df['u_in_lag3']\n",
        "    df['u_out_diff3'] = df['u_out'] - df['u_out_lag3']\n",
        "    df['u_in_diff4'] = df['u_in'] - df['u_in_lag4']\n",
        "    df['u_out_diff4'] = df['u_out'] - df['u_out_lag4']\n",
        "    print(\"Step-4...Completed\")\n",
        "    \n",
        "    df['one'] = 1\n",
        "    df['count'] = (df['one']).groupby(df['breath_id']).cumsum()\n",
        "    df['u_in_cummean'] =df['u_in_cumsum'] /df['count']\n",
        "    \n",
        "    df['breath_id_lag']=df['breath_id'].shift(1).fillna(0)\n",
        "    df['breath_id_lag2']=df['breath_id'].shift(2).fillna(0)\n",
        "    df['breath_id_lagsame']=np.select([df['breath_id_lag']==df['breath_id']],[1],0)\n",
        "    df['breath_id_lag2same']=np.select([df['breath_id_lag2']==df['breath_id']],[1],0)\n",
        "    df['breath_id__u_in_lag'] = df['u_in'].shift(1).fillna(0)\n",
        "    df['breath_id__u_in_lag'] = df['breath_id__u_in_lag'] * df['breath_id_lagsame']\n",
        "    df['breath_id__u_in_lag2'] = df['u_in'].shift(2).fillna(0)\n",
        "    df['breath_id__u_in_lag2'] = df['breath_id__u_in_lag2'] * df['breath_id_lag2same']\n",
        "    print(\"Step-5...Completed\")\n",
        "    \n",
        "    df['time_step_diff'] = df.groupby('breath_id')['time_step'].diff().fillna(0)\n",
        "    df['ewm_u_in_mean'] = (df\\\n",
        "                           .groupby('breath_id')['u_in']\\\n",
        "                           .ewm(halflife=9)\\\n",
        "                           .mean()\\\n",
        "                           .reset_index(level=0,drop=True))\n",
        "    df[[\"15_in_sum\",\"15_in_min\",\"15_in_max\",\"15_in_mean\"]] = (df\\\n",
        "                                                              .groupby('breath_id')['u_in']\\\n",
        "                                                              .rolling(window=15,min_periods=1)\\\n",
        "                                                              .agg({\"15_in_sum\":\"sum\",\n",
        "                                                                    \"15_in_min\":\"min\",\n",
        "                                                                    \"15_in_max\":\"max\",\n",
        "                                                                    \"15_in_mean\":\"mean\"\n",
        "                                                                    #\"15_in_std\":\"std\"\n",
        "                                                               })\\\n",
        "                                                               .reset_index(level=0,drop=True))\n",
        "    print(\"Step-6...Completed\")\n",
        "    \n",
        "    #df['u_in_diff_1_2'] = df['u_in_lag1'] - df['u_in_lag2']\n",
        "    #df['u_out_diff_1_2'] = df['u_out_lag1'] - df['u_out_lag2']\n",
        "    #df['u_in_lagback_diff_1_2'] = df['u_in_lag_back1'] - df['u_in_lag_back2']\n",
        "    #df['u_out_lagback_diff_1_2'] = df['u_out_lag_back1'] - df['u_out_lag_back2']\n",
        "    \n",
        "    df['u_in_lagback_diff1'] = df['u_in'] - df['u_in_lag_back1']\n",
        "    df['u_out_lagback_diff1'] = df['u_out'] - df['u_out_lag_back1']\n",
        "    df['u_in_lagback_diff2'] = df['u_in'] - df['u_in_lag_back2']\n",
        "    df['u_out_lagback_diff2'] = df['u_out'] - df['u_out_lag_back2']\n",
        "    print(\"Step-7...Completed\")\n",
        "    \n",
        "    df['R_cat'] = df['R'].astype(str)\n",
        "    df['C_cat'] = df['C'].astype(str)\n",
        "    df['R__C'] = df[\"R\"].astype(str) + '__' + df[\"C\"].astype(str)\n",
        "    df = pd.get_dummies(df)\n",
        "    print(\"Step-8...Completed\")\n",
        "    \n",
        "    return df\n",
        "\n",
        "\n",
        "print(\"Train data...\\n\")\n",
        "train = add_features(train_df)\n",
        "\n",
        "print(\"\\nTest data...\\n\")\n",
        "test = add_features(test_df)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train data...\n",
            "\n",
            "Step-1...Completed\n",
            "Step-2...Completed\n",
            "Step-3...Completed\n",
            "Step-4...Completed\n",
            "Step-5...Completed\n",
            "Step-6...Completed\n",
            "Step-7...Completed\n",
            "Step-8...Completed\n",
            "\n",
            "Test data...\n",
            "\n",
            "Step-1...Completed\n",
            "Step-2...Completed\n",
            "Step-3...Completed\n",
            "Step-4...Completed\n",
            "Step-5...Completed\n",
            "Step-6...Completed\n",
            "Step-7...Completed\n",
            "Step-8...Completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-21T17:28:07.762733Z",
          "iopub.execute_input": "2021-10-21T17:28:07.763099Z",
          "iopub.status.idle": "2021-10-21T17:28:07.795291Z",
          "shell.execute_reply.started": "2021-10-21T17:28:07.763065Z",
          "shell.execute_reply": "2021-10-21T17:28:07.794126Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PgRvxlmB9co0",
        "outputId": "63ed3b1d-3c3f-4d1a-e87c-7dc9c11363d8"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((800000, 76),\n",
              "    id  breath_id   R   C  ...  R__C_50__50  R__C_5__10  R__C_5__20  R__C_5__50\n",
              " 0   1          1  20  50  ...            0           0           0           0\n",
              " 1   2          1  20  50  ...            0           0           0           0\n",
              " 2   3          1  20  50  ...            0           0           0           0\n",
              " 3   4          1  20  50  ...            0           0           0           0\n",
              " 4   5          1  20  50  ...            0           0           0           0\n",
              " \n",
              " [5 rows x 76 columns])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zcyv-iig9co0",
        "outputId": "ffe1e730-2380-457e-ffa9-8cc28a54ca93"
      },
      "source": [
        "del train_df\n",
        "del test_df\n",
        "gc.collect()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "117"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qip05b0r9co0",
        "outputId": "a5d349fd-bf37-433f-8712-df291a4f52da"
      },
      "source": [
        "targets = train[['pressure']].to_numpy().reshape(-1, 80)\n",
        "\n",
        "train.drop(['pressure','id', 'breath_id','one','count',\n",
        "            'breath_id_lag','breath_id_lag2','breath_id_lagsame',\n",
        "            'breath_id_lag2same'], axis=1, inplace=True)\n",
        "\n",
        "test = test.drop(['id', 'breath_id','one','count','breath_id_lag',\n",
        "                  'breath_id_lag2','breath_id_lagsame',\n",
        "                  'breath_id_lag2same'], axis=1)\n",
        "\n",
        "print(f\"train: {train.shape} \\ntest: {test.shape}\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train: (800000, 67) \n",
            "test: (4024000, 67)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYWzQtL49co1",
        "outputId": "e99eac2c-108c-48f6-892f-82f91f05a0ed"
      },
      "source": [
        "scaler = RobustScaler()\n",
        "train = scaler.fit_transform(train)\n",
        "test = scaler.transform(test)\n",
        "\n",
        "train = train.reshape(-1, 80, train.shape[-1])\n",
        "test = test.reshape(-1, 80, train.shape[-1])\n",
        "\n",
        "print(f\"train: {train.shape} \\ntest: {test.shape} \\ntargets: {targets.shape}\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train: (10000, 80, 67) \n",
            "test: (50300, 80, 67) \n",
            "targets: (10000, 80)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Osu14DeV9co1"
      },
      "source": [
        "## Hardware config"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CisxazED9co1",
        "outputId": "f82d1950-5a7f-4e90-b2b8-33f8507e2e24"
      },
      "source": [
        "try:\n",
        "    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n",
        "    tf.config.experimental_connect_to_cluster(tpu)\n",
        "    tf.tpu.experimental.initialize_tpu_system(tpu)\n",
        "    tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)\n",
        "    BATCH_SIZE = tpu_strategy.num_replicas_in_sync * 64\n",
        "    print(\"Running on TPU:\", tpu.master())\n",
        "    print(f\"Batch Size: {BATCH_SIZE}\")\n",
        "    \n",
        "except ValueError:\n",
        "    tpu_strategy = tf.distribute.get_strategy()\n",
        "    BATCH_SIZE = 512\n",
        "    print(f\"Running on {strategy.num_replicas_in_sync} replicas\")\n",
        "    print(f\"Batch Size: {BATCH_SIZE}\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Clearing out eager caches\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Clearing out eager caches\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.21.194.242:8470\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.21.194.242:8470\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n",
            "WARNING:absl:`tf.distribute.experimental.TPUStrategy` is deprecated, please use  the non experimental symbol `tf.distribute.TPUStrategy` instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running on TPU: grpc://10.21.194.242:8470\n",
            "Batch Size: 512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GAZMw10A9co1"
      },
      "source": [
        "## Rescaling layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qLou5BDt9co2"
      },
      "source": [
        "@tf.custom_gradient\n",
        "def round_with_gradients(x):\n",
        "    def grad(dy):\n",
        "        return dy\n",
        "    return tf.round(x), grad\n",
        "\n",
        "class ScaleLayer(tf.keras.layers.Layer):\n",
        "    def __init__(self):\n",
        "        super(ScaleLayer, self).__init__()\n",
        "        self.min = tf.constant(pressure_min, dtype=np.float32)\n",
        "        self.max = tf.constant(pressure_max, dtype=np.float32)\n",
        "        self.step = tf.constant(pressure_step, dtype=np.float32)\n",
        "\n",
        "    def call(self, inputs):\n",
        "        steps = tf.math.divide(tf.math.add(inputs, -self.min), self.step)\n",
        "        int_steps = round_with_gradients(steps)\n",
        "        rescaled_steps = tf.math.add(tf.math.multiply(int_steps, self.step), self.min)\n",
        "        clipped = tf.clip_by_value(rescaled_steps, self.min, self.max)\n",
        "        return clipped"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Ft0JBPC9co2"
      },
      "source": [
        "## Keras DNN Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_HuMFGY39co2"
      },
      "source": [
        "def dnn_model():\n",
        "    \n",
        "    x_input = Input(shape=(train.shape[-2:]))\n",
        "    \n",
        "    x1 = Bidirectional(LSTM(units=768, return_sequences=True))(x_input)\n",
        "    x2 = Bidirectional(LSTM(units=512, return_sequences=True))(x1)\n",
        "    x3 = Bidirectional(LSTM(units=256, return_sequences=True))(x2)\n",
        "    x4 = Bidirectional(LSTM(units=128, return_sequences=True))(x3)\n",
        "    \n",
        "    z2 = Bidirectional(GRU(units=256, return_sequences=True))(x2)\n",
        "    z3 = Bidirectional(GRU(units=128, return_sequences=True))(Add()([x3, z2]))\n",
        "    z4 = Bidirectional(GRU(units=64, return_sequences=True))(Add()([x4, z3]))\n",
        "    \n",
        "    x = Concatenate(axis=2)([x4, z2, z3, z4])\n",
        "    \n",
        "    x = Dense(units=128, activation='selu')(x)\n",
        "    \n",
        "    x = Dense(units=1)(x)\n",
        "    x_output = ScaleLayer()(x)\n",
        "\n",
        "    model = Model(inputs=x_input, outputs=x_output, \n",
        "                  name='DNN_Model')\n",
        "    return model"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_d8ybRJc9co2",
        "outputId": "0a0e4c14-ea3b-463a-b76b-761224908e3b"
      },
      "source": [
        "model = dnn_model()\n",
        "model.summary()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"DNN_Model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 80, 67)]     0                                            \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional (Bidirectional)   (None, 80, 1536)     5136384     input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_1 (Bidirectional) (None, 80, 1024)     8392704     bidirectional[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_2 (Bidirectional) (None, 80, 512)      2623488     bidirectional_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_4 (Bidirectional) (None, 80, 512)      1969152     bidirectional_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "add (Add)                       (None, 80, 512)      0           bidirectional_2[0][0]            \n",
            "                                                                 bidirectional_4[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_3 (Bidirectional) (None, 80, 256)      656384      bidirectional_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_5 (Bidirectional) (None, 80, 256)      493056      add[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 80, 256)      0           bidirectional_3[0][0]            \n",
            "                                                                 bidirectional_5[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_6 (Bidirectional) (None, 80, 128)      123648      add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 80, 1152)     0           bidirectional_3[0][0]            \n",
            "                                                                 bidirectional_4[0][0]            \n",
            "                                                                 bidirectional_5[0][0]            \n",
            "                                                                 bidirectional_6[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 80, 128)      147584      concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 80, 1)        129         dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "scale_layer (ScaleLayer)        (None, 80, 1)        0           dense_1[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 19,542,529\n",
            "Trainable params: 19,542,529\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "e-M0ZMvC9co3",
        "outputId": "2ae44f89-5222-4e4a-abbf-7028cc2a5489"
      },
      "source": [
        "plot_model(\n",
        "    model, \n",
        "    to_file='Google_Brain_Keras_Model.png', \n",
        "    show_shapes=True,\n",
        "    show_layer_names=True\n",
        ")\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rxWQVBYm-0VK",
        "outputId": "dfc524c5-6d51-4708-bdc9-1e2baad7187e"
      },
      "source": [
        "del model\n",
        "gc.collect()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9461"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1v9SMji9co3"
      },
      "source": [
        "#reset Keras Session\n",
        "def reset_keras():\n",
        "    sess = tf.compat.v1.keras.backend.get_session()\n",
        "    tf.compat.v1.keras.backend.clear_session()\n",
        "    sess.close()\n",
        "    sess = tf.compat.v1.keras.backend.get_session()\n",
        "\n",
        "    # use the same config as you used to create the session\n",
        "    config = tf.compat.v1.ConfigProto()\n",
        "    config.gpu_options.per_process_gpu_memory_fraction = 1\n",
        "    config.gpu_options.visible_device_list = \"0\"\n",
        "    tf.compat.v1.keras.backend.set_session(tf.compat.v1.Session(config=config))\n",
        "    gc.collect()\n",
        "\n",
        "reset_keras()"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDsDMBnE9co3"
      },
      "source": [
        "train_pred = np.zeros(targets.shape)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SYJkCi1-9co3",
        "outputId": "e42720d7-0fcd-4455-a023-d1627a0df350"
      },
      "source": [
        "with tpu_strategy.scope():\n",
        "    \n",
        "    VERBOSE = 1\n",
        "    EPOCHS = 300\n",
        "    FOLDS = 7\n",
        "    if DEBUG:\n",
        "      EPOCHS = 100\n",
        "    test_preds = []\n",
        "    kf = KFold(n_splits=FOLDS, shuffle=True, random_state=2021)\n",
        "    \n",
        "    for fold, (train_idx, test_idx) in enumerate(kf.split(train, targets)):\n",
        "        X_train, X_valid = train[train_idx], train[test_idx]\n",
        "        y_train, y_valid = targets[train_idx], targets[test_idx]\n",
        "        \n",
        "        model = dnn_model()\n",
        "        model.compile(optimizer=\"adam\", loss=\"mae\")\n",
        "\n",
        "        lr = ReduceLROnPlateau(monitor=\"val_loss\", factor=0.75, \n",
        "                               patience=10, verbose=VERBOSE)\n",
        "        \n",
        "        chk_point = ModelCheckpoint(f'./Bidirect_LSTM_model_{fold+1}C.h5',\n",
        "                                    monitor='val_loss', verbose=VERBOSE, \n",
        "                                    save_best_only=True, mode='min')\n",
        "\n",
        "        es = EarlyStopping(monitor=\"val_loss\", patience=50, \n",
        "                           verbose=VERBOSE, mode=\"min\", \n",
        "                           restore_best_weights=True)\n",
        "        \n",
        "        model.fit(X_train, y_train, \n",
        "                  validation_data=(X_valid, y_valid), \n",
        "                  epochs=EPOCHS,\n",
        "                  verbose=VERBOSE,\n",
        "                  batch_size=BATCH_SIZE, \n",
        "                  callbacks=[lr, chk_point, es])\n",
        "        \n",
        "        y_true = y_valid.squeeze().reshape(-1, 1)\n",
        "        y_pred = model.predict(X_valid, batch_size=BATCH_SIZE).squeeze().reshape(-1, 1)\n",
        "        train_pred[test_idx] = y_pred.reshape(-1, 80)\n",
        "        score = mean_absolute_error(y_true, y_pred)\n",
        "        print(f\"Fold-{fold+1} | OOF Score: {score}\")\n",
        "        \n",
        "        test_preds.append(model.predict(test, batch_size=BATCH_SIZE).squeeze().reshape(-1, 1).squeeze())\n",
        "        \n",
        "        del X_train, X_valid, y_train, y_valid, model\n",
        "        reset_keras()\n",
        "        gc.collect()\n",
        "\n",
        "        if DEBUG:\n",
        "          break"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "17/17 [==============================] - 91s 3s/step - loss: 5.1366 - val_loss: 2.6632\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 2.66316, saving model to ./Bidirect_LSTM_model_1C.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 2.0798 - val_loss: 1.6075\n",
            "\n",
            "Epoch 00002: val_loss improved from 2.66316 to 1.60751, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 3/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 1.7664 - val_loss: 1.6837\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 1.60751\n",
            "Epoch 4/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 1.2329 - val_loss: 1.0421\n",
            "\n",
            "Epoch 00004: val_loss improved from 1.60751 to 1.04208, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 5/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.9524 - val_loss: 0.8615\n",
            "\n",
            "Epoch 00005: val_loss improved from 1.04208 to 0.86151, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 6/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.8562 - val_loss: 0.8325\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.86151 to 0.83251, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 7/100\n",
            "17/17 [==============================] - 9s 514ms/step - loss: 0.8372 - val_loss: 0.8601\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 0.83251\n",
            "Epoch 8/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.7678 - val_loss: 0.8002\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.83251 to 0.80022, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 9/100\n",
            "17/17 [==============================] - 9s 515ms/step - loss: 0.7227 - val_loss: 0.6734\n",
            "\n",
            "Epoch 00009: val_loss improved from 0.80022 to 0.67342, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 10/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.6385 - val_loss: 0.6539\n",
            "\n",
            "Epoch 00010: val_loss improved from 0.67342 to 0.65387, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 11/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.6003 - val_loss: 0.6738\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 0.65387\n",
            "Epoch 12/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.5946 - val_loss: 0.5742\n",
            "\n",
            "Epoch 00012: val_loss improved from 0.65387 to 0.57418, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 13/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.5693 - val_loss: 0.5490\n",
            "\n",
            "Epoch 00013: val_loss improved from 0.57418 to 0.54899, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 14/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.5407 - val_loss: 0.5246\n",
            "\n",
            "Epoch 00014: val_loss improved from 0.54899 to 0.52460, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 15/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.5065 - val_loss: 0.5088\n",
            "\n",
            "Epoch 00015: val_loss improved from 0.52460 to 0.50882, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 16/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.5192 - val_loss: 0.5752\n",
            "\n",
            "Epoch 00016: val_loss did not improve from 0.50882\n",
            "Epoch 17/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.5597 - val_loss: 0.5096\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.50882\n",
            "Epoch 18/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.4672 - val_loss: 0.4882\n",
            "\n",
            "Epoch 00018: val_loss improved from 0.50882 to 0.48818, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 19/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.5055 - val_loss: 0.5620\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 0.48818\n",
            "Epoch 20/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.4872 - val_loss: 0.4972\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 0.48818\n",
            "Epoch 21/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.4574 - val_loss: 0.5379\n",
            "\n",
            "Epoch 00021: val_loss did not improve from 0.48818\n",
            "Epoch 22/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.4776 - val_loss: 0.5699\n",
            "\n",
            "Epoch 00022: val_loss did not improve from 0.48818\n",
            "Epoch 23/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.4601 - val_loss: 0.4711\n",
            "\n",
            "Epoch 00023: val_loss improved from 0.48818 to 0.47111, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 24/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.4575 - val_loss: 0.4979\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.47111\n",
            "Epoch 25/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.4621 - val_loss: 0.4494\n",
            "\n",
            "Epoch 00025: val_loss improved from 0.47111 to 0.44943, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 26/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.4541 - val_loss: 0.4872\n",
            "\n",
            "Epoch 00026: val_loss did not improve from 0.44943\n",
            "Epoch 27/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.4570 - val_loss: 0.4814\n",
            "\n",
            "Epoch 00027: val_loss did not improve from 0.44943\n",
            "Epoch 28/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.4596 - val_loss: 0.5344\n",
            "\n",
            "Epoch 00028: val_loss did not improve from 0.44943\n",
            "Epoch 29/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.4699 - val_loss: 0.4929\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.44943\n",
            "Epoch 30/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.4310 - val_loss: 0.4308\n",
            "\n",
            "Epoch 00030: val_loss improved from 0.44943 to 0.43082, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 31/100\n",
            "17/17 [==============================] - 9s 539ms/step - loss: 0.3994 - val_loss: 0.4341\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.43082\n",
            "Epoch 32/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.4227 - val_loss: 0.4347\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.43082\n",
            "Epoch 33/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.4198 - val_loss: 0.4561\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.43082\n",
            "Epoch 34/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.4071 - val_loss: 0.4351\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 0.43082\n",
            "Epoch 35/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.3817 - val_loss: 0.4152\n",
            "\n",
            "Epoch 00035: val_loss improved from 0.43082 to 0.41521, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 36/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.3625 - val_loss: 0.4132\n",
            "\n",
            "Epoch 00036: val_loss improved from 0.41521 to 0.41317, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 37/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.3812 - val_loss: 0.4358\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.41317\n",
            "Epoch 38/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.3675 - val_loss: 0.4148\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.41317\n",
            "Epoch 39/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.3655 - val_loss: 0.3910\n",
            "\n",
            "Epoch 00039: val_loss improved from 0.41317 to 0.39098, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 40/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.3513 - val_loss: 0.4076\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.39098\n",
            "Epoch 41/100\n",
            "17/17 [==============================] - 9s 517ms/step - loss: 0.3543 - val_loss: 0.3889\n",
            "\n",
            "Epoch 00041: val_loss improved from 0.39098 to 0.38891, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 42/100\n",
            "17/17 [==============================] - 9s 513ms/step - loss: 0.3578 - val_loss: 0.4555\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.38891\n",
            "Epoch 43/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.3865 - val_loss: 0.4417\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.38891\n",
            "Epoch 44/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.3378 - val_loss: 0.3785\n",
            "\n",
            "Epoch 00044: val_loss improved from 0.38891 to 0.37848, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 45/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.3529 - val_loss: 0.4177\n",
            "\n",
            "Epoch 00045: val_loss did not improve from 0.37848\n",
            "Epoch 46/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.3761 - val_loss: 0.4127\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.37848\n",
            "Epoch 47/100\n",
            "17/17 [==============================] - 9s 513ms/step - loss: 0.3413 - val_loss: 0.4019\n",
            "\n",
            "Epoch 00047: val_loss did not improve from 0.37848\n",
            "Epoch 48/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.3252 - val_loss: 0.4097\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.37848\n",
            "Epoch 49/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.3217 - val_loss: 0.3950\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 0.37848\n",
            "Epoch 50/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.3204 - val_loss: 0.3874\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 0.37848\n",
            "Epoch 51/100\n",
            "17/17 [==============================] - 9s 514ms/step - loss: 0.3180 - val_loss: 0.3727\n",
            "\n",
            "Epoch 00051: val_loss improved from 0.37848 to 0.37270, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 52/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.3099 - val_loss: 0.3692\n",
            "\n",
            "Epoch 00052: val_loss improved from 0.37270 to 0.36916, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 53/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.3096 - val_loss: 0.3799\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.36916\n",
            "Epoch 54/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.3076 - val_loss: 0.3741\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 0.36916\n",
            "Epoch 55/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.3270 - val_loss: 0.3742\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 0.36916\n",
            "Epoch 56/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.3099 - val_loss: 0.3731\n",
            "\n",
            "Epoch 00056: val_loss did not improve from 0.36916\n",
            "Epoch 57/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.3051 - val_loss: 0.3785\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.36916\n",
            "Epoch 58/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.3046 - val_loss: 0.3911\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 0.36916\n",
            "Epoch 59/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.3040 - val_loss: 0.3646\n",
            "\n",
            "Epoch 00059: val_loss improved from 0.36916 to 0.36456, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 60/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.2898 - val_loss: 0.3744\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 0.36456\n",
            "Epoch 61/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.2988 - val_loss: 0.3717\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.36456\n",
            "Epoch 62/100\n",
            "17/17 [==============================] - 9s 509ms/step - loss: 0.3145 - val_loss: 0.3856\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 0.36456\n",
            "Epoch 63/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.3329 - val_loss: 0.3815\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 0.36456\n",
            "Epoch 64/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.2979 - val_loss: 0.3714\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 0.36456\n",
            "Epoch 65/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.2887 - val_loss: 0.3737\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 0.36456\n",
            "Epoch 66/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.2886 - val_loss: 0.3846\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 0.36456\n",
            "Epoch 67/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.2727 - val_loss: 0.3607\n",
            "\n",
            "Epoch 00067: val_loss improved from 0.36456 to 0.36068, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 68/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.2662 - val_loss: 0.3599\n",
            "\n",
            "Epoch 00068: val_loss improved from 0.36068 to 0.35993, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 69/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.2752 - val_loss: 0.3517\n",
            "\n",
            "Epoch 00069: val_loss improved from 0.35993 to 0.35174, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 70/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.2604 - val_loss: 0.3551\n",
            "\n",
            "Epoch 00070: val_loss did not improve from 0.35174\n",
            "Epoch 71/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.2821 - val_loss: 0.3581\n",
            "\n",
            "Epoch 00071: val_loss did not improve from 0.35174\n",
            "Epoch 72/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.2593 - val_loss: 0.3557\n",
            "\n",
            "Epoch 00072: val_loss did not improve from 0.35174\n",
            "Epoch 73/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.2641 - val_loss: 0.3501\n",
            "\n",
            "Epoch 00073: val_loss improved from 0.35174 to 0.35014, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 74/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.2608 - val_loss: 0.3736\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 0.35014\n",
            "Epoch 75/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.2546 - val_loss: 0.3486\n",
            "\n",
            "Epoch 00075: val_loss improved from 0.35014 to 0.34856, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 76/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.2555 - val_loss: 0.3588\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 0.34856\n",
            "Epoch 77/100\n",
            "17/17 [==============================] - 9s 513ms/step - loss: 0.2826 - val_loss: 0.3592\n",
            "\n",
            "Epoch 00077: val_loss did not improve from 0.34856\n",
            "Epoch 78/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.2607 - val_loss: 0.3383\n",
            "\n",
            "Epoch 00078: val_loss improved from 0.34856 to 0.33835, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 79/100\n",
            "17/17 [==============================] - 9s 513ms/step - loss: 0.2632 - val_loss: 0.3694\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 0.33835\n",
            "Epoch 80/100\n",
            "17/17 [==============================] - 9s 509ms/step - loss: 0.2686 - val_loss: 0.3398\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 0.33835\n",
            "Epoch 81/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.2632 - val_loss: 0.3419\n",
            "\n",
            "Epoch 00081: val_loss did not improve from 0.33835\n",
            "Epoch 82/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.2443 - val_loss: 0.3434\n",
            "\n",
            "Epoch 00082: val_loss did not improve from 0.33835\n",
            "Epoch 83/100\n",
            "17/17 [==============================] - 9s 548ms/step - loss: 0.2330 - val_loss: 0.3343\n",
            "\n",
            "Epoch 00083: val_loss improved from 0.33835 to 0.33431, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 84/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.2391 - val_loss: 0.3550\n",
            "\n",
            "Epoch 00084: val_loss did not improve from 0.33431\n",
            "Epoch 85/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.2632 - val_loss: 0.3458\n",
            "\n",
            "Epoch 00085: val_loss did not improve from 0.33431\n",
            "Epoch 86/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.2606 - val_loss: 0.3603\n",
            "\n",
            "Epoch 00086: val_loss did not improve from 0.33431\n",
            "Epoch 87/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.2493 - val_loss: 0.3445\n",
            "\n",
            "Epoch 00087: val_loss did not improve from 0.33431\n",
            "Epoch 88/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.2301 - val_loss: 0.3334\n",
            "\n",
            "Epoch 00088: val_loss improved from 0.33431 to 0.33343, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 89/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.2256 - val_loss: 0.3400\n",
            "\n",
            "Epoch 00089: val_loss did not improve from 0.33343\n",
            "Epoch 90/100\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 0.2410 - val_loss: 0.3481\n",
            "\n",
            "Epoch 00090: val_loss did not improve from 0.33343\n",
            "Epoch 91/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.2506 - val_loss: 0.3474\n",
            "\n",
            "Epoch 00091: val_loss did not improve from 0.33343\n",
            "Epoch 92/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.2387 - val_loss: 0.3780\n",
            "\n",
            "Epoch 00092: val_loss did not improve from 0.33343\n",
            "Epoch 93/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.2586 - val_loss: 0.3657\n",
            "\n",
            "Epoch 00093: val_loss did not improve from 0.33343\n",
            "Epoch 94/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.2502 - val_loss: 0.3453\n",
            "\n",
            "Epoch 00094: val_loss did not improve from 0.33343\n",
            "Epoch 95/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.2265 - val_loss: 0.3328\n",
            "\n",
            "Epoch 00095: val_loss improved from 0.33343 to 0.33278, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 96/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.2301 - val_loss: 0.3455\n",
            "\n",
            "Epoch 00096: val_loss did not improve from 0.33278\n",
            "Epoch 97/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.2257 - val_loss: 0.3332\n",
            "\n",
            "Epoch 00097: val_loss did not improve from 0.33278\n",
            "Epoch 98/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.2226 - val_loss: 0.3315\n",
            "\n",
            "Epoch 00098: val_loss improved from 0.33278 to 0.33152, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 99/100\n",
            "17/17 [==============================] - 9s 512ms/step - loss: 0.2223 - val_loss: 0.3494\n",
            "\n",
            "Epoch 00099: val_loss did not improve from 0.33152\n",
            "Epoch 100/100\n",
            "17/17 [==============================] - 9s 511ms/step - loss: 0.2108 - val_loss: 0.3256\n",
            "\n",
            "Epoch 00100: val_loss improved from 0.33152 to 0.32555, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Fold-1 | OOF Score: 0.3255526891906173\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-fd4042574470>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mDEBUG\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m           \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, exception_type, exception_value, traceback)\u001b[0m\n\u001b[1;32m    431\u001b[0m                          \"tf.distribute.set_strategy() out of `with` scope.\"),\n\u001b[1;32m    432\u001b[0m             e)\n\u001b[0;32m--> 433\u001b[0;31m     \u001b[0m_pop_per_thread_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    434\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribution_strategy_context.py\u001b[0m in \u001b[0;36m_pop_per_thread_mode\u001b[0;34m()\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_pop_per_thread_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m   \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_distribution_strategy_stack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: pop from empty list"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUanGPDq9co3"
      },
      "source": [
        "train_pred = train_pred.flatten()\n",
        "targets = targets.flatten()\n",
        "score = mean_absolute_error(targets, train_pred)\n",
        "print(f\"Train Score: {score}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QHx2oF849co4"
      },
      "source": [
        "## Create submission file"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tS9VjV7K9co4"
      },
      "source": [
        "submission = pd.read_csv('../input/ventilator-pressure-prediction/sample_submission.csv')\n",
        "submission[\"pressure\"] = sum(test_preds)/5\n",
        "submission.to_csv('submission.csv', index=False)\n",
        "\n",
        "# ENSEMBLE FOLDS WITH MEDIAN\n",
        "#取中位数\n",
        "submission[\"pressure\"] = np.median(np.vstack(test_preds),axis=0)\n",
        "submission.to_csv('submission_median.csv', index=False)\n",
        "\n",
        "\n",
        "# ENSEMBLE FOLDS WITH MEDIAN AND ROUND PREDICTIONS\n",
        "submission[\"pressure\"] =\\\n",
        "    np.round( (submission.pressure - pressure_min)/pressure_step ) * pressure_step + pressure_min\n",
        "submission.pressure = np.clip(submission.pressure, pressure_min, pressure_max)\n",
        "submission.to_csv('submission_median_round.csv', index=False)"
      ]
    }
  ]
}