Patient Readmission within 30 Days Prediction Model

notebook.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kvvDY0LVhuaW"
      },
      "source": [
        "## Introduction\n",
        "\n",
        "Hospital readmissions are common, expensive, and a key target of the Medicare Value Based Purchasing (VBP) program. Validated risk assessment tools such as the HOSPITAL score and LACE index have been developed to identify patients at high risk of hospital readmission so they can be targeted for interventions aimed at reducing the rate of readmission.\n",
        "\n",
        "This analysis aims to evaluate the utility of HOSPITAL score and LACE index for predicting hospital readmission within 30 days using two machine learning decision tree algorithms."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jK9tCTcwqq4k"
      },
      "source": [
        "## Installing TensorFlow Decision Forests\n",
        "\n",
        "Install TF-DF by running the following cell."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pa1Pf37RhEYN"
      },
      "outputs": [],
      "source": [
        "!pip install tensorflow_decision_forests"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lk26uBSCe8Du",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3287d672-2e56-4b55-9f07-b552affea07a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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": [
        "!pip install wurlitzer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3oinwbhXlggd"
      },
      "source": [
        "## Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "52W45tmDjD64"
      },
      "outputs": [],
      "source": [
        "import tensorflow_decision_forests as tfdf\n",
        "\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import auc"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0LPPwWxYxtDM"
      },
      "source": [
        "The hidden code cell limits the output height in colab.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2AhqJz3VmQM-"
      },
      "outputs": [],
      "source": [
        "#@title\n",
        "\n",
        "from IPython.core.magic import register_line_magic\n",
        "from IPython.display import Javascript\n",
        "from IPython.display import display as ipy_display\n",
        "\n",
        "# Some of the model training logs can cover the full\n",
        "# screen if not compressed to a smaller viewport.\n",
        "# This magic allows setting a max height for a cell.\n",
        "@register_line_magic\n",
        "def set_cell_height(size):\n",
        "  ipy_display(\n",
        "      Javascript(\"google.colab.output.setIframeHeight(0, true, {maxHeight: \" +\n",
        "                 str(size) + \"})\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8gVQ-txtjFU4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f68153e2-e8c4-4839-893b-88b23ec79863"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.2.6\n"
          ]
        }
      ],
      "source": [
        "print(tfdf.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QGRtRECujKeu"
      },
      "source": [
        "## Data Analysis and Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3qsSU1RfmNiP"
      },
      "source": [
        "### Load the dataset and convert it in a tf.Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "44Jq6g_mJFmj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "outputId": "51edda1c-f94b-46f0-b9e6-2eb6b447f7c7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     MRN        FIN   Admit Dt   Disch Dt  LOS  \\\n",
              "0  14578  260024823 2015-11-10 2015-11-13    3   \n",
              "1  17833  259706133 2015-10-28 2015-11-04    7   \n",
              "2  27160  261315212 2015-12-25 2015-12-27    2   \n",
              "\n",
              "                                Disch Disp  Age Sex                   Attd MD  \\\n",
              "0          Discharged to Home or Self Care   93   F  PAPIREDDY MD, MURALIDHAR   \n",
              "1  Disch/Trans to Home Care or Home Health   71   M       AL-JOHANY MD, HAMID   \n",
              "2          Discharged to Home or Self Care   85   M      PENDYALA MD, VENKATA   \n",
              "\n",
              "  Prin Dx  ... C_SEVERELR  C_MET CP_1 CP_2  CP_3  CP_4  CP_6  Total_CP  \\\n",
              "0   K22.2  ...      False  False    0    2     0     0     0         2   \n",
              "1  A41.02  ...      False  False    2    0     0     0     0         2   \n",
              "2  I50.43  ...      False  False    2    2     0     0     0         4   \n",
              "\n",
              "   LACE_CP1  LACE_CP2  \n",
              "0         2         0  \n",
              "1         2         0  \n",
              "2         0         5  \n",
              "\n",
              "[3 rows x 64 columns]"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MRN</th>\n",
              "      <th>FIN</th>\n",
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              "      <th>LOS</th>\n",
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              "      <th>CP_3</th>\n",
              "      <th>CP_4</th>\n",
              "      <th>CP_6</th>\n",
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              "      <th>LACE_CP1</th>\n",
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              "      <td>Disch/Trans to Home Care or Home Health</td>\n",
              "      <td>71</td>\n",
              "      <td>M</td>\n",
              "      <td>AL-JOHANY MD, HAMID</td>\n",
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              "      <td>2015-12-27</td>\n",
              "      <td>2</td>\n",
              "      <td>Discharged to Home or Self Care</td>\n",
              "      <td>85</td>\n",
              "      <td>M</td>\n",
              "      <td>PENDYALA MD, VENKATA</td>\n",
              "      <td>I50.43</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# Download the dataset\n",
        "!wget -q https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5374974/bin/peerj-05-3137-s001.xlsx\n",
        "\n",
        "# Load a dataset into a Pandas Dataframe.\n",
        "dataset_df = pd.DataFrame(pd.read_excel(\"peerj-05-3137-s001.xlsx\",sheet_name='Detail'))\n",
        "\n",
        "# Display the first 3 examples.\n",
        "dataset_df.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "23AewWT1lkIK"
      },
      "source": [
        "The dataset contains a mix of numerical (e.g. `Hemoglobin Last Result Admit Dt`), boolean\n",
        "(e.g. `C_MILDLR`) and missing features. TF-DF supports all these feature types natively (differently than NN based models), therefore there is no need for preprocessing in the form of one-hot encoding, normalization or extra `is_present` feature.\n",
        "\n",
        "**Note:** Labels are a bit different: Keras metrics expect integers.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "Also\n",
        "\n",
        "Not all the features will be relavant predictors for the model, as such we will `pop()` things like `Admit Dt`, `MRN`, `FIN`, etc since they do not provide useful information.\n",
        "\n",
        "Moreover, we must convert BOOLEAN types to INT64 since currently tensor dtype `<dtype: 'bool'>` are non supported  for semantic Semantic.CATEGORICAL of those respective features"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "REMOVE_COLS = ['MRN', 'FIN', 'Admit Dt', 'Disch Dt', 'Attd MD', 'Prin Dx Desc', 'Px Code', 'Px Desc', 'Px Date' , 'AllDx', 'HOSPITAL_HR' ,'LACE_HR', 'LACE_CP1', 'LACE_CP2', 'DaysToReadmit','LastAdmitted']\n",
        "BOOL_COLS = ['C_MI',\t'C_Stroke',\t'C_PVD',\t'C_DMWO',\t'C_CHF',\t'C_DMW',\t'C_CLD',\t'C_MILDLR',\t'C_TUMOR',\t'C_DEMENTIA',\t'C_CTD',\t'C_AIDS',\t'C_SEVERELR',\t'C_MET']\n",
        "\n",
        "removed_col_df = pd.concat([dataset_df.pop(COL) for COL in REMOVE_COLS], axis=1)\n",
        "\n",
        "for COL in BOOL_COLS:\n",
        "  dataset_df[COL] = dataset_df[COL].map(dataset_df[COL].unique().tolist().index)"
      ],
      "metadata": {
        "id": "DGBRlLf-OQOZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uO_jz2sj0IBZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e63c3ebc-e43e-4226-f445-172c8a1e758b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label classes: [0, 1]\n"
          ]
        }
      ],
      "source": [
        "# Encode the categorical label into an integer.\n",
        "\n",
        "LABEL = \"30Readmit\"\n",
        "\n",
        "classes = dataset_df[LABEL].unique().tolist()\n",
        "print(f\"Label classes: {classes}\")\n",
        "\n",
        "dataset_df[LABEL] = dataset_df[LABEL].map(classes.index)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "3YrW4OyKRv4Q",
        "outputId": "ca3ed7bf-6d85-48ba-c6f9-da55d113a036"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     LOS                               Disch Disp  Age Sex  Prin Dx  \\\n",
              "0      3          Discharged to Home or Self Care   93   F    K22.2   \n",
              "1      7  Disch/Trans to Home Care or Home Health   71   M   A41.02   \n",
              "2      2          Discharged to Home or Self Care   85   M   I50.43   \n",
              "3      9  Disch/Trans to Home Care or Home Health   73   F  I82.412   \n",
              "4      4    Disch/Trans to Skill Nursing Facility   80   F    I48.0   \n",
              "..   ...                                      ...  ...  ..      ...   \n",
              "458    3          Discharged to Home or Self Care   52   F   K80.50   \n",
              "459    5          Discharged to Home or Self Care   61   F      N12   \n",
              "460    5          Discharged to Home or Self Care   45   M   E11.21   \n",
              "461    5          Discharged to Home or Self Care   27   F   J93.11   \n",
              "462    5          Discharged to Home or Self Care   27   F   J93.12   \n",
              "\n",
              "     # Admits in Year  #ED Visits/6 Mth Prior  \\\n",
              "0                   5                     2.0   \n",
              "1                   1                     1.0   \n",
              "2                   1                     NaN   \n",
              "3                   1                     NaN   \n",
              "4                   1                     NaN   \n",
              "..                ...                     ...   \n",
              "458                 1                     NaN   \n",
              "459                 1                     NaN   \n",
              "460                 1                     NaN   \n",
              "461                 2                     NaN   \n",
              "462                 2                     NaN   \n",
              "\n",
              "     Hemoglobin Last Result Admit Dt  Sodium Last Result Admit Dt  30Readmit  \\\n",
              "0                                NaN                          NaN          0   \n",
              "1                                NaN                          NaN          0   \n",
              "2                                NaN                          NaN          0   \n",
              "3                                NaN                          NaN          0   \n",
              "4                                NaN                        129.0          0   \n",
              "..                               ...                          ...        ...   \n",
              "458                              NaN                          NaN          0   \n",
              "459                              NaN                        136.0          0   \n",
              "460                              NaN                        137.0          0   \n",
              "461                              NaN                        138.0          1   \n",
              "462                              NaN                        135.0          0   \n",
              "\n",
              "     ...  C_CTD  C_AIDS  C_SEVERELR  C_MET  CP_1  CP_2  CP_3  CP_4  CP_6  \\\n",
              "0    ...      0       0           0      0     0     2     0     0     0   \n",
              "1    ...      0       0           0      0     2     0     0     0     0   \n",
              "2    ...      0       0           0      0     2     2     0     0     0   \n",
              "3    ...      0       0           0      0     1     2     0     0     0   \n",
              "4    ...      0       0           0      0     1     0     0     0     0   \n",
              "..   ...    ...     ...         ...    ...   ...   ...   ...   ...   ...   \n",
              "458  ...      0       0           0      0     0     2     0     0     0   \n",
              "459  ...      0       0           0      0     2     4     0     0     0   \n",
              "460  ...      0       0           0      0     2     6     0     0     0   \n",
              "461  ...      0       0           0      0     0     0     0     0     0   \n",
              "462  ...      0       0           0      0     0     0     0     0     0   \n",
              "\n",
              "     Total_CP  \n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-be9ff64c-e52e-42e7-9b68-2c7fb3e123b8')\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "                                                     [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",
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vwJjLFhbtozI"
      },
      "source": [
        "Next split the dataset into training and testing:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u7DEIxn2oB3U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93873de1-fcab-411f-df66-80f7d46547d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313 examples in training, 150 examples for testing.\n"
          ]
        }
      ],
      "source": [
        "# Split the dataset into a training and a testing dataset.\n",
        "\n",
        "def split_dataset(dataset, test_ratio=0.30):\n",
        "  \"\"\"Splits a panda dataframe in two.\"\"\"\n",
        "  test_indices = np.random.rand(len(dataset)) < test_ratio\n",
        "  return dataset[~test_indices], dataset[test_indices]\n",
        "\n",
        "\n",
        "train_ds_pd, test_ds_pd = split_dataset(dataset_df)\n",
        "print(\"{} examples in training, {} examples for testing.\".format(\n",
        "    len(train_ds_pd), len(test_ds_pd)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uWq7uQcCuBzO"
      },
      "source": [
        "Convert the pandas dataframe (`pd.Dataframe`) into tensorflow datasets (`tf.data.Dataset`):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qtXgUBKluTX0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d9424ec-4951-470d-dca9-80b198dbb291"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Some of the feature names have been changed automatically to be compatible with SavedModels because fix_feature_names=True.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Some of the feature names have been changed automatically to be compatible with SavedModels because fix_feature_names=True.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Some of the feature names have been changed automatically to be compatible with SavedModels because fix_feature_names=True.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow_decision_forests/keras/core.py:2542: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
            "  features_dataframe = dataframe.drop(label, 1)\n",
            "WARNING:absl:Some of the feature names have been changed automatically to be compatible with SavedModels because fix_feature_names=True.\n"
          ]
        }
      ],
      "source": [
        "train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_ds_pd, label=LABEL)\n",
        "test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_ds_pd, label=LABEL)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Expo"
      ],
      "metadata": {
        "id": "HwfPsSyT1oCP"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "_kg_hide-input": true,
        "id": "Eke0gQWKS3uE"
      },
      "source": [
        "# Distribution graphs (histogram/bar graph) of column data\n",
        "def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):\n",
        "    nunique = df.nunique()\n",
        "    df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]] # For displaying purposes, pick columns that have between 1 and 50 unique values\n",
        "    nRow, nCol = df.shape\n",
        "    columnNames = list(df)\n",
        "    nGraphRow = (nCol + nGraphPerRow - 1) / nGraphPerRow\n",
        "    plt.figure(num = None, figsize = (6 * nGraphPerRow, 8 * nGraphRow), dpi = 80, facecolor = 'w', edgecolor = 'k')\n",
        "    for i in range(min(nCol, nGraphShown)):\n",
        "        plt.subplot(nGraphRow, nGraphPerRow, i + 1)\n",
        "        columnDf = df.iloc[:, i]\n",
        "        if (not np.issubdtype(type(columnDf.iloc[0]), np.number)):\n",
        "            valueCounts = columnDf.value_counts()\n",
        "            valueCounts.plot.bar()\n",
        "        else:\n",
        "            columnDf.hist()\n",
        "        plt.ylabel('counts')\n",
        "        plt.xticks(rotation = 90)\n",
        "        plt.title(f'{columnNames[i]} (column {i})')\n",
        "    plt.tight_layout(pad = 1.0, w_pad = 1.0, h_pad = 1.0)\n",
        "    plt.show()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "_kg_hide-input": true,
        "id": "XHPkeEBrS3uI"
      },
      "source": [
        "# Correlation matrix\n",
        "def plotCorrelationMatrix(df, graphWidth):\n",
        "    # filename = df.dataframeName\n",
        "    df = df.dropna('columns') # drop columns with NaN\n",
        "    df = df[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values\n",
        "    if df.shape[1] < 2:\n",
        "        print(f'No correlation plots shown: The number of non-NaN or constant columns ({df.shape[1]}) is less than 2')\n",
        "        return\n",
        "    corr = df.corr()\n",
        "    plt.figure(num=None, figsize=(graphWidth, graphWidth), dpi=80, facecolor='w', edgecolor='k')\n",
        "    corrMat = plt.matshow(corr, fignum = 1)\n",
        "    plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)\n",
        "    plt.yticks(range(len(corr.columns)), corr.columns)\n",
        "    plt.gca().xaxis.tick_bottom()\n",
        "    plt.colorbar(corrMat)\n",
        "    plt.title(f'Correlation Matrix', fontsize=15)\n",
        "    plt.show()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "_kg_hide-input": true,
        "id": "JtEYW3yUS3uM"
      },
      "source": [
        "# Scatter and density plots\n",
        "def plotScatterMatrix(df, plotSize, textSize):\n",
        "    df = df.select_dtypes(include =[np.number]) # keep only numerical columns\n",
        "    # Remove rows and columns that would lead to df being singular\n",
        "    df = df.dropna('columns')\n",
        "    df = df[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values\n",
        "    columnNames = list(df)\n",
        "    if len(columnNames) > 10: # reduce the number of columns for matrix inversion of kernel density plots\n",
        "        columnNames = columnNames[:10]\n",
        "    df = df[columnNames]\n",
        "    ax = pd.plotting.scatter_matrix(df, alpha=0.75, figsize=[plotSize, plotSize], diagonal='kde')\n",
        "    corrs = df.corr().values\n",
        "    for i, j in zip(*plt.np.triu_indices_from(ax, k = 1)):\n",
        "        ax[i, j].annotate('Corr. coef = %.3f' % corrs[i, j], (0.8, 0.2), xycoords='axes fraction', ha='center', va='center', size=textSize)\n",
        "    plt.suptitle('Scatter and Density Plot')\n",
        "    plt.show()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plotPerColumnDistribution(dataset_df, 10, 2)"
      ],
      "metadata": {
        "id": "rEFte_xq1sk0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0297fa80-d1ab-4438-ba96-8f953c69e8c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x14080 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plotCorrelationMatrix(dataset_df, 20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9uYzgFHo14gO",
        "outputId": "41a1b883-27fb-4d36-f262-72d3fe66fad9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: FutureWarning: In a future version of pandas all arguments of DataFrame.dropna will be keyword-only\n",
            "  after removing the cwd from sys.path.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1600 with 2 Axes>"
            ],
            "image/png": 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3N78WRdh1111z3nnn5ec//3k23HDDD21XW1u7xKi/G2+8Ma+88spi2z4oOv77cXxSp59+eqZMmZKxY8dm9OjR6dGjR4YOHZoFCxas8L4BAD4NjMwDAFZIr169Mm7cuBx88MHZYostcsQRR2TLLbfMwoUL8+CDD+bGG2/MsGHDkiRbb711hg4dmiuuuKJ5ausjjzySsWPH5oADDlhsJduPq6amJldeeWWGDBmSvn375sgjj0yXLl3yyiuv5N57702nTp3yu9/9bqnPHTRoUNZee+0MHTo0J510UkqlUq699tqlTm/dZpttcsMNN2TkyJHZbrvt0qFDh+y7775L3e+FF16YIUOGZODAgTn66KMzb968/OxnP8taa621zOmvK6qmpiajRo36yHb77LNPzj333Bx55JEZNGhQnn766fz6179Oz549F2vXq1evdO7cOZdddlk6duyY9u3b5wtf+MIyr2m4NOPHj8+ll16as88+OwMGDEiSXHXVVRk8eHDOPPPMXHDBBR9rfwAAn0ZG5gEAK2y//fbLU089lYMOOii33XZbvvWtb+W73/1uJk+enB//+Mf56U9/2tz2yiuvzDnnnJNHH300p5xySsaPH5/vfe97uf7661c4j8GDB+ehhx7Ktttum5///Oc58cQTc/XVV2fDDTfMt7/97Q993rrrrpvf//732WijjTJq1KhcdNFF2WOPPZZaXDrhhBNy6KGH5qqrrsqhhx6aE0888UP3u/vuu+cPf/hD1l133Zx11lm56KKLsv322+eBBx742IWwIpxxxhk59dRTc9ddd+Xkk0/OhAkTcscdd2STTTZZrF3r1q0zduzY1NbWZsSIEfnGN76R+++//2PFeu+993LUUUelf//++f73v9+8faeddsrJJ5+cH//4x3n44YdXynEBAKzOSuWPc0VlAAAAAFZbG25Um788+pmWTqPidt2+JtOmTWvpNJaLkXkAAAAAUCUU8wAAAACgSijmAQAAAECVUMwDAAAAgCrRqqUTAAAAAGDV0WSp1FWakXkAAAAAUCUU8wAAAACgSijmAQAAAECVUMwDAAAAgCphAQwAAAAAmjWm1NIpsAxG5gEAAABAlVDMAwAAAIAqoZgHAAAAAFVCMQ8AAAAAqoQFMAAAAABIkpRjAYxVnZF5AAAAAFAlFPMAAAAAoEoo5gEAAABAlVDMAwAAAIAqYQEMAAAAAP6llKbyp3EBjHJLJ7DcjMwDAAAAgCqhmAcAAAAAVUIxDwAAAACqhGIeAAAAAFQJC2AAAAAA0KwxFsBYlRmZBwAAAABVQjEPAAAAAKqEYh4AAAAAVAnXzAMAAACgWeOncuxXU0snsNw+jb0DAAAAAFVJMQ8AAAAAqoRiHgAAAABUCcU8AAAAAKgSFsAAAAAAIElSTtJULrV0GiyDkXkAAAAAUCUU8wAAAACgSijmAQAAAECVUMwDAAAAgCphAQwAAAAAmjXGAhirMiPzAAAAAKBKKOYBAAAAQJVQzAMAAACAKqGYBwAAAABVwgIYAAAAADRrLBv7tSrTOwAAAABQJRTzAAAAAKBKKOYBAAAAQJVQzAMAAACAKmEBDAAAAACSJOWU0mTs1ypN7wAAAABAlVDMAwAAAIAqoZgHAAAAAFVCMQ8AAAAAqoQFMAAAAABo1phSS6fAMhiZBwAAAABVwsi8KtO2TSnrr1dbaIzXZ3codP8fKFeglFwqFx8jSVKJOJX6j5HV5Fgq8f5KkjatFxUeY1FTsZ/5D6zVel7hMWbOa194jCQpNRUfo7ZdQ/FBkmRW8f1frin+Q9nYtvAQSZLOa8wpPMas2ZV5H1fke381+m0pr0YDCGoai49RrsxPS2VU6nxvNbLanIdXqO8r8npV4Nwlqcx3ZcV+WioQqHbevCxYsKD4QPAxKeZVmfXXq82UCZsWGqPPpScUuv8PLFyr+F/fVsXXJt6PM6f4X5KG9pU5W6mdV4E/6tco/lgWdq7MGdGgbZ8vPMaTr29ceIwk+XG/GwuPcfztRxceI0nWeL34s+4eQyYVHiNJFn5vg8JjzP9M8ZW2VwdWpnJw8YFXFR7ju5cdVXiMJGlsV3yMcqXOBCvwlVyJ35ZSBYpsSdJ+WvEx5m1YmT+3yzXF90slzl2SpKlN8TFq5xcfI0kWrLt69EvNwsJDJEkWrF/8l1jbNyvzP9ENHYvv+5oFlflMLlqr+H5ZdMFPCo8Bn4RiHgAAAADNGis11YlPRO8AAAAAQJVQzAMAAACAKqGYBwAAAABVQjEPAAAAAKqEBTAAAAAAaNaUyqxKzCdjZB4AAAAAVAnFvBXUo0eP1NfXL7Zt1qxZGT58eHr27JnNN9882223Xe68887mxxctWpSTTjopffv2zdZbb50+ffpk9OjRlU4dAAAAgCpjmu1KVi6Xs9dee6V///554YUX0qpVq9TX12fvvffO1VdfnT322COXXHJJpk+fnieffDKtWrXK/Pnz89JLL7V06gAAAACs4ozMW8n+9Kc/5eWXX87o0aPTqtX7tdK6urp8//vfz3nnnZckmTZtWjbYYIPmx9u1a5e+ffu2WM4AAAAAVAfFvJVswoQJ2WabbdKmTZvFtg8cODCPP/54kuTYY4/N7373u2yxxRY59thjc/3116exsbEl0gUAAABoVk7SmJpP3a2aVFe2q4m+ffvmpZdeyi9+8Yt07949Z599dvbbb7+lth09enS6du3afJs9u1zhbAEAAABYVSjmrWQDBgzI448/nkWLFi22/aGHHsqAAQOa77dp0ya77bZbRo0alfvvvz933nln3n777SX2N3LkyEybNq351qGD5aEBAAAAPq0U81ay3XbbLZtsskm+/e1vp6GhIUlSX1+f//qv/8qoUaOSJH/+85/z6quvNj/n8ccfzzrrrJPOnTu3SM4AAAAAVAer2a4Ee+65Z1q3bt18/+GHH84555yTz372s2nTpk06duyYyy67LHvuuWeSZMqUKTnllFMyf/78tGnTJh06dMhtt92Wmhq1VQAAAAA+nGLeCpo8efJSt//yl7/80OccdthhOeywwwrKCAAAAOCTaywbbPRhJk6cmKFDh+att97KWmutlauvvjp9+/ZdrE1TU1NOO+20/OEPf0irVq2y7rrr5pe//GV69+69UnLQOwAAAACwHIYPH57jjjsuL7zwQk4//fQMGzZsiTa33357HnjggTz55JN56qmn8sUvfjFnnHHGSstBMQ8AAAAAPsIbb7yRxx57rHm25YEHHpipU6fmxRdfXKxdqVTKggULMn/+/JTL5bz77rvp2rXrSsvDNFsAAAAA+AhTp07NRhttlFat3i+nlUqldOvWLVOmTFlsCu2+++6be++9NxtuuGE6duyYLl265P77719peRiZBwAAAMCn2uzZs9O1a9fm2+jRoz/xvh577LH8/e9/zyuvvJLp06fni1/8YkaMGLHScjUyDwAAAIB/KaXpUzj2q0OHDpk2bdoy22yyySZ59dVX09DQkFatWqVcLmfKlCnp1q3bYu2uueaa7LbbbuncuXOSZOjQofnSl7600nL99PUOAAAAAHxMG2ywQQYMGJDrrrsuSXLzzTena9euS6xS27Nnz4wfPz4LFy5Mkvz+97/PlltuudLyMDIPAAAAAJbD5ZdfnmHDhuX8889Pp06dctVVVyVJjjnmmOy3337Zb7/98q1vfSvPPvtstt5667Ru3TobbrhhLrvsspWWQ6lcLpdX2t4oXOtOndPrlLMLjfGPEy4tdP8f2Oac4wuP0VRbKjxGksz7TPExahcUHyNJahYWH6PUVHyM2vnFx0iSOV2L/wpt+3al3scV+DnoMq/4GEk6/2mNwmO8vXUF3shJOr1YW3iMRe0LD5EF61Xm9Wo7o/hJBwu3nFt4jCTJlOLfx+VWlTkNbDW3+O+xBZsU/wO2xottC4+RJK1nFx9jwTrFx0gqc/4yt2cFTl6SrDGpTeEx2s2ozGeyYc0KnFtUIMTsfhU6QX6vAmNgKjRnrtNzxZ9XNBX/UUmSvNe7ofAYjedd8pHTLldH623UJmMe6NPSaVTct3Z+q2r62zRbAAAAAKgSptkCAAAAkCQpl5PGcmVmB/HJGJkHAAAAAFVCMQ8AAAAAqoRiHgAAAABUCdfMAwAAAKBZo7FfqzS9AwAAAABVQjEPAAAAAKqEYt5K8N5776VDhw45+uijWzoVAAAAAFZjinkrwQ033JBtttkmt9xyS2bPnt3S6QAAAACwmlLMWwnGjBmT008/PTvvvHNuuOGGJMmiRYtywgkn5HOf+1y23377nHrqqRk8eHDzc6699tp84QtfyIABA7LzzjvnySefbKHsAQAAAP5/TeWaT92tmljNdgX94x//yNSpU7PnnnumoaEhP/rRj3L00UfniiuuyMSJE/PMM88kSfbaa6/m5zzwwAP5zW9+kz//+c9p27Zt/vKXv+TQQw9tbgsAAAAAS1NdpcdV0JgxY3LEEUektrY2e+21VyZNmpRnn302f/rTn3LYYYeldevWad26dYYOHdr8nNtuuy1PPvlkvvCFL6Suri4nnnhi3n777cybN2+J/Y8ePTpdu3ZtvjUtXFDJwwMAAABgFWJk3gpYtGhRrr322rRu3Trjxo1LksydOzdjxoxZom2pVGr+d7lcztChQ3P++ed/ZIyRI0dm5MiRzfdbd+q8EjIHAAAAoBoZmbcCbr/99vTs2TOvvPJKJk+enMmTJ+fhhx/Otddem1133TXjxo3LokWLsmjRolxzzTXNz9tvv/1y3XXXZcqUKUmSpqamPPbYYy11GAAAAABUCSPzVsCYMWPyzW9+c7FtW2yxRbp06ZINNtggPXr0SJ8+fbL22mtn2223zfTp05MkO+20Uy644IJ85StfSUNDQxYuXJi999472267bUscBgAAAECSpJyk0divVZpi3gq48847l7p9woQJSd5f9KJjx45ZtGhRvvnNb2bgwIHNbQ455JAccsghFckTAAAAgNWDYl6Bdt999yxYsCDz58/PjjvumJNOOqmlUwIAAACgiinmFehvf/tbS6cAAAAAwGrEJGgAAAAAqBJG5gEAAADQrLFcaukUWAYj8wAAAACgSijmAQAAAECVUMwDAAAAgCqhmAcAAAAAVcICGFWmXJMsXKtcaIxtzjm+0P1/4PGz/7vwGFs/8o3CYyRJw4trFR6j9ezK1N4bOhQfY699Hy48xnYdJhUeI0l+cH3x77GDDr+v8BhJ8pvbdik8RuOUNQqPkSRzvjy7+CBzWxcfI0lju9rCY9QsKjxEWs+qzHdYY7tifyOTJFMr8z5uM6v4C08v6lh4iH/FKb5fWr3RpvAY83ouLDxGkrR+qvhjWbBBQ+ExkqTDP4v/c6PDC8W/XklSU4HuX7B2ZS4431SBn7BWc4uPUZpZmd/ipg7Ff146/b0y7+OFnYuPUTuv+BhJUmqyQENxSmky9muVpncAAAAAoEoo5gEAAABAlVDMAwAAAIAqoZgHAAAAAFXCAhgAAAAANGssG/u1KtM7AAAAAFAlFPMAAAAAoEoo5gEAAABAlfhYxbx//vOf+eIXv5gk+drXvpYJEyZ8ZPuampqcd955y2x30EEH5eqrr/44qSRJjjnmmNx7771JkltvvTUPP/zwx95HXV1d3nvvveVuP3fu3Hzuc5/L3Xff3bxt5syZ6d69+yeKDwAAAADL62MV8/74xz9m9913T2NjY5544onU1dUts/2vfvWr7LbbbrnqqqtSLpdXKNGlufLKK7Prrrsm+eTFvPr6+nTs2HG526+55poZO3Zshg8fnlmzZiVJTjzxxBx22GHZfvvtP3b8/1dDQ8MK7wMAAADgkygnaUrpU3erJstVzLv88suz/fbb54wzzsi4ceNSV1eXd955J4MGDcq111671Oc0Njbm6quvzk9/+tN07Ngx48ePb37sueeey6BBg9K3b98ccMABeffdd5sfGzZsWI477rjsvvvu2XTTTXPUUUflkUceyeDBg9OzZ8+MHDmyue3gwYNz66235s4778ztt9+eCy+8MHV1dbnyyiszceLE7LDDDtl6663Tr1+/jBo1aql5lkqlzJw5M0nSo0ePnHXWWRk4cGA23XTT/Od//udSnzNw4MAccsgh+cLbjxwAACAASURBVPa3v53bb789Tz/9dM4+++w8+uij2W233bLtttumf//+ufHGG5O8X6Dbc889s+2226Zv37459NBDM2fOnCTJfffdl759++boo49OXV1dfvvb3y5PlwAAAADwKdRqeRoNHz48w4cPT+/evfPEE0/kF7/4Rcrlck455ZQPfc5dd92Vrl27pk+fPjn66KMzZsyY5im6hx9+eEaMGJGjjz46Tz/9dLbddtsceuihzc99+umnc++996ampiZ9+vTJO++8kz/+8Y9ZuHBhevbsmaOPPjp9+/Ztbr/XXntlv/32S11dXXNOJ598cvbZZ59873vfS5K8/fbby/WCzJw5Mw899FDeeuut9OrVK0ceeWS6dOmyRLtzzjkn2223XX7/+9/nnnvuydy5c3PcccflzjvvzEYbbZS33norAwYMyKBBg7Lxxhtn3LhxWXfddVMul3PCCSfkZz/7Wb773e8mSZ599tlceumlGTNmzHLlCAAAAMCn03JPs502bVo22GCDtGrVKo8//ni22WabZbYfM2ZMjjrqqCTJN7/5zdx5551555138u6776a+vj7Dhg1LkvTr1y877rjjYs/df//9065du7Rp0yb9+vXLnnvumdatW6d9+/bp06dPJk6c+JH57rzzzvnlL3+Z73//+7n77rvTuXPn5TrOD4qK6623Xnr27JlJkyYttV2bNm1y0kkn5Qtf+EK22mqrPPjgg/nnP/+ZIUOGpK6uLrvvvnuS5Pnnn0+5XM5PfvKT9O/fP1tttVXuuOOO1NfXN++rZ8+e2WWXXZYaZ/To0enatWvzrWnBguU6DgAAAABWPx85Mm/q1KnZd999M2vWrMyZMyd1dXV54YUX8sQTT6R3795LnRb65ptv5o477sgjjzyS888/P0myaNGi/PrXv84RRxyxRPtSafG5ye3atWv+d21t7RL3l+e6cgceeGAGDRqUP/7xj/n5z3+eiy++OHfeeedHPu/jxKqtrU1tbW2SpFwup2/fvnnwwQeXaHfddddl/Pjxuf/++9OpU6f89Kc/XWzacYcOHT40xsiRIxebWtxqOYuSAAAAAJ9EY/ljLbFAhX1k72yyySapr6/PkCFDct111+W3v/1ttt9++zz99NMfen23a665JgcccECmTp2ayZMnZ/LkybnpppsyZsyYdOrUKf37988111yTJHnmmWfy17/+dYUPpFOnTs0LUiTJxIkT85nPfCZHHHFELrjggsJXmh00aFAmTZqUe+65p3lbfX19Fi5cmHfeeSfrrbdeOnXqlPfee+8TrdwLAAAAAMtdar3//vuz0047Na9ouyxjxozJN7/5zcW27bHHHpk+fXomTJiQa665JldccUW23HLLjBo1KjvvvPMny/7fHH744fmf//mf9O/fP1deeWVuuumm9OvXL/3798/BBx+cyy67bIVjLMvaa6+dO+64I+eff3623nrr9OnTJ9/97nfT1NSUI444InPnzs1mm22WIUOGZKeddio0FwAAAABWT6VyuVxu6SRYfq06d063M88qNMZaLxa6+2aPn/3fhcfY+pFvFB4jSea8uFbhMdZ4vTLDnBvaFx9jr32LHSmbJNt1WPr1Lle2H1xf/HvsGwfcV3iMJPnNbUu/dufK1Nim8BBJkla9ZhceY8Hc1oXHSJL2z7T76EYrqqn4EA1rFh8jScqtij+taWxbeIgkSZuZpY9utIIWdazMaWDjGsXHqVlY/OvVsN6iwmMkSaeniv+yfHfzj75szcrQ4Z/Ltd7eiim+65MkNQuLj9GwRvExkqSpAj9hreYWH2Nu1wr8gCVp6lD856XT3ytzkrSoY/ExaucVHyNJ5mzaWHiMhv+6ONOmTSs8zqpm7Q3b5kf3b9/SaVTceV98qWr62yRoAAAAAKgSFfivMgAAAACqRaOxX6s0vQMAAAAAVUIxDwAAAACqhGIeAAAAAFQJxTwAAAAAqBIWwAAAAADgX0ppKpdaOgmWQTGvypTKSat5xcZoqq3Mh3brR75ReIwnP/+bwmMkyeZPHV94jHJt4SHe11R8iFueGFB4jNvW2LrwGEnSpqH4GFdPGFh8kCRtKxCjzazKfL8snNSh8BitKtD3SdLYujJxitZqfmXiLOxYfIy271TmfVxqLD5G27crcywL1ik+RqkCv1+171Tm1Hlh5+JjtJpVmROLip2/VEDDmsXHqMT7OKnM90tTBX6/2r1RmYlmi+YXfzCN7QoPkSQpVeD8pRJ9nyRtZhTf/xU63YOPzTRbAAAAAKgSinkAAAAAUCUU8wAAAACgSrhmHgAAAABJknKSRmO/Vml6BwAAAACqhGIeAAAAAFQJxTwAAAAAqBKKeQAAAABQJaqmmPelL30pW221Verq6rLTTjvliSeeSJJMnDgxgwYNyuc+97lst912eeaZZ5qf06NHj2y22Wapq6vLZpttlh/96EcrPa/11lsvkydP/tjPq6ury3vvvZckufjii/Paa6+t5MwAAAAAPr6mcs2n7lZNqibb//mf/8lTTz2V+vr6jBw5MsOGDUuSDB8+PMcdd1xeeOGFnH766c3bP3DDDTekvr4+48ePzw9/+MM88sgjlU9+Kerr69OxY8ckinkAAAAALJ+qKeZ17ty5+d+zZs1KqVTKG2+8kcceeyyHHXZYkuTAAw/M1KlT8+KLLy7x/C5dumTzzTfPyy+/nCR57bXX8vWvfz2f//zn069fv4waNaq57WmnnZbtttsudXV12XnnnfP88883P3b77bdniy22yFZbbZX/+I//WCxGjx49MmrUqAwaNCibbLJJLrvsslx11VUZOHBgevTokeuvv765balUysyZM3Puuedm+vTpOfjgg1NXV5f6+vqV84IBAAAAsNqpmmJekhxxxBHZZJNNcuaZZ+baa6/N1KlTs9FGG6VVq1ZJ3i+QdevWLVOmTFniuc8991xmzJiRwYMHJ0mGDh2ab33rW3nkkUfyxBNP5LHHHsuNN96YJDn99NPz6KOPpr6+PieccEJOPvnkJMkbb7yRI488MjfffHOeeuqp9O7dOzNmzFgszpw5c/Lggw/m3nvvzbe//e288soreeihh3LjjTfmxBNPXCKvs846KxtvvHHzCMK6urqV+ZIBAAAAsBpp1dIJfBzXXHNNkmTs2LE5/fTTc955533kcw4++ODU1NTk+eefz09+8pOsv/76mTNnTv70pz/l9ddfb243e/bs5hF4f/zjH/Ozn/0s7733XpqamvL2228nSR5++OFstdVW6dOnT5Lk6KOPXqJAd/DBBydJevfunXbt2uWggw5Kkmy77bZ5++23M3PmzMVGGX6U0aNHZ/To0c33mxYsWO7nAgAAALB6qapi3geGDh2aESNGpGvXrnn11VfT0NCQVq1apVwuZ8qUKenWrVtz2xtuuCF1dXW55557su+++2a33XbLpptumuT94ly7du0W2/eUKVPyf/7P/8mjjz6aXr165amnnsrOO++81DxKpdIS2/59f7W1tc33S6VSSqVSGhoaPtaxjhw5MiNHjmy+33qt5S8EAgAAAHxcjVmy3sGqoyqm2c6cOTPTp09vvn/rrbdm3XXXzQYbbJABAwbkuuuuS5LcfPPN6dq1a3r37r3EPnbfffccf/zxGTVqVDp06JBdd911sdVtp0+fnmnTpmXWrFlp3bp1Ntpoo5TL5fz85z9vbjNw4MA89dRTee6555Ikv/rVr7Jw4cIVPr5OnTpl1qxZK7wfAAAAAFZvVTEyb9asWfna176WefPmpaamJuuvv35+//vfp1Qq5fLLL8+wYcNy/vnnp1OnTrnqqqs+dD9nnnlmevfunccffzy//vWvM3LkyGy55ZYplUpp3759Lr/88my99dY55JBD0rdv36y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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plotScatterMatrix(dataset_df, 20, 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7-JB30if19EE",
        "outputId": "d17018c2-0b4c-45d3-f388-59934bb4b602"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.dropna will be keyword-only\n",
            "  \"\"\"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 100 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYAoyfYtqHG4"
      },
      "source": [
        "### Train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xete-FbuqJCV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "fb12c3a5-34b7-452f-8014-7af1adb47aea"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "google.colab.output.setIframeHeight(0, true, {maxHeight: 300})"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Use /tmp/tmp6qx89i6y as temporary training directory\n",
            "Reading training dataset...\n",
            "WARNING:tensorflow:6 out of the last 6 calls to <function CoreModel._consumes_training_examples_until_eof at 0x7ff077c127a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:6 out of the last 6 calls to <function CoreModel._consumes_training_examples_until_eof at 0x7ff077c127a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training dataset read in 0:00:00.657313. Found 331 examples.\n",
            "Training model...\n",
            "Model trained in 0:00:00.212588\n",
            "Compiling model...\n",
            "WARNING:tensorflow:6 out of the last 6 calls to <function CoreModel.make_predict_function.<locals>.predict_function_trained at 0x7ff068c07050> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:6 out of the last 6 calls to <function CoreModel.make_predict_function.<locals>.predict_function_trained at 0x7ff068c07050> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model compiled.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff069186f10>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "%set_cell_height 300\n",
        "\n",
        "# Specify the model.\n",
        "RF_MODEL = tfdf.keras.RandomForestModel()\n",
        "\n",
        "# Train the model.\n",
        "RF_MODEL.fit(x=train_ds)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OBnjxdip-MC0"
      },
      "source": [
        "#### Remarks\n",
        "\n",
        "-   No input features are specified. Therefore, all the columns will be used as\n",
        "    input features except for the label. The feature used by the model are shown\n",
        "    in the training logs and in the `model.summary()`.\n",
        "-   DFs natively process numerical, categorical, categorical-set features and\n",
        "    missing-values. **Numerical features do not need to be normalized.** Categorical\n",
        "    string values do not need to be encoded in a dictionary.\n",
        "-   No training hyper-parameters are specified. Therefore the default\n",
        "    hyper-parameters will be used. Default hyper-parameters provide\n",
        "    reasonable results in most situations.\n",
        "-   Calling `compile` on the model before the `fit` is optional. Compile can be\n",
        "    used to provide extra evaluation metrics.\n",
        "-   Training algorithms do not need validation datasets. If a validation dataset\n",
        "    is provided, it will only be used to show metrics.\n",
        "-   Add a `verbose` argument to `RandomForestModel` to control the amount of\n",
        "    displayed training logs. Set `verbose=0` to hide most of the logs. Set \n",
        "    `verbose=2` to show all the logs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vBSz-jE0Qss_"
      },
      "source": [
        "### Plotting the training logs\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WynFJCEbhuF_"
      },
      "source": [
        "Let's plot it:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xzPH7Gggh0g1"
      },
      "outputs": [],
      "source": [
        "def plot_training_logs(model):\n",
        "  logs = model.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()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_training_logs(RF_MODEL)\n",
        "RF_MODEL.make_inspector().evaluation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "4NMge7dGgyVL",
        "outputId": "68743fec-2019-432e-ff2e-d2853b107132"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Evaluation(num_examples=331, accuracy=0.9063444108761329, loss=0.4712202442961877, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "phTUr6F1t-_E"
      },
      "source": [
        "## Train Different Models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vHgPr4Pt43hv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d215501-f57e-4df5-bcbe-0dcc2c48e43b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Use /tmp/tmpy8gdf6ck as temporary training directory\n",
            "Reading training dataset...\n",
            "Training dataset read in 0:00:00.716170. Found 331 examples.\n",
            "Training model...\n",
            "Model trained in 0:00:01.026464\n",
            "Compiling model...\n",
            "Model compiled.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff06802f990>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "# Classical and complex ~ spicy!\n",
        "GBDT_BFG_MODEL = tfdf.keras.GradientBoostedTreesModel(\n",
        "    num_trees=500, growing_strategy=\"BEST_FIRST_GLOBAL\", max_depth=8)\n",
        "GBDT_BFG_MODEL.fit(x=train_ds)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "GBDT_BFG_MODEL.make_inspector().evaluation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z_eN4GyUf9qt",
        "outputId": "11564669-140e-40f9-f6af-9ecd96f06d8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Evaluation(num_examples=None, accuracy=0.9411764740943909, loss=0.36920592188835144, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uECgPGDc2P4p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ca7f450-3044-4609-8260-04b1302cce68"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Use /tmp/tmpp2hgjoif as temporary training directory\n",
            "Reading training dataset...\n",
            "Training dataset read in 0:00:00.737338. Found 331 examples.\n",
            "Training model...\n",
            "Model trained in 0:00:05.052629\n",
            "Compiling model...\n",
            "Model compiled.\n",
            "WARNING:tensorflow:5 out of the last 28 calls to <function CoreModel.yggdrasil_model_path_tensor at 0x7ff067d69200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 28 calls to <function CoreModel.yggdrasil_model_path_tensor at 0x7ff067d69200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff06803fe90>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "# Just Complex\n",
        "GBDT_RAN_MODEL = tfdf.keras.GradientBoostedTreesModel(\n",
        "    num_trees=500,\n",
        "    growing_strategy=\"BEST_FIRST_GLOBAL\",\n",
        "    max_depth=8,\n",
        "    split_axis=\"SPARSE_OBLIQUE\",\n",
        "    categorical_algorithm=\"RANDOM\",\n",
        "    )\n",
        "GBDT_RAN_MODEL.fit(x=train_ds)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "GBDT_RAN_MODEL.make_inspector().evaluation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7H3XVewPgiCZ",
        "outputId": "3ec4a8bf-3b6c-4167-dd30-499746fdb147"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Evaluation(num_examples=None, accuracy=0.9411764740943909, loss=0.41400885581970215, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xk7wEmUZu3V0"
      },
      "source": [
        "If you're not sure what hyperparameters you should adjust things to, then you can use TensorFlow's reccomendation and take advatage of their hyperparameter templates."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LtrRhMhj3hSu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5d92d88-4f3e-4d56-a188-6694cf57a526"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resolve hyper-parameter template \"benchmark_rank1\" to \"benchmark_rank1@v1\" -> {'growing_strategy': 'BEST_FIRST_GLOBAL', 'categorical_algorithm': 'RANDOM', 'split_axis': 'SPARSE_OBLIQUE', 'sparse_oblique_normalization': 'MIN_MAX', 'sparse_oblique_num_projections_exponent': 1.0}.\n",
            "Use /tmp/tmpckrm1ar3 as temporary training directory\n",
            "Reading training dataset...\n",
            "Training dataset read in 0:00:00.747998. Found 331 examples.\n",
            "Training model...\n",
            "Model trained in 0:00:00.572663\n",
            "Compiling model...\n",
            "Model compiled.\n",
            "WARNING:tensorflow:6 out of the last 30 calls to <function CoreModel.yggdrasil_model_path_tensor at 0x7ff067d35e60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:6 out of the last 30 calls to <function CoreModel.yggdrasil_model_path_tensor at 0x7ff067d35e60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff067d15f50>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "# A good template of hyper-parameters.\n",
        "TEMPLATE_PARAM_MODEL = tfdf.keras.GradientBoostedTreesModel(hyperparameter_template=\"benchmark_rank1\")\n",
        "TEMPLATE_PARAM_MODEL.fit(x=train_ds)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TEMPLATE_PARAM_MODEL.make_inspector().evaluation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bpNetZaNjwvU",
        "outputId": "5eaf68a1-09c5-499b-8904-6466bd123665"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Evaluation(num_examples=None, accuracy=0.9411764740943909, loss=0.3887302577495575, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tSdtNJUArBpl"
      },
      "source": [
        "# Evaluate the model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_DICT = {\n",
        "  \"Random Forest\":                                           RF_MODEL,\n",
        "  \"Gradient Boosted Decision Tree w/ BFG\":                   GBDT_BFG_MODEL,\n",
        "  \"Gradient Boosted Decision Tree w/ SO and RAND\":           GBDT_RAN_MODEL, \n",
        "  \"Gradient Boosted Decision Tree w/ Templated Hyperparam\":  TEMPLATE_PARAM_MODEL, \n",
        "}\n",
        "\n",
        "SELECT = \"Gradient Boosted Decision Tree w/ BFG\" #@param [\"Random Forest\", \"Gradient Boosted Decision Tree w/ BFG\", \"Gradient Boosted Decision Tree w/ SO and RAND\", \"Gradient Boosted Decision Tree w/ Templated Hyperparam\"]\n",
        "\n",
        "MODEL = MODEL_DICT[SELECT]"
      ],
      "metadata": {
        "id": "erL6177dhsO0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Udtu_uS1paSu"
      },
      "source": [
        "Let's evaluate our model on the test dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xUy4ULEMtDXB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f47d6cf8-2bea-4afb-8edd-e2cfa15fd63b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 365ms/step - loss: 0.0000e+00 - accuracy: 0.9167\n",
            "\n",
            "loss: 0.0000\n",
            "accuracy: 0.9167\n"
          ]
        }
      ],
      "source": [
        "MODEL.compile(metrics=[\"accuracy\"])\n",
        "evaluation = MODEL.evaluate(test_ds, return_dict=True)\n",
        "print()\n",
        "\n",
        "for name, value in evaluation.items():\n",
        "  print(f\"{name}: {value:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_training_logs(MODEL)\n",
        "MODEL.make_inspector().evaluation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "Uo1-bwRCiscD",
        "outputId": "4029513c-ea66-4bd0-9241-c41afaff679b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Evaluation(num_examples=None, accuracy=0.9411764740943909, loss=0.36920592188835144, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-8R02_SXpbq"
      },
      "source": [
        "## Plot the model\n",
        "\n",
        "Let's see what decisions the model is making so we can try and interpret it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KUIxf8N6Yjl0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "62e7bbb1-c3f8-4fd1-fa40-52f6909f087e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<script src=\"https://d3js.org/d3.v6.min.js\"></script>\n",
              "<div id=\"tree_plot_d6e46f2c75c841a3ae762190cc4b4e41\"></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\": \"REGRESSION\", \"value\": 4.145508825104116e-09, \"num_examples\": 297.0, \"standard_deviation\": 0.2826278025881535}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"#_Admits_in_Year\", \"threshold\": 7.5}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 0.5645068883895874, \"num_examples\": 13.0, \"standard_deviation\": 0.4985185278495917}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"C_MI\", \"threshold\": 0.5}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 0.3077065050601959, \"num_examples\": 6.0, \"standard_deviation\": 0.4714045333309032}}, {\"value\": {\"type\": \"REGRESSION\", \"value\": 0.7846214771270752, \"num_examples\": 7.0, \"standard_deviation\": 0.45175396161448705}}]}, {\"value\": {\"type\": \"REGRESSION\", \"value\": -0.02584010176360607, \"num_examples\": 284.0, \"standard_deviation\": 0.2498511824811361}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"#_Admits_in_Year\", \"threshold\": 3.5}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 0.13631519675254822, \"num_examples\": 56.0, \"standard_deviation\": 0.39729635827837145}, \"condition\": {\"type\": \"CATEGORICAL_IS_IN\", \"attribute\": \"Disch_Disp\", \"mask\": [\"Discharged to Home or Self Care\", \"Disch/Xfer to Another Rehab Fac/Unit\", \"Dis/Tran to HC Inst not def in this list\"]}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 0.2659764587879181, \"num_examples\": 30.0, \"standard_deviation\": 0.4582575817492969}, \"condition\": {\"type\": \"CATEGORICAL_IS_IN\", \"attribute\": \"Prin_Dx\", \"mask\": [\"A41.9\", \"I50.33\", \"I21.4\"]}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 0.5163568258285522, \"num_examples\": 12.0, \"standard_deviation\": 0.500000012795837}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"HOSPITAL_Total\", \"threshold\": 6.5}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 0.140786275267601, \"num_examples\": 5.0, \"standard_deviation\": 0.4000000108250154}}, {\"value\": {\"type\": \"REGRESSION\", \"value\": 0.7846214771270752, \"num_examples\": 7.0, \"standard_deviation\": 0.45175396161448705}}]}, {\"value\": {\"type\": \"REGRESSION\", \"value\": 0.09905620664358139, \"num_examples\": 18.0, \"standard_deviation\": 0.37267800636352477}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"Total_CP\", \"threshold\": 4.5}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": -0.10959409922361374, \"num_examples\": 10.0}}, {\"value\": {\"type\": \"REGRESSION\", \"value\": 0.3598690927028656, \"num_examples\": 8.0, \"standard_deviation\": 0.4841229310582473}}]}]}, {\"value\": {\"type\": \"REGRESSION\", \"value\": -0.01329395454376936, \"num_examples\": 26.0, \"standard_deviation\": 0.26646936225268625}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"Age\", \"threshold\": 58.5}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": -0.10959409922361374, \"num_examples\": 14.0}}, {\"value\": {\"type\": \"REGRESSION\", \"value\": 0.09905620664358139, \"num_examples\": 12.0, \"standard_deviation\": 0.37267800636352477}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"C_DMWO\", \"threshold\": 0.5}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 0.3077065050601959, \"num_examples\": 6.0, \"standard_deviation\": 0.4714045333309032}}, {\"value\": {\"type\": \"REGRESSION\", \"value\": -0.10959409922361374, \"num_examples\": 6.0}}]}]}]}, {\"value\": {\"type\": \"REGRESSION\", \"value\": -0.06566771119832993, \"num_examples\": 228.0, \"standard_deviation\": 0.1840015572470472}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"T\", \"threshold\": 1.0}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": -0.017711391672492027, \"num_examples\": 109.0, \"standard_deviation\": 0.2607829502695175}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"E\", \"threshold\": 0.5}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": -0.10959409922361374, \"num_examples\": 35.0}}, {\"value\": {\"type\": \"REGRESSION\", \"value\": 0.025746643543243408, \"num_examples\": 74.0, \"standard_deviation\": 0.31051690826123}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"Age\", \"threshold\": 40.5}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": -0.0007330667576752603, \"num_examples\": 69.0, \"standard_deviation\": 0.2817713423765729}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"LACE_TOTAL\", \"threshold\": 11.5}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 0.05022316053509712, \"num_examples\": 47.0, \"standard_deviation\": 0.3337103737853635}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"C_MI\", \"threshold\": 0.5}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": -0.05268946290016174, \"num_examples\": 22.0, \"standard_deviation\": 0.2082989008477938}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"Age\", \"threshold\": 83.5}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 0.140786275267601, \"num_examples\": 5.0, \"standard_deviation\": 0.4000000108250154}}, {\"value\": {\"type\": \"REGRESSION\", \"value\": -0.10959409922361374, \"num_examples\": 17.0}}]}, {\"value\": {\"type\": \"REGRESSION\", \"value\": 0.140786275267601, \"num_examples\": 25.0, \"standard_deviation\": 0.4000000108250154}, \"condition\": {\"type\": \"CATEGORICAL_IS_IN\", \"attribute\": \"Disch_Disp\", \"mask\": [\"Discharged to Home or Self Care\", \"Disch/Trans to Skill Nursing Facility\", \"Dsch/Tran to Another Short Term Gen Hosp\", \"Disch/Trans Psych Hosp or Distinct Unit\"]}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": 0.45945221185684204, \"num_examples\": 11.0, \"standard_deviation\": 0.4979296106442739}}, {\"value\": {\"type\": \"REGRESSION\", \"value\": -0.10959409922361374, \"num_examples\": 14.0}}]}]}, {\"value\": {\"type\": \"REGRESSION\", \"value\": -0.10959409922361374, \"num_examples\": 22.0}}]}, {\"value\": {\"type\": \"REGRESSION\", \"value\": 0.39116665720939636, \"num_examples\": 5.0, \"standard_deviation\": 0.4898979614259706}}]}]}, {\"value\": {\"type\": \"REGRESSION\", \"value\": -0.10959409922361374, \"num_examples\": 119.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"Sodium_Last_Result_Admit_Dt\", \"threshold\": 131.5}, \"children\": [{\"value\": {\"type\": \"REGRESSION\", \"value\": -0.10959409922361374, \"num_examples\": 107.0}}, {\"value\": {\"type\": \"REGRESSION\", \"value\": -0.10959409922361374, \"num_examples\": 12.0}}]}]}]}]}, \"#tree_plot_d6e46f2c75c841a3ae762190cc4b4e41\")\n",
              "</script>\n"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ],
      "source": [
        "tfdf.model_plotter.plot_model_in_colab(MODEL, max_depth=100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ob3ovQ2seVY"
      },
      "source": [
        "## Model structure and feature importance\n",
        "\n",
        "The overall structure of the model is show with `.summary()`. You will see:\n",
        "\n",
        "-   **Type**: The learning algorithm used to train the model (`Random Forest` in\n",
        "    our case).\n",
        "-   **Task**: The problem solved by the model (`Classification` in our case).\n",
        "-   **Input Features**: The input features of the model.\n",
        "-   **Variable Importance**: Different measures of the importance of each\n",
        "    feature for the model.\n",
        "-   **Out-of-bag evaluation**: The out-of-bag evaluation of the model. This is a\n",
        "    cheap and efficient alternative to cross-validation.\n",
        "-   **Number of {trees, nodes} and other metrics**: Statistics about the\n",
        "    structure of the decisions forests.\n",
        "\n",
        "**Remark:** The summary's content depends on the learning algorithm (e.g.\n",
        "Out-of-bag is only available for Random Forest) and the hyper-parameters (e.g.\n",
        "the *mean-decrease-in-accuracy* variable importance can be disabled in the\n",
        "hyper-parameters)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kzXME28Lq7Il"
      },
      "outputs": [],
      "source": [
        "%set_cell_height 300\n",
        "MODEL.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G3xuB3jN1Cww"
      },
      "outputs": [],
      "source": [
        "# The input features\n",
        "MODEL.make_inspector().features()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BZ2RBbU51L6s"
      },
      "outputs": [],
      "source": [
        "# The feature importances\n",
        "MODEL.make_inspector().variable_importances()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0zvyRJVk1aEk"
      },
      "source": [
        "The content of the summary and the inspector depends on the learning algorithm (`tfdf.keras.RandomForestModel` in this case) and its hyper-parameters (e.g. `compute_oob_variable_importances=True` will trigger the computation of Out-of-bag variable importances for the Random Forest learner)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tFVmrHtWXYKY"
      },
      "source": [
        "## Model Self Evaluation\n",
        "\n",
        "During training TFDF models can self evaluate even if no validation dataset is provided to the `fit()` method. The exact logic depends on the model. For example, Random Forest will use Out-of-bag evaluation while Gradient Boosted Trees will use internal train-validation.\n",
        "\n",
        "**Note:** While this evaluation is  computed during training, it is NOT computed on the training dataset and can be used as a low quality evaluation.\n",
        "\n",
        "The model self evaluation is available with the inspector's `evaluation()`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BZPzyIMmYmsI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b55bce57-2eb8-4c67-f010-8e07604e8d16"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Evaluation(num_examples=331, accuracy=0.9063444108761329, loss=0.4712202442961877, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "MODEL.make_inspector().evaluation()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZbRk7xvpTKQG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        },
        "outputId": "e704391d-d1a6-497d-a365-7b059adc68d3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "google.colab.output.setIframeHeight(0, true, {maxHeight: 150})"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[TrainLog(num_trees=1, evaluation=Evaluation(num_examples=122, accuracy=0.8278688524590164, loss=6.204235108172307, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=11, evaluation=Evaluation(num_examples=330, accuracy=0.9, loss=1.6499466230923479, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=21, evaluation=Evaluation(num_examples=331, accuracy=0.9003021148036254, loss=1.3571963676017937, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=31, evaluation=Evaluation(num_examples=331, accuracy=0.9063444108761329, loss=1.2571703259723424, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=41, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=1.2533868869423326, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=51, evaluation=Evaluation(num_examples=331, accuracy=0.9154078549848943, loss=1.1530768757297734, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=61, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=0.8579525930126088, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=71, evaluation=Evaluation(num_examples=331, accuracy=0.9154078549848943, loss=0.8585209877615699, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=81, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=0.859407025065802, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=91, evaluation=Evaluation(num_examples=331, accuracy=0.9123867069486404, loss=0.858898223504379, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=101, evaluation=Evaluation(num_examples=331, accuracy=0.9123867069486404, loss=0.8594060534065494, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=111, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=0.8604458858599475, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=121, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=0.6672277172571582, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=131, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=0.5709820516306198, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=141, evaluation=Evaluation(num_examples=331, accuracy=0.9063444108761329, loss=0.5728827878844072, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=151, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=0.5716951022633658, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=161, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=0.5745543754656779, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=171, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=0.5743048918458811, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=181, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=0.480029194567949, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=191, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=0.47882828131329347, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=201, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=0.4789242806712171, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=211, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=0.47605788713079117, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=221, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=0.4735586020442744, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=231, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=0.47433275769090455, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=241, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=0.4743300385497443, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=251, evaluation=Evaluation(num_examples=331, accuracy=0.9063444108761329, loss=0.47242383636658014, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=261, evaluation=Evaluation(num_examples=331, accuracy=0.9063444108761329, loss=0.47181769005751684, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=271, evaluation=Evaluation(num_examples=331, accuracy=0.9063444108761329, loss=0.47255830698593143, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=281, evaluation=Evaluation(num_examples=331, accuracy=0.9093655589123867, loss=0.472263897327408, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=291, evaluation=Evaluation(num_examples=331, accuracy=0.9063444108761329, loss=0.4718118366191664, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)),\n",
              " TrainLog(num_trees=300, evaluation=Evaluation(num_examples=331, accuracy=0.9063444108761329, loss=0.4712202442961877, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None))]"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "%set_cell_height 150\n",
        "MODEL.make_inspector().training_logs()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w1xzugBRhwuN"
      },
      "source": [
        "## TensorBoard\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5R_m-JmvU9tu",
        "colab": {
          "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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