REM_Sleep_Classification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cYuYrzBp5Ebp"
      },
      "source": [
        "## Installing Tensorflow Decision forest \n",
        "\n",
        "TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models. The library is a collection of Keras models and supports classification, regression and ranking.\n",
        "\n",
        "TF-DF is a wrapper around the Yggdrasil Decision Forest C++ libraries. Models trained with TF-DF are compatible with Yggdrasil Decision Forests' models, and vice versa."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9PRfQHg95NdX",
        "outputId": "b74ec1a5-da97-4940-a67e-1eaf8c6abeb3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow_decision_forests\n",
            "  Downloading tensorflow_decision_forests-0.2.7-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.0 MB)\n",
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            "  Downloading flatbuffers-1.12-py2.py3-none-any.whl (15 kB)\n",
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            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow~=2.9.1->tensorflow_decision_forests) (1.3.1)\n",
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            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow~=2.9.1->tensorflow_decision_forests) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow~=2.9.1->tensorflow_decision_forests) (3.0.4)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow~=2.9.1->tensorflow_decision_forests) (3.2.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->tensorflow~=2.9.1->tensorflow_decision_forests) (3.0.9)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->tensorflow_decision_forests) (2022.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->tensorflow_decision_forests) (2.8.2)\n",
            "Installing collected packages: tensorflow-estimator, tensorboard, keras, gast, flatbuffers, wurlitzer, tensorflow, tensorflow-decision-forests\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.8.0\n",
            "    Uninstalling tensorflow-estimator-2.8.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.8.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.8.0\n",
            "    Uninstalling tensorboard-2.8.0:\n",
            "      Successfully uninstalled tensorboard-2.8.0\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.8.0\n",
            "    Uninstalling keras-2.8.0:\n",
            "      Successfully uninstalled keras-2.8.0\n",
            "  Attempting uninstall: gast\n",
            "    Found existing installation: gast 0.5.3\n",
            "    Uninstalling gast-0.5.3:\n",
            "      Successfully uninstalled gast-0.5.3\n",
            "  Attempting uninstall: flatbuffers\n",
            "    Found existing installation: flatbuffers 2.0\n",
            "    Uninstalling flatbuffers-2.0:\n",
            "      Successfully uninstalled flatbuffers-2.0\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.8.2+zzzcolab20220719082949\n",
            "    Uninstalling tensorflow-2.8.2+zzzcolab20220719082949:\n",
            "      Successfully uninstalled tensorflow-2.8.2+zzzcolab20220719082949\n",
            "Successfully installed flatbuffers-1.12 gast-0.4.0 keras-2.9.0 tensorboard-2.9.1 tensorflow-2.9.1 tensorflow-decision-forests-0.2.7 tensorflow-estimator-2.9.0 wurlitzer-3.0.2\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: wurlitzer in /usr/local/lib/python3.7/dist-packages (3.0.2)\n"
          ]
        }
      ],
      "source": [
        "!pip3 install tensorflow_decision_forests --upgrade\n",
        "!pip install wurlitzer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Sd8ILQ58kYA"
      },
      "source": [
        "### Importing all the important libraries and modules "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jjaIpcwanEl3",
        "outputId": "977f5b5e-2183-4e44-937d-9ce861e6b503"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting mne\n",
            "  Downloading mne-1.1.0-py3-none-any.whl (7.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 7.5 MB 8.2 MB/s \n",
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            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mne) (1.4.4)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->mne) (1.15.0)\n",
            "Installing collected packages: mne\n",
            "Successfully installed mne-1.1.0\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import tensorflow_decision_forests as tfdf\n",
        "import pandas as pd\n",
        "import urllib.request\n",
        "from datetime import datetime\n",
        "!pip install mne\n",
        "import mne\n",
        "\n",
        "def to_timestamp(x: str, acq_time: datetime):\n",
        "    date = datetime.strptime(x, '%H:%M:%S')\n",
        "    date = datetime(acq_time.year, acq_time.month, acq_time.day, date.hour, date.minute, date.second)\n",
        "    if date.hour < 12:\n",
        "        date = datetime(date.year, date.month, date.day + 1, date.hour, date.minute, date.second)\n",
        "    return (date.day - acq_time.day) * 24 * 3600 + (date.hour - acq_time.hour) * 3600 + (\n",
        "                date.minute - acq_time.minute) * 60 + date.second - acq_time.second\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U0AQj-qg8tvj"
      },
      "source": [
        "### Defining the subsample function for the data\n",
        "\n",
        "Subsampling is a statistical method for selecting a subset of a larger data set. The subset is a representation of the larger data set. If a data set is too large to fit in memory, a subsample representing the larger data set must be chosen.\n",
        "\n",
        "Subsampling accelerates the training of machine learning models. A subsample of the data can be used because training a machine learning model on a large data set takes time. This is useful when the data set is too large to fit in memory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zq-su2083Cbi"
      },
      "outputs": [],
      "source": [
        "def subsample(data: np.ndarray, fs: int, fs_new: int, axis=0):\n",
        "    assert fs % fs_new == 0\n",
        "    factor = int(fs / fs_new)\n",
        "    if data.shape[axis] % factor != 0: \n",
        "        print('Subsampling led to loss of %i samples, in an online setting consider using a BlockBuffer with a '\n",
        "              'buffer size of a multiple of %i samples.' % (data.shape[axis] % factor, factor))\n",
        "    idx_mask = np.arange(data.shape[axis], step=factor)\n",
        "    return data.take(idx_mask, axis)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AL4qjOVu9D8b"
      },
      "source": [
        "### Defining the functions for following:\n",
        "\n",
        "1. Reading the annotations file\n",
        "2. Merging the data file and annotations file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EVIp3vZQ5xGT"
      },
      "outputs": [],
      "source": [
        "def read_annotation_file(path_filename: str, acq_time: datetime):\n",
        "    df_annotations_data = pd.read_csv(path_filename, sep='\\t', skiprows=20)\n",
        "    df_annotations_data = df_annotations_data.loc[df_annotations_data[\"Event\"].str.startswith(\"SLEEP\")]\n",
        "    df_annotations_data = df_annotations_data.rename(columns={\"Time [hh:mm:ss]\": \"onset\", \"Duration[s]\": \"duration\"})\n",
        "    df_annotations_data[\"timestamp\"] = df_annotations_data.onset.apply(lambda x: to_timestamp(x, acq_time))\n",
        "\n",
        "    return df_annotations_data\n",
        "\n",
        "\n",
        "def merge_data_file_and_annotations(data, df_annotations, fs_new):\n",
        "    raw_data = data.get_data()\n",
        "    fs = int(data.info[\"sfreq\"])\n",
        "    df = pd.DataFrame(raw_data.T, columns=data.info.ch_names)\n",
        "    df[\"times\"] = data.times\n",
        "    df = pd.DataFrame(data=subsample(df.to_numpy(), fs, fs_new), columns=df.columns)\n",
        "    \n",
        "    labels = [np.nan] * df.shape[0]\n",
        "    for i in range(df_annotations.shape[0] - 1):\n",
        "        row_start = df_annotations.iloc[i]\n",
        "        row_end = df_annotations.iloc[i + 1]\n",
        "        start_pos = row_start.timestamp * fs_new\n",
        "        end_pos = row_end.timestamp * fs_new\n",
        "        if start_pos < len(labels):\n",
        "            labels[start_pos:end_pos] = [row_start[\"Sleep Stage\"]]*(end_pos-start_pos)\n",
        "    \n",
        "    df[\"sleepstage\"] = labels\n",
        "    df = df.dropna()\n",
        "    \n",
        "    return df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a30yMqRq9W4E"
      },
      "source": [
        "### Downloading and importing the data into directories"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "gWuDysgv3tGz",
        "outputId": "6e005db1-61df-49ea-8b33-6e6d42a54315"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data file: https://physionet.org/files/capslpdb/1.0.0/rbd1.edf?download\n",
            "Downloading annotations data file: https://physionet.org/files/capslpdb/1.0.0/rbd1.txt?download\n",
            "Loading File: rbd1.edf\n",
            "Extracting EDF parameters from /content/rbd1.edf...\n",
            "EDF file detected\n",
            "Setting channel info structure...\n",
            "Creating raw.info structure...\n",
            "Loading Annotations file: rbd1_annotations.txt\n",
            "\n",
            "\n",
            " Example Annotations file: \n",
            "\n"
          ]
        },
        {
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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            "text/plain": [
              "  Sleep Stage          Position     onset     Event  duration Location  \\\n",
              "0           W  Unknown Position  23:07:19  SLEEP-S0        30  ROC-LOC   \n",
              "1           W  Unknown Position  23:07:49  SLEEP-S0        30  ROC-LOC   \n",
              "2           W  Unknown Position  23:08:19  SLEEP-S0        30  ROC-LOC   \n",
              "3           W  Unknown Position  23:08:49  SLEEP-S0        30  ROC-LOC   \n",
              "\n",
              "   timestamp  \n",
              "0       9270  \n",
              "1       9300  \n",
              "2       9330  \n",
              "3       9360  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Subject ID\n",
        "subject_name = 'rbd1'\n",
        "# New frequency\n",
        "fs_new = 128\n",
        "# CSV Filename\n",
        "new_filename = f'{subject_name}_data_and_annotations.csv'\n",
        "              \n",
        "data_filename = f'{subject_name}.edf'\n",
        "filename_annotations = f'{subject_name}_annotations.txt'\n",
        "url_data = f'https://physionet.org/files/capslpdb/1.0.0/{subject_name}.edf?download'\n",
        "url_annotations = f'https://physionet.org/files/capslpdb/1.0.0/{subject_name}.txt?download'\n",
        "\n",
        "print(f'Downloading data file: {url_data}')\n",
        "urllib.request.urlretrieve(url_data, data_filename)\n",
        "\n",
        "print(f'Downloading annotations data file: {url_annotations}')\n",
        "urllib.request.urlretrieve(url_annotations, filename_annotations)\n",
        "\n",
        "\n",
        "print(f'Loading File: {data_filename}')\n",
        "data_polysomnography = mne.io.read_raw_edf(data_filename)\n",
        "\n",
        "print(f'Loading Annotations file: {filename_annotations}')\n",
        "df_annotations_polysomnography = read_annotation_file(filename_annotations, data_polysomnography.info[\"meas_date\"])\n",
        "\n",
        "print(\"\\n\\n Example Annotations file: \\n\")\n",
        "df_annotations_polysomnography.head(4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Z58ERpu5H76B",
        "outputId": "8b799017-3a69-4502-d5d9-aa88f8029f2f"
      },
      "outputs": [
        {
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              "      <td>SLEEP-S0</td>\n",
              "      <td>30</td>\n",
              "      <td>ROC-LOC</td>\n",
              "      <td>30240</td>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
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            "text/plain": [
              "    Sleep Stage          Position     onset      Event  duration Location  \\\n",
              "927           R  Unknown Position  04:54:49  SLEEP-REM        30  ROC-LOC   \n",
              "928           R  Unknown Position  04:55:19  SLEEP-REM        30  ROC-LOC   \n",
              "929           W  Unknown Position  04:55:49   SLEEP-S0        30  ROC-LOC   \n",
              "930           W  Unknown Position  04:56:19   SLEEP-S0        30  ROC-LOC   \n",
              "931           W  Unknown Position  04:56:49   SLEEP-S0        30  ROC-LOC   \n",
              "\n",
              "     timestamp  \n",
              "927      30120  \n",
              "928      30150  \n",
              "929      30180  \n",
              "930      30210  \n",
              "931      30240  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_annotations_polysomnography.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DWFDRp_J7viz",
        "outputId": "6722167d-ccc1-4815-9a81-e8fb2e44fb11"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Merge files annotation and polysomnography data. Downsampling data from 512.0 to 128 Hz\n"
          ]
        }
      ],
      "source": [
        "print(f'Merge files annotation and polysomnography data. Downsampling data from {data_polysomnography.info[\"sfreq\"]} to {fs_new} Hz')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FLEY7p9r9o1v"
      },
      "source": [
        "### Merging the data of both annotation and data file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "-xW2s1h39dlV",
        "outputId": "741e0771-defc-4d06-f9d8-3084e8ef8dfe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Data Merged\n"
          ]
        },
        {
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              "      <td>-0.000032</td>\n",
              "      <td>5.799756e-07</td>\n",
              "      <td>4.273504e-07</td>\n",
              "      <td>6.715507e-07</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>0.000299</td>\n",
              "      <td>-0.000792</td>\n",
              "      <td>-3.803311e-06</td>\n",
              "      <td>9270.000000</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1186561</th>\n",
              "      <td>-0.000023</td>\n",
              "      <td>1.266789e-06</td>\n",
              "      <td>2.274115e-06</td>\n",
              "      <td>4.578755e-08</td>\n",
              "      <td>1.678877e-07</td>\n",
              "      <td>-0.000006</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>-0.000167</td>\n",
              "      <td>5.189255e-07</td>\n",
              "      <td>-1.526252e-07</td>\n",
              "      <td>-9.157509e-07</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>0.000360</td>\n",
              "      <td>-0.000862</td>\n",
              "      <td>1.392386e-06</td>\n",
              "      <td>9270.007812</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1186562</th>\n",
              "      <td>-0.000021</td>\n",
              "      <td>-1.678877e-07</td>\n",
              "      <td>1.633089e-06</td>\n",
              "      <td>5.647131e-07</td>\n",
              "      <td>3.205128e-07</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>-0.000001</td>\n",
              "      <td>-0.000352</td>\n",
              "      <td>-4.884005e-07</td>\n",
              "      <td>-6.410256e-07</td>\n",
              "      <td>-6.715507e-07</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.000295</td>\n",
              "      <td>-0.000809</td>\n",
              "      <td>4.959182e-08</td>\n",
              "      <td>9270.015625</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1186563</th>\n",
              "      <td>-0.000019</td>\n",
              "      <td>1.236264e-06</td>\n",
              "      <td>1.205739e-06</td>\n",
              "      <td>1.877289e-06</td>\n",
              "      <td>6.257631e-07</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>-0.000004</td>\n",
              "      <td>-0.000383</td>\n",
              "      <td>-2.442002e-07</td>\n",
              "      <td>3.970467e-23</td>\n",
              "      <td>1.923077e-06</td>\n",
              "      <td>-0.000015</td>\n",
              "      <td>0.000259</td>\n",
              "      <td>-0.000783</td>\n",
              "      <td>-3.917754e-06</td>\n",
              "      <td>9270.023438</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ddf37804-4a0d-4e08-8a05-63742d4b0516')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ddf37804-4a0d-4e08-8a05-63742d4b0516 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ddf37804-4a0d-4e08-8a05-63742d4b0516');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          ROC-LOC        Fp2-F4         F4-C4         C4-P4         P4-O2  \\\n",
              "1186560 -0.000021  2.304640e-06  6.868132e-07  2.594628e-07  1.984127e-07   \n",
              "1186561 -0.000023  1.266789e-06  2.274115e-06  4.578755e-08  1.678877e-07   \n",
              "1186562 -0.000021 -1.678877e-07  1.633089e-06  5.647131e-07  3.205128e-07   \n",
              "1186563 -0.000019  1.236264e-06  1.205739e-06  1.877289e-06  6.257631e-07   \n",
              "\n",
              "            C4-A1  EMG1-EMG2  ECG1-ECG2       DX1-DX2       SX1-SX2  \\\n",
              "1186560 -0.000009   0.000005  -0.000032  5.799756e-07  4.273504e-07   \n",
              "1186561 -0.000006   0.000003  -0.000167  5.189255e-07 -1.526252e-07   \n",
              "1186562  0.000002  -0.000001  -0.000352 -4.884005e-07 -6.410256e-07   \n",
              "1186563  0.000007  -0.000004  -0.000383 -2.442002e-07  3.970467e-23   \n",
              "\n",
              "                F8-T4     F7-T3    TORACE    ADDOME           MIC  \\\n",
              "1186560  6.715507e-07  0.000005  0.000299 -0.000792 -3.803311e-06   \n",
              "1186561 -9.157509e-07  0.000010  0.000360 -0.000862  1.392386e-06   \n",
              "1186562 -6.715507e-07  0.000004  0.000295 -0.000809  4.959182e-08   \n",
              "1186563  1.923077e-06 -0.000015  0.000259 -0.000783 -3.917754e-06   \n",
              "\n",
              "               times sleepstage  \n",
              "1186560  9270.000000          W  \n",
              "1186561  9270.007812          W  \n",
              "1186562  9270.015625          W  \n",
              "1186563  9270.023438          W  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_data = merge_data_file_and_annotations(data_polysomnography, df_annotations_polysomnography, fs_new)\n",
        "df_data.to_csv(new_filename, index=False)\n",
        "print(\"Data Merged\")\n",
        "\n",
        "df_data.head(4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "5G_vtkYkIjg4",
        "outputId": "01dccbca-da0c-468a-d1f7-e51e57ed4102"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ROC-LOC</th>\n",
              "      <th>Fp2-F4</th>\n",
              "      <th>F4-C4</th>\n",
              "      <th>C4-P4</th>\n",
              "      <th>P4-O2</th>\n",
              "      <th>C4-A1</th>\n",
              "      <th>EMG1-EMG2</th>\n",
              "      <th>ECG1-ECG2</th>\n",
              "      <th>DX1-DX2</th>\n",
              "      <th>SX1-SX2</th>\n",
              "      <th>F8-T4</th>\n",
              "      <th>F7-T3</th>\n",
              "      <th>TORACE</th>\n",
              "      <th>ADDOME</th>\n",
              "      <th>MIC</th>\n",
              "      <th>times</th>\n",
              "      <th>sleepstage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3870715</th>\n",
              "      <td>-0.000005</td>\n",
              "      <td>-0.000002</td>\n",
              "      <td>-7.783883e-07</td>\n",
              "      <td>-7.173382e-07</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>-1.922763e-06</td>\n",
              "      <td>-0.000013</td>\n",
              "      <td>4.273504e-07</td>\n",
              "      <td>-1.905824e-21</td>\n",
              "      <td>-3.052503e-07</td>\n",
              "      <td>1.526252e-06</td>\n",
              "      <td>0.000876</td>\n",
              "      <td>0.000432</td>\n",
              "      <td>-3.700313e-07</td>\n",
              "      <td>30239.960938</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3870716</th>\n",
              "      <td>-0.000003</td>\n",
              "      <td>-0.000002</td>\n",
              "      <td>-1.388889e-06</td>\n",
              "      <td>-7.478632e-07</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>-0.000002</td>\n",
              "      <td>6.104010e-07</td>\n",
              "      <td>-0.000019</td>\n",
              "      <td>4.273504e-07</td>\n",
              "      <td>-4.578755e-07</td>\n",
              "      <td>4.884005e-07</td>\n",
              "      <td>3.357753e-07</td>\n",
              "      <td>0.000675</td>\n",
              "      <td>0.000477</td>\n",
              "      <td>-1.308461e-06</td>\n",
              "      <td>30239.968750</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3870717</th>\n",
              "      <td>-0.000003</td>\n",
              "      <td>-0.000002</td>\n",
              "      <td>-1.785714e-06</td>\n",
              "      <td>-1.816239e-06</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>-0.000005</td>\n",
              "      <td>-5.585169e-06</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>4.578755e-07</td>\n",
              "      <td>-6.105006e-08</td>\n",
              "      <td>4.578755e-07</td>\n",
              "      <td>-1.831502e-07</td>\n",
              "      <td>0.000353</td>\n",
              "      <td>0.000562</td>\n",
              "      <td>-1.819638e-06</td>\n",
              "      <td>30239.976562</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3870718</th>\n",
              "      <td>-0.000004</td>\n",
              "      <td>-0.000002</td>\n",
              "      <td>-2.335165e-06</td>\n",
              "      <td>-2.518315e-06</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>-0.000005</td>\n",
              "      <td>2.838365e-06</td>\n",
              "      <td>0.000018</td>\n",
              "      <td>-3.357753e-07</td>\n",
              "      <td>2.442002e-07</td>\n",
              "      <td>-7.326007e-07</td>\n",
              "      <td>-3.021978e-06</td>\n",
              "      <td>0.000558</td>\n",
              "      <td>0.000473</td>\n",
              "      <td>3.471427e-07</td>\n",
              "      <td>30239.984375</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3870719</th>\n",
              "      <td>-0.000005</td>\n",
              "      <td>-0.000001</td>\n",
              "      <td>-2.121490e-06</td>\n",
              "      <td>-1.907814e-06</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>-0.000003</td>\n",
              "      <td>-7.935213e-07</td>\n",
              "      <td>0.000016</td>\n",
              "      <td>-5.494505e-07</td>\n",
              "      <td>-6.105006e-08</td>\n",
              "      <td>3.663004e-07</td>\n",
              "      <td>-2.777778e-06</td>\n",
              "      <td>0.001477</td>\n",
              "      <td>0.000158</td>\n",
              "      <td>2.338445e-06</td>\n",
              "      <td>30239.992188</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  </svg>\n",
              "      </button>\n",
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              "  <style>\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "          document.querySelector('#df-6a9f75ae-94c1-478c-9ade-b2ba954b23a4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6a9f75ae-94c1-478c-9ade-b2ba954b23a4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          ROC-LOC    Fp2-F4         F4-C4         C4-P4     P4-O2     C4-A1  \\\n",
              "3870715 -0.000005 -0.000002 -7.783883e-07 -7.173382e-07  0.000009  0.000002   \n",
              "3870716 -0.000003 -0.000002 -1.388889e-06 -7.478632e-07  0.000009 -0.000002   \n",
              "3870717 -0.000003 -0.000002 -1.785714e-06 -1.816239e-06  0.000007 -0.000005   \n",
              "3870718 -0.000004 -0.000002 -2.335165e-06 -2.518315e-06  0.000006 -0.000005   \n",
              "3870719 -0.000005 -0.000001 -2.121490e-06 -1.907814e-06  0.000006 -0.000003   \n",
              "\n",
              "            EMG1-EMG2  ECG1-ECG2       DX1-DX2       SX1-SX2         F8-T4  \\\n",
              "3870715 -1.922763e-06  -0.000013  4.273504e-07 -1.905824e-21 -3.052503e-07   \n",
              "3870716  6.104010e-07  -0.000019  4.273504e-07 -4.578755e-07  4.884005e-07   \n",
              "3870717 -5.585169e-06   0.000001  4.578755e-07 -6.105006e-08  4.578755e-07   \n",
              "3870718  2.838365e-06   0.000018 -3.357753e-07  2.442002e-07 -7.326007e-07   \n",
              "3870719 -7.935213e-07   0.000016 -5.494505e-07 -6.105006e-08  3.663004e-07   \n",
              "\n",
              "                F7-T3    TORACE    ADDOME           MIC         times  \\\n",
              "3870715  1.526252e-06  0.000876  0.000432 -3.700313e-07  30239.960938   \n",
              "3870716  3.357753e-07  0.000675  0.000477 -1.308461e-06  30239.968750   \n",
              "3870717 -1.831502e-07  0.000353  0.000562 -1.819638e-06  30239.976562   \n",
              "3870718 -3.021978e-06  0.000558  0.000473  3.471427e-07  30239.984375   \n",
              "3870719 -2.777778e-06  0.001477  0.000158  2.338445e-06  30239.992188   \n",
              "\n",
              "        sleepstage  \n",
              "3870715          W  \n",
              "3870716          W  \n",
              "3870717          W  \n",
              "3870718          W  \n",
              "3870719          W  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_data.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DozzGL1W9w0a"
      },
      "source": [
        "### Visualizing the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "apxVh6SysfFP",
        "outputId": "d0da5f23-d540-428f-af87-61eb900872c8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "_ = plt.plot(df_data.times/3600, df_data.sleepstage)\n",
        "_ = plt.xlabel(\"Time (h)\")\n",
        "_ = plt.ylabel(\"Sleep Stage\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "m3-BregWBszr",
        "outputId": "fbd98169-4272-4995-8829-acfd7f248095"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ROC-LOC</th>\n",
              "      <th>Fp2-F4</th>\n",
              "      <th>F4-C4</th>\n",
              "      <th>C4-P4</th>\n",
              "      <th>P4-O2</th>\n",
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              "      <th>F7-T3</th>\n",
              "      <th>TORACE</th>\n",
              "      <th>ADDOME</th>\n",
              "      <th>MIC</th>\n",
              "      <th>times</th>\n",
              "      <th>sleepstage</th>\n",
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              "      <th>0</th>\n",
              "      <td>-0.000021</td>\n",
              "      <td>2.304640e-06</td>\n",
              "      <td>6.868132e-07</td>\n",
              "      <td>2.594628e-07</td>\n",
              "      <td>1.984127e-07</td>\n",
              "      <td>-0.000009</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>-0.000032</td>\n",
              "      <td>5.799756e-07</td>\n",
              "      <td>4.273504e-07</td>\n",
              "      <td>6.715507e-07</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>0.000299</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.000023</td>\n",
              "      <td>1.266789e-06</td>\n",
              "      <td>2.274115e-06</td>\n",
              "      <td>4.578755e-08</td>\n",
              "      <td>1.678877e-07</td>\n",
              "      <td>-0.000006</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>-0.000167</td>\n",
              "      <td>5.189255e-07</td>\n",
              "      <td>-1.526252e-07</td>\n",
              "      <td>-9.157509e-07</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>0.000360</td>\n",
              "      <td>-0.000862</td>\n",
              "      <td>1.392386e-06</td>\n",
              "      <td>9270.007812</td>\n",
              "      <td>W</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.000021</td>\n",
              "      <td>-1.678877e-07</td>\n",
              "      <td>1.633089e-06</td>\n",
              "      <td>5.647131e-07</td>\n",
              "      <td>3.205128e-07</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>-0.000001</td>\n",
              "      <td>-0.000352</td>\n",
              "      <td>-4.884005e-07</td>\n",
              "      <td>-6.410256e-07</td>\n",
              "      <td>-6.715507e-07</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.000295</td>\n",
              "      <td>-0.000809</td>\n",
              "      <td>4.959182e-08</td>\n",
              "      <td>9270.015625</td>\n",
              "      <td>W</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.000019</td>\n",
              "      <td>1.236264e-06</td>\n",
              "      <td>1.205739e-06</td>\n",
              "      <td>1.877289e-06</td>\n",
              "      <td>6.257631e-07</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>-0.000004</td>\n",
              "      <td>-0.000383</td>\n",
              "      <td>-2.442002e-07</td>\n",
              "      <td>3.970467e-23</td>\n",
              "      <td>1.923077e-06</td>\n",
              "      <td>-0.000015</td>\n",
              "      <td>0.000259</td>\n",
              "      <td>-0.000783</td>\n",
              "      <td>-3.917754e-06</td>\n",
              "      <td>9270.023438</td>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cc299a5b-70eb-42a9-ba39-611bf729671b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cc299a5b-70eb-42a9-ba39-611bf729671b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    ROC-LOC        Fp2-F4         F4-C4         C4-P4         P4-O2     C4-A1  \\\n",
              "0 -0.000021  2.304640e-06  6.868132e-07  2.594628e-07  1.984127e-07 -0.000009   \n",
              "1 -0.000023  1.266789e-06  2.274115e-06  4.578755e-08  1.678877e-07 -0.000006   \n",
              "2 -0.000021 -1.678877e-07  1.633089e-06  5.647131e-07  3.205128e-07  0.000002   \n",
              "3 -0.000019  1.236264e-06  1.205739e-06  1.877289e-06  6.257631e-07  0.000007   \n",
              "\n",
              "   EMG1-EMG2  ECG1-ECG2       DX1-DX2       SX1-SX2         F8-T4     F7-T3  \\\n",
              "0   0.000005  -0.000032  5.799756e-07  4.273504e-07  6.715507e-07  0.000005   \n",
              "1   0.000003  -0.000167  5.189255e-07 -1.526252e-07 -9.157509e-07  0.000010   \n",
              "2  -0.000001  -0.000352 -4.884005e-07 -6.410256e-07 -6.715507e-07  0.000004   \n",
              "3  -0.000004  -0.000383 -2.442002e-07  3.970467e-23  1.923077e-06 -0.000015   \n",
              "\n",
              "     TORACE    ADDOME           MIC        times sleepstage  \n",
              "0  0.000299 -0.000792 -3.803311e-06  9270.000000          W  \n",
              "1  0.000360 -0.000862  1.392386e-06  9270.007812          W  \n",
              "2  0.000295 -0.000809  4.959182e-08  9270.015625          W  \n",
              "3  0.000259 -0.000783 -3.917754e-06  9270.023438          W  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import urllib.request\n",
        "from datetime import datetime\n",
        "import mne\n",
        "\n",
        "df = pd.read_csv(new_filename)\n",
        "df.head(4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QQOKkbHX_mlm"
      },
      "source": [
        "### Visualizing the data for following:\n",
        "\n",
        "1. Times\n",
        "2. ROC-LOC\n",
        "3. F4-C4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "rKNZOgzf7Way",
        "outputId": "6e10e76a-49f1-49a1-8d72-1bbe2802d71e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f3e00d588d0>]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 2160x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize = (30,5))\n",
        "plt.plot(df[\"times\"], df[\"ROC-LOC\"])\n",
        "plt.plot(df[\"times\"], df[\"F4-C4\"])\n",
        "#plt.plot(df[\"times\"], df[\"sleepstage\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HEyPg8AnmpkZ",
        "outputId": "6fda0529-100b-4dc2-ac94-e8f0cb547a7f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['ROC-LOC', 'Fp2-F4', 'F4-C4', 'C4-P4', 'P4-O2', 'C4-A1', 'EMG1-EMG2',\n",
              "       'ECG1-ECG2', 'DX1-DX2', 'SX1-SX2', 'F8-T4', 'F7-T3', 'TORACE', 'ADDOME',\n",
              "       'MIC', 'times', 'sleepstage'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fD7xB6t2_1LQ"
      },
      "source": [
        "### Applying the MinMaxScaler on the data\n",
        "\n",
        "Transform features by scaling each feature to a given range.\n",
        "\n",
        "This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "AS4i7PpfuoWR",
        "outputId": "9178c3ff-352b-4acc-9ad6-34bc79efbaa0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MinMaxScaler()\n",
            "[7.28980075e-04 4.52274115e-04 4.03586691e-04 4.44001832e-04\n",
            " 3.66071429e-04 3.62652625e-04 5.91173393e-04 9.21369060e-04\n",
            " 3.77960928e-04 3.53663004e-04 8.42185592e-04 7.52106227e-04\n",
            " 5.98955156e-03 6.06275699e-03 8.99481193e-05]\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e1ec9ebd-19ad-4e0b-861e-8d6d52975d9a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>times</th>\n",
              "      <th>sleepstage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.405702</td>\n",
              "      <td>0.690211</td>\n",
              "      <td>0.410654</td>\n",
              "      <td>0.502975</td>\n",
              "      <td>0.615501</td>\n",
              "      <td>0.594384</td>\n",
              "      <td>0.528498</td>\n",
              "      <td>0.503567</td>\n",
              "      <td>0.425484</td>\n",
              "      <td>0.470777</td>\n",
              "      <td>0.530494</td>\n",
              "      <td>0.459260</td>\n",
              "      <td>0.527172</td>\n",
              "      <td>0.426842</td>\n",
              "      <td>0.353739</td>\n",
              "      <td>9270.000000</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.404493</td>\n",
              "      <td>0.689496</td>\n",
              "      <td>0.412976</td>\n",
              "      <td>0.502735</td>\n",
              "      <td>0.615469</td>\n",
              "      <td>0.598248</td>\n",
              "      <td>0.527198</td>\n",
              "      <td>0.433279</td>\n",
              "      <td>0.425392</td>\n",
              "      <td>0.469908</td>\n",
              "      <td>0.529608</td>\n",
              "      <td>0.462576</td>\n",
              "      <td>0.532224</td>\n",
              "      <td>0.420962</td>\n",
              "      <td>0.389555</td>\n",
              "      <td>9270.007812</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.405557</td>\n",
              "      <td>0.688509</td>\n",
              "      <td>0.412038</td>\n",
              "      <td>0.503316</td>\n",
              "      <td>0.615629</td>\n",
              "      <td>0.606675</td>\n",
              "      <td>0.523542</td>\n",
              "      <td>0.337109</td>\n",
              "      <td>0.423858</td>\n",
              "      <td>0.469176</td>\n",
              "      <td>0.529744</td>\n",
              "      <td>0.458199</td>\n",
              "      <td>0.526893</td>\n",
              "      <td>0.425437</td>\n",
              "      <td>0.380299</td>\n",
              "      <td>9270.015625</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.407419</td>\n",
              "      <td>0.689475</td>\n",
              "      <td>0.411413</td>\n",
              "      <td>0.504787</td>\n",
              "      <td>0.615950</td>\n",
              "      <td>0.612105</td>\n",
              "      <td>0.521506</td>\n",
              "      <td>0.320761</td>\n",
              "      <td>0.424230</td>\n",
              "      <td>0.470136</td>\n",
              "      <td>0.531192</td>\n",
              "      <td>0.444228</td>\n",
              "      <td>0.523863</td>\n",
              "      <td>0.427642</td>\n",
              "      <td>0.352950</td>\n",
              "      <td>9270.023438</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e1ec9ebd-19ad-4e0b-861e-8d6d52975d9a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e1ec9ebd-19ad-4e0b-861e-8d6d52975d9a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e1ec9ebd-19ad-4e0b-861e-8d6d52975d9a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          0         1         2         3         4         5         6  \\\n",
              "0  0.405702  0.690211  0.410654  0.502975  0.615501  0.594384  0.528498   \n",
              "1  0.404493  0.689496  0.412976  0.502735  0.615469  0.598248  0.527198   \n",
              "2  0.405557  0.688509  0.412038  0.503316  0.615629  0.606675  0.523542   \n",
              "3  0.407419  0.689475  0.411413  0.504787  0.615950  0.612105  0.521506   \n",
              "\n",
              "          7         8         9        10        11        12        13  \\\n",
              "0  0.503567  0.425484  0.470777  0.530494  0.459260  0.527172  0.426842   \n",
              "1  0.433279  0.425392  0.469908  0.529608  0.462576  0.532224  0.420962   \n",
              "2  0.337109  0.423858  0.469176  0.529744  0.458199  0.526893  0.425437   \n",
              "3  0.320761  0.424230  0.470136  0.531192  0.444228  0.523863  0.427642   \n",
              "\n",
              "         14        times sleepstage  \n",
              "0  0.353739  9270.000000          W  \n",
              "1  0.389555  9270.007812          W  \n",
              "2  0.380299  9270.015625          W  \n",
              "3  0.352950  9270.023438          W  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#sklearn.preprocessing.MinMaxScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "\n",
        "new_df = df.iloc[:,:-2]\n",
        "new_df.head(4)\n",
        "print(scaler.fit(new_df))\n",
        "\n",
        "print(scaler.data_max_)\n",
        "df_norm = scaler.transform(new_df)\n",
        "df_norm = pd.DataFrame(df_norm)\n",
        "df_norm['times'] = df['times']\n",
        "df_norm['sleepstage'] = df['sleepstage']\n",
        "df_norm.head(4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fxMRmhVaATDf"
      },
      "source": [
        "#### Visualzing the transformed data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "xDtRr1tpuxMh",
        "outputId": "b0326230-0165-4963-ebd5-948fb6719be9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-58081d36-c122-4975-baf0-1f35208c52aa\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ROC-LOC</th>\n",
              "      <th>Fp2-F4</th>\n",
              "      <th>F4-C4</th>\n",
              "      <th>C4-P4</th>\n",
              "      <th>P4-O2</th>\n",
              "      <th>C4-A1</th>\n",
              "      <th>EMG1-EMG2</th>\n",
              "      <th>ECG1-ECG2</th>\n",
              "      <th>DX1-DX2</th>\n",
              "      <th>SX1-SX2</th>\n",
              "      <th>F8-T4</th>\n",
              "      <th>F7-T3</th>\n",
              "      <th>TORACE</th>\n",
              "      <th>ADDOME</th>\n",
              "      <th>MIC</th>\n",
              "      <th>times</th>\n",
              "      <th>sleepstage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.000021</td>\n",
              "      <td>2.304640e-06</td>\n",
              "      <td>6.868132e-07</td>\n",
              "      <td>2.594628e-07</td>\n",
              "      <td>1.984127e-07</td>\n",
              "      <td>-0.000009</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>-0.000032</td>\n",
              "      <td>5.799756e-07</td>\n",
              "      <td>4.273504e-07</td>\n",
              "      <td>6.715507e-07</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>0.000299</td>\n",
              "      <td>-0.000792</td>\n",
              "      <td>-3.803311e-06</td>\n",
              "      <td>9270.000000</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.000023</td>\n",
              "      <td>1.266789e-06</td>\n",
              "      <td>2.274115e-06</td>\n",
              "      <td>4.578755e-08</td>\n",
              "      <td>1.678877e-07</td>\n",
              "      <td>-0.000006</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>-0.000167</td>\n",
              "      <td>5.189255e-07</td>\n",
              "      <td>-1.526252e-07</td>\n",
              "      <td>-9.157509e-07</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>0.000360</td>\n",
              "      <td>-0.000862</td>\n",
              "      <td>1.392386e-06</td>\n",
              "      <td>9270.007812</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.000021</td>\n",
              "      <td>-1.678877e-07</td>\n",
              "      <td>1.633089e-06</td>\n",
              "      <td>5.647131e-07</td>\n",
              "      <td>3.205128e-07</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>-0.000001</td>\n",
              "      <td>-0.000352</td>\n",
              "      <td>-4.884005e-07</td>\n",
              "      <td>-6.410256e-07</td>\n",
              "      <td>-6.715507e-07</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.000295</td>\n",
              "      <td>-0.000809</td>\n",
              "      <td>4.959182e-08</td>\n",
              "      <td>9270.015625</td>\n",
              "      <td>W</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.000019</td>\n",
              "      <td>1.236264e-06</td>\n",
              "      <td>1.205739e-06</td>\n",
              "      <td>1.877289e-06</td>\n",
              "      <td>6.257631e-07</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>-0.000004</td>\n",
              "      <td>-0.000383</td>\n",
              "      <td>-2.442002e-07</td>\n",
              "      <td>3.970467e-23</td>\n",
              "      <td>1.923077e-06</td>\n",
              "      <td>-0.000015</td>\n",
              "      <td>0.000259</td>\n",
              "      <td>-0.000783</td>\n",
              "      <td>-3.917754e-06</td>\n",
              "      <td>9270.023438</td>\n",
              "      <td>W</td>\n",
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              "  </svg>\n",
              "      </button>\n",
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              "  <style>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-58081d36-c122-4975-baf0-1f35208c52aa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-58081d36-c122-4975-baf0-1f35208c52aa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    ROC-LOC        Fp2-F4         F4-C4         C4-P4         P4-O2     C4-A1  \\\n",
              "0 -0.000021  2.304640e-06  6.868132e-07  2.594628e-07  1.984127e-07 -0.000009   \n",
              "1 -0.000023  1.266789e-06  2.274115e-06  4.578755e-08  1.678877e-07 -0.000006   \n",
              "2 -0.000021 -1.678877e-07  1.633089e-06  5.647131e-07  3.205128e-07  0.000002   \n",
              "3 -0.000019  1.236264e-06  1.205739e-06  1.877289e-06  6.257631e-07  0.000007   \n",
              "\n",
              "   EMG1-EMG2  ECG1-ECG2       DX1-DX2       SX1-SX2         F8-T4     F7-T3  \\\n",
              "0   0.000005  -0.000032  5.799756e-07  4.273504e-07  6.715507e-07  0.000005   \n",
              "1   0.000003  -0.000167  5.189255e-07 -1.526252e-07 -9.157509e-07  0.000010   \n",
              "2  -0.000001  -0.000352 -4.884005e-07 -6.410256e-07 -6.715507e-07  0.000004   \n",
              "3  -0.000004  -0.000383 -2.442002e-07  3.970467e-23  1.923077e-06 -0.000015   \n",
              "\n",
              "     TORACE    ADDOME           MIC        times sleepstage  \n",
              "0  0.000299 -0.000792 -3.803311e-06  9270.000000          W  \n",
              "1  0.000360 -0.000862  1.392386e-06  9270.007812          W  \n",
              "2  0.000295 -0.000809  4.959182e-08  9270.015625          W  \n",
              "3  0.000259 -0.000783 -3.917754e-06  9270.023438          W  "
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_norm.columns = df.columns \n",
        "\n",
        "df_sel = df_norm[['ROC-LOC', 'Fp2-F4', 'F4-C4', 'C4-P4', 'P4-O2', 'C4-A1', 'EMG1-EMG2','ECG1-ECG2', 'DX1-DX2', 'SX1-SX2', 'F8-T4', 'F7-T3', 'TORACE', 'ADDOME',\n",
        "                  'MIC', 'times', 'sleepstage']]\n",
        "df.head(4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0oJfJRGxA1-O"
      },
      "source": [
        "### Making the dataset excluding the columns of times, sleepstage"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4IYW3IMwmrYa",
        "outputId": "2758b011-9180-44ea-efa1-40f4cf888a78"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['ROC-LOC',\n",
              " 'Fp2-F4',\n",
              " 'F4-C4',\n",
              " 'C4-P4',\n",
              " 'P4-O2',\n",
              " 'C4-A1',\n",
              " 'EMG1-EMG2',\n",
              " 'ECG1-ECG2',\n",
              " 'DX1-DX2',\n",
              " 'SX1-SX2',\n",
              " 'F8-T4',\n",
              " 'F7-T3',\n",
              " 'TORACE',\n",
              " 'ADDOME',\n",
              " 'MIC']"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "columns_electrodes = [el for el in df_sel.columns if el not in [\"times\", \"sleepstage\"]]\n",
        "columns_electrodes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_OQ8S0RfBFR9"
      },
      "source": [
        "#### Puting Sleep Stage Ids accordingly and visualizing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "gpsLpde00rZb",
        "outputId": "0ff69162-1651-4537-f9fd-67e715985cac"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ROC-LOC</th>\n",
              "      <th>Fp2-F4</th>\n",
              "      <th>F4-C4</th>\n",
              "      <th>C4-P4</th>\n",
              "      <th>P4-O2</th>\n",
              "      <th>C4-A1</th>\n",
              "      <th>EMG1-EMG2</th>\n",
              "      <th>ECG1-ECG2</th>\n",
              "      <th>DX1-DX2</th>\n",
              "      <th>SX1-SX2</th>\n",
              "      <th>F8-T4</th>\n",
              "      <th>F7-T3</th>\n",
              "      <th>TORACE</th>\n",
              "      <th>ADDOME</th>\n",
              "      <th>MIC</th>\n",
              "      <th>times</th>\n",
              "      <th>sleepstage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.405702</td>\n",
              "      <td>0.690211</td>\n",
              "      <td>0.410654</td>\n",
              "      <td>0.502975</td>\n",
              "      <td>0.615501</td>\n",
              "      <td>0.594384</td>\n",
              "      <td>0.528498</td>\n",
              "      <td>0.503567</td>\n",
              "      <td>0.425484</td>\n",
              "      <td>0.470777</td>\n",
              "      <td>0.530494</td>\n",
              "      <td>0.459260</td>\n",
              "      <td>0.527172</td>\n",
              "      <td>0.426842</td>\n",
              "      <td>0.353739</td>\n",
              "      <td>9270.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.404493</td>\n",
              "      <td>0.689496</td>\n",
              "      <td>0.412976</td>\n",
              "      <td>0.502735</td>\n",
              "      <td>0.615469</td>\n",
              "      <td>0.598248</td>\n",
              "      <td>0.527198</td>\n",
              "      <td>0.433279</td>\n",
              "      <td>0.425392</td>\n",
              "      <td>0.469908</td>\n",
              "      <td>0.529608</td>\n",
              "      <td>0.462576</td>\n",
              "      <td>0.532224</td>\n",
              "      <td>0.420962</td>\n",
              "      <td>0.389555</td>\n",
              "      <td>9270.007812</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.405557</td>\n",
              "      <td>0.688509</td>\n",
              "      <td>0.412038</td>\n",
              "      <td>0.503316</td>\n",
              "      <td>0.615629</td>\n",
              "      <td>0.606675</td>\n",
              "      <td>0.523542</td>\n",
              "      <td>0.337109</td>\n",
              "      <td>0.423858</td>\n",
              "      <td>0.469176</td>\n",
              "      <td>0.529744</td>\n",
              "      <td>0.458199</td>\n",
              "      <td>0.526893</td>\n",
              "      <td>0.425437</td>\n",
              "      <td>0.380299</td>\n",
              "      <td>9270.015625</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.407419</td>\n",
              "      <td>0.689475</td>\n",
              "      <td>0.411413</td>\n",
              "      <td>0.504787</td>\n",
              "      <td>0.615950</td>\n",
              "      <td>0.612105</td>\n",
              "      <td>0.521506</td>\n",
              "      <td>0.320761</td>\n",
              "      <td>0.424230</td>\n",
              "      <td>0.470136</td>\n",
              "      <td>0.531192</td>\n",
              "      <td>0.444228</td>\n",
              "      <td>0.523863</td>\n",
              "      <td>0.427642</td>\n",
              "      <td>0.352950</td>\n",
              "      <td>9270.023438</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.410442</td>\n",
              "      <td>0.689623</td>\n",
              "      <td>0.411859</td>\n",
              "      <td>0.506223</td>\n",
              "      <td>0.617554</td>\n",
              "      <td>0.612838</td>\n",
              "      <td>0.520671</td>\n",
              "      <td>0.443304</td>\n",
              "      <td>0.424741</td>\n",
              "      <td>0.469587</td>\n",
              "      <td>0.531873</td>\n",
              "      <td>0.444781</td>\n",
              "      <td>0.526550</td>\n",
              "      <td>0.424120</td>\n",
              "      <td>0.370990</td>\n",
              "      <td>9270.031250</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.412207</td>\n",
              "      <td>0.689959</td>\n",
              "      <td>0.412261</td>\n",
              "      <td>0.506941</td>\n",
              "      <td>0.618869</td>\n",
              "      <td>0.612438</td>\n",
              "      <td>0.525088</td>\n",
              "      <td>0.540887</td>\n",
              "      <td>0.424834</td>\n",
              "      <td>0.470959</td>\n",
              "      <td>0.530494</td>\n",
              "      <td>0.449246</td>\n",
              "      <td>0.528323</td>\n",
              "      <td>0.421526</td>\n",
              "      <td>0.403334</td>\n",
              "      <td>9270.039062</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.410756</td>\n",
              "      <td>0.687962</td>\n",
              "      <td>0.410163</td>\n",
              "      <td>0.506941</td>\n",
              "      <td>0.618805</td>\n",
              "      <td>0.618467</td>\n",
              "      <td>0.527370</td>\n",
              "      <td>0.569025</td>\n",
              "      <td>0.423858</td>\n",
              "      <td>0.470136</td>\n",
              "      <td>0.530630</td>\n",
              "      <td>0.457801</td>\n",
              "      <td>0.526183</td>\n",
              "      <td>0.422892</td>\n",
              "      <td>0.367624</td>\n",
              "      <td>9270.046875</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.408701</td>\n",
              "      <td>0.686975</td>\n",
              "      <td>0.406457</td>\n",
              "      <td>0.507112</td>\n",
              "      <td>0.619478</td>\n",
              "      <td>0.619133</td>\n",
              "      <td>0.524695</td>\n",
              "      <td>0.553312</td>\n",
              "      <td>0.424880</td>\n",
              "      <td>0.469953</td>\n",
              "      <td>0.529131</td>\n",
              "      <td>0.458110</td>\n",
              "      <td>0.524507</td>\n",
              "      <td>0.423811</td>\n",
              "      <td>0.391343</td>\n",
              "      <td>9270.054688</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.409281</td>\n",
              "      <td>0.686407</td>\n",
              "      <td>0.404358</td>\n",
              "      <td>0.507795</td>\n",
              "      <td>0.621211</td>\n",
              "      <td>0.620499</td>\n",
              "      <td>0.528744</td>\n",
              "      <td>0.531530</td>\n",
              "      <td>0.425020</td>\n",
              "      <td>0.469450</td>\n",
              "      <td>0.529557</td>\n",
              "      <td>0.455635</td>\n",
              "      <td>0.525752</td>\n",
              "      <td>0.421791</td>\n",
              "      <td>0.375986</td>\n",
              "      <td>9270.062500</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.411240</td>\n",
              "      <td>0.685987</td>\n",
              "      <td>0.406144</td>\n",
              "      <td>0.508719</td>\n",
              "      <td>0.621916</td>\n",
              "      <td>0.626262</td>\n",
              "      <td>0.522291</td>\n",
              "      <td>0.517469</td>\n",
              "      <td>0.424323</td>\n",
              "      <td>0.471371</td>\n",
              "      <td>0.530834</td>\n",
              "      <td>0.459171</td>\n",
              "      <td>0.526673</td>\n",
              "      <td>0.420095</td>\n",
              "      <td>0.398548</td>\n",
              "      <td>9270.070312</td>\n",
              "      <td>0</td>\n",
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            "text/plain": [
              "    ROC-LOC    Fp2-F4     F4-C4     C4-P4     P4-O2     C4-A1  EMG1-EMG2  \\\n",
              "0  0.405702  0.690211  0.410654  0.502975  0.615501  0.594384   0.528498   \n",
              "1  0.404493  0.689496  0.412976  0.502735  0.615469  0.598248   0.527198   \n",
              "2  0.405557  0.688509  0.412038  0.503316  0.615629  0.606675   0.523542   \n",
              "3  0.407419  0.689475  0.411413  0.504787  0.615950  0.612105   0.521506   \n",
              "4  0.410442  0.689623  0.411859  0.506223  0.617554  0.612838   0.520671   \n",
              "5  0.412207  0.689959  0.412261  0.506941  0.618869  0.612438   0.525088   \n",
              "6  0.410756  0.687962  0.410163  0.506941  0.618805  0.618467   0.527370   \n",
              "7  0.408701  0.686975  0.406457  0.507112  0.619478  0.619133   0.524695   \n",
              "8  0.409281  0.686407  0.404358  0.507795  0.621211  0.620499   0.528744   \n",
              "9  0.411240  0.685987  0.406144  0.508719  0.621916  0.626262   0.522291   \n",
              "\n",
              "   ECG1-ECG2   DX1-DX2   SX1-SX2     F8-T4     F7-T3    TORACE    ADDOME  \\\n",
              "0   0.503567  0.425484  0.470777  0.530494  0.459260  0.527172  0.426842   \n",
              "1   0.433279  0.425392  0.469908  0.529608  0.462576  0.532224  0.420962   \n",
              "2   0.337109  0.423858  0.469176  0.529744  0.458199  0.526893  0.425437   \n",
              "3   0.320761  0.424230  0.470136  0.531192  0.444228  0.523863  0.427642   \n",
              "4   0.443304  0.424741  0.469587  0.531873  0.444781  0.526550  0.424120   \n",
              "5   0.540887  0.424834  0.470959  0.530494  0.449246  0.528323  0.421526   \n",
              "6   0.569025  0.423858  0.470136  0.530630  0.457801  0.526183  0.422892   \n",
              "7   0.553312  0.424880  0.469953  0.529131  0.458110  0.524507  0.423811   \n",
              "8   0.531530  0.425020  0.469450  0.529557  0.455635  0.525752  0.421791   \n",
              "9   0.517469  0.424323  0.471371  0.530834  0.459171  0.526673  0.420095   \n",
              "\n",
              "        MIC        times  sleepstage  \n",
              "0  0.353739  9270.000000           0  \n",
              "1  0.389555  9270.007812           0  \n",
              "2  0.380299  9270.015625           0  \n",
              "3  0.352950  9270.023438           0  \n",
              "4  0.370990  9270.031250           0  \n",
              "5  0.403334  9270.039062           0  \n",
              "6  0.367624  9270.046875           0  \n",
              "7  0.391343  9270.054688           0  \n",
              "8  0.375986  9270.062500           0  \n",
              "9  0.398548  9270.070312           0  "
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Conversion sleep stages to Id's\n",
        "sleep_stages_ids = {\"W\": 0, \"S1\": 0, \"S2\": 0, \"S3\":0, \"S4\":0, \"R\": 1}\n",
        "sleep_stages_parsed = df[\"sleepstage\"].apply(lambda x: sleep_stages_ids[x])\n",
        "\n",
        "# Convert the sleep stage to ID's\n",
        "df_sel[\"sleepstage\"] = sleep_stages_parsed\n",
        "\n",
        "df_sel.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U8HGQbktBu7X"
      },
      "source": [
        "### Visualizing the data "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "lQegy1G-pv_N",
        "outputId": "0b2c16d9-25cc-4c30-fcf3-6b64e3a1f411"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fs = int(1/(df_sel.times[1] - df_sel.times[0]))\n",
        "\n",
        "# Number of points to plot per second, in this case all the points are being ploted\n",
        "number_points_1_sec = 10/fs\n",
        "marker_every_sec = int(number_points_1_sec*fs)\n",
        "indexes_plot = np.arange(0, df.shape[0], marker_every_sec)\n",
        "\n",
        "def plot_multiple(df_data, columns_plot, ax2, markevery=1):\n",
        "  \"\"\"\n",
        "  This function receives a dataframe, the column names that one desires to plot \n",
        "  and the Axes plot\n",
        "  \"\"\"\n",
        "\n",
        "  plt_columns = columns_plot.copy()\n",
        "  # Plot also sleep stage\n",
        "  plt_columns.append(\"sleepstage\")\n",
        "\n",
        "  n_rows = len(plt_columns)\n",
        "  dmin = df_data[plt_columns].min().min()\n",
        "  dmax = df_data[plt_columns].max().max()\n",
        "  dr = (dmax - dmin) * 0.7  # Crowd them a bit.\n",
        "  y0 = dmin\n",
        "  y1 = (n_rows - 1) * dr + dmax\n",
        "  ax2.set_ylim(y0, y1)\n",
        "  ticklocs=[]\n",
        "  segs = []\n",
        "  ax2.set_xlim([df_data.times.min(), df_data.times.max()])\n",
        "  ax2.set_ylim(y0, y1)\n",
        "\n",
        "  for i, column_name in enumerate(plt_columns):\n",
        "      segs.append(df_data.iloc[np.arange(0, df_data.shape[0], markevery)][[\"times\", column_name]])\n",
        "      ticklocs.append(i * dr)\n",
        "  \n",
        "\n",
        "  offsets = np.zeros((n_rows, 2), dtype=float)\n",
        "  offsets[:, 1] = ticklocs\n",
        "\n",
        "  lines = LineCollection(segs, offsets=offsets, transOffset=None)\n",
        "  ax2.add_collection(lines)\n",
        "\n",
        "  # Set the yticks to use axes coordinates on the y axis\n",
        "  _ = ax2.set_yticks(ticklocs)\n",
        "  # Set the yticks to use axes coordinates on the y axis\n",
        "  ax2.set_yticks(ticklocs)\n",
        "  ax2.set_yticklabels(plt_columns)\n",
        "\n",
        "  ax2.set_xlabel('Time (s)')\n",
        "  \n",
        "from matplotlib.collections import LineCollection\n",
        "\n",
        "f, ax_multiple = plt.subplots(1, 1, figsize=(15, 8)) \n",
        "plot_multiple(df_sel, columns_electrodes, ax_multiple, markevery=marker_every_sec)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "weJQX1mGCWzp"
      },
      "source": [
        "### Implementation of FastICA \n",
        "A fast algorithm for Independent Component Analysis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "QFB5iemMrJBT",
        "outputId": "8528ea4c-22bd-4b09-b57f-5b560fcc18fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['component#0', 'component#1', 'component#2', 'component#3', 'component#4', 'component#5', 'component#6', 'component#7', 'component#8', 'component#9', 'component#10', 'component#11', 'component#12', 'component#13', 'component#14']\n",
            "Modified DataFrame:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-aaf679a2-7ff0-4053-8a00-d151bfde8ff4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>component#0</th>\n",
              "      <th>component#1</th>\n",
              "      <th>component#2</th>\n",
              "      <th>component#3</th>\n",
              "      <th>component#4</th>\n",
              "      <th>component#5</th>\n",
              "      <th>component#6</th>\n",
              "      <th>component#7</th>\n",
              "      <th>component#8</th>\n",
              "      <th>component#9</th>\n",
              "      <th>component#10</th>\n",
              "      <th>component#11</th>\n",
              "      <th>component#12</th>\n",
              "      <th>component#13</th>\n",
              "      <th>component#14</th>\n",
              "      <th>times</th>\n",
              "      <th>sleepstage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.495620</td>\n",
              "      <td>0.406276</td>\n",
              "      <td>0.561871</td>\n",
              "      <td>0.433764</td>\n",
              "      <td>0.470621</td>\n",
              "      <td>0.574675</td>\n",
              "      <td>0.459297</td>\n",
              "      <td>0.363239</td>\n",
              "      <td>0.407139</td>\n",
              "      <td>0.530053</td>\n",
              "      <td>0.310249</td>\n",
              "      <td>0.502493</td>\n",
              "      <td>0.528269</td>\n",
              "      <td>0.644890</td>\n",
              "      <td>0.501301</td>\n",
              "      <td>9270.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.503015</td>\n",
              "      <td>0.406937</td>\n",
              "      <td>0.562276</td>\n",
              "      <td>0.426839</td>\n",
              "      <td>0.469897</td>\n",
              "      <td>0.574761</td>\n",
              "      <td>0.463498</td>\n",
              "      <td>0.363915</td>\n",
              "      <td>0.409506</td>\n",
              "      <td>0.529039</td>\n",
              "      <td>0.311314</td>\n",
              "      <td>0.432443</td>\n",
              "      <td>0.527493</td>\n",
              "      <td>0.608303</td>\n",
              "      <td>0.502864</td>\n",
              "      <td>9270.007812</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.500179</td>\n",
              "      <td>0.410681</td>\n",
              "      <td>0.562067</td>\n",
              "      <td>0.429775</td>\n",
              "      <td>0.469350</td>\n",
              "      <td>0.576169</td>\n",
              "      <td>0.460321</td>\n",
              "      <td>0.363862</td>\n",
              "      <td>0.408753</td>\n",
              "      <td>0.528872</td>\n",
              "      <td>0.312516</td>\n",
              "      <td>0.335989</td>\n",
              "      <td>0.524712</td>\n",
              "      <td>0.616330</td>\n",
              "      <td>0.505906</td>\n",
              "      <td>9270.015625</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.497373</td>\n",
              "      <td>0.412995</td>\n",
              "      <td>0.561461</td>\n",
              "      <td>0.430587</td>\n",
              "      <td>0.470312</td>\n",
              "      <td>0.575652</td>\n",
              "      <td>0.446574</td>\n",
              "      <td>0.363475</td>\n",
              "      <td>0.406921</td>\n",
              "      <td>0.530065</td>\n",
              "      <td>0.311514</td>\n",
              "      <td>0.319356</td>\n",
              "      <td>0.523043</td>\n",
              "      <td>0.643482</td>\n",
              "      <td>0.510979</td>\n",
              "      <td>9270.023438</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aaf679a2-7ff0-4053-8a00-d151bfde8ff4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-aaf679a2-7ff0-4053-8a00-d151bfde8ff4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-aaf679a2-7ff0-4053-8a00-d151bfde8ff4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   component#0  component#1  component#2  component#3  component#4  \\\n",
              "0     0.495620     0.406276     0.561871     0.433764     0.470621   \n",
              "1     0.503015     0.406937     0.562276     0.426839     0.469897   \n",
              "2     0.500179     0.410681     0.562067     0.429775     0.469350   \n",
              "3     0.497373     0.412995     0.561461     0.430587     0.470312   \n",
              "\n",
              "   component#5  component#6  component#7  component#8  component#9  \\\n",
              "0     0.574675     0.459297     0.363239     0.407139     0.530053   \n",
              "1     0.574761     0.463498     0.363915     0.409506     0.529039   \n",
              "2     0.576169     0.460321     0.363862     0.408753     0.528872   \n",
              "3     0.575652     0.446574     0.363475     0.406921     0.530065   \n",
              "\n",
              "   component#10  component#11  component#12  component#13  component#14  \\\n",
              "0      0.310249      0.502493      0.528269      0.644890      0.501301   \n",
              "1      0.311314      0.432443      0.527493      0.608303      0.502864   \n",
              "2      0.312516      0.335989      0.524712      0.616330      0.505906   \n",
              "3      0.311514      0.319356      0.523043      0.643482      0.510979   \n",
              "\n",
              "         times  sleepstage  \n",
              "0  9270.000000           0  \n",
              "1  9270.007812           0  \n",
              "2  9270.015625           0  \n",
              "3  9270.023438           0  "
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.decomposition import FastICA\n",
        "ica_model = FastICA(n_components=len(columns_electrodes), random_state=0)\n",
        "\n",
        "# Apply ICA only to the electrodes (exclude sleep stages and times)\n",
        "independent_sources = ica_model.fit_transform(df_sel[columns_electrodes])\n",
        "\n",
        "# Apply the Min-Max Scaler\n",
        "scaled_independent_sources = MinMaxScaler().fit_transform(independent_sources)*df_sel[\"sleepstage\"].max()\n",
        "\n",
        "# The fit_transform returns a matrix, convert it to dataframe\n",
        "components_columns_name = [f'component#{el}' for el in range(len(columns_electrodes))]\n",
        "print(components_columns_name)\n",
        "print(\"Modified DataFrame:\")\n",
        "df_sources = pd.DataFrame(data=scaled_independent_sources, columns=components_columns_name)\n",
        "df_sources[\"times\"] = df_sel[\"times\"]\n",
        "df_sources[\"sleepstage\"] = df_sel[\"sleepstage\"]\n",
        "df_sources.head(4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4CPF-m0-dcVt",
        "outputId": "e49b1ed2-cc79-4376-f56f-e74db683f5b8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['component#0', 'component#1', 'component#2', 'component#3', 'component#4', 'component#5', 'component#6', 'component#7', 'component#8', 'component#9', 'component#10', 'component#11', 'component#12', 'component#13', 'component#14']\n"
          ]
        }
      ],
      "source": [
        "print(components_columns_name)\n",
        "components_columns_name.append('times')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4udVHe5iH7w",
        "outputId": "6dca8c6a-dc93-4dca-8922-f4f612c08245"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['component#0', 'component#1', 'component#2', 'component#3', 'component#4', 'component#5', 'component#6', 'component#7', 'component#8', 'component#9', 'component#10', 'component#11', 'component#12', 'component#13', 'component#14', 'times']\n"
          ]
        }
      ],
      "source": [
        "print(components_columns_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VdlBPeVBaoVz"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler=MinMaxScaler()\n",
        "df_sources[components_columns_name] = scaler.fit_transform(df_sources[components_columns_name])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "KcdRLr0Iea3Q",
        "outputId": "4d5f1ab1-6eec-4a2f-aecf-8092afb307a9"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-6c02c8c8-6cf3-4bd7-bca5-3ccf86cebc9d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>component#0</th>\n",
              "      <th>component#1</th>\n",
              "      <th>component#2</th>\n",
              "      <th>component#3</th>\n",
              "      <th>component#4</th>\n",
              "      <th>component#5</th>\n",
              "      <th>component#6</th>\n",
              "      <th>component#7</th>\n",
              "      <th>component#8</th>\n",
              "      <th>component#9</th>\n",
              "      <th>component#10</th>\n",
              "      <th>component#11</th>\n",
              "      <th>component#12</th>\n",
              "      <th>component#13</th>\n",
              "      <th>component#14</th>\n",
              "      <th>times</th>\n",
              "      <th>sleepstage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.495620</td>\n",
              "      <td>0.406276</td>\n",
              "      <td>0.561871</td>\n",
              "      <td>0.433764</td>\n",
              "      <td>0.470621</td>\n",
              "      <td>0.574675</td>\n",
              "      <td>0.459297</td>\n",
              "      <td>0.363239</td>\n",
              "      <td>0.407139</td>\n",
              "      <td>0.530053</td>\n",
              "      <td>0.310249</td>\n",
              "      <td>0.502493</td>\n",
              "      <td>0.528269</td>\n",
              "      <td>0.644890</td>\n",
              "      <td>0.501301</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.503015</td>\n",
              "      <td>0.406937</td>\n",
              "      <td>0.562276</td>\n",
              "      <td>0.426839</td>\n",
              "      <td>0.469897</td>\n",
              "      <td>0.574761</td>\n",
              "      <td>0.463498</td>\n",
              "      <td>0.363915</td>\n",
              "      <td>0.409506</td>\n",
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              "      <td>0.608303</td>\n",
              "      <td>0.502864</td>\n",
              "      <td>3.725562e-07</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.500179</td>\n",
              "      <td>0.410681</td>\n",
              "      <td>0.562067</td>\n",
              "      <td>0.429775</td>\n",
              "      <td>0.469350</td>\n",
              "      <td>0.576169</td>\n",
              "      <td>0.460321</td>\n",
              "      <td>0.363862</td>\n",
              "      <td>0.408753</td>\n",
              "      <td>0.528872</td>\n",
              "      <td>0.312516</td>\n",
              "      <td>0.335989</td>\n",
              "      <td>0.524712</td>\n",
              "      <td>0.616330</td>\n",
              "      <td>0.505906</td>\n",
              "      <td>7.451123e-07</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.497373</td>\n",
              "      <td>0.412995</td>\n",
              "      <td>0.561461</td>\n",
              "      <td>0.430587</td>\n",
              "      <td>0.470312</td>\n",
              "      <td>0.575652</td>\n",
              "      <td>0.446574</td>\n",
              "      <td>0.363475</td>\n",
              "      <td>0.406921</td>\n",
              "      <td>0.530065</td>\n",
              "      <td>0.311514</td>\n",
              "      <td>0.319356</td>\n",
              "      <td>0.523043</td>\n",
              "      <td>0.643482</td>\n",
              "      <td>0.510979</td>\n",
              "      <td>1.117669e-06</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.496819</td>\n",
              "      <td>0.413220</td>\n",
              "      <td>0.561477</td>\n",
              "      <td>0.425692</td>\n",
              "      <td>0.469518</td>\n",
              "      <td>0.575270</td>\n",
              "      <td>0.445673</td>\n",
              "      <td>0.362915</td>\n",
              "      <td>0.405930</td>\n",
              "      <td>0.531043</td>\n",
              "      <td>0.310895</td>\n",
              "      <td>0.441899</td>\n",
              "      <td>0.521174</td>\n",
              "      <td>0.627057</td>\n",
              "      <td>0.521385</td>\n",
              "      <td>1.490225e-06</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6c02c8c8-6cf3-4bd7-bca5-3ccf86cebc9d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6c02c8c8-6cf3-4bd7-bca5-3ccf86cebc9d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   component#0  component#1  component#2  component#3  component#4  \\\n",
              "0     0.495620     0.406276     0.561871     0.433764     0.470621   \n",
              "1     0.503015     0.406937     0.562276     0.426839     0.469897   \n",
              "2     0.500179     0.410681     0.562067     0.429775     0.469350   \n",
              "3     0.497373     0.412995     0.561461     0.430587     0.470312   \n",
              "4     0.496819     0.413220     0.561477     0.425692     0.469518   \n",
              "\n",
              "   component#5  component#6  component#7  component#8  component#9  \\\n",
              "0     0.574675     0.459297     0.363239     0.407139     0.530053   \n",
              "1     0.574761     0.463498     0.363915     0.409506     0.529039   \n",
              "2     0.576169     0.460321     0.363862     0.408753     0.528872   \n",
              "3     0.575652     0.446574     0.363475     0.406921     0.530065   \n",
              "4     0.575270     0.445673     0.362915     0.405930     0.531043   \n",
              "\n",
              "   component#10  component#11  component#12  component#13  component#14  \\\n",
              "0      0.310249      0.502493      0.528269      0.644890      0.501301   \n",
              "1      0.311314      0.432443      0.527493      0.608303      0.502864   \n",
              "2      0.312516      0.335989      0.524712      0.616330      0.505906   \n",
              "3      0.311514      0.319356      0.523043      0.643482      0.510979   \n",
              "4      0.310895      0.441899      0.521174      0.627057      0.521385   \n",
              "\n",
              "          times  sleepstage  \n",
              "0  0.000000e+00           0  \n",
              "1  3.725562e-07           0  \n",
              "2  7.451123e-07           0  \n",
              "3  1.117669e-06           0  \n",
              "4  1.490225e-06           0  "
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_sources.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s9twptwKe49z",
        "outputId": "337353c0-2c48-492f-a15b-69078ca1ef09"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([False, False, False, False, False, False, False, False, False,\n",
              "       False, False, False, False, False, False, False, False])"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_sources.isnull().any().values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7pkQSISBO4EV"
      },
      "outputs": [],
      "source": [
        "lt=  ['component#0', 'component#1', 'component#3', 'component#4', 'component#7','component#8', 'component#9','component#10','component#11','component#12','component#13','component#14' ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "N-U0xVWnOrCW",
        "outputId": "eee2331e-4594-4701-f772-d3226afdc8c4"
      },
      "outputs": [
        {
          "data": {
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              "      <td>0.574675</td>\n",
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              "          const element = document.querySelector('#df-40ce6ec8-3373-4eac-af63-d9cb99ff733e');\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   component#2  component#5  component#6         times  sleepstage\n",
              "0     0.561871     0.574675     0.459297  0.000000e+00           0\n",
              "1     0.562276     0.574761     0.463498  3.725562e-07           0\n",
              "2     0.562067     0.576169     0.460321  7.451123e-07           0\n",
              "3     0.561461     0.575652     0.446574  1.117669e-06           0\n",
              "4     0.561477     0.575270     0.445673  1.490225e-06           0"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_sourcess = df_sources.drop(lt,axis=1)\n",
        "df_sourcess.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1ciPeOose5AE"
      },
      "outputs": [],
      "source": [
        "x = df_sourcess.drop(\"sleepstage\",axis=1)\n",
        "y = df_sourcess[\"sleepstage\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nc1U8-c8e5Cs",
        "outputId": "8e6df1b1-5488-46ca-fff4-caf14a5222ca"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "y.unique()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fcEOK92_e5FS",
        "outputId": "481abfa4-6762-4490-add5-7d5b519f8136"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(2415744, 4)\n",
            "(268416, 4)\n",
            "(2415744,)\n",
            "(268416,)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,train_size = 0.9,stratify=y)\n",
        "\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yJ8oU8gr6yIY"
      },
      "outputs": [],
      "source": [
        "from scipy.stats import mode\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CxXCH2MUsbGM"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mgsT2N2QfMO2"
      },
      "source": [
        "# **Applying Navie Bayes classification**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "eC9IPViUe5H0",
        "outputId": "f265cdfc-9248-45d5-b4bf-a22bdafd95d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Naive Bayes test accuracy score:  0.9020363912732475\n",
            "Naive Bayes train accuracy score:  0.9027285175912679\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "import tensorflow as tf\n",
        "\n",
        "nb= GaussianNB()\n",
        "nb.fit(x_train, y_train)\n",
        "print(\"Naive Bayes test accuracy score: \",nb.score(x_test, y_test))\n",
        "print(\"Naive Bayes train accuracy score: \",nb.score(x_train, y_train))\n",
        "\n",
        "preds5 = nb.predict(x_test)\n",
        "cm = tf.math.confusion_matrix(labels=y_test,predictions=preds5)\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(cm,annot=True,fmt='d')\n",
        "plt.xlabel('Predicated')\n",
        "plt.ylabel('Truth')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "La3Lz4TZ__FD"
      },
      "source": [
        "# **Applying logistic regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "-qM0l1cq_5V-",
        "outputId": "70a52d4d-138b-4525-e68b-c0b118d073b0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "logistic regression test accuracy score:  0.9152844837863615\n",
            "logistic regression train accuracy score:  0.9155535520320034\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model3 = LogisticRegression()\n",
        "model3.fit(x_train, y_train)\n",
        "\n",
        "print(\"logistic regression test accuracy score: \",model3.score(x_test, y_test))\n",
        "print(\"logistic regression train accuracy score: \",model3.score(x_train, y_train))\n",
        "\n",
        "preds3 = model3.predict(x_test)\n",
        "cm = tf.math.confusion_matrix(labels=y_test,predictions=preds3)\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(cm,annot=True,fmt='d')\n",
        "plt.xlabel('Predicated')\n",
        "plt.ylabel('Truth')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pc92gQiSEXv8"
      },
      "source": [
        "## Using TensorFlow Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vMY3S8n3EegM"
      },
      "outputs": [],
      "source": [
        "import tensorflow_decision_forests as tfdf\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZHEeIn4Hivr",
        "outputId": "b050808b-4d95-41d2-ed1b-a1d5c242da4b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Use /tmp/tmpghsb3ovt as temporary training directory\n",
            "Reading training dataset...\n",
            "Training dataset read in 0:01:04.835630. Found 2415744 examples.\n",
            "Training model...\n",
            "Model trained in 0:11:09.174104\n",
            "Compiling model...\n",
            "WARNING:tensorflow:AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7f3e093f47a0> and will run it as-is.\n",
            "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
            "Cause: could not get source code\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7f3e093f47a0> and will run it as-is.\n",
            "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
            "Cause: could not get source code\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING: AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7f3e093f47a0> and will run it as-is.\n",
            "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
            "Cause: could not get source code\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
            "Model compiled.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f3e016e1bd0>"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tuner = tfdf.tuner.RandomSearch(num_trials=20)\n",
        "model1 = tfdf.keras.RandomForestModel(random_seed=18,tuner = tuner)\n",
        "model1.fit(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pRGA2dCFHvBf",
        "outputId": "9e780190-6557-4fd2-d327-72cec9b424d9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "8388/8388 [==============================] - 22s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
            "\n",
            "loss: 0.0000\n",
            "accuracy: 1.0000\n"
          ]
        }
      ],
      "source": [
        "model1.compile(metrics=[\"accuracy\"])\n",
        "evaluation = model1.evaluate(x_test,y_test,return_dict=True)\n",
        "print()\n",
        "\n",
        "for name, value in evaluation.items():\n",
        "  print(f\"{name}: {value:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PAOd_J78Djij",
        "outputId": "0282a855-6e3d-43bf-d62e-e02966e87028"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"random_forest_model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            "=================================================================\n",
            "Total params: 1\n",
            "Trainable params: 0\n",
            "Non-trainable params: 1\n",
            "_________________________________________________________________\n",
            "Type: \"RANDOM_FOREST\"\n",
            "Task: CLASSIFICATION\n",
            "Label: \"__LABEL\"\n",
            "\n",
            "Input Features (4):\n",
            "\tdata:0.0\n",
            "\tdata:0.1\n",
            "\tdata:0.2\n",
            "\tdata:0.3\n",
            "\n",
            "No weights\n",
            "\n",
            "Variable Importance: MEAN_MIN_DEPTH:\n",
            "    1.  \"__LABEL\"  6.952873 ################\n",
            "    2. \"data:0.1\"  5.836930 #############\n",
            "    3. \"data:0.0\"  4.660335 ##########\n",
            "    4. \"data:0.2\"  2.234418 ####\n",
            "    5. \"data:0.3\"  0.408420 \n",
            "\n",
            "Variable Importance: NUM_AS_ROOT:\n",
            "    1. \"data:0.3\" 219.000000 ################\n",
            "    2. \"data:0.2\" 81.000000 \n",
            "\n",
            "Variable Importance: NUM_NODES:\n",
            "    1. \"data:0.3\" 10707.000000 ################\n",
            "    2. \"data:0.0\" 1864.000000 #\n",
            "    3. \"data:0.2\" 1192.000000 \n",
            "    4. \"data:0.1\" 722.000000 \n",
            "\n",
            "Variable Importance: SUM_SCORE:\n",
            "    1. \"data:0.3\" 342597295.227745 ################\n",
            "    2. \"data:0.2\" 522517.658883 \n",
            "    3. \"data:0.0\" 275406.331352 \n",
            "    4. \"data:0.1\" 54181.845337 \n",
            "\n",
            "\n",
            "Hyperparameter optimizer:\n",
            "\n",
            "Best parameters: *empty*\n",
            "Num steps: 1\n",
            "Best score: 0.999997\n",
            "\n",
            "Step #0 score:0.999997 parameters:{ *empty* }\n",
            "\n",
            "\n",
            "Winner take all: true\n",
            "Out-of-bag evaluation: accuracy:0.999997 logloss:3.86566e-05\n",
            "Number of trees: 300\n",
            "Total number of nodes: 29270\n",
            "\n",
            "Number of nodes by tree:\n",
            "Count: 300 Average: 97.5667 StdDev: 76.3519\n",
            "Min: 21 Max: 531 Ignored: 0\n",
            "----------------------------------------------\n",
            "[  21,  46) 89  29.67%  29.67% ##########\n",
            "[  46,  72) 50  16.67%  46.33% ######\n",
            "[  72,  97) 40  13.33%  59.67% ####\n",
            "[  97, 123) 42  14.00%  73.67% #####\n",
            "[ 123, 148) 21   7.00%  80.67% ##\n",
            "[ 148, 174) 14   4.67%  85.33% ##\n",
            "[ 174, 199) 13   4.33%  89.67% #\n",
            "[ 199, 225)  9   3.00%  92.67% #\n",
            "[ 225, 250)  6   2.00%  94.67% #\n",
            "[ 250, 276)  6   2.00%  96.67% #\n",
            "[ 276, 302)  2   0.67%  97.33%\n",
            "[ 302, 327)  2   0.67%  98.00%\n",
            "[ 327, 353)  2   0.67%  98.67%\n",
            "[ 353, 378)  2   0.67%  99.33%\n",
            "[ 378, 404)  0   0.00%  99.33%\n",
            "[ 404, 429)  1   0.33%  99.67%\n",
            "[ 429, 455)  0   0.00%  99.67%\n",
            "[ 455, 480)  0   0.00%  99.67%\n",
            "[ 480, 506)  0   0.00%  99.67%\n",
            "[ 506, 531]  1   0.33% 100.00%\n",
            "\n",
            "Depth by leafs:\n",
            "Count: 14785 Average: 7.96659 StdDev: 2.93433\n",
            "Min: 2 Max: 15 Ignored: 0\n",
            "----------------------------------------------\n",
            "[  2,  3)  154   1.04%   1.04% #\n",
            "[  3,  4)  856   5.79%   6.83% ####\n",
            "[  4,  5) 1063   7.19%  14.02% #####\n",
            "[  5,  6) 1200   8.12%  22.14% ######\n",
            "[  6,  7) 1490  10.08%  32.22% ########\n",
            "[  7,  8) 1764  11.93%  44.15% #########\n",
            "[  8,  9) 1935  13.09%  57.23% ##########\n",
            "[  9, 10) 1871  12.65%  69.89% ##########\n",
            "[ 10, 11) 1458   9.86%  79.75% ########\n",
            "[ 11, 12) 1194   8.08%  87.83% ######\n",
            "[ 12, 13)  814   5.51%  93.33% ####\n",
            "[ 13, 14)  465   3.15%  96.48% ##\n",
            "[ 14, 15)  291   1.97%  98.44% ##\n",
            "[ 15, 15]  230   1.56% 100.00% #\n",
            "\n",
            "Number of training obs by leaf:\n",
            "Count: 14785 Average: 49017.5 StdDev: 163189\n",
            "Min: 5 Max: 1090835 Ignored: 0\n",
            "----------------------------------------------\n",
            "[       5,   54546) 12882  87.13%  87.13% ##########\n",
            "[   54546,  109088)   697   4.71%  91.84% #\n",
            "[  109088,  163629)    29   0.20%  92.04%\n",
            "[  163629,  218171)   283   1.91%  93.95%\n",
            "[  218171,  272712)    28   0.19%  94.14%\n",
            "[  272712,  327254)    83   0.56%  94.70%\n",
            "[  327254,  381795)   237   1.60%  96.31%\n",
            "[  381795,  436337)   157   1.06%  97.37%\n",
            "[  436337,  490878)    89   0.60%  97.97%\n",
            "[  490878,  545420)     0   0.00%  97.97%\n",
            "[  545420,  599962)     2   0.01%  97.98%\n",
            "[  599962,  654503)     0   0.00%  97.98%\n",
            "[  654503,  709045)     0   0.00%  97.98%\n",
            "[  709045,  763586)     1   0.01%  97.99%\n",
            "[  763586,  818128)     0   0.00%  97.99%\n",
            "[  818128,  872669)     5   0.03%  98.03%\n",
            "[  872669,  927211)     8   0.05%  98.08%\n",
            "[  927211,  981752)    29   0.20%  98.28%\n",
            "[  981752, 1036294)    71   0.48%  98.76%\n",
            "[ 1036294, 1090835]   184   1.24% 100.00%\n",
            "\n",
            "Attribute in nodes:\n",
            "\t10707 : data:0.3 [NUMERICAL]\n",
            "\t1864 : data:0.0 [NUMERICAL]\n",
            "\t1192 : data:0.2 [NUMERICAL]\n",
            "\t722 : data:0.1 [NUMERICAL]\n",
            "\n",
            "Attribute in nodes with depth <= 0:\n",
            "\t219 : data:0.3 [NUMERICAL]\n",
            "\t81 : data:0.2 [NUMERICAL]\n",
            "\n",
            "Attribute in nodes with depth <= 1:\n",
            "\t659 : data:0.3 [NUMERICAL]\n",
            "\t165 : data:0.2 [NUMERICAL]\n",
            "\t76 : data:0.0 [NUMERICAL]\n",
            "\n",
            "Attribute in nodes with depth <= 2:\n",
            "\t1446 : data:0.3 [NUMERICAL]\n",
            "\t336 : data:0.2 [NUMERICAL]\n",
            "\t160 : data:0.0 [NUMERICAL]\n",
            "\t4 : data:0.1 [NUMERICAL]\n",
            "\n",
            "Attribute in nodes with depth <= 3:\n",
            "\t2382 : data:0.3 [NUMERICAL]\n",
            "\t490 : data:0.2 [NUMERICAL]\n",
            "\t273 : data:0.0 [NUMERICAL]\n",
            "\t37 : data:0.1 [NUMERICAL]\n",
            "\n",
            "Attribute in nodes with depth <= 5:\n",
            "\t4653 : data:0.3 [NUMERICAL]\n",
            "\t715 : data:0.2 [NUMERICAL]\n",
            "\t658 : data:0.0 [NUMERICAL]\n",
            "\t183 : data:0.1 [NUMERICAL]\n",
            "\n",
            "Condition type in nodes:\n",
            "\t14485 : HigherCondition\n",
            "Condition type in nodes with depth <= 0:\n",
            "\t300 : HigherCondition\n",
            "Condition type in nodes with depth <= 1:\n",
            "\t900 : HigherCondition\n",
            "Condition type in nodes with depth <= 2:\n",
            "\t1946 : HigherCondition\n",
            "Condition type in nodes with depth <= 3:\n",
            "\t3182 : HigherCondition\n",
            "Condition type in nodes with depth <= 5:\n",
            "\t6209 : HigherCondition\n",
            "Node format: NOT_SET\n",
            "\n",
            "Training OOB:\n",
            "\ttrees: 1, Out-of-bag evaluation: accuracy:0.999994 logloss:0.000202745\n",
            "\ttrees: 7, Out-of-bag evaluation: accuracy:0.999992 logloss:0.000151155\n",
            "\ttrees: 13, Out-of-bag evaluation: accuracy:0.999994 logloss:5.79783e-05\n",
            "\ttrees: 19, Out-of-bag evaluation: accuracy:0.999995 logloss:4.23631e-05\n",
            "\ttrees: 24, Out-of-bag evaluation: accuracy:0.999994 logloss:3.87982e-05\n",
            "\ttrees: 28, Out-of-bag evaluation: accuracy:0.999995 logloss:3.67066e-05\n",
            "\ttrees: 34, Out-of-bag evaluation: accuracy:0.999996 logloss:3.26485e-05\n",
            "\ttrees: 40, Out-of-bag evaluation: accuracy:0.999995 logloss:3.93074e-05\n",
            "\ttrees: 46, Out-of-bag evaluation: accuracy:0.999996 logloss:3.71694e-05\n",
            "\ttrees: 52, Out-of-bag evaluation: accuracy:0.999996 logloss:3.37509e-05\n",
            "\ttrees: 58, Out-of-bag evaluation: accuracy:0.999996 logloss:3.2847e-05\n",
            "\ttrees: 63, Out-of-bag evaluation: accuracy:0.999996 logloss:3.18778e-05\n",
            "\ttrees: 69, Out-of-bag evaluation: accuracy:0.999996 logloss:3.39579e-05\n",
            "\ttrees: 74, Out-of-bag evaluation: accuracy:0.999997 logloss:3.22086e-05\n",
            "\ttrees: 79, Out-of-bag evaluation: accuracy:0.999997 logloss:3.12213e-05\n",
            "\ttrees: 84, Out-of-bag evaluation: accuracy:0.999997 logloss:3.0614e-05\n",
            "\ttrees: 90, Out-of-bag evaluation: accuracy:0.999996 logloss:2.96636e-05\n",
            "\ttrees: 95, Out-of-bag evaluation: accuracy:0.999997 logloss:2.88192e-05\n",
            "\ttrees: 100, Out-of-bag evaluation: accuracy:0.999996 logloss:3.60457e-05\n",
            "\ttrees: 106, Out-of-bag evaluation: accuracy:0.999997 logloss:3.45335e-05\n",
            "\ttrees: 111, Out-of-bag evaluation: accuracy:0.999996 logloss:3.39205e-05\n",
            "\ttrees: 117, Out-of-bag evaluation: accuracy:0.999996 logloss:3.25566e-05\n",
            "\ttrees: 122, Out-of-bag evaluation: accuracy:0.999996 logloss:3.18366e-05\n",
            "\ttrees: 128, Out-of-bag evaluation: accuracy:0.999996 logloss:3.16968e-05\n",
            "\ttrees: 133, Out-of-bag evaluation: accuracy:0.999996 logloss:3.10735e-05\n",
            "\ttrees: 139, Out-of-bag evaluation: accuracy:0.999996 logloss:3.02747e-05\n",
            "\ttrees: 145, Out-of-bag evaluation: accuracy:0.999996 logloss:2.96712e-05\n",
            "\ttrees: 151, Out-of-bag evaluation: accuracy:0.999997 logloss:2.93234e-05\n",
            "\ttrees: 157, Out-of-bag evaluation: accuracy:0.999996 logloss:2.87759e-05\n",
            "\ttrees: 163, Out-of-bag evaluation: accuracy:0.999996 logloss:2.81761e-05\n",
            "\ttrees: 168, Out-of-bag evaluation: accuracy:0.999997 logloss:2.77939e-05\n",
            "\ttrees: 173, Out-of-bag evaluation: accuracy:0.999997 logloss:2.73814e-05\n",
            "\ttrees: 178, Out-of-bag evaluation: accuracy:0.999997 logloss:2.6982e-05\n",
            "\ttrees: 182, Out-of-bag evaluation: accuracy:0.999997 logloss:2.67983e-05\n",
            "\ttrees: 188, Out-of-bag evaluation: accuracy:0.999996 logloss:2.68045e-05\n",
            "\ttrees: 192, Out-of-bag evaluation: accuracy:0.999996 logloss:2.6811e-05\n",
            "\ttrees: 198, Out-of-bag evaluation: accuracy:0.999996 logloss:2.67734e-05\n",
            "\ttrees: 204, Out-of-bag evaluation: accuracy:0.999996 logloss:2.63431e-05\n",
            "\ttrees: 210, Out-of-bag evaluation: accuracy:0.999996 logloss:2.60214e-05\n",
            "\ttrees: 216, Out-of-bag evaluation: accuracy:0.999996 logloss:2.56911e-05\n",
            "\ttrees: 222, Out-of-bag evaluation: accuracy:0.999996 logloss:2.58309e-05\n",
            "\ttrees: 228, Out-of-bag evaluation: accuracy:0.999996 logloss:2.57625e-05\n",
            "\ttrees: 234, Out-of-bag evaluation: accuracy:0.999997 logloss:2.53072e-05\n",
            "\ttrees: 239, Out-of-bag evaluation: accuracy:0.999996 logloss:2.52207e-05\n",
            "\ttrees: 245, Out-of-bag evaluation: accuracy:0.999997 logloss:2.49508e-05\n",
            "\ttrees: 251, Out-of-bag evaluation: accuracy:0.999997 logloss:2.47406e-05\n",
            "\ttrees: 257, Out-of-bag evaluation: accuracy:0.999997 logloss:2.45586e-05\n",
            "\ttrees: 263, Out-of-bag evaluation: accuracy:0.999996 logloss:2.45766e-05\n",
            "\ttrees: 269, Out-of-bag evaluation: accuracy:0.999996 logloss:2.4413e-05\n",
            "\ttrees: 275, Out-of-bag evaluation: accuracy:0.999996 logloss:2.41508e-05\n",
            "\ttrees: 280, Out-of-bag evaluation: accuracy:0.999996 logloss:3.65796e-05\n",
            "\ttrees: 285, Out-of-bag evaluation: accuracy:0.999996 logloss:3.73881e-05\n",
            "\ttrees: 291, Out-of-bag evaluation: accuracy:0.999997 logloss:3.94446e-05\n",
            "\ttrees: 297, Out-of-bag evaluation: accuracy:0.999997 logloss:3.89376e-05\n",
            "\ttrees: 300, Out-of-bag evaluation: accuracy:0.999997 logloss:3.86566e-05\n",
            "\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "print(model1.summary())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JY4yFmqhIBzg"
      },
      "source": [
        "Let's evaluate our model on the test dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wHZNU9RVDo3P",
        "outputId": "b7d9a2cf-a4f3-4384-e02b-38a668f452e3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "8388/8388 [==============================] - 21s 2ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[0.0, 1.0]"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model1.evaluate(x_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fEB6cTdkAD1J",
        "outputId": "a79bb269-13a2-438e-f76d-e91ca71c8457"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "8388/8388 [==============================] - 17s 2ms/step\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([[0.],\n",
              "       [0.],\n",
              "       [0.],\n",
              "       ...,\n",
              "       [0.],\n",
              "       [0.],\n",
              "       [0.]], dtype=float32)"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model1.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "7zQmVoMz_OKP",
        "outputId": "b85f2819-c105-4921-8d80-f05999eb0ae4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "8388/8388 [==============================] - 17s 2ms/step\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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AbzNzxVrxNa5V9j9Vju+RyZEkSXrRMnNmZu5X2Wauw2nnAbsBk4ElwL+2dZDryNVqkiQ1VObArlbLzEdXfY6I84Hvl6+dwLjKoWNLjB7iTwDbRcQmpTpUPX7VtRZHxCbAtuX4Hlk5kiSpqfpxzlGdiBhV+foWYNVKtrnA0WWl2XhgAnAjcBMwoaxM24zuSdtzszvLuw54Wzl/GnBl5VrTyue3AT/OPrJCK0eSJKntIuJi4GBgp4hYDJwGHBwRk4EEHgBOBMjMOyLiMuBOYAXwgcxcWa5zMnA1MByYlZl3lFucAlwSEWcAtwAXlPgFwEUR0UH3hPCj+xzrQJfUerL88UWDc2DSEDdi9EEDPQSpsVYs6+x1FVWrPT39TS37W7vNBfP6deztZOVIkqSG8t1q9ZxzJEmSVGHlSJKkprJyVMvkSJKkpur7lWiNZFtNkiSpwsqRJEkN5YTseiZHkiQ1lclRLdtqkiRJFVaOJElqKidk1zI5kiSpoZxzVM+2miRJUoWVI0mSmsq2Wi2TI0mSGsq2Wj3bapIkSRVWjiRJairbarVMjiRJaqg0OaplciRJUlOZHNVyzpEkSVKFlSNJkhrKtlo9kyNJkprK5KiWbTVJkqQKK0eSJDWUbbV6JkeSJDWUyVE922qSJEkVVo4kSWooK0f1TI4kSWqqjIEewaBkW02SJKnCypEkSQ1lW62eyZEkSQ2VXbbV6thWkyRJqrByJElSQ9lWq2dyJElSQ6Wr1WrZVpMkSaqwciRJUkPZVqtn5UiSpIbKrmjZ1peImBURj0XE7ZXYP0fE3RGxMCKuiIjtSnyXiHguIm4t239Uztk3Im6LiI6IOCciosR3iIh5EXFv+Xf7Eo9yXEe5zz59jdXkSJIk9YcLgSlrxeYBkzJzb+DXwCcq++7LzMlle18lfh5wAjChbKuueSpwbWZOAK4t3wEOqxw7o5zfK5MjSZIaKrN1W9/3yuuBpWvFrsnMFeXrfGBsb9eIiFHANpk5PzMTmAMcWXZPBWaXz7PXis/JbvOB7cp1emRyJElSQ7WyrRYRMyJiQWWbsZ7DeQ/ww8r38RFxS0T8d0QcVGJjgMWVYxaXGMDIzFxSPj8CjKyc81AP59RyQrYkSXrRMnMmMHNDzo2IfwBWAN8soSXAKzLziYjYF/iviNhrPcaSEbEO9ax6JkeSJDXUYHh9SES8G/hr4I2lVUZmvgC8UD7/MiLuA/4E6GTN1tvYEgN4NCJGZeaS0jZ7rMQ7gXE9nFPLtpokSQ3Vn3OO6kTEFODvgSMy89lK/KURMbx83pXuydSLStvs6Yg4oKxSOw64spw2F5hWPk9bK35cWbV2APBUpf1Wy8qRJElqu4i4GDgY2CkiFgOn0b067SXAvLIif35ZmfY64PSIWA50Ae/LzFWTuU+ie+XbCLrnKK2ap3QWcFlETAceBI4q8auAw4EO4Fng+D7Hmhua7rXZ8scXDc6BSUPciNEH9X2QpLZYsayzX/tci/70kJb9rd31tmsGvkfXIlaOJElqKN+tVs85R5IkSRVWjiRJaijfrVbP5EiSpIbqsq1Wy7aaJElShZUjSZIaygnZ9UyOJElqqMHwhOzByLaaJElShZUjSZIaapA+B3rAmRxJktRQttXqrVNyFBGvBXapHp+Zc9o0JkmSpAHTZ3IUERcBuwG3AitLOAGTI0mSNmI+56jeulSO9gMm5mB9Q60kSdogLuWvty6r1W4HXt7ugUiSJA0GPVaOIuJ7dLfPtgbujIgbgRdW7c/MI9o/PEmS1C72hOr11lb7l34bhSRJ6nfOOarXY3KUmf8NEBFnZ+Yp1X0RcTbw320emyRJUr9blzlHb6qJHdbqgUiSpP6VGS3bhpLe5hy9HzgJ2C0iFlZ2bQ38rN0DkyRJ7eWco3q9zTn6FvBD4PPAqZX47zJzaVtHJUmSNEB6m3P0FPBURJyy1q6tImKrzPzfdg7snfv+XTsvL0lS4zkhu966PATyB3Qv6Q9gc2A8cA+wVxvHJUmS2myozRVqlT6To8z80+r3iNiH7rlIkiRJQ846vXi2KjNvjojXtGMwkiSp/9hWq7cuL579SOXrMGAf4OG2jUiSJPULF6vVW5fK0daVzyvonoP03fYMR5Ik9RcrR/V6TY4iYjiwdWZ+rJ/GI0mSNKB6ewjkJpm5IiIO7M8BSZKk/uFqtXq9VY5upHt+0a0RMRf4NvDMqp2ZeXmbxyZJktqoa6AHMEity5yjzYEngDfwh+cdJWByJEmShpzekqOXlZVqt/OHpGgVJ7hLkrSRS2yr1ektORoObAW1vzmTI0mSNnJd/jWv1VtytCQzT++3kUiSJA0CvSVH1tokSRrCuvxTX2tYL/ve2G+jkCRJ/S6Jlm19iYhZEfFYRNxeie0QEfMi4t7y7/YlHhFxTkR0RMTC8l7XVedMK8ffGxHTKvF9I+K2cs45ERG93aM3PSZHmbm0z59UkiRp3VwITFkrdipwbWZOAK4t3wEOAyaUbQZwHnQnOsBpwGuA/YHTKsnOecAJlfOm9HGPHvVWOZIkSUNYVwu3vmTm9cDahZepwOzyeTZwZCU+J7vNB7aLiFHAocC8zFyamU8C84ApZd82mTk/MxOYs9a16u7Ro3V5zpEkSRqCBsFS/pGZuaR8fgQYWT6PAR6qHLe4xHqLL66J93aPHlk5kiRJL1pEzIiIBZVtxvqcXyo+bX24wLrew8qRJEkN1crXh2TmTGDmep72aESMyswlpTX2WIl3AuMqx40tsU7g4LXiPynxsTXH93aPHlk5kiSpofpzzlEP5gKrVpxNA66sxI8rq9YOAJ4qrbGrgUMiYvsyEfsQ4Oqy7+mIOKCsUjturWvV3aNHVo4kSVLbRcTFdFd9doqIxXSvOjsLuCwipgMPAkeVw68CDgc6gGeB46F7JX1EfBa4qRx3emV1/Ul0r4gbAfywbPRyjx6ZHEmS1FD9OSE7M4/pYdcfPVexzA36QA/XmQXMqokvACbVxJ+ou0dvTI4kSWqorgFfrDY4OedIkiSpwsqRJEkN5bvV6pkcSZLUUG19qNBGzLaaJElShZUjSZIaqpUPgRxKTI4kSWqornDOUR3bapIkSRVWjiRJaignZNczOZIkqaGcc1TPtpokSVKFlSNJkhrK14fUMzmSJKmhfEJ2PdtqkiRJFVaOJElqKFer1TM5kiSpoZxzVM+2miRJUoWVI0mSGsrnHNUzOZIkqaGcc1TPtpokSVKFlSNJkhrKCdn1TI4kSWoo5xzVs60mSZJUYeVIkqSGsnJUz+RIkqSGSucc1bKtJkmSVGHlSJKkhrKtVs/kSJKkhjI5qmdbTZIkqcLKkSRJDeXrQ+qZHEmS1FA+IbuebTVJkqQKK0eSJDWUE7LrmRxJktRQJkf1bKtJkiRVmBxJktRQ2cKtNxGxR0TcWtmejogPR8RnIqKzEj+8cs4nIqIjIu6JiEMr8Skl1hERp1bi4yPiFyV+aURstqG/F5MjSZIaqitat/UmM+/JzMmZORnYF3gWuKLs/tKqfZl5FUBETASOBvYCpgD/HhHDI2I4cC5wGDAROKYcC3B2udbuwJPA9A39vZgcSZLUUF0t3NbDG4H7MvPBXo6ZClySmS9k5v1AB7B/2Toyc1FmLgMuAaZGRABvAL5Tzp8NHLl+w/oDkyNJkvSiRcSMiFhQ2Wb0cOjRwMWV7ydHxMKImBUR25fYGOChyjGLS6yn+I7AbzNzxVrxDWJyJElSQ7VyzlFmzszM/SrbzLXvV+YBHQF8u4TOA3YDJgNLgH9tz0+6flzKL0lSQ3X1/wtEDgNuzsxHAVb9CxAR5wPfL187gXGV88aWGD3EnwC2i4hNSvWoevx6s3IkSZL6yzFUWmoRMaqy7y3A7eXzXODoiHhJRIwHJgA3AjcBE8rKtM3obtHNzcwErgPeVs6fBly5oYO0ciRJUkP150MgI2JL4E3AiZXwFyJiMt2duQdW7cvMOyLiMuBOYAXwgcxcWa5zMnA1MByYlZl3lGudAlwSEWcAtwAXbOhYTY4kSWqo/myqZeYzdE+crsaO7eX4zwGfq4lfBVxVE19E92q2F822miRJUoWVI0mSGsp3q9UzOZIkqaH6erJ1U9lWkyRJqrByJElSQw3Ac442CiZHkiQ1lKlRPdtqkiRJFVaOJElqKFer1TM5kiSpoZxzVM+2miRJUoWVI0mSGsq6UT2TI0mSGso5R/Vsq0mSJFVYOZIkqaGckF3P5EiSpIYyNapnW02SJKnCypEkSQ3lhOx6JkeSJDVU2lirZVtNkiSpwsqRJEkNZVutnsmRJEkN5VL+erbVJEmSKqwcSZLUUNaN6pkcSZLUULbV6tlWkyRJqrBypF4NGzaMs77/ryx95AnOes8ZTDpwb4795LsZFsHzzz7PuR/9Mo88+AjTPj2dSf9nEgCbjXgJ2+64Le/e+10A7DR6J9539snsOHonSDjz3afzm8WP9XgtSRvm0EMO5otfPJ3hw4Yx6xsX84V/Pnegh6RBztVq9UyO1KvD3/PXdHY8xIittgDghDPexxdOOJPOjsUccuxh/M0Hj+Lcj53D7M9esPqcKe/+K8bvtevq7yd/8cNc/tVvs/CGX7H5FpvT1dXV67Ukrb9hw4Zxzpc/x5TDj2Hx4iXM//lVfO/713DXXfcO9NA0iPkQyHq21dSjHV6+I/u8YT+uvWTeH4LJ6kRpi623YOmjS//ovD8/4nX8z5XXAzB2wjiGbzKchTf8CoDnn32eZc8vW+drSVo3+7/6Vdx33wPcf///snz5ci677EqOePOhAz0saaNk5Ug9Ov609/KfZ85m861GrI6dd8pX+eSFn2bZ88t47vfP8ckjP77GOTuNeSkvG/cybv/ZbQCMGj+aZ55+ho997VReNm4kt93wK7551hy6urr6vJakdTd6zMt5aPHDq78v7lzC/q9+1QCOSBsD22r1+r1yFBHH97JvRkQsiIgFi37/QD+OSmvb5w378dQTv2XR7fetEf/r9x7Bme/+LO87YDrXfftapn16+hr7D3zzQcy/6merW2fDNxnOnq+eyJwzvsGpb/4oL3vFSA5++xvW6VqSpPbKFv7PUDIQbbV/6mlHZs7MzP0yc79dt9qlH4ektb1yvz3Z7y/359wbZvJ3X/kYk167N5/4xqfZec9d6Lj11wD87Hs/ZY99X7nGeQcecRA3zP3p6u9PLHmcB+68n8ceepSulV3cdPUvGD9pN7bZYZs+ryVp3T3c+Qjjxo5e/X3smFE8/LALHKQN0Za2WkQs7GkXMLId91RrfesLF/GtL1wEwMQDJnHEjCP5wglncv6C2YwaP5ol9z/M3gdNZnHHQ6vPGb3bGLbcZkt+/cu7V8fu+1UHW2yzJdvssA1PL32aSa/dm/tu6+D3T/2eLbbessdrSVo/Ny24ld13H88uu4yjs/MRjjpqKsce94GBHpYGOdtq9do152gkcCjw5FrxAH7WpnuqzbpWdvG1U8/lY/9xCl1dyTNP/Z5///hXVu8/8M0H8bPv3bDmOV1dXPS5b/CP3/osEbDotvu49uJr+ryWpPWzcuVKPvThT3HVD77F8GHDuHD2pdx5568Helga5LpyaLXDWiWyDb+YiLgA+EZm3lCz71uZ+c6+rvH2naf6vzFpAFyxZMFAD0FqrBXLOqM/73fszm9t2d/aix68vF/H3k5tmXOUmdPrEqOyr8/ESJIktV+2cOtLRDwQEbdFxK0RsaDEdoiIeRFxb/l3+xKPiDgnIjoiYmFE7FO5zrRy/L0RMa0S37dcv6Ocu8HJms85kiSpobrIlm3r6PWZOTkz9yvfTwWuzcwJwLXlO8BhwISyzQDOg+5kCjgNeA2wP3DaqoSqHHNC5bwpG/p7MTmSJEkDZSowu3yeDRxZic/JbvOB7SJiFN3zmedl5tLMfBKYB0wp+7bJzPnZPV9oTuVa683kSJKkhmrlc46qzyos24w/uh1cExG/rOwbmZlLyudH+MOK9jFAdQnz4hLrLb64Jr5BfEK2JEkN1cql/Jk5E5jZyyF/npmdEfEyYF5E3F3dmZkZEYNiMZaVI0mS1HaZ2Vn+fQy4gu45Q4+Wlhjl38fK4Z3AuMrpY0ust/jYmvgGMTmSJKmh+mtCdkRsGYGRYysAAAlsSURBVBFbr/oMHALcDswFVq04mwZcWT7PBY4rq9YOAJ4q7bergUMiYvsyEfsQ4Oqy7+mIOKCsUjuucq31ZltNkqSG6sd3oo0Eriir6zcBvpWZP4qIm4DLImI68CBwVDn+KuBwoAN4FjgeIDOXRsRngZvKcadn5tLy+STgQmAE8MOybRCTI0mS1FaZuQj4s5r4E8Aba+IJ1L7/JjNnAbNq4guASS96sJgcSZLUWL5brZ7JkSRJDdWOV4gNBU7IliRJqrByJElSQ63Haz8axeRIkqSGcs5RPZMjSZIaqh+X8m9UnHMkSZJUYeVIkqSGcs5RPZMjSZIayqX89WyrSZIkVVg5kiSpoVytVs/kSJKkhnK1Wj3bapIkSRVWjiRJaihXq9UzOZIkqaFcrVbPtpokSVKFlSNJkhrKtlo9kyNJkhrK1Wr1bKtJkiRVWDmSJKmhupyQXcvkSJKkhjI1qmdbTZIkqcLKkSRJDeVqtXomR5IkNZTJUT3bapIkSRVWjiRJaihfH1LP5EiSpIayrVbPtpokSVKFlSNJkhrK14fUMzmSJKmhnHNUz7aaJElShZUjSZIaygnZ9UyOJElqKNtq9WyrSZIkVZgcSZLUUF1ky7beRMS4iLguIu6MiDsi4kMl/pmI6IyIW8t2eOWcT0RER0TcExGHVuJTSqwjIk6txMdHxC9K/NKI2GxDfy8mR5IkNVS28H/6sAL4aGZOBA4APhARE8u+L2Xm5LJdBVD2HQ3sBUwB/j0ihkfEcOBc4DBgInBM5Tpnl2vtDjwJTN/Q34vJkSRJaqvMXJKZN5fPvwPuAsb0cspU4JLMfCEz7wc6gP3L1pGZizJzGXAJMDUiAngD8J1y/mzgyA0dr8mRJEkN1ZXZsi0iZkTEgso2o+6eEbEL8CrgFyV0ckQsjIhZEbF9iY0BHqqctrjEeorvCPw2M1esFd8gJkeSJDVUK9tqmTkzM/erbDPXvl9EbAV8F/hwZj4NnAfsBkwGlgD/2q+/gB64lF+SJLVdRGxKd2L0zcy8HCAzH63sPx/4fvnaCYyrnD62xOgh/gSwXURsUqpH1ePXm5UjSZIaqpVttd6UOUEXAHdl5hcr8VGVw94C3F4+zwWOjoiXRMR4YAJwI3ATMKGsTNuM7knbc7P7gU3XAW8r508DrtzQ34uVI0mSGqofXzx7IHAscFtE3Fpin6R7tdlkIIEHgBMBMvOOiLgMuJPulW4fyMyVABFxMnA1MByYlZl3lOudAlwSEWcAt9CdjG2QGKxPx3z7zlMH58CkIe6KJQsGeghSY61Y1hn9eb9XvuzVLftbe/djN/Xr2NvJypEkSQ3VVzusqUyOJElqqH5sq21UnJAtSZJUYeVIkqSGsq1Wz+RIkqSGsq1Wz7aaJElShZUjSZIaKrNroIcwKJkcSZLUUF221WrZVpMkSaqwciRJUkMN1rdkDDSTI0mSGsq2Wj3bapIkSRVWjiRJaijbavVMjiRJaiifkF3PtpokSVKFlSNJkhrK14fUMzmSJKmhnHNUz+RIkqSGcil/PeccSZIkVVg5kiSpoWyr1TM5kiSpoVzKX8+2miRJUoWVI0mSGsq2Wj2TI0mSGsrVavVsq0mSJFVYOZIkqaFsq9UzOZIkqaFcrVbPtpokSVKFlSNJkhrKF8/WMzmSJKmhbKvVs60mSZJUYeVIkqSGcrVaPZMjSZIayjlH9WyrSZIkVVg5kiSpoWyr1bNyJElSQ2Vmy7a+RMSUiLgnIjoi4tR++PE2mMmRJElqq4gYDpwLHAZMBI6JiIkDO6qemRxJktRQ2cKtD/sDHZm5KDOXAZcAU1v6w7TQoJ1z9O0Hr4yBHoM2XETMyMyZAz0OqWn8z57Wx4plnS37WxsRM4AZldDMyv8tjgEequxbDLymVfduNStHapcZfR8iqQ38z54GRGbOzMz9KttGm6SbHEmSpHbrBMZVvo8tsUHJ5EiSJLXbTcCEiBgfEZsBRwNzB3hMPRq0c4600dtoy6nSRs7/7GnQycwVEXEycDUwHJiVmXcM8LB6FD4ASpIk6Q9sq0mSJFWYHEmSJFWYHKmlNqbHw0tDSUTMiojHIuL2gR6LtLEzOVLLbGyPh5eGmAuBKQM9CGkoMDlSK21Uj4eXhpLMvB5YOtDjkIYCkyO1Ut3j4ccM0FgkSdogJkeSJEkVJkdqpY3q8fCSJNUxOVIrbVSPh5ckqY7JkVomM1cAqx4Pfxdw2WB+PLw0lETExcDPgT0iYnFETB/oMUkbK18fIkmSVGHlSJIkqcLkSJIkqcLkSJIkqcLkSJIkqcLkSJIkqcLkSNqIRMTKiLg1Im6PiG9HxBYv4loXRsTbyuevb8hLgiNickQcvgHn/SQi9lvf8ySpP5gcSRuX5zJzcmZOApYB76vujIhNNuSimfnezLxzA06dDKx3ciRJg5nJkbTx+imwe0QcHBE/jYi5wJ0RMTwi/jkiboqIhRFxIkB0+2pE3BMR/w942aoLVSs5ETElIm6OiF9FxLUltn9E/DwibomIn0XEHuUp6KcD7yjVrHdExJYRMSsibizHTi3nj4iISyLiroi4AhjRv78qSVp3G/T/ZUoaWKVCdBjwoxLaB5iUmfdHxAzgqcx8dUS8BPifiLgGeBWwBzARGAncCcxa67ovBc4HXleutUPZdTdwUGauiIi/BM7MzL+JiH8E9svMk8v5ZwI/zsz3RMR2wI0lETsReDYz94yIvYGb2/SrkaQXzeRI2riMiIhby+efAhcArwVuzMz7S/wQYO9V84mAbYEJwOuAizNzJfBwRPy45voHANevulZmLq1cY3ZETAAS2LSH8R0CHBERHyvfNwdeUe59TrnmwohYuJ4/tyT1G5MjaePyXGZOrgYiAuCZagj4YGZevdZxL2Zu0GeB6zLzLRGxC/CTHo4L4G8y856aMUrSRsE5R9LQczXw/ojYFCAi/iQitgSup3t+0PCIGAW8vubc+cDrImJ8OXdVW21boLN8fnfl+N8BW6917w9GyYYi4lUlfj3wzhKbBOz9on5CSWojkyNp6Pk63fOJbo6I24Gv0V0lvgK4t+ybQ/cb3NeQmb8BZgCXR8SvgEvLri8An4+IW1iz4nwdMHHVhGy6K0ybAgsj4o7yHeA8YKuIuIvuSdy/bOHPK0ktFZk50GOQJEkaNKwcSZIkVZgcSZIkVZgcSZIkVZgcSZIkVZgcSZIkVZgcSZIkVZgcSZIkVfx/y1OJ0ma2RbYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "preds = model1.predict(x_test)\n",
        "cm = tf.math.confusion_matrix(labels=y_test,predictions=preds)\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(cm,annot=True,fmt='d')\n",
        "plt.xlabel('Predicated')\n",
        "plt.ylabel('Truth')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "cKvAzMCKKX42",
        "outputId": "7b747f59-f528-4252-f573-4658c9084ce1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "<script src=\"https://d3js.org/d3.v6.min.js\"></script>\n",
              "<div id=\"tree_plot_4965e5729323436ea8346039035ba305\"></div>\n",
              "<script>\n",
              "/*\n",
              " * Copyright 2021 Google LLC.\n",
              " * Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              " * you may not use this file except in compliance with the License.\n",
              " * You may obtain a copy of the License at\n",
              " *\n",
              " *     https://www.apache.org/licenses/LICENSE-2.0\n",
              " *\n",
              " * Unless required by applicable law or agreed to in writing, software\n",
              " * distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              " * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              " * See the License for the specific language governing permissions and\n",
              " * limitations under the License.\n",
              " */\n",
              "\n",
              "/**\n",
              " *  Plotting of decision trees generated by TF-DF.\n",
              " *\n",
              " *  A tree is a recursive structure of node objects.\n",
              " *  A node contains one or more of the following components:\n",
              " *\n",
              " *    - A value: Representing the output of the node. If the node is not a leaf,\n",
              " *      the value is only present for analysis i.e. it is not used for\n",
              " *      predictions.\n",
              " *\n",
              " *    - A condition : For non-leaf nodes, the condition (also known as split)\n",
              " *      defines a binary test to branch to the positive or negative child.\n",
              " *\n",
              " *    - An explanation: Generally a plot showing the relation between the label\n",
              " *      and the condition to give insights about the effect of the condition.\n",
              " *\n",
              " *    - Two children : For non-leaf nodes, the children nodes. The first\n",
              " *      children (i.e. \"node.children[0]\") is the negative children (drawn in\n",
              " *      red). The second children is the positive one (drawn in green).\n",
              " *\n",
              " */\n",
              "\n",
              "/**\n",
              " * Plots a single decision tree into a DOM element.\n",
              " * @param {!options} options Dictionary of configurations.\n",
              " * @param {!tree} raw_tree Recursive tree structure.\n",
              " * @param {string} canvas_id Id of the output dom element.\n",
              " */\n",
              "function display_tree(options, raw_tree, canvas_id) {\n",
              "  console.log(options);\n",
              "\n",
              "  // Determine the node placement.\n",
              "  const tree_struct = d3.tree().nodeSize(\n",
              "      [options.node_y_offset, options.node_x_offset])(d3.hierarchy(raw_tree));\n",
              "\n",
              "  // Boundaries of the node placement.\n",
              "  let x_min = Infinity;\n",
              "  let x_max = -x_min;\n",
              "  let y_min = Infinity;\n",
              "  let y_max = -x_min;\n",
              "\n",
              "  tree_struct.each(d => {\n",
              "    if (d.x > x_max) x_max = d.x;\n",
              "    if (d.x < x_min) x_min = d.x;\n",
              "    if (d.y > y_max) y_max = d.y;\n",
              "    if (d.y < y_min) y_min = d.y;\n",
              "  });\n",
              "\n",
              "  // Size of the plot.\n",
              "  const width = y_max - y_min + options.node_x_size + options.margin * 2;\n",
              "  const height = x_max - x_min + options.node_y_size + options.margin * 2 +\n",
              "      options.node_y_offset - options.node_y_size;\n",
              "\n",
              "  const plot = d3.select(canvas_id);\n",
              "\n",
              "  // Tool tip\n",
              "  options.tooltip = plot.append('div')\n",
              "                        .attr('width', 100)\n",
              "                        .attr('height', 100)\n",
              "                        .style('padding', '4px')\n",
              "                        .style('background', '#fff')\n",
              "                        .style('box-shadow', '4px 4px 0px rgba(0,0,0,0.1)')\n",
              "                        .style('border', '1px solid black')\n",
              "                        .style('font-family', 'sans-serif')\n",
              "                        .style('font-size', options.font_size)\n",
              "                        .style('position', 'absolute')\n",
              "                        .style('z-index', '10')\n",
              "                        .attr('pointer-events', 'none')\n",
              "                        .style('display', 'none');\n",
              "\n",
              "  // Create canvas\n",
              "  const svg = plot.append('svg').attr('width', width).attr('height', height);\n",
              "  const graph =\n",
              "      svg.style('overflow', 'visible')\n",
              "          .append('g')\n",
              "          .attr('font-family', 'sans-serif')\n",
              "          .attr('font-size', options.font_size)\n",
              "          .attr(\n",
              "              'transform',\n",
              "              () => `translate(${options.margin},${\n",
              "                  - x_min + options.node_y_offset / 2 + options.margin})`);\n",
              "\n",
              "  // Plot bounding box.\n",
              "  if (options.show_plot_bounding_box) {\n",
              "    svg.append('rect')\n",
              "        .attr('width', width)\n",
              "        .attr('height', height)\n",
              "        .attr('fill', 'none')\n",
              "        .attr('stroke-width', 1.0)\n",
              "        .attr('stroke', 'black');\n",
              "  }\n",
              "\n",
              "  // Draw the edges.\n",
              "  display_edges(options, graph, tree_struct);\n",
              "\n",
              "  // Draw the nodes.\n",
              "  display_nodes(options, graph, tree_struct);\n",
              "}\n",
              "\n",
              "/**\n",
              " * Draw the nodes of the tree.\n",
              " * @param {!options} options Dictionary of configurations.\n",
              " * @param {!graph} graph D3 search handle containing the graph.\n",
              " * @param {!tree_struct} tree_struct Structure of the tree (node placement,\n",
              " *     data, etc.).\n",
              " */\n",
              "function display_nodes(options, graph, tree_struct) {\n",
              "  const nodes = graph.append('g')\n",
              "                    .selectAll('g')\n",
              "                    .data(tree_struct.descendants())\n",
              "                    .join('g')\n",
              "                    .attr('transform', d => `translate(${d.y},${d.x})`);\n",
              "\n",
              "  nodes.append('rect')\n",
              "      .attr('x', 0.5)\n",
              "      .attr('y', 0.5)\n",
              "      .attr('width', options.node_x_size)\n",
              "      .attr('height', options.node_y_size)\n",
              "      .attr('stroke', 'lightgrey')\n",
              "      .attr('stroke-width', 1)\n",
              "      .attr('fill', 'white')\n",
              "      .attr('y', -options.node_y_size / 2);\n",
              "\n",
              "  // Brackets on the right of condition nodes without children.\n",
              "  non_leaf_node_without_children =\n",
              "      nodes.filter(node => node.data.condition != null && node.children == null)\n",
              "          .append('g')\n",
              "          .attr('transform', `translate(${options.node_x_size},0)`);\n",
              "\n",
              "  non_leaf_node_without_children.append('path')\n",
              "      .attr('d', 'M0,0 C 10,0 0,10 10,10')\n",
              "      .attr('fill', 'none')\n",
              "      .attr('stroke-width', 1.0)\n",
              "      .attr('stroke', '#F00');\n",
              "\n",
              "  non_leaf_node_without_children.append('path')\n",
              "      .attr('d', 'M0,0 C 10,0 0,-10 10,-10')\n",
              "      .attr('fill', 'none')\n",
              "      .attr('stroke-width', 1.0)\n",
              "      .attr('stroke', '#0F0');\n",
              "\n",
              "  const node_content = nodes.append('g').attr(\n",
              "      'transform',\n",
              "      `translate(0,${options.node_padding - options.node_y_size / 2})`);\n",
              "\n",
              "  node_content.append(node => create_node_element(options, node));\n",
              "}\n",
              "\n",
              "/**\n",
              " * Creates the D3 content for a single node.\n",
              " * @param {!options} options Dictionary of configurations.\n",
              " * @param {!node} node Node to draw.\n",
              " * @return {!d3} D3 content.\n",
              " */\n",
              "function create_node_element(options, node) {\n",
              "  // Output accumulator.\n",
              "  let output = {\n",
              "    // Content to draw.\n",
              "    content: d3.create('svg:g'),\n",
              "    // Vertical offset to the next element to draw.\n",
              "    vertical_offset: 0\n",
              "  };\n",
              "\n",
              "  // Conditions.\n",
              "  if (node.data.condition != null) {\n",
              "    display_condition(options, node.data.condition, output);\n",
              "  }\n",
              "\n",
              "  // Values.\n",
              "  if (node.data.value != null) {\n",
              "    display_value(options, node.data.value, output);\n",
              "  }\n",
              "\n",
              "  // Explanations.\n",
              "  if (node.data.explanation != null) {\n",
              "    display_explanation(options, node.data.explanation, output);\n",
              "  }\n",
              "\n",
              "  return output.content.node();\n",
              "}\n",
              "\n",
              "\n",
              "/**\n",
              " * Adds a single line of text inside of a node.\n",
              " * @param {!options} options Dictionary of configurations.\n",
              " * @param {string} text Text to display.\n",
              " * @param {!output} output Output display accumulator.\n",
              " */\n",
              "function display_node_text(options, text, output) {\n",
              "  output.content.append('text')\n",
              "      .attr('x', options.node_padding)\n",
              "      .attr('y', output.vertical_offset)\n",
              "      .attr('alignment-baseline', 'hanging')\n",
              "      .text(text);\n",
              "  output.vertical_offset += 10;\n",
              "}\n",
              "\n",
              "/**\n",
              " * Adds a single line of text inside of a node with a tooltip.\n",
              " * @param {!options} options Dictionary of configurations.\n",
              " * @param {string} text Text to display.\n",
              " * @param {string} tooltip Text in the Tooltip.\n",
              " * @param {!output} output Output display accumulator.\n",
              " */\n",
              "function display_node_text_with_tooltip(options, text, tooltip, output) {\n",
              "  const item = output.content.append('text')\n",
              "                   .attr('x', options.node_padding)\n",
              "                   .attr('alignment-baseline', 'hanging')\n",
              "                   .text(text);\n",
              "\n",
              "  add_tooltip(options, item, () => tooltip);\n",
              "  output.vertical_offset += 10;\n",
              "}\n",
              "\n",
              "/**\n",
              " * Adds a tooltip to a dom element.\n",
              " * @param {!options} options Dictionary of configurations.\n",
              " * @param {!dom} target Dom element to equip with a tooltip.\n",
              " * @param {!func} get_content Generates the html content of the tooltip.\n",
              " */\n",
              "function add_tooltip(options, target, get_content) {\n",
              "  function show(d) {\n",
              "    options.tooltip.style('display', 'block');\n",
              "    options.tooltip.html(get_content());\n",
              "  }\n",
              "\n",
              "  function hide(d) {\n",
              "    options.tooltip.style('display', 'none');\n",
              "  }\n",
              "\n",
              "  function move(d) {\n",
              "    options.tooltip.style('display', 'block');\n",
              "    options.tooltip.style('left', (d.pageX + 5) + 'px');\n",
              "    options.tooltip.style('top', d.pageY + 'px');\n",
              "  }\n",
              "\n",
              "  target.on('mouseover', show);\n",
              "  target.on('mouseout', hide);\n",
              "  target.on('mousemove', move);\n",
              "}\n",
              "\n",
              "/**\n",
              " * Adds a condition inside of a node.\n",
              " * @param {!options} options Dictionary of configurations.\n",
              " * @param {!condition} condition Condition to display.\n",
              " * @param {!output} output Output display accumulator.\n",
              " */\n",
              "function display_condition(options, condition, output) {\n",
              "  threshold_format = d3.format('r');\n",
              "\n",
              "  if (condition.type === 'IS_MISSING') {\n",
              "    display_node_text(options, `${condition.attribute} is missing`, output);\n",
              "    return;\n",
              "  }\n",
              "\n",
              "  if (condition.type === 'IS_TRUE') {\n",
              "    display_node_text(options, `${condition.attribute} is true`, output);\n",
              "    return;\n",
              "  }\n",
              "\n",
              "  if (condition.type === 'NUMERICAL_IS_HIGHER_THAN') {\n",
              "    format = d3.format('r');\n",
              "    display_node_text(\n",
              "        options,\n",
              "        `${condition.attribute} >= ${threshold_format(condition.threshold)}`,\n",
              "        output);\n",
              "    return;\n",
              "  }\n",
              "\n",
              "  if (condition.type === 'CATEGORICAL_IS_IN') {\n",
              "    display_node_text_with_tooltip(\n",
              "        options, `${condition.attribute} in [...]`,\n",
              "        `${condition.attribute} in [${condition.mask}]`, output);\n",
              "    return;\n",
              "  }\n",
              "\n",
              "  if (condition.type === 'CATEGORICAL_SET_CONTAINS') {\n",
              "    display_node_text_with_tooltip(\n",
              "        options, `${condition.attribute} intersect [...]`,\n",
              "        `${condition.attribute} intersect [${condition.mask}]`, output);\n",
              "    return;\n",
              "  }\n",
              "\n",
              "  if (condition.type === 'NUMERICAL_SPARSE_OBLIQUE') {\n",
              "    display_node_text_with_tooltip(\n",
              "        options, `Sparse oblique split...`,\n",
              "        `[${condition.attributes}]*[${condition.weights}]>=${\n",
              "            threshold_format(condition.threshold)}`,\n",
              "        output);\n",
              "    return;\n",
              "  }\n",
              "\n",
              "  display_node_text(\n",
              "      options, `Non supported condition ${condition.type}`, output);\n",
              "}\n",
              "\n",
              "/**\n",
              " * Adds a value inside of a node.\n",
              " * @param {!options} options Dictionary of configurations.\n",
              " * @param {!value} value Value to display.\n",
              " * @param {!output} output Output display accumulator.\n",
              " */\n",
              "function display_value(options, value, output) {\n",
              "  if (value.type === 'PROBABILITY') {\n",
              "    const left_margin = 0;\n",
              "    const right_margin = 50;\n",
              "    const plot_width = options.node_x_size - options.node_padding * 2 -\n",
              "        left_margin - right_margin;\n",
              "\n",
              "    let cusum = Array.from(d3.cumsum(value.distribution));\n",
              "    cusum.unshift(0);\n",
              "    const distribution_plot = output.content.append('g').attr(\n",
              "        'transform', `translate(0,${output.vertical_offset + 0.5})`);\n",
              "\n",
              "    distribution_plot.selectAll('rect')\n",
              "        .data(value.distribution)\n",
              "        .join('rect')\n",
              "        .attr('height', 10)\n",
              "        .attr(\n",
              "            'x',\n",
              "            (d, i) =>\n",
              "                (cusum[i] * plot_width + left_margin + options.node_padding))\n",
              "        .attr('width', (d, i) => d * plot_width)\n",
              "        .style('fill', (d, i) => d3.schemeSet1[i]);\n",
              "\n",
              "    const num_examples =\n",
              "        output.content.append('g')\n",
              "            .attr('transform', `translate(0,${output.vertical_offset})`)\n",
              "            .append('text')\n",
              "            .attr('x', options.node_x_size - options.node_padding)\n",
              "            .attr('alignment-baseline', 'hanging')\n",
              "            .attr('text-anchor', 'end')\n",
              "            .text(`(${value.num_examples})`);\n",
              "\n",
              "    const distribution_details = d3.create('ul');\n",
              "    distribution_details.selectAll('li')\n",
              "        .data(value.distribution)\n",
              "        .join('li')\n",
              "        .append('span')\n",
              "        .text(\n",
              "            (d, i) =>\n",
              "                'class ' + i + ': ' + d3.format('.3%')(value.distribution[i]));\n",
              "\n",
              "    add_tooltip(options, distribution_plot, () => distribution_details.html());\n",
              "    add_tooltip(options, num_examples, () => 'Number of examples');\n",
              "\n",
              "    output.vertical_offset += 10;\n",
              "    return;\n",
              "  }\n",
              "\n",
              "  if (value.type === 'REGRESSION') {\n",
              "    display_node_text(\n",
              "        options,\n",
              "        'value: ' + d3.format('r')(value.value) + ` (` +\n",
              "            d3.format('.6')(value.num_examples) + `)`,\n",
              "        output);\n",
              "    return;\n",
              "  }\n",
              "\n",
              "  display_node_text(options, `Non supported value ${value.type}`, output);\n",
              "}\n",
              "\n",
              "/**\n",
              " * Adds an explanation inside of a node.\n",
              " * @param {!options} options Dictionary of configurations.\n",
              " * @param {!explanation} explanation Explanation to display.\n",
              " * @param {!output} output Output display accumulator.\n",
              " */\n",
              "function display_explanation(options, explanation, output) {\n",
              "  // Margin before the explanation.\n",
              "  output.vertical_offset += 10;\n",
              "\n",
              "  display_node_text(\n",
              "      options, `Non supported explanation ${explanation.type}`, output);\n",
              "}\n",
              "\n",
              "\n",
              "/**\n",
              " * Draw the edges of the tree.\n",
              " * @param {!options} options Dictionary of configurations.\n",
              " * @param {!graph} graph D3 search handle containing the graph.\n",
              " * @param {!tree_struct} tree_struct Structure of the tree (node placement,\n",
              " *     data, etc.).\n",
              " */\n",
              "function display_edges(options, graph, tree_struct) {\n",
              "  // Draw an edge between a parent and a child node with a bezier.\n",
              "  function draw_single_edge(d) {\n",
              "    return 'M' + (d.source.y + options.node_x_size) + ',' + d.source.x + ' C' +\n",
              "        (d.source.y + options.node_x_size + options.edge_rounding) + ',' +\n",
              "        d.source.x + ' ' + (d.target.y - options.edge_rounding) + ',' +\n",
              "        d.target.x + ' ' + d.target.y + ',' + d.target.x;\n",
              "  }\n",
              "\n",
              "  graph.append('g')\n",
              "      .attr('fill', 'none')\n",
              "      .attr('stroke-width', 1.2)\n",
              "      .selectAll('path')\n",
              "      .data(tree_struct.links())\n",
              "      .join('path')\n",
              "      .attr('d', draw_single_edge)\n",
              "      .attr(\n",
              "          'stroke', d => (d.target === d.source.children[0]) ? '#0F0' : '#F00');\n",
              "}\n",
              "\n",
              "display_tree({\"margin\": 10, \"node_x_size\": 160, \"node_y_size\": 28, \"node_x_offset\": 180, \"node_y_offset\": 33, \"font_size\": 10, \"edge_rounding\": 20, \"node_padding\": 2, \"show_plot_bounding_box\": false}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.8187088532559741, 0.18129114674402586], \"num_examples\": 2415744.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"data:0.2\", \"threshold\": -0.20086996257305145}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.8236817570588177, 0.17631824294118223], \"num_examples\": 2250306.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"data:0.3\", \"threshold\": 1.2662147283554077}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.12007188820145222, 0.8799281117985478], \"num_examples\": 290451.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"data:0.3\", \"threshold\": 1.424818515777588}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.025716396560667747, 0.9742836034393323], \"num_examples\": 190851.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"data:0.3\", \"threshold\": 1.7222013473510742}}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.3008734939759036, 0.6991265060240964], \"num_examples\": 99600.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"data:0.3\", \"threshold\": 1.3752574920654297}}]}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.9279569151799495, 0.07204308482005047], \"num_examples\": 1959855.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"data:0.2\", \"threshold\": 0.29396456480026245}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.9056493427762444, 0.09435065722375563], \"num_examples\": 140211.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"data:0.2\", \"threshold\": 1.8605494499206543}}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.9296758047178459, 0.0703241952821541], \"num_examples\": 1819644.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"data:0.3\", \"threshold\": -0.17112997174263}}]}]}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.751066864928251, 0.24893313507174894], \"num_examples\": 165438.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"data:0.3\", \"threshold\": 1.2165911197662354}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.21771257810377614, 0.7822874218962238], \"num_examples\": 36810.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"data:0.3\", \"threshold\": 1.7222007513046265}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [1.0, 0.0], \"num_examples\": 1969.0}}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.17350248270715538, 0.8264975172928446], \"num_examples\": 34841.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"data:0.2\", \"threshold\": -4.158421516418457}}]}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.9036990390894672, 0.0963009609105327], \"num_examples\": 128628.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"data:0.3\", \"threshold\": -0.1712145060300827}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.8242554942326519, 0.17574450576734815], \"num_examples\": 70483.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"data:0.1\", \"threshold\": 1.2990427017211914}}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [1.0, 0.0], \"num_examples\": 58145.0}}]}]}]}, \"#tree_plot_4965e5729323436ea8346039035ba305\")\n",
              "</script>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tfdf.model_plotter.plot_model_in_colab(model1, tree_idx=0, max_depth=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AEduZ6cnOZZt",
        "outputId": "fd7e9d4a-9f5c-42b5-bd2d-1c56988ec8f2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[\"data:0.0\" (1; #0),\n",
              " \"data:0.1\" (1; #1),\n",
              " \"data:0.2\" (1; #2),\n",
              " \"data:0.3\" (1; #3)]"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model1.make_inspector().features()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t9GAZI8TOaw8",
        "outputId": "0413a57a-555b-4374-8b9e-50983a0d550e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'MEAN_MIN_DEPTH': [(\"__LABEL\" (4; #4), 6.9528727637458045),\n",
              "  (\"data:0.1\" (1; #1), 5.836930074667347),\n",
              "  (\"data:0.0\" (1; #0), 4.660334775724733),\n",
              "  (\"data:0.2\" (1; #2), 2.234418224080396),\n",
              "  (\"data:0.3\" (1; #3), 0.4084199668344952)],\n",
              " 'NUM_AS_ROOT': [(\"data:0.3\" (1; #3), 219.0), (\"data:0.2\" (1; #2), 81.0)],\n",
              " 'NUM_NODES': [(\"data:0.3\" (1; #3), 10707.0),\n",
              "  (\"data:0.0\" (1; #0), 1864.0),\n",
              "  (\"data:0.2\" (1; #2), 1192.0),\n",
              "  (\"data:0.1\" (1; #1), 722.0)],\n",
              " 'SUM_SCORE': [(\"data:0.3\" (1; #3), 342597295.2277448),\n",
              "  (\"data:0.2\" (1; #2), 522517.6588834489),\n",
              "  (\"data:0.0\" (1; #0), 275406.3313518614),\n",
              "  (\"data:0.1\" (1; #1), 54181.845337143255)]}"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model1.make_inspector().variable_importances()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0YnJGtIZmpe4",
        "outputId": "3b7935c5-5af9-4626-acc9-ef9e86fa9c38"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[TrainLog(num_trees=1, evaluation=Evaluation(num_examples=888893, accuracy=0.9999943750260155, loss=0.000202744602448273, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=7, evaluation=Evaluation(num_examples=2318375, accuracy=0.9999918046045182, loss=0.00015115527424869134, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=13, evaluation=Evaluation(num_examples=2409552, accuracy=0.9999941897912973, loss=5.797834348520313e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=19, evaluation=Evaluation(num_examples=2415372, accuracy=0.9999946178062841, loss=4.2363091456181435e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=24, evaluation=Evaluation(num_examples=2415701, accuracy=0.9999942045807821, loss=3.8798192920527125e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=28, evaluation=Evaluation(num_examples=2415739, accuracy=0.9999954465279569, loss=3.670664892818312e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=34, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999958604885286, loss=3.2648539513780625e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=40, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999954465373815, loss=3.930742461458119e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=46, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999958604885286, loss=3.716941824061376e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=52, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999958604885286, loss=3.375086489882177e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=58, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999958604885286, loss=3.284699700622674e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=63, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999958604885286, loss=3.1877825789116124e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=69, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=3.395791445253143e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=74, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=3.2208561449332054e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=79, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=3.1221267897840736e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=84, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=3.061404241636856e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=90, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=2.9663628599482247e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=95, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=2.8819166516353152e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=100, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=3.6045686690648664e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=106, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=3.4533506194523624e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=111, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=3.3920518735217216e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=117, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999958604885286, loss=3.255655803886167e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=122, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=3.183661799586415e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=128, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=3.169675094454682e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=133, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=3.107354792547901e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=139, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=3.0274660403361136e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=145, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=2.9671186020575413e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=151, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=2.932337833839146e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=157, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=2.8775881188733372e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=163, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=2.8176127310375406e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=168, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=2.7793920603442477e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=173, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=2.738137085516148e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=178, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=2.698201055789111e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=182, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=2.6798269214993475e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=188, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=2.6804461949719816e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=192, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=2.6811027926846004e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=198, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=2.6773415107416657e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=204, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=2.6343078249283546e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=210, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=2.6021430706176738e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=216, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=2.569114453344163e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=222, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=2.5830875599566927e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=228, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=2.5762477600901005e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=234, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=2.5307208417008258e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=239, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=2.5220684908416527e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=245, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=2.4950753058921143e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=251, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=2.4740597721857812e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=257, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=2.4558566826944205e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=263, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=2.457658792631925e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=269, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999958604885286, loss=2.441303311195069e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=275, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999958604885286, loss=2.4150759913542344e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=280, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999958604885286, loss=3.657959026349604e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=285, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999962744396758, loss=3.7388131375283084e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=291, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=3.9444588380285314e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=297, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=3.893756101189032e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=300, evaluation=Evaluation(num_examples=2415744, accuracy=0.9999966883908229, loss=3.8656574517943775e-05, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None))]"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model1.make_inspector().training_logs()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "VsV4MwBAnI1p",
        "outputId": "96fe2112-c9c2-43ec-c7bd-91ceac0469d5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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hmxmbTOWE93b3c8mqemLRZOrMNesbOTU8xtH+M+f13X2ol3jCz6n4dM36RvYfG8xqjUohzDQzsLO1hb2H+xkZD7dTPCRLRicLB+Sm4uyNm5qxLGeOis2JwVEOnBg+Z+Zi+/pGRicSPPPC0Hn9nzia3BdpoZXEpoL4Ar2H7Z09RCsruDaL/YyKQVCp7BJ3b3X3DwZt73f3+4LHo+7+Jnff4u43uHtX2rkfDM671N2/Odc1g/bKoG178HN30O7ufldw7Cp335N2zmeD197i7v+Q7/cjlYrWq8BGRCQvFNiUqY7uPppiVfMug5syWznhRMJ5fNreOKkb9ZlmYXZ19hCNVJyzm/r29Y2MTyZ4+vj5N6zFZraZgbbWZuIJZ/eh8KWrj/Sd4dTw+ILX16Q01UXZunp5wRe/58KurmRgcW4APPffFcBNmxe24P6C+houLuBapfbOHnZc1ERNVfh1cFK8plLRVBlNRCQvFNiUqY7ufrYtoKTwbOWED/acZnA0fs7mkpetrqe6smLGClbtnae49qLGc27cUjf2HYeLfz+b2WYGdmxsoipi8woqFrox50zaWpt57Pn+JV+Fqf1AD8trKtm6ZvlU2/oVtayoi84Y2LR3nuKyC+tpXla94Ndua21m98HFX6vUe3qcp48PLtX1NTKD1Aar/drLRkQkLxTYlKGh0QmeO7GwksKzlRM+uzfO2WtXRSq4cu3562b6R8Z56tjgeUHB6oZaVi2vXhLrbGabGYhFK7lmfdO89rPp6O6nurKCSy8MX347k7bWFsYnEzz6fPEHiXNp7zrFTZubiVScDcTNbGr9VrrRiUn2HOrL2YaWO1tbODMxOVUUY7E81JVau7XkNuaUWUQrK1hWXalUNBGRPFFgU4aeOBKUFF5AYHPB8plTdDq6+1lWXUnrymXntM+00Puhrt6p8r3TzXTDWozmmhnY2drMk0cHGAiZdtLR3c9VaxuoiuTu/5bXb1pBpGJ+M0fFprt3hO7eM7P+nXSeHGYwbQPYvYf7GYsncjbTcdPmFcm1SgcWNx2tvfMUddEIVwdFOKQ0NMaq6FcqmohIXiiwKUN7F1A4IN1M5YQ7uvu5el3DOd+sp15rLH7uQu9dnaemyvdOt319E4d6Rugr4pSNTDMDba3NJDy5/0omE5MJnjyaXfntuSyrrmTbuoYlXUAgNevVtuX893lqA9jugbT+p6gwuGGB62tSGmNRrliz+GuV2jt7uGHTipwGulJ4K+qi2sdGRCRP9IlZhjq6+9nUUjeV752tna0t55QTHp2Y5OnjgzPenKfa9qbNwkwv3ztT/44iLlWcaWZg+4ZGaqoqQgUVPzk+xFg8kbPCAenaWlvYd2SAodGl+S1xe+cpWpZFufiCZecdO7sBbF9a/x6uWtfI8pqFl8xOaQuq3J0ZX5y1Si8MjNJ18nTO0umkeDTGokX9hY2IyFKmwKbMnC0pvPAb6KkUneDG/aljA8QTPuO11zXV0rIsOrUG58TQKM9NK9+b7up1DVQYMxYcKBaZZgaqKyNcv3FFqHU2qRvzXM/YQHLmaDLh7D6U1y068sLdae/sYWdry4yFLlIbwKbSFk+Pxenozv2Gljtbmxd1rdKurlNTryulpSlWpapoIiJ5osCmzBwbGOXk0FhObqCnp+jsnaFwQMrZhd7JG8Op9KJZvpGuq67kklX1Rb3OJszMwM7WZp55cYiTQ2NzXmtvdz8ty6pZ21g7Z79sXHtRE9HKikVfI5ILnSdPc2JobM5AJX0D2D3P9wX7IuU2ILh+4woqF3GtUvuBHhpqq9i6ennmzrKkNGnGRkQkbxTYlJmpqmU5mhloa22ZKifc0d3P2sZaLqivmbFvcqH3aQbOTLCr8/zyvTP1f/xI8oa12ISdGUgFbqkKV7NJzaJlW357LjVVEa7b0LQk19nsCgKJud7n5Aaw4xzpO0N75ymqIsaOi3KzviZlWXUl29Y3Lsp7ODVLtbmZiorc/z1IYTXFogyNxc8ppCIiIrmhwKbMdHT3Ea2s4PIcfROcnqKTKcUttVHnviP9tHf2nFe+9/z+jfSPTHCoZyQnY82l3Yd6Q80MXLlmOfXVlXPeEA+MTNB18jTX5GF9TUpbazP7jw8uuW+K2zt7WNtYO+dGsukbwO7q7OGaDU3URnO/oWVbazP7jvSfU4EtH7p7z3C0/wxtW5SGVopW1CVneFUZTUQk9xTYlJmO7n6uWLN8xgX72Uil6Hzt8WMc6TszZ2Bz9foGzOAb+45zuHckY1AwtVFnd/HtwbKrsyfUzEBlpIIbN6+YmnmYSWp/lHysr0lJ3SRnmjkqJomEs6urh52tzXPOZKU2gH3w2ZM8eXQgbxta7gyq3O0+mN+1Su0hZqlk6UoVbVFlNBGR3FNgU0YmJhM8keOSwqkUnXsfOwLMvL4mZXlNFa0rl031nal8b7qLL6inLhopygIC7fOYGdjZ2sKhnhGO9p+Z8XhHdz9m5HW/kqvXNRKLRpZUOtrTLwzSPzKR8QY/tQHsf3QcJeGzr9taqGs3BGuV8vwetnf2sLK++ry9oKQ0NKUCmyU2eyoishQosCkjz7wwxOhEIuczA22tzUxMOpEK48o1c9+cb1/fyMSkz1q+N12kwrhqXUPRFRAYGJngyWPhZwZS/WarjtbR3c+Wlcuoz2F54umqIhXcsGnFktqoM/V+hakMlvq7qqmqyNvMV01VhB0X5XetkntylqotwyyVLF1NQSqaKqOJiOReZaEHIIsnFSBcE6xJyJWdrc389fcPcNmF9RlnMLavb+TLjx6ZtXzv+f2b+Mx/dTE6MUlNVebZEXenu/cME4n8Lcx9qKsHn8fMwKWr6llRF+V7T7844zqax7v7eeVlF+R6mOdpa23mz545yaPP99EYy18QlSsPPHOSzS11rG7IXCkuFczMti9SrrS1NvN/vvMsfafHaapb2D5QM+k8OczJDFXgZGlrUiqaiEjeKLApI48d7qO5Lsr6FbktKXzthiZi0eS32Zns2Jjs89KQC6Ov2ZD8Jv6pYwNcF6LS1X90HOX3vvh4qGsvRF00wrb14VLHKiqMttZmvr7vON988oUZ+1wX4r1bqJcEqX+/8DfteX+tXPnlmzaE6nftRU2YwU9dnN8NLXe2tgDPsqurh5+5anXOrz81S7VZG3OWKgU2IiL5o8CmTLg7D3f1cuPmFTlPcampivDvv93G6uWZA6bLLlzOvb+1k23rwqUL3bAxGcw81NUbKrDZ1Znc/+Pu264Idf1sbW5ZRnVl+Mpb7/+5rfz01lUzHotGKnjl5fmfsbliTQOf+/UblswNlZnxspCBytrGWr7y2y/h8tX1eR3T1esaqItG2NWZn8Bm7+Hkfka5/vJBikdtNEJNVYWqoomI5IECmzJxuDe5eP03X9Gal+tfdmH48tFhApSUprooW1cnNwG96+YtGft3dPdzzYZGbtu+NvRrLIYL6muKYkwvu2RloYeQN/msKpeS77VK+dzPSIpHUyxKr4oHiIjknIoHlInUguelmLvf1trMnkN9jE5MztlvaHSC504ML8oNrpSvttYWOk+e5sXB0Zxed2Bkgq5T+d3PSIpDUyxK/xKZORURWUoU2JSJ9s4eVi2vZnNLXaGHMm9tW5oZiyfYm6Hs8xNHBnBfnG/upXztzFDlLlsdi7CfkRSHproqzdiIiOSBApsy4O7s6jxFW8hKZMXm+o0riFTYnJtcAuzt1o2h5N/W1ctpqK3KeTpax+H872ckxaExFtUaGxGRPFBgUwaeOzHMqeHxUPuBFKP6miquXteQcf+Qju5+NrXUTe3sLZIPFRXGzs3NOd/PpqO7L+/7GUlxWBGLLpkiHiIiS4kCmzLQfiD5zfLOzUszsIHk2Du6+zk9Fp/xuLtPLbwWybe2Lc0c6TtDd+9ITq6nv9/y0hSrov/MBJMJL/RQRERKigKbMtDe2cP6FbWsXxEr9FCy1tbaQjzh7D7UO+PxYwOjnBwa042hLIpUEY5cpaMd7h2hb2SC7SocUBYaY1HcYfCM0tFERHJJgU2Jm0w4D3X10LbEN/y77qImopGKWRdsdxzW+hpZPK0rl7Gyvjpn6WgdWh9WVlbUaZNOEZF8UGBT4vYfG2RwNE7blqWbhgbJTe2u2dA4641kR3cf0coKLl8dfj8dkWyZGW2tyXU27gtPJ9p7uJ/aqgiXrsrvBqNSHBpjyXVUCmxERHJLgU2JS6XKLOX1NSltrS08eWyAgRmqCXV093PFmuVEK/UnLYujrbWZk0NjdJ4cXvC1Orr7uWptA5UR/f2Wg6agwEnfaaWiiYjkkj5FS1x7Zw9bLljGBctrCj2UBWvb0ow7PHTw3FmbickETxwdUBqPLKq21mR650LT0cbik+w/Nqj1NWVEqWgiIvmhwKaEjccT7D7UO7XQeanbtq6R2qrIeetsnnlhiNGJhAIbWVTrV8RY11RL+4GFBTZPHx9ifFJ/v+VEqWgiIvmR18DGzBrN7Mtm9hMze9rMdubz9eRc+470MzI+WTKBTbSygus3reDHB86tRJVaeH3N+qZCDEvKWFtrM7u6ekgsoGxvx+E+QIUDysmy6kqqIkafNukUEcmpfM/YfBT4lrtfBmwDns7z60ma9s4ezODGTaUR2EDyRvK5E8OcGBqdauvo7mdFXZT1K2oLODIpR22tLQycmWD/8cGsr9HR3c8F9dWsblj66aISjpnRGIvSrxkbEZGcyltgY2YNwMuAzwC4+7i79+fr9eR87Z2n2Lp6OU1BPncpSM0+paejpTY2NLNCDUvK1M4Z/h7nS3+/5akpVkXvaQU2IiK5lM8Zm03ASeAfzGyvmX3azOqmdzKzO81sj5ntOXnyZB6HU15GJyZ57Pn+kklDS7liTQP1NZVTN5KDoxN0nhxWGo8UxKrlNbSurMt6o86+0+Mc6hlR4YAy1BSLKhVNRCTH8hnYVALXAn/j7tcAp4H3TO/k7p9y9x3uvmPlypV5HE55efT5PsYnE1OVm0pFpMK4aXPzVCWqfd0DuGt9ghROW2sLjxzsZWIyMe9zO46U9sacZnarmT1jZgfM7Lz//ptZtZl9MTj+sJltTDv23qD9GTN7TaZrmtk7gjY3s5a09j80s47g50kzmzSzFcGxQ2b2RHBsT77eh5k0xaL0acZGRCSn8hnYHAGOuPvDwfMvkwx0ZBG0d54iUmFcv2lFoYeSc22tzRzuHaG7d4SO7uTC620lemMoxa+ttZnT45PsOzIw73M7DvdjBlevK72/XzOLAJ8AXgtsBd5sZlundbsD6HP3LcCHgQ8F524FbgeuAG4FPmlmkQzX/DHwauD59Bdw97909+3uvh14L/BDd+9N63JzcHxHrn73MJrqqjRjIyKSY3kLbNz9BaDbzC4Nml4F7M/X68m52jt72LaugWXVlYUeSs6lZqF2dfXQ0d3P5pV1NNRWFXhUUq5u2pxaZzP/dLSO7n4uuaC+JP9/CtwAHHD3LncfB74A3Datz23APcHjLwOvsuRio9uAL7j7mLsfBA4E15v1mu6+190PZRjTm4HPL/xXW7imoHiAe/YV9URE5Fz5ror2O8C/mNk+YDvwZ3l+PQGGRifYd2Sg5NLQUi5ZtYzmuijtB05NLbwWKZSmuihbVy+f90ad7s7jR0r673ct0J32/EjQNmMfd48DA0DzHOeGueaMzCxGcvbn3rRmB75jZo+a2Z1hrpMrTbEo8YQzNBZfzJcVESlpef2a0N07gEWd3hfYfaiXyYSXXOGAFDNjZ2sz393/IqfHJ7mmdG8MZYloa23mcw89z+jEJDVVkVDnHOoZoX9kQoUDFs/PAT+elob2Unc/amYXAN81s5+4+4PTTwyCnjsBNmzYkJPBpDbp7D89wfIazTiLiORCvmdspADaD/QQrazg2otKd8PKttYWTo9PArBdG3NKgbVtaWY8nuCxYLPNMFLrw0p4xuYosD7t+bqgbcY+ZlYJNAA9c5wb5pqzuZ1paWjufjT49wTwFZKpbufJR5GbFUEZ/j7tZSMikjMKbIpIfDLBRA5+2jt7uG5DU+hvjpei1GxUdWUFl62uL/BopNxdv3EFkQrjxwdOhf7/6d7D/cSiES5ZVbJ/v7uBi81sk5lFSQYW903rcx/w1ish9cIAACAASURBVODxG4Hve3LRyX3A7UHVtE3AxcAjIa95nmBftZcDX01rqzOz+tRj4Bbgyax/23lqjCUDm14FNiIiOVOSK1YL7fs/eZH3feVJvvm7Lwu9qP2rHUf53S92kKt1pH/w05fk5kJF6qLmGGsaaljTWEtVRPG5FFZ9TRVXr2vgEz/o5BM/6Ax93o2bkgFRKXL3uJm9A/g2EAE+6+5PmdndwB53v4/kBs7/ZGYHgF6SgQpBvy+RLDgTB+5y90lIlnWefs2g/Z3Au4ELgX1mdr+7vy0Yzs8D33H302lDXAV8JdgYtRL4V3f/Vr7ej+lSMzb9CmxERHJGgU0efP3x4xwbGGVXZw+3XnlhqHO++cQLtCyr5q07L1rw61dGKvjFHeszd1zCzIyP/9K11JbwrJQsLX9625U88MyJeZ1z82UX5Gk0xcHd7wfun9b2/rTHo8CbZjn3g8AHw1wzaP8Y8LFZrvWPwD9Oa+sCtmX4FfKmKVhj03daJZ9FRHJFgU2OuftUdaRdnadCBTaJhLOrq4fXXLGKd7zy4nwPsWRcu0Fra6R4XLm2gSvXNhR6GLJELK+posK0xkZEJJeUw5NjB0+d5oXBUSIVFrr86/7jgwycmSjZ8swiInKuigqjMRZVYCMikkMKbHIsFcy86bp1PHdimBNDoxnP2RWcs7NEyzOLiMj5GmNVSkUTEckhBTY5tquzh9UNNbz5hg1TzzNp7zxF68o6Vi2vyffwRESkSDRpxkZEJKcU2ORQIuE81NXDztZmrlzbQH1NZcbAZmIywSMHe5WGJiJSZpKBjWZsRERyRYFNDj17Yoie0+O0tbYQqTBu2tzMrq65A5t9RwY4PT45tS+LiIiUh6ZYFX2nNWMjIpIrCmxyqP3AuWtl2lqbeb5nhCN9I7Oes6vzFAA3bVZgIyJSTlbUKRVNRCSXFNjkUHtnDxubY6xtrAWYSi+bKx2tvbOHrauX0xRs1iYiIuWhMRZlLJ7gzPhkoYciIlISFNjkSHwywcNdPexMWytzyaplNNdFZw1sRicm2fN8n9LQRETKUGqTzl7N2oiI5IQCmxx56tggQ2Pxc4IUM2NnazPtnT24+3nnPHa4j/F4grYtCmxERMpNaqZe62xERHJDgU2OpPavmb5Wpq21hRcGRzl46vR55+zq7CFSYVy/ccWijFFERIpHUywZ2PSrMpqISE4osMmR9s5TXLqqnpX11ee0p2Zw2mdIR2vv7OGqtQ3U11QtyhhFRKR4KBVNRCS3FNjkwHg8we5DvVPV0NJd1BxjTUPNeetshsfiPN7dr/U1IiJlKpWK1q/ARkQkJxTY5EBHdz+jE4kZg5TkOpsWdnX1kEicXWez+1Av8YRrY04RkTLVWJucsek7rVQ0EZFcUGCTA+2dp6gwuHGWvWjaWpvpPT3OMy8OTbXt6uwhGqnguouaFmuYIiJSRCojFdTXVGovGxGRHFFgkwPtnT1cubaBhtqZ18rsnGGdTXvnKa7Z0EhtNLIoYxQRkeKjTTpFRHJHgc0CnRmfZO/hvhnX16SsaaxlY3OMXZ2ngGQ+9VPHBpWGJiJS5hpjUXpV7llEJCcU2CzQnud7mZjMvFZmZ2sLD3f1Ep9M8FBXL+5o/xoRkTK3Ilalcs8iIjmiwGaB2jt7qKwwrt8491qZttZmhsbiPHlskF2dp6itirBtXeMijVJERIpRU0ypaCIiuaLAZoHaO3u4ZkMjsWjlnP1SG3e2d56ivbOH6zetIFqpt19EpJw1xqL0KRVNRCQndGe9AIOjEzxxpJ+dIdbKrKyv5tJV9dzXcYznTgxr/xoREWFFXRWnxycZjycKPRQRkSVPgc0CPNLVS8IJHaTsbG3mJy8kSz4rsBERkcaYNukUEckVBTYL0N7ZQ3VlBddsCLdWJhXM1NdUcsWahnwOTUREloCmILDpVWAjIrJgCmwW4KGuHnZsbKK6MtxeNDdubqbCkuttIhWW59GJiEixa6pL7n/Wd1qV0UREFmruFe8yp2MDZ9iRoRpauobaKv7s56/SbI2IiABnZ2yUiiYisnAKbLLk7gyPxllWPb+38PYbNuRpRCIistQoFU1EJHeUipal0YkE8YRTX1NV6KGIiMgS1RhLfoZok04RkYVTYJOlodHkh1B9jSa9REQkOzVVEWLRCL3ay0ZEZMEU2GRpaCwOKLAREZGFaYpF6VMqmojIgimwydLQqAIbERFZuKa6KqWiiYjkgAKbLJ1NRdMaGxERyV5TLKpUNBGRHFBgk6XhYMZmvlXRRERE0jXFoir3LCKSAwpssqRUNBERyYWmWBV9SkUTEVkwBTZZGlQqmojInMzsVjN7xswOmNl7ZjhebWZfDI4/bGYb0469N2h/xsxek+maZvaOoM3NrCWt/RVmNmBmHcHP+8OOb7E0xqIMnJkgPpko1BBEREqCApssDSkVTURkVmYWAT4BvBbYCrzZzLZO63YH0OfuW4APAx8Kzt0K3A5cAdwKfNLMIhmu+WPg1cDzMwznR+6+Pfi5ex7jWxQNtckvyIaDapsiIpIdBTZZGh6LUxeNEKmwQg9FRKQY3QAccPcudx8HvgDcNq3PbcA9weMvA68yMwvav+DuY+5+EDgQXG/Wa7r7Xnc/lOPxLYpYNALAmYnJQry8iEjJUGCTpaHRCaWhiYjMbi3Qnfb8SNA2Yx93jwMDQPMc54a55kx2mtnjZvZNM7tiHuMDwMzuNLM9Zrbn5MmTIV5ufmqDwGZkXIGNiMhCKLDJ0tBonGUqHCAiUuweAy5y923AXwP/Md8LuPun3H2Hu+9YuXJlzgdYWxXM2CiwERFZEAU2WRoei6simojI7I4C69OerwvaZuxjZpVAA9Azx7lhrnkOdx909+Hg8f1AVVBcYN7XypdYNPlZolQ0EZGFUWCTpcHRuFLRRERmtxu42Mw2mVmUZDGA+6b1uQ94a/D4jcD33d2D9tuDqmmbgIuBR0Je8xxmdmGwbgczu4Hk515PNtfKl9po8qNYqWgiIgujKYcsDY1OsK6xttDDEBEpSu4eN7N3AN8GIsBn3f0pM7sb2OPu9wGfAf7JzA4AvSSDC4J+XwL2A3HgLnefhGRZ5+nXDNrfCbwbuBDYZ2b3u/vbSAZMv2VmceAMcHsQPM04vkV4a85TWxXM2IyrKpqIyEIosMnS8KhS0URE5hKkft0/re39aY9HgTfNcu4HgQ+GuWbQ/jHgYzO0fxz4eNjxFYKqoomI5IZS0bI0pMBGRERyIKaqaCIiOaHAJgsTkwnOTEyyrFprbEREZGFqoqqKJiKSCxmnHMysAtgGrCGZn/yku5/I98CK2elgd2jN2IiIyELFqjRjIyKSC7PemZtZK/BHwKuB54CTQA1wiZmNAH8H3OPuicUYaDEZGlVgIyIiuVEZqSAaqdAaGxGRBZrrzvx/AX8DvD2oIDPFzC4A/j/gV4B78je84jQ4OgEosBERkdyoqapQKpqIyALNemfu7m+e49gJ4CN5GdESMDw1Y6M1NiIisnCxaCUjKvcsIrIgYdbYvGGG5gHgiUxrbczsEDAETAJxd9+RzSCLjVLRREQkl2LRiNbYiIgsUJg78zuAncAPguevAB4FNpnZ3e7+TxnOv9ndT2U/xOIzNJZMRVtWrcBGREqXmdUArwN+irQCMsA3CrWZZamqjUYY1RobEZEFCXNnXglc7u4vApjZKuBzwI3Ag0CmwKbkKBVNREqdmf1PkkHNA8DDwAmCAjLAXwRBzx+4+76CDbKE1FZpxkZEZKHCBDbrU0FN4ETQ1mtmExnOdeA7ZubA37n7p6Z3MLM7gTsBNmzYEHLYhTWoVDQRKX2PuPsHZjn2V0ERmaXxH+0loDYamUpzFhGR7IS5M3/AzL4O/Fvw/BeCtjqgP8O5L3X3o8EH4HfN7Cfu/mB6hyDY+RTAjh07fKaLFJuh0ThVEaO6UvubikhpcvdvZDh+guQXXZIDsWiEk0NjhR6GiMiSFiawuYtkMPOS4PnngHuDEtA3z3Wiux8N/j1hZl8BbiCZvrakDY9NUF9ThZkVeigiInllZl8jOfuebgDYQ3ImfnTxR1V6lIomIrJwGQObIID5cvATWjCjU+HuQ8HjW4C7sxplkRkajatwgIiUiy5gJfD54Pkvkqx2eQnw9yT3M5MFqo1WKrAREVmgMOWebwL+GrgciAIR4LS7L89w6irgK8GsRiXwr+7+rYUNtzgMjca1vkZEykWbu1+f9vxrZrbb3a83M1VGy5FYNMIZ7WMjIrIgYe7OPw7cTnKNzQ7gLSS/qZuTu3cB2xY0uiI1rMBGRMrHMjPb4O6HAcxsA7AsODZeuGGVllg0wpmJSdxdac4iIlkKtfrd3Q8AEXefdPd/AG7N77CK2+DoBMuqVepZRMrCHwD/ZWY/MLMHgB8B/z1IMb6noCMrITVVERIOY/FEoYciIrJkhZl2GDGzKNBhZv8bOE7IgKhUDY3GWa4ZGxEpA+5+v5ldDFwWND2TVjDgIwUaVsmJRSMAnBmfpKYqUuDRiIgsTWEClF8J+r0DOA2sJ1klrWwNjykVTUTKysXApSTTi/+bmb2lwOMpOVOBzYQKCIiIZCtMVbTngxmbjcC/k/y2rmzzqt2d4bE4yxTYiEgZMLMPAK8AtgL3A68F/otk6X/JkdQsjSqjiYhkL+OMjZn9LNAJfIxkIYEDZvbafA+sWI2MTzKZcOprtMZGRMrCG4FXAS+4+6+RnLVpKOyQSk8smvyy7IwCGxGRrIWZdvi/wM1BAQHMrBX4BvDNfA6sWA2PJctxKhVNRMrEGXdPmFnczJYDJ0imJEsOpVLRRlTyWUQka2HuzodSQU2gi+TmbGVpaHQCQBt0iki52GNmjSQ343wUGAZ2FXZIpadWa2xERBZs1rtzM3tD8HCPmd0PfAlw4E3A7kUYW1EaHE1+m7ZcqWgiUgbc/beDh39rZt8Clrv7vkKOqRTVVp2tiiYiItmZa9rh59Ievwi8PHh8EqjJ24iK3PCoUtFEpLwEX3S9lOSXW/8FKLDJsbOpaApsRESyNevdebBIVKYZCgIbVUUTkXJgZp8EtgCfD5rebmavdve7CjiskpNKRRtRKpqISNbmdXduZo+5+7X5GsxSkFpjo6poIlImXglc7u4OYGb3AE8VdkilJ5WKNqoZGxGRrIXZoDOd5WUUS4iqoolImTkAbEh7vj5okxxKlXtWKpqISPZmDWzM7F3Bvy9Ja/5G3kdU5FLFA+qiCmxEpHSZ2dfM7D6gHnjazB4wsweAp4M2yaFIhRGtrGBkQuWeRUSyNdfd+a8BHwX+GrgWwN3ftxiDKmZDoxMsq64kUlH2k1ciUtr+T6EHUG5i0YhS0UREFmCuwOZpM3sOWGNm6RVwDHB3vzq/QytOw6NxpaGJSMlz9x9ObzOz17n71wsxnnJQWxVRKpqIyALMVRXtzWZ2IfBt4PWLN6TiNjQa1+acIlKu7gYU2ORJbTSiqmgiIgswZ/EAd3/B3bcBx0nmVNcDx9z9+cUYXKE8eXSAr+87NuOxobEJzdiISLlSDm4exaIRbdApIrIAGauimdnLgeeATwCfBJ41s5fle2CF9H+/8wy//6XHmZhMnHcsmYqmUs8iUtrM7HvBvx9Ka377PK9xq5k9Y2YHzOw9MxyvNrMvBscfNrONacfeG7Q/Y2avyXRNM3tH0OZm1pLW/ktmts/MnjCzdjPblnbsUNDeYWZ75vO75UOsqlKBjYjIAoQp9/xXwC3u/nJ3fxnwGuDD+R1W4cQnE+w+1Md4PMFPjg+dd3xoNK7NOUWkHKw2szbg9WZ2jZldC8TN7Nrg8ZzMLELyC7HXAluBN5vZ1mnd7gD63H0Lyc+VDwXnbgVuB64AbgU+aWaRDNf8MfBqYHpGwUHg5e5+FfCnwKemHb/Z3be7+45Mv1O+1SgVTURkQcLcoVe5+zOpJ+7+rJmV7JTF/uODU3vV7O3u46p1DeccHxyNs1yBjYiUvvcDfwKsI/kFVzonuXHnXG4ADrh7F4CZfQG4Ddif1uc24H8Ej78MfNzMLGj/gruPAQfN7EBwPWa7prvvDdrOHah7e9rTh4LfpyjFqiK8MHCm0MMQEVmywszY7DGzT5vZK4KfvwcKPmWfLw919QCwrLqSjsP95x0fHptQKpqIlDx3/7K7vxb43+5+87SfTEENwFqgO+35kaBtxj7uHgcGgOY5zg1zzbncAXwz7bkD3zGzR83sznlcJy9i0QhnNGMjIpK1MFMPvwXcBbwzeP4jkmttStJDXb20rqxjU8syOrrPDWwmJhOMTiRUFU1Eyoa7/6mZvR5Ira18YCmWfDazm0kGNi9Na36pux81swuA75rZT9z9wRnOvRO4E2DDhg15G2ONigeIiCxIxhkbdx9z979y9zcAd7n7h4P0gJITn0yw+2AvN25u5poNjXSdOs3AyMTU8aHRZIqaqqKJSLkwsz8H3kUyhWw/8C4z+7MQpx4F1qc9Xxe0zdjHzCqBBqBnjnPDXHOm3+Fq4NPAbe7ek2p396PBvyeAr3A23e0c7v4pd9/h7jtWrlyZ6eWyFtM+NiIiCxImFS3dN/IyiiKx//ggQ2NxbtrczPb1jQB0HDk7azM8FdgoFU1EysbPAj/t7p9198+SXMz/uhDn7QYuNrNNZhYlWQzgvml97gPeGjx+I/B9d/eg/fagatom4GLgkZDXPIeZbQD+HfgVd382rb3OzOpTj4FbgCdD/F55k0pFS74FIiIyX/MNbEp6D4PU+pqbNq3g6nUNmHHOOpvB0eTsjVLRRKTMNKY9bpi1V5pgzcw7SG7y/DTwJXd/yszuDlLbAD4DNAfFAX4feE9w7lPAl0jOEH2LZLbA5GzXBDCzd5rZEZKzOPvM7NPBa7yf5LqdT04r67wK+C8ze5xk0PQNd//W/N6W3KqNVuIOY/HztxoQEZHM5nuH/vd5GUWReKirl80r67hgeQ0AW1Yuo6O7b+p4KhVNVdFEpIz8ObDXzH5A8sutlxEEIJm4+/3A/dPa3p/2eBR40yznfhD4YJhrBu0fAz42Q/vbgLfN0N4FbJveXki1VcnvGkfGJ6mpihR4NCIiS0+YDTr/KfXY3T85va1UTCac3Qd7uWlz81Tb9vWNdHT3T6UFpMpAax8bESkX7v554CaS6Vz3Ajvd/YuFHVVpikWTny0j4/ECj0REZGkKk4p2RfqTYIO06/IznMLZfyy5vubGTSum2rZvaKRvZILDvSMADAWpaFpjIyLlxN2Pu/t9wIXu/kKhx1OqaqPJWRpVRhMRyc6sgY2ZvdfMhoCrzWww+BkCTgBfXbQRLpKp9TXTZmyAqbLPqoomImXuNws9gFJWG6SfaS8bEZHszBrYuPufu3s98Jfuvjz4qXf3Znd/7yKOcVE81NXD5pY6VgXrawAuXVVPbVWEvUEBgalUNBUPEJHyVNIFZAotFszYqOSziEh2wtyhf9PMXja9caZNzJaqyYTzyMFeXrdtzTntlZEKrlrbMDVjMzg6QTRSoUWdIlI2zGyTux8Mnv7cDG2SI0pFExFZmDBrbP4w7edPgK8B/yOPY5q3kfE4t3z4h3xx9+Gszk+tr7lp84rzjm3f0Mj+Y4OMxScZGo0rDU1Eys29qQfufiR4+OUCjaWkpYoHKBVNRCQ7Ge/S3f3n0p+b2XrgI3kbURaikQqefXGYFwfHsjr/4YPnr69J2b6+kfHJBE8fH2J4NK6KaCJSFszsMpLFYxrM7A1ph5YDNTOfJQuRWmOjVDQRkexkc5d+BLg81wNZiMpIBdFIRdYfBg919bBp2vqalGs2BAUEDvcxNDqhGRsRKReXAq8juTln+hdcQ8BvFGREJe5sKprKPYuIZCPjXbqZ/TXgwdMKYDvwWD4HlY2aqoqsPgwmE87DB3t53dWrZzy+uqGWVcur6ejuT6aiVavUs4iUPnf/KvBVM9vp7rsKPZ5yoOIBIiILE2b6YU/a4zjweXf/cZ7Gk7VYtDKrvOSnjw8yNBqfMQ0tJbVRZ01VhPUrYgsZpojIUnOnmZ03Q+Puv16IwZSyGpV7FhFZkDBrbO4xsyhwSdD0TH6HlJ1YNJLVt1yp/Wtu3DRXYNPEt596kfqaSrauWZ71GEVElqCvpz2uAX4eOFagsZS0SIVRXVmhqmgiIlkKk4r2CuAe4BDJPQzWm9lbi63cc01VhNEsvuV6qKuHjc0xLmyYfS1saqPOodE4y2uUiiYi5cPd701/bmafB/6rQMMpedl+SSciIuFS0f4vcIu7PwNgZpcAnweuy+fA5iubD4PU+pqfvWrm9TUpV69roMIg4dqcU0TK3sXABYUeRKnKNq1aRETCBTZVqaAGwN2fNbOim7aojUYYGp1f8YAw62sA6qoruWRVPT95YUhV0USkrJjZEMkCMhb8+wLwRwUdVAlLFsJRYCMiko1QxQPM7NPAPwfPf4lzCwoUhdqqCCeH5rePzdT6mhk25pxu+/rGILApuphORCRv3L2+0GMoJ7FoJSMq9ywikpWKEH1+C9gPvDP42R+0FZVsUtEe6uplY3OM1Q21Gfum1tlog04RKTdm9noz+z/Bz+sKPZ5SVqs1NiIiWQtTFW0M+Kvgp2jN98NgMuE8crCH11459/qalJde3MKq5dVcdqG+vBSR8mFmfwFcD/xL0PQuM2tz9z8u4LBKVm1VhP6R8UIPQ0RkSZo1sDGzrwGfAr7l7hPTjm0GfhU45O6fzesIQ6qtqpxXVbSe4TEGR+NcsTZc+eZ1TTEe/uNXZzs8EZGl6meA7e6eADCze4C9gAKbPIhFIxzr14yNiEg25pqx+Q3g94GPmFkvcJLkHgabgAPAx4OdqYtCMhUtjrtjZhn7p2Z3VOVMRCSjRqA3eNxQyIGUOqWiiYhkb9a7end/AXg38G4z2wisBs4Az7r7yKKMbh5qoxESDuOTCaorIxn7nw4WZ8aiCmxERObw58BeM/sBycpoLwPeU9ghla5YNKJyzyIiWQp1V+/uh0hu0Fm0aquSwcyZ8clQgU2qnGYsmrmviEi5cvfPm9kDJNfZAPxR8MWX5EFtVUTlnkVEslQy0xW1QYAyMj5JYyxz/9PBB0ddtQIbEZHpzGxj8KUW7n4cuG/acQPWuvuRAgyvZNUGG3QmEk5FRea0ahEROatkApvUzEvYKfwzQSpabVXJvAUiIrn0l2ZWAXwVeJSz6yy3ADcDrwI+ACiwyaHUZ9lofFKp0iIi85Txv5pm9nPAN1IVcYpVeipaGKfHNGMjIjIbd3+TmW0luSnzr5NcZzkCPA3cD3zQ3UcLOMSSNPUl3bgCGxGR+QrzX81fJFkZ7V7gs+7+kzyPKSvpqWhhjAQzO7VaYyMiMiN33w/8/4UeRzmpqTr7WdZc4LGIiCw1FZk6uPsvA9cAncA/mtkuM7vTzELtVGlmETPba2ZfX+BY5zTfVLSRsWQqWp2+ERMRkSIx388yERE5K2NgA+Dug8CXgS+QTEf4eeAxM/udEKe/i2TqQl6l1sqk1s5kkprZSaWwiYiIFFpsntkHIiJyVsbAxsxeb2ZfAR4AqoAb3P21wDbgDzKcuw74WeDTCx/q3OadijYep6aqQlVnRESkaNTMc72oiIicFSYP6xeAD7v7g+mN7j5iZndkOPcjJDf5DJW2thDzTkUbn1QamohIBmb2EqDD3U+b2S8D1wIfdffnCzy0kpQqGHBmIlz2gYiInBUmFe1/AI+knphZrZltBHD37812kpm9Djjh7o/OdfFgvc4eM9tz8uTJMGOeUW10ft9ynRmfVOEAEZHM/gYYMbPULH0n8LnCDql0KRVNRCR7YQKbfwPSSz1PBm2ZvAR4vZkdIrk255Vm9s/TO7n7p9x9h7vvWLlyZYjLzmze5Z7H45qxERHJLO7uDtwGfNzdP8EizMKXq/l+lomIyFlhAptKdx9PPQkeRzOd5O7vdfd17r4RuB34flBhLS+qIhVURWyqjHMmI5qxEREJY8jM3gv8MvCNYNPOqgKPqWTVqiqaiEjWwgQ2J83s9aknZnYbcCp/Q8peTVUk9LdcI+OT2pxTRCSzXwTGgDvc/QVgHfCXhR1S6VIqmohI9sIENr8J/LGZHTazbuCPgLfP50Xc/QF3f102A5yPWHR+gU2qRLSIiMxqiGSxgB+Z2SXAduDzYU40s1vN7BkzO2Bm75nheLWZfTE4/nBq/WZw7L1B+zNm9ppM1zSzdwRtbmYtae1mZh8Lju0zs2vTjr3VzJ4Lft46z/clL2oqFdiIiGQr4529u3cCN5nZsuD5cN5HlaVYtHIeqWhxzdiIiGT2IPBTZtYEfAfYTXIW55fmOsnMIsAngJ8GjgC7zew+d9+f1u0OoM/dt5jZ7cCHgF80s60kU5ivANYA/xkEVcxxzR8DXye5NUG61wIXBz83kiyGcKOZrQA+AOwAHHg0uFbfvN6dHKuoMGqqKhhVKpqIyLyFmrIws58l+QFTY5bc98Xd787juLKSTEULv0FnTGtsREQysbTy/p909/9tZo+HOO8G4IC7dwGY2RdIFiBID2xuI1l5E5KbQH/ckh8ytwFfcPcx4KCZHQiux2zXdPe9Qdv0cdwGfC4ogPCQmTWa2WrgFcB33b03OO+7wK2EnI3Kp1i0kpGQn2UiInJWmA06/5bkt3O/AxjwJuCiPI8rK7FoJPw+NmPxqf0CRERkVmZmO0nO0HwjaAuTxrwW6E57fiRom7GPu8eBAaB5jnPDXDPsOEJfK1fbEoRVWxVRKpqISBbCfDi1uftbSKYL/E9gJ3BJhnMKIhYN92Hg7oxMaMZGRCSE3wXeC3zF3Z8ys83ADwo8pkWVq20JwprPelERETkrzJTFaPDviJmtAXqA1fkbUvZqqiKcHBrL2G8snsAdlXsWEcnA3X8I/NDMlpnZsiAN7J0hTj0KrE97vi5om6nPETOrBBpIfsbMdW6ma4Ydx1GS6Wjp7Q9kuNaiqJ1H9oGIiJwVZsbma2bWOIo5kAAAIABJREFUSLK852PAIeBf8zmobIVNRTs9lsxd1gadIiJzM7OrzGwv8BSw38weNbMrQpy6G7jYzDaZWZRkMYD7pvW5D0hVI3sjyf3OPGi/Paiatonkwv9HQl5zuvuAtwTV0W4CBtz9OPBt4BYzawoKI9wStBWcUtFERLIz5519sBHb99y9H7jXzL4O1Lj7wKKMbp7Cfhik+mjGRkQko78Dft/dfwBgZq8A/h5om+skd4+b2TtIBgsR4LNBKtvdwB53vw/4DPBPQXGAXpKBCkG/L5EsNBAH7nL3yeD1z7tm0P5O4N3AhcA+M7vf3d8G3A/8DHAAGAF+LXiNXjP7U5LBEsDdqUIChRaLRjg1PJ65o4iInGPOwMbdE2b2CeCa4PkYyY3ailJtNMLoPAIbzdiIiGRUlwpqILkvmZnVhTnR3e8nGVikt70/7fEoyYI0M537QeCDYa4ZtH8M+NgM7Q7cNctrfBb47Jy/RAEoFU1EJDthUtG+Z2a/YDPU0Cw2sWiEkYlJkp9js0uV0VTxABGRjLrM7E/MbGPw8z6gq9CDKmW1VZUqHiAikoUwgc3bgX8Dxsxs0MyGzGwwz+PKSm1VhMmEMz6ZmLNfasZGgY2ISEa/DqwE/j34WRm0SZ4kK3xqHxsRkfnKmIvl7vWLMZBcqA1Sy0bHE1RXzh60nA1slIomIjIXd+8jXBU0yZGwWxeIiMi5Mt7Zm9nLZmp39wdzP5yFSc3AjEzEaaBq1n5TqWjVmrEREZmJmX0NmDWv191fv4jDKSs1VRHG4gkSCaeiouizwEVEikaYKYs/THtcA9wA/6+9O49vs7rzPf75SZZsyUmc2HEg+0ZYwg5hhy5AC8wwpAsdaGeYLlBmOrTT5fZe6Mzc3k6nnVva2zIb05atpXQoMHSBaaFAgS7sGAIJEJasJCGQxM6CV1ny7/6hR47iSLacWJYf+ft+vfSy9CznOcdKfPTT+Z3z8AxwZllqtB8SsWygMlRuslLRRESG9P8qXYHxKtc3dfVmqK9VZoGISKlKSUX7k/zXZjYb+Oey1Wg/5JZvHmoIX6loIiKDC27MKRWQzOvLFNiIiJRuX/5ibgQOG+mKjIT8b7kG09mjVdFEREphZivYOyVtJ9ACfM3dW0e/VtWtrsTsAxER2VMpc2z+jd2dWgQ4Bni2nJXaVyWnovVmiEWNWLSUReFERMa1e4EMcGvw+mIgCbwJ/BD4k8Knyb7KZRPoXjYiIsNTyohNS97zNPATd3+0TPXZLyWnovWklYYmIlKas939uLzXK8zsWXc/zsz+vGK1qmK7U9G05LOIyHCU8un+TqDb3TMAZhY1s6S7d5a3asPXP2LTO3hn0JnKKA1NRKQ0UTM70d2fAjCzE4DcH1B98i6D3Jd0SkUTERmeUgKbB4GzgfbgdQK4Hzi1XJXaV/3D96mhb9CpwEZEpCSXATeZ2QTAgF3ApWZWD/zfitasSu3+kk6BjYjIcJQS2NS5ey6owd3bzSxZxjrts0SJw/edKaWiiYiUwt2fBo40s4bg9c683XdUplbVLVliWrWIiOyplNnzHWbWn19tZscDXeWr0r7LfcvVPcS3XB0asRERKYmZNZjZd8iO3j9oZt/OBTlSHkpFExHZN6UMW3wO+C8ze4NsGsKBwEVlrdU+itdEqInYkN9ydaUyTJ0QH6VaiYiE2k3AC8CfBq8vAX4AfKBiNapyuYwCLR4gIjI8pdyg82kzOxQ4JNj0irv3lrda+y4Rjw4Z2HSk0sypHZPZdCIiY81Cd/9g3ut/MLPnKlabcWD3HJvB54uKiMiehkxFM7MrgHp3f8HdXwAmmNlfl79q+yYRiw6ZitaVypCMKRVNRKQEXWZ2eu6FmZ3GGE1HrhZ1sQhm0KURGxGRYSlljs0n3X1H7oW7bwc+Wb4q7Z9kCSM2nakM9bVaPEBEpAR/BVxrZuvMbB3w78BfVrZK1c3MSMSG7stERGRPpXy6j5qZubtD9j42wJidoFJXQmfQmUr3T84UEZHi3P154GgzmxS83mVmnwOWV7Zm1S0Ri2q5ZxGRYSplxObXwO1mdpaZnQX8JNg2JiXjg6eipdJ99GZcqWgiIsPg7rvcfVfw8gsVrcw4kIhHtSqaiMgwlTJicyVwOfCp4PUDwPVlq9F+SsZrBl1JJtdRJJWKJiKyr6zSFah2paRVi4jInoYcsXH3Pnf/nrtf6O4XAi8B/1b+qu2boVLROnuzQY/uYyMiss+80hWodol4DZ1KRRMRGZaShi3M7Fjgw2TvY7AW+Fk5K7U/hkpF6+jJ9B8nIiKFmdnbFA5gDEiMcnXGnUQsQrdGbEREhqVoYGNmB5MNZj4MbANuB8zd3z1KddsnQw3f96eixZWKJiJSjLtPrHQdxrNkvIYtb3dXuhoiIqEy2Kf7l4E/AOe7+yoAM/v8qNRqP9QNsZJMRzD/pl4jNiIiMkaVcrNpERHZ02BzbD4AbAYeNrPrgxXRxvyE0eQQK8nk9mm5ZxERGasSMa2KJiIyXEUDG3f/hbtfDBwKPAx8DphmZt81s/eOVgWHKxmPku5zUum+gvtz34DpBp0iIjJWJeO6j42IyHCVsipah7vf6u5/AswClpFdAnpMqgvuT1OsQ8iloiV0HxsRERmjlIomIjJ8pdygs5+7b3f369z9rHJVaH/lFgUoNoTfpREbEREZ45KxGlLpPjJ9WllbRKRUwwpswiARzzap2E06cyM2Wu5ZRETGqlxfpnQ0EZHSVV9gEwtGbIp0Bl2pDGZQW1N1TRcRkSqRCLIPin1JJyIie6u6T/e5kZhiqWgdPRnq4zWYjfkF3kREZJxKxgbvy0REZG9VF9jklnEuOmLTm9ZSzyIiMqbl+iktICAiUrrqC2xig3cG2REbBTYiIuVmZuea2StmtsrMriqwv9bMbg/2P2lm8/L2fSnY/oqZnTNUmWY2PyhjVVBmPNh+jZk9FzxeNbMdeedk8vbdXa7fw74Y6ks6ERHZW9UFNkOlonWmMv25yyIiUh5mFgWuBc4DFgMfNrPFAw67FNju7gcB1wBXB+cuBi4GDgfOBf7DzKJDlHk1cE1Q1vagbNz98+5+jLsfA/wb8LO863fl9rn7BSP8K9gvSkUTERm+qgtshvqWqzOV1oiNiEj5nQiscvc17p4CbgOWDjhmKXBz8PxO4CzLToBcCtzm7j3uvhZYFZRXsMzgnDODMgjKfF+BOn0Y+MmItbCMkv2LByiwEREpVdUFNsnY4J1BdsRGgY2ISJnNBDbkvd4YbCt4jLungZ1A0yDnFtveBOwIyih4LTObC8wHHsrbXGdmLWb2hJkVCoQqRss9i4gMX9XlZNXlOoMiS2R2pTIcOKluNKskIiKVdzFwp7vnRwpz3X2TmS0AHjKzFe6+euCJZnY5cDnAnDlzRqWyif6bTWu5ZxGRUlXdiE08GiEasaLfcnWk0ro5p4hI+W0CZue9nhVsK3iMmdUADUDrIOcW294KTA7KKHatixmQhubum4Kfa4DfAscWaoi7X+fuS9x9SXNzc+HWjrDkEAvhiIjI3qousDEzkrFo0c6gK5UhWavARkSkzJ4GFgWrlcXJBhYDVx67G/ho8PxC4CF392D7xcGqafOBRcBTxcoMznk4KIOgzLtyFzGzQ4EpwON526aYWW3wfCpwGvDSiLV+P2m5ZxGR4au6VDSAuniU7kFHbKqy2SIiY4a7p83s08B9QBS4yd1fNLOvAi3ufjdwI3CLma0C2sgGKgTH3UE20EgDV+RSyAqVGVzySuA2M/sasCwoO+disosReN62w4Dvm1kf2S/5vuHuYyawqa2JYEbRvkxERPZWlZ/wk/HCIzaZPqe7t6//XjciIlI+7n4PcM+AbV/Oe94NfKjIuV8Hvl5KmcH2NWRXTStU1lcKbHsMOHLQBlTQUNkHIiKyt6pLRYPsTToLdQa5eTf1SkUTEZExLhGvUWAjIjIM1RnYFElF6wxWl9ENOkVEZKxLxCNaFU1EZBiqMrAplorW2ROM2GhVNBERGeOSsRrdx0ZEZBjKFtiYWZ2ZPWVmz5vZi2b2D+W61kDFUtFy27Tcs4iIjHWJIl/SiYhIYeXMyeoBznT3djOLAY+Y2b3u/kQZrwlkU80KpaJ19WaH9LUqmoiIjHXJeJQuBTYiIiUr24iNZ7UHL2PBwwc5ZcRkV5LZOy+5o0cjNiIiEg6JWFSpaCIiw1DWOTZmFjWz54AtwAPu/mSBYy43sxYza9m6deuIXLfY8P3uVDSN2IiIyNiW0IiNiMiwlDWwcfeMux8DzAJONLMjChxznbsvcfclzc3NI3LdoVZF04iNiIiMdcUWwhERkcJGZVU0d98BPAycOxrXS8ai9Gac3kzfHtv7R2x0HxsRERnjEkXSqkVEpLByrorWbGaTg+cJ4D3Ay+W6Xr5EMCIzMDd594iNUtFERGRsyy6E0zf0gSIiApR3VbTpwM1mFiUbQN3h7r8s4/X69Qc2qQyT6mL923MjNomYRmxERGRsS8ajpDJ9pDN91ESr8rZzIiIjqmyBjbsvB44tV/mDyc2hGZib3JnKUBeLEI1YJaolIiJSsv6+rDfDJAU2IiJDqsq/lLkRmYGryXSm0kpDExGRUKgL+rJuLSAgIlKS6gxsguAld0POnM5URiuiiYhIKBTLPhARkcKqM7CJFUlF61FgIyIi4aDARkRkeKoysEnGi6Si9WaUiiYiIqGQS0UbmH0gIiKFVWVgU3S55560RmxERCQUcl/EdaW05LOISCmqM7ApuniARmxERCQcdqeiacRGRKQUVRnYFF/uWSM2IiISDsWyD0REpLCqDGyKpqKlMtTXKrAREZGxr9hCOCIiUlhVBjbxaISIFU5FS8SUiiYiImNfsYVwRESksKoMbMyMZLxmj2+53F2paCIiEhpKRRMRGZ6qDGwgu0xm/hKZPek++hySSkUTEZEQiEcjRCNGe48WDxARKUXVBjbJeHSP4fvc6E0ypsBGRETGPjPjoOYJvLBpZ6WrIiISClUd2HTuEdhkv/FK1mqOjYiIhMPJCxppWbed3ozuZSMiMpSqDWyyqWgFRmw0x0ZERELi5AVNdPVmWL5RozYiIkOp2sCmWCpavW7QKSIiIXHi/EYAnljTWuGaiIiMfVUd2OyRihZMvkxoxEZEREKiaUIthxwwUYGNiEgJqjawqYtF6S6QiqYRGxERCRPNsxERKU3VBjYDR2w6UhqxERGR8NE8GxGR0lRxYFPTvxIa7L5zc73uYyMiIiGieTYiIqWp2sAmm4q2e9i+o/8+NkpFExGR8NA8GxGR0lRtYJOMR0ll+kgHOcldSkUTEZGQ0jwbEZGhVW1gk4hlA5jcvWw6UxliUSNeU7VNFhEZU8zsXDN7xcxWmdlVBfbXmtntwf4nzWxe3r4vBdtfMbNzhirTzOYHZawKyowH2z9mZlvN7LngcVneOR81s9eCx0fL9XsYCZpnIyIytKr9lJ8bmcnNrelMZfqDHRERKS8ziwLXAucBi4EPm9niAYddCmx394OAa4Crg3MXAxcDhwPnAv9hZtEhyrwauCYoa3tQds7t7n5M8LghuEYj8H+Ak4ATgf9jZlNG9JcwgjTPRkRkaFUb2CSDwKazP7BJU1+r+TUiIqPkRGCVu69x9xRwG7B0wDFLgZuD53cCZ5mZBdtvc/ced18LrArKK1hmcM6ZQRkEZb5viPqdAzzg7m3uvh14gGwQNSZpno2IyNCqNrAZmIrWkcpofo2IyOiZCWzIe70x2FbwGHdPAzuBpkHOLba9CdgRlFHoWh80s+VmdqeZzR5G/QAws8vNrMXMWrZu3Vq8xWWmeTYiIoOr3sBmwIhNVyqjm3OKiIw//w3Mc/ejyI7K3DzE8Xtx9+vcfYm7L2lubh7xCpZK82xERAZXtYFNMghicnNsOnrSGrERERk9m4DZea9nBdsKHmNmNUAD0DrIucW2twKTgzL2uJa7t7p7T7D9BuD4YdRvTNE8GxGRwVVtYDMwFa2rN0O9AhsRkdHyNLAoWK0sTnYxgLsHHHM3kFuN7ELgIXf3YPvFwapp84FFwFPFygzOeTgog6DMuwDMbHre9S4AVgbP7wPea2ZTgkUD3htsG7M0z0ZEZHBVm5u1OxUtm3Ld0ZNm9pRkJaskIjJuuHvazD5NNliIAje5+4tm9lWgxd3vBm4EbjGzVUAb2UCF4Lg7gJeANHCFu2cACpUZXPJK4DYz+xqwLCgb4G/M7IKgnDbgY8E12szsH8kGSwBfdfe2Mv06RszJCxq5o2UjvZk+YtGq/W5SRGSfVH1g05U3xyapERsRkVHj7vcA9wzY9uW8593Ah4qc+3Xg66WUGWxfQ3bVtIHbvwR8qcg1bgJuGrQRY8zJC5q4+fH1LN+4k+PnjtnVqUVEKqJqv+5JDrxBZ68CGxERCTfNsxERKa5qA5uBq6J19mRIaFU0EREJMc2zEREprmoDm9qaCGbQ3ZuhN9NHKtOnxQNERCT0dD8bEZHCqjawMTOSsSidqUz/qI2WexYRkbDT/WxERAqr2sAGIBGvoTOV6V9AoL5WqWgiIhJummcjIlJYlQc2Ebp7M3QESz5r8QAREQk7zbMRESmsqgObZKyGzlS6f8QmqcUDRESkCpy8oJGn17WxZmt7pasiIjJmVHVgk4hn59h09GjERkREqscnTp9PMl7DX9z0FG/t6q50dURExoTqDmxiUbp7M3T25kZsFNiIiEj4zW2q54cfP4HtHSk+etNT7OzqrXSVREQqrqoDm2QwYtPZo1Q0ERGpLkfNmsz3L1nC6q3tXHbz03QHX+KJiIxXVR3Y1MWjdPVm6NTiASIiUoVOXzSVay46hpb12/n0rctI6942IlIhO7t6eWtXN319XrE6VPUQRjIWpSuVoUupaCIiUqXOP2oG2ztS/O+7XuRvf76Cqz94FGa2T2W5O1fc+iztPRn+1zmHcMTMhhGurYhUm51dvXz/d6v5waPr6OrNUFsTYdaUBLMbk8xpTDJ7SpLZjUnmTU0yr6meulj5Po9Xd2DTv3iAUtFERKR6XXLKPLa2p/jXB18j3ef80/uP3KcPD/+9fDP3rHiT2poI5//bI7z/2Jn8j/cezKwpyTLUWkTCrCuV4QePreV7v13Nru40f3L0DE6YN4UNbZ1saOtiw/ZOnlm/nbe703ucN72hjr/748M4/6gZI16nqv6kn0tF60qlMYO6WFVn3omIyDj2+bMXETXjmt+8yqot7Xzvz49nxuREyed3ptL8069WcsTMSfz40pP4/u/XcNMja/nV8s187LR5XPGug2hIxsrYAhEJg1S6j9tbNvCvD77G1rd7ePchzXzxnEM4fEbhEd6dnb283tbJ2tYO1m3LPg6YVFeWulV1YJOM1ZBK97GrO00yFt3noXkREZGxzsz47NmLWDxjEp+//Tku+PdHuPYjx3HSgqaSzr/24VW8uaubf//IsUxOxrny3EO55OS5fPv+V7n+D2u4/ekNfObMg/jEafOJRNSfiownu7p7aVnXxhNr2rj3hc1saOvixHmN/MefHccJ8xoHPbchGePIZANHzip/amt1BzbBnJrWjhTJ2qpuqoiICADvWXwAv7jiVC7/0TP82Q1P8vX3H8FFJ8wZ9Jz1rR1c//u1vP/YmSzJ+5AyY3KCb//p0Vx6+nz+770r+dqvVvLM+u1cc9ExZc2TF5HKW9/awY+fWM8Ta9p48Y2d9DnEoxGOmzuZry49gncd3DzmBg2q+tN+XS6wae/RwgEiIjJuHDRtIr/49Gl8+tZlXPnTFZgZf7pkdtHj//GXK4lFjavOO7Tg/sUzJnHLpSdxwx/W8PV7VrLlhie5/i+W0FgfL1cTKsbduaNlA9MbEpx+0FSNTsm49YU7nmfFxp0cO2cynzlzESctaOS4OVPG9JcaVR3YJGO5wCalhQNERGRcmVQX47pLjueTP2rhyp8up7YmwtJjZu513G9f2cJvVr7FVecdOmTe+2VnLGDG5ASfu/05Pvjdx/jhx09gblN9uZpQEU+tbePKn64AYObkBBceP4sPLZmlBRRkXNnV3cuy17fz1+86iC+ec0ilq1Oyqp5Nn+hPRdOIjYiIjD91sSjXXbKEE+Y18oU7nufXL7y5x/5Uuo+v/vIl5k+t5+OnzSupzD86cjq3XnYSOzpTfOA/HmPZ69vLUPPKuf4Pa5mSjPEvFx/DguZ6/vWh1zjjmw9zyY1P8t/Pv0FPWjdCler35Jo2+hxOPai0OXpjxbgIbNo6UgpsRERkXErEo9z0sRM4cmYDn/nJs/z2lS39+25+bB1rtnbw5fMXU1tTej+5ZF4jP/3UqdTX1vDh65/gvhffHPqkEFiztZ0HX36LS06ey9JjZnLLpSfx+//5bj5z5iJWb2nnMz9ZxtH/cD/vu/ZR/vbnK/jxE+tZ9vp2ulIKdqS6PLZ6G7U1EY6bM6XSVRmWqs7PyqWi9bluzikiIuPXhNoabv7EiXzk+if4y1ue4QcfP4GDpk3gXx58jTMPnca7D5027DIXNE/gZ399Kpfe3MJf/fgZPnT8LE5e0MSSuY3MbkwMe1Lx0+va+OavX+a1Le1cdvp8PnH6/FFPI7/xkbXEohEuOWVe/7bZjUm+8J6D+exZi3hk1TZ+98pWXtq8k18+/wa3Pvk6ANGIcerCJpYeM5NzDj+AiXVaFlvC7bFVrZwwr3FMz6cppKoDm0ReMFOvOTYiIjKONSRi3HLpSVz0/ce57OYWjp41mZ50hv99/uJ9LnPqhFpu++TJfPmuF7h3xZvc0bIRgGkTa1kybwpL5jZy4vxGFk+fVHQS/stv7uJbv36FB1/ewrSJtRw9azL/7/5Xufnx9Xz2rEVcdMJsYtHyJ5i0tvdw5zMbef8xM2meWLvX/mjEeOfBzbzz4GYgu8jAph1dvPjGLpa9voNfrXiDL/7X8/ztzyOcdeg0Ljh6Bu8+dFroPhiKbH27h1feepulx478DTTLrWyf9s1sNvAj4ADAgevc/V/Kdb1C8kdpEhqxERGRca6xPs5/XnYSF133BI+vaeWv3rmQ+VP3b/J/Ih7lWx86mm988ChefettWtZvp2VdGy3rtnPPimyKWvPEWs48ZBpnHjaN0w+aSn1tDRvaOrnmgVf5+XObmFBbw/869xA+fup8EvEoz6xv4xv3vszf/+IFbnxkLV987yGce8SBRMu4QtmPn3idnnQfl50xv6TjzYxZU5LMmpLknMMP5MpzD2HZhh3c/dwb/HL5Zu594U0m1tbwjkOaOX7OFI6bO4XF0ycRr6nqWQBSBR5bvQ2A0xZOrXBNhq+cwxhp4H+4+7NmNhF4xswecPeXynjNPSTyRmmUiiYiIgLTJtVx6ydP4o6nN5b8Ib4U0Yhx2PRJHDZ9EpecPBeAN3d289jqbTz08hbueWEzt7dsIF4T4ZhZk1m2YTsRMy5/xwI+9c6FTE7uXjr6+LmN3PGXp/Dgyi18876XueLWZ2msj3PmodM4+7ADOGNRNjgaKd29GW55Yh3vPqSZRQdM3KcyzIzj5kzhuDlT+Ps/Pown1rRx13ObeHTVNn61fDMA8ZoIR85s4KhZDUyqi1EXi1IXi+z+WRNl+uQEhx44USM9UjGPrWplUl0NR8ws/w01R1rZAht33wxsDp6/bWYrgZnA6AU2eX8UtNyziIhI1vSGBJ89e1HZr3NgQx0fOG4WHzhuFr2ZPp5e18ZDK7fw2OpWLjx+Fp8962AObCi8xLSZcfbiA3j3odO4/8U3ue/FN3ngpbe485mNxGsinLqwidMPmsriGZNYPH3SHoHRcP1i2Sa2taf45BkL9rmMfDXRCKcvmsrpi7LfeL+5s5tlr2/n2de38+zrO7jtqQ109RZfcCAaMRY213P4jAYOnzGJw2c0cMTMSZq7I6Pi0dXbOHlBU1lHSMtlVD7tm9k84FjgyQL7LgcuB5gzZ/A7Iw9X/iiNRmxEREQqJxaNcOrCqZw6zPSWaMQ478jpnHfkdNKZPlrWb+c3L73Fb1a+xW9f2dp/3MzJCQ6bPpHFMxo4YV525KSUUZ2+PueGR9ayePokTllYnqVtD2yo629D/nV70n1092boTmfo7u2jM5VmQ1snL76xixff2MXjq1v5+bJN/ecsaK7n6FmTOXJmA0fPbmDx9Aal2suIer21k43bu0YsyB9tZQ9szGwC8FPgc+6+a+B+d78OuA5gyZIlPpLXrq2JYAbukBzBIWsREREZfTXRCCcvaOLkBU38/fmL2dbew8rNu3jpjV28tHkXKzfv4qGXt9DnUBMxjpjZwEnzswsYLJnXSENi7xGP3726lVVb2vnni44Z9kpu+yMSMRLx6F6ByeEzGjj3iN0B0Lb2Hl7YtJMVG3eyfNNOHlu9rT/YiUaMuY1JFjRPYGFzPQubJ7CguZ4FzRNorN/3ESwZvx7Nza8J2f1rcsr6ad/MYmSDmv9095+V81pFrk8iFqUzlelf+llERESqw9QJtZyxqJkzFjX3b2vvSfPs+u08ubaVp9a28YNH1/H936/BDBZPn8RpB03llIVNnDivkfraGq7/wxqmN9Txx0dNH+RKlTN1Qi3vOmQa7zpk95Lcb+3qZvnGnazYuIPXtrSzems7v391K6lMX/8xk+pqaJ5YS9OEWpon1DJ1QpypE2qZOrGWWVMSzGuqZ3pDHTWjsOKchMejq7YxbWItC5snVLoq+6Scq6IZcCOw0t2/U67rDCUZzwY29bUKbERERKrdhNoa3nFwM+8IlmXu7s2w7PUdPLm2lcdXt/LDR9dx3e/XUBMxjpzVwLLXd/Cl8w4dlSWlR8oBk+p4z+I63rP4gP5tmT5n4/ZOVm9tZ83WDta3drKtvYfW9hQrN+9ia3sPb3en9ygnFjVmT0kytynJ3KZ65jVlR38WNNczoyFRdIluqU59fc7jq1t5x8HNozp6OZLKOWJzGnAJsMLMngu2/a2731PGa+4lt6pIQosHiIiwoANYAAAQbUlEQVSIjDt1sSinLGzilIVNfO5s6EplaFnfxmOrW3lsdSsLptZz8YkjO8e3EqIRY25TPXOb6jnz0MLHdPdm2Nbew8btXaxv7WBda2f257ZOnlrbRkdq94IGdbEI86dmg5yFU+uZ3Zhk5uQEMyYnOLChTqu2VaFX3nqb1o4Up5ZprtloKOeqaI8AFQ/3cosG1GtynYiIyLiXiEf3Sl8bL+pi0f5775y8YM8Pr+7O1vYe1mzt6B/1WbO1nRUbd3Lvis30DZgFPXVCLTOnJJg1JcHsKcnsz8Yks6dkgx8FPuHz6Krc/Jrw3b8mp+qHMXIjNVo1RERERKQwM2PaxDqmTazbK+jpSWd4c2c3m3Z08caObt7Y0cWm7V1s2tHFi5t2cv+Lb9Kb2TPymTohO5cnF/xMm1jH5ESMycnsIxmvIZft5Hmn5p47vsf2aMSImBGNZOsaNaMmakydUKsgaoQ8trqV+VPrmTE5Uemq7LPqD2xi2ZzZeqWiiYiMKjM7F/gXIArc4O7fGLC/FvgRcDzQClzk7uuCfV8CLgUywN+4+32DlWlm84HbgCbgGeASd0+Z2ReAy8jeNHor8Al3Xx+ckwFWBNV53d0vKMfvQSTsamui/WluhWT6nC1vd7OhrYuN2zvZtL2LjXmBzwMvvrXHwgYjrbE+zvSGOqY31HFgQx3TGxLZhRPq4zTWx2mqr6VxQpz6eDS0c0fKrTfTx5NrWnnfsTMrXZX9UvWf9nM35tR9bERERo+ZRYFrgfcAG4Gnzexud8+/SfOlwHZ3P8jMLgauBi4ys8XAxcDhwAzgN2Z2cHBOsTKvBq5x99vM7HtB2d8FlgFL3L3TzD4FfBO4KCiry92PKdsvQWSciEaM6Q0JpjckOHF+4177+/qct3vS7OzsZXtnih1dvXT0pPeYr7BnvGH92wxwsqlymT7oc+9/pNJ9bNnVw+Zd3cGIUjct67ezo7O3YD3jNRGm1sdpnJANdprq4zRNiNM0oZbG+jixqNHdG9xbqP9nhlSmLxghilATMaIRIxY1opEIjfUxpjdk0+9mTK4L7Q3hl2/cQUcqE+o0NBgHgU0uBU33sRERGVUnAqvcfQ2Amd0GLAXyA5ulwFeC53cC/x6sqLkUuM3de4C1ZrYqKI9CZZrZSuBM4CPBMTcH5X7X3R/Ou94TwJ+PZCNFZGiRiNGQiNGQiDGnKVn263WlsosktHakaOvoYVt7iraO7KO1PbuttSPFqi3tbGvvoSddfDSptiZCPBoh4066z0ln+vaab5RvSjLGjMkJDphUlz03OD8ePJ9Yl/095NLyGoKfkxIxJtbGqItFKjKq9OiqVszglAXhXTgAxkNgk1sVTfmXIiKjaSawIe/1RuCkYse4e9rMdpJNJZtJNgjJPzeXH1GozCZgh7unCxyf71Lg3rzXdWbWQjZN7Rvu/ovSmiYiY1kiHs0uZNA4dBDl7nSmMrS2p0j39VEXi5KIRamLRamtiRRc8rqvLwhy+vpo60jtnne0o4s3gsdbu7rpSfeRCh69mT560n10pNJ7zB0aKBox6uNRJtbFmFBbQ31tlPraGurjNSRro3v8rK2J4GRHsfrnJgXPa2MRErEoiXgNyeBGsMlYtqyGRIyGZIwJ8Zr+9j26ahuLp09iSshv7Fr1gU0ynv2HGdVa7CIi45aZ/TmwBHhn3ua57r7JzBYAD5nZCndfXeDcy4HLAebMCf+ywCKym5llA4dhZPZEIkY8YsSJkIzXMGtK6aNQmT7n7e5ednb1sqOzlx1d2ec7g/S89u407T1p3u5O097T2//8rV3ddPRk6Eyl6UhlSA0yylRyOwwmBSNpG7d3cenp8/e7zEqr+sDmgqNncMCkukpXQ0RkvNkEzM57PSvYVuiYjWZWAzSQXURgsHMLbW8FJptZTTBqs8e1zOxs4O+AdwbpbQC4+6bg5xoz+y1wLLBXYOPu1wHXASxZsmSQ71pFRAYXjRiTk3EmJ+PM3Y+sr3QwAmQGEcufk5R93pPO0NWboSuVoTO1+/nb3Wl25QVTucfBB0zkT5fMGokmVlTVBzZL5jWyZN7eE9lERKSsngYWBauVbSK7GMBHBhxzN/BR4HHgQuAhd3czuxu41cy+Q3bxgEXAU2TnEe9VZnDOw0EZtwVl3gVgZscC3wfOdfctuQub2RSg0917zGwq2ZtKf7MMvwcRkRFXE41QE40U3Z+bzzPeVH1gIyIioy+YM/Np4D6ySzPf5O4vmtlXgRZ3vxu4EbglWBygjWygQnDcHWQXGkgDV7h7BqBQmcElrwRuM7OvkV0J7cZg+7eACcB/BRNyc8s6HwZ838z6gAjZOTb5CxuIiEjImA82g2mULVmyxFtaWipdDRGRcc3MnnH3JZWux1ikfkpEpPKK9VPFx7BERERERERCQoGNiIiIiIiEngIbEREREREJPQU2IiIiIiISegpsREREREQk9BTYiIiIiIhI6CmwERERERGR0FNgIyIiIiIioafARkREREREQk+BjYiIiIiIhJ4CGxERERERCT1z90rXoZ+ZbQXW7+PpU4FtI1idsULtChe1K1zUrsLmunvzSFWmmqifKkjtChe1K1zUrsIK9lNjKrDZH2bW4u5LKl2PkaZ2hYvaFS5ql4yman1f1K5wUbvCRe0aHqWiiYiIiIhI6CmwERERERGR0KumwOa6SlegTNSucFG7wkXtktFUre+L2hUuale4qF3DUDVzbEREREREZPyqphEbEREREREZp0If2JjZuWb2ipmtMrOrKl2f/WFm68xshZk9Z2YtwbZGM3vAzF4Lfk6pdD2HYmY3mdkWM3shb1vBdljWvwbv33IzO65yNR9ckXZ9xcw2Be/Zc2b2R3n7vhS06xUzO6cytR6amc02s4fN7CUze9HMPhtsD/V7Nki7Qv2emVmdmT1lZs8H7fqHYPt8M3syqP/tZhYPttcGr1cF++dVsv7jkfqpsUf9VP++Mf83D9RPhe09q2g/5e6hfQBRYDWwAIgDzwOLK12v/WjPOmDqgG3fBK4Knl8FXF3pepbQjncAxwEvDNUO4I+AewEDTgaerHT9h9murwBfLHDs4uDfYy0wP/h3Gq10G4q0azpwXPB8IvBqUP9Qv2eDtCvU71nwe58QPI8BTwbvwx3AxcH27wGfCp7/NfC94PnFwO2VbsN4eqifGpsP9VPh+ZsX1FX9VIjes0r2U2EfsTkRWOXua9w9BdwGLK1wnUbaUuDm4PnNwPsqWJeSuPvvgbYBm4u1YynwI896AphsZtNHp6bDU6RdxSwFbnP3HndfC6wi++91zHH3ze7+bPD8bWAlMJOQv2eDtKuYULxnwe+9PXgZCx4OnAncGWwf+H7l3sc7gbPMzEapuqJ+akxSPwWE5G8eqJ/KE4r3rJL9VNgDm5nAhrzXGxn8H8RY58D9ZvaMmV0ebDvA3TcHz98EDqhM1fZbsXZUw3v46WCo+6a8FIxQtisY/j2W7LcrVfOeDWgXhPw9M7OomT0HbAEeIPut3Q53TweH5Ne9v13B/p1A0+jWeFwLzb+rEqmfCud7GOq/efnUT4WjXZXqp8Ie2FSb0939OOA84Aoze0f+Ts+O0YV+GbtqaUfgu8BC4BhgM/DtylZn35nZBOCnwOfcfVf+vjC/ZwXaFfr3zN0z7n4MMIvst3WHVrhKMn6onwqf0P/Ny1E/FR6V6qfCHthsAmbnvZ4VbAsld98U/NwC/JzsP4S3csOnwc8tlavhfinWjlC/h+7+VvCftw+4nt1DwqFql5nFyP5R/U93/1mwOfTvWaF2Vct7BuDuO4CHgVPIplrUBLvy697frmB/A9A6ylUdz0L372ow6qeAkL2H1fI3T/0UEKJ25Yx2PxX2wOZpYFGwykKc7ISjuytcp31iZvVmNjH3HHgv8ALZ9nw0OOyjwF2VqeF+K9aOu4G/CFYwORnYmTesPOYNyNl9P9n3DLLtujhY6WM+sAh4arTrV4ogj/VGYKW7fydvV6jfs2LtCvt7ZmbNZjY5eJ4A3kM2L/th4MLgsIHvV+59vBB4KPhmU0aH+qnwCPXfvGLC/jcP1E8Rsvesov3UwNUEwvYgu/LFq2Rz9/6u0vXZj3YsILvSxfPAi7m2kM0xfBB4DfgN0FjpupbQlp+QHTrtJZtDeWmxdpBdOePa4P1bASypdP2H2a5bgnovD/5jTs87/u+Cdr0CnFfp+g/SrtPJDt8vB54LHn8U9vdskHaF+j0DjgKWBfV/AfhysH0B2Q5uFfBfQG2wvS54vSrYv6DSbRhvD/VTY++hfqr/+DH/Ny+op/qpEL1nleynLChQREREREQktMKeiiYiIiIiIqLARkREREREwk+BjYiIiIiIhJ4CGxERERERCT0FNiIiIiIiEnoKbCR0zMzN7Nt5r79oZl8ZobJ/aGYXDn3kfl/nQ2a20sweHrB9npl9pNzXFxGR8lE/JVIZCmwkjHqAD5jZ1EpXJF/e3XRLcSnwSXd/94Dt84CCHcYwyxcRkcpRPyVSAQpsJIzSwHXA5wfuGPhNlpm1Bz/fZWa/M7O7zGyNmX3DzP7MzJ4ysxVmtjCvmLPNrMXMXjWz84Pzo2b2LTN72syWm9lf5pX7BzO7G3ipQH0+HJT/gpldHWz7Mtmbct1oZt8acMo3gDPM7Dkz+7yZfczM7jazh4AHgzt/3xTUe5mZLR2iftPN7PdBeS+Y2Rn7+DsXEZHSqZ9SPyUVoMhawupaYLmZfXMY5xwNHAa0AWuAG9z9RDP7LPAZ4HPBcfOAE4GFwMNmdhDwF8BOdz/BzGqBR83s/uD444Aj3H1t/sXMbAZwNXA8sB2438ze5+5fNbMzgS+6e8uAOl4VbM91VB8Lyj/K3dvM7J+Ah9z9E2Y2GXjKzH4D/FmR+n0AuM/dv25mUSA5jN+XiIjsO/VT6qdklCmwkVBy911m9iPgb4CuEk972t03A5jZaiD3B38FkD/Ufoe79wGvmdka4FDgvcBRed+yNQCLgBTw1MDOInAC8Ft33xpc8z+BdwC/KLG+OQ+4e1vw/L3ABWb2xeB1HTBnkPo9DdxkZjHgF+7+3DCvLSIi+0D9lPopGX0KbCTM/hl4FvhB3rY0QYqlmUWAeN6+nrznfXmv+9jz/4IPuI4DBnzG3e/L32Fm7wI69q36Jcsv34APuvsrA+pRsH7BvncAfwz80My+4+4/KmttRUQkR/3U7nqon5Ky0xwbCa3g26E7yE5wzFlHdkgd4AIgtg9Ff8jMIkE+8wLgFeA+4FPBN0qY2cFmVj9EOU8B7zSzqcHw+oeB3w1xztvAxEH23wd8JuggMLNj87bvVT8zmwu85e7XAzeQTRcQEZFRoH5K/ZSMLo3YSNh9G/h03uvrgbvM7Hng1+zbt1Svk/1jPwn4K3fvNrMbyOY0Pxv8sd4KvG+wQtx9s5ldBTxM9husX7n7XUNcezmQCer/Q7I5z/n+kew3gMuDb/rWAueT7QwK1e9dwP80s16gnWwOtoiIjB71U+qnZJSY+8DRTBERERERkXBRKpqIiIiIiISeAhsREREREQk9BTYiIiIiIhJ6CmxERERERCT0FNiIiIiIiEjoKbAREREREZHQU2AjIiIiIiKhp8BGRERERERC7/8DgQ/sQ7AEzt0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1008x432 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "logs = model1.make_inspector().training_logs()\n",
        "\n",
        "plt.figure(figsize=(14, 6))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot([log.num_trees for log in logs], [log.evaluation.accuracy for log in logs])\n",
        "plt.xlabel(\"Number of trees\")\n",
        "plt.ylabel(\"Accuracy (out-of-bag)\")\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot([log.num_trees for log in logs], [log.evaluation.loss for log in logs])\n",
        "plt.xlabel(\"Number of trees\")\n",
        "plt.ylabel(\"Logloss (out-of-bag)\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "09Am1e23nVWS"
      },
      "outputs": [],
      "source": [
        "# This cell start TensorBoard that can be slow.\n",
        "# Load the TensorBoard notebook extension\n",
        "%load_ext tensorboard\n",
        "# Google internal version\n",
        "# %load_ext google3.learning.brain.tensorboard.notebook.extension"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eFHKyT-YngZW"
      },
      "outputs": [],
      "source": [
        "model1.make_inspector().export_to_tensorboard(\"/tmp/tensorboard_logs\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 821,
          "resources": {
            "https://localhost:6006/?tensorboardColab=true": {
              "data": "<!doctype html><meta name="tb-relative-root" content="./"><!doctype html><!--
@license
Copyright 2019 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
--><html><head><meta charset="utf-8">
<title>TensorBoard</title>
<link rel="shortcut icon" 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">
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">

<style>
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/uYECMKoHcO9x1wdmbyHIm3-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/sTdaA6j0Psb920Vjv-mrzH-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/_VYFx-s824kXq_Ul2BHqYH-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/tnj4SB6DNbdaQnsM8CFqBX-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/oMMgfZMQthOryQo9n22dcuvvDin1pK8aKteLpeZ5c0A.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/Ks_cVxiCiwUWVsFWFA3Bjn-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/NJ4vxlgWwWbEsv18dAhqnn-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/isZ-wbCXNKAbnjo6_TwHToX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/77FXFjRbGzN4aCrSFhlh3oX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/jSN2CGVDbcVyCnfJfjSdfIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/UX6i4JxQDm3fVTc1CPuwqoX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
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@font-face {
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@font-face {
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}
</style>



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",
              "headers": [
                [
                  "content-type",
                  "font/woff2"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "UvAje4UEnk4n",
        "outputId": "b2acdd1f-b80e-4229-ec7e-dbf1cd479a52"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": [
              "\n",
              "        (async () => {\n",
              "            const url = new URL(await google.colab.kernel.proxyPort(6006, {'cache': true}));\n",
              "            url.searchParams.set('tensorboardColab', 'true');\n",
              "            const iframe = document.createElement('iframe');\n",
              "            iframe.src = url;\n",
              "            iframe.setAttribute('width', '100%');\n",
              "            iframe.setAttribute('height', '800');\n",
              "            iframe.setAttribute('frameborder', 0);\n",
              "            document.body.appendChild(iframe);\n",
              "        })();\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "%tensorboard --logdir \"/tmp/tensorboard_logs\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JleOQX9sMq0n"
      },
      "outputs": [],
      "source": [
        "# print(\"Random Fores Score at testing: \",model1.score(x_test, y_test))\n",
        "# print(\"Random Fores Score at testing: \",model1.score(x_train, y_train))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fuQBeO8SIYyP"
      },
      "source": [
        "## Gardient Boosted Trees Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "NSlp1e3aIgKQ",
        "outputId": "b91eb029-1183-4872-8d68-152243291432"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Use /tmp/tmpsz4sah5g as temporary training directory\n",
            "Reading training dataset...\n",
            "Training dataset read in 0:01:00.782806. Found 2415744 examples.\n",
            "Training model...\n",
            "Model trained in 0:17:23.815370\n",
            "Compiling model...\n",
            "Model compiled.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f3df8291210>"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tuner = tfdf.tuner.RandomSearch(num_trials=20)\n",
        "model2 = tfdf.keras.GradientBoostedTreesModel(tuner=tuner,random_seed=18)\n",
        "\n",
        "model2.fit(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "hjaXaJE9CdTX",
        "outputId": "eb25ea5f-11fe-455a-9eb5-4861c1a7e6de"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "8388/8388 [==============================] - 20s 2ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
            "\n",
            "loss: 0.0000\n",
            "accuracy: 1.0000\n"
          ]
        }
      ],
      "source": [
        "model2.compile(metrics=[\"accuracy\"])\n",
        "evaluation = model2.evaluate(x_test,y_test,return_dict=True)\n",
        "print()\n",
        "\n",
        "for name, value in evaluation.items():\n",
        "  print(f\"{name}: {value:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "5I6eLV5dokw-",
        "outputId": "c15ba027-227a-4e90-c89a-a7f96cec8c65"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "logs = model2.make_inspector().training_logs()\n",
        "\n",
        "plt.figure(figsize=(12, 4))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot([log.num_trees for log in logs], [log.evaluation.accuracy for log in logs])\n",
        "plt.xlabel(\"Number of trees\")\n",
        "plt.ylabel(\"Accuracy (out-of-bag)\")\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot([log.num_trees for log in logs], [log.evaluation.loss for log in logs])\n",
        "plt.xlabel(\"Number of trees\")\n",
        "plt.ylabel(\"Logloss (out-of-bag)\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "DZLwIO-Norav"
      },
      "outputs": [],
      "source": [
        "model2.make_inspector().export_to_tensorboard(\"/tmp/tensorboard2_logs\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "xTfYAspQo78q",
        "outputId": "0799ae16-aeb7-47fa-84af-600c96ebab60"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": [
              "\n",
              "        (async () => {\n",
              "            const url = new URL(await google.colab.kernel.proxyPort(6007, {'cache': true}));\n",
              "            url.searchParams.set('tensorboardColab', 'true');\n",
              "            const iframe = document.createElement('iframe');\n",
              "            iframe.src = url;\n",
              "            iframe.setAttribute('width', '100%');\n",
              "            iframe.setAttribute('height', '800');\n",
              "            iframe.setAttribute('frameborder', 0);\n",
              "            document.body.appendChild(iframe);\n",
              "        })();\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "%tensorboard --logdir \"/tmp/tensorboard2_logs\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "NUXjEMsbKt1a",
        "outputId": "ea91a87f-1a81-4013-b023-10d98b137900"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "8388/8388 [==============================] - 20s 2ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[0.0, 0.9999962449073792]"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model2.evaluate(x_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "6oPtdJohAKGe",
        "outputId": "93e58982-bd40-48f9-e5fc-dfaaac1c2aa5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "8388/8388 [==============================] - 16s 2ms/step\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([[5.8095606e-10],\n",
              "       [5.8095606e-10],\n",
              "       [1.1698649e-09],\n",
              "       ...,\n",
              "       [5.8095606e-10],\n",
              "       [2.5126687e-10],\n",
              "       [2.5126687e-10]], dtype=float32)"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model2.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YUAYLHK5AQCJ"
      },
      "outputs": [],
      "source": [
        "preds2 = model2.predict(x_test)\n",
        "cm2 = tf.math.confusion_matrix(labels=y_test,predictions=preds2)\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(cm2,annot=True,fmt='d')\n",
        "plt.xlabel('Predicated')\n",
        "plt.ylabel('Truth')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SziECgDYK4A8"
      },
      "outputs": [],
      "source": [
        "tfdf.model_plotter.plot_model_in_colab(model2, tree_idx=0, max_depth=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZtHXq_3rLU25"
      },
      "outputs": [],
      "source": [
        "model2.make_inspector().features()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P5nNSVVrLbAK"
      },
      "outputs": [],
      "source": [
        "model2.make_inspector().variable_importances()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Qm-F95oGaVE"
      },
      "source": [
        "## Score Fusion"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}