Bayesian Network with Pomegranate.ipynb

bayesian-network-with-pomegranate.ipynb

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      "name": "Bayesian Network with Pomegranate.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/gist/jkapila/a9eb527d724c75e2edec73ea06529354/bayesian-network-with-pomegranate.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6OQMgzlHaduN"
      },
      "source": [
        "# Bayesian Network Learning, Inference and Plotting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iQKQGqFGasPj"
      },
      "source": [
        "## Preparing data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4WszboFgaduT"
      },
      "source": [
        "### Step 1) Import the required libraries and reading data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDIWCridaduU"
      },
      "source": [
        "# do it only once, need not to run everytime on local\n",
        "# if on colab and you are using notebook again and again u need to do it before using the environment\n",
        "# !apt install graphviz libgraphviz-dev\n",
        "# !pip install --user pomegranate causalnex pyagrum pygraphviz"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VNRsITuRaduV"
      },
      "source": [
        "# data mainpulation and other basic requirement\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Graph library for all needs\n",
        "import networkx as nx\n",
        "\n",
        "# plotting and vizulization\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "import seaborn as sns\n",
        "plt.style.use('seaborn-ticks')\n",
        "%matplotlib inline"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uHwC-5H4ZJZB"
      },
      "source": [
        "### Step 2) Get the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
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        "id": "N4G30kFeaduW",
        "outputId": "c8c96af4-dcd5-4a7b-f721-3844eba7c5a5"
      },
      "source": [
        "#Reading Data \n",
        "# Add any file as per you convince\n",
        "# using adult data as example\n",
        "col_names = 'age,workclass,fnlwgt,education,education_num,marital_status,occupation,relationship,race,sex,capital_gain,capital_loss,hours_per_week,country,target'\n",
        "df= pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data',header=None,names=col_names.split(','))\n",
        "df.head()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>education_num</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "      <th>country</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>77516</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>2174</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>83311</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>215646</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>Private</td>\n",
              "      <td>234721</td>\n",
              "      <td>11th</td>\n",
              "      <td>7</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>Private</td>\n",
              "      <td>338409</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>Cuba</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age          workclass  fnlwgt  ... hours_per_week         country  target\n",
              "0   39          State-gov   77516  ...             40   United-States   <=50K\n",
              "1   50   Self-emp-not-inc   83311  ...             13   United-States   <=50K\n",
              "2   38            Private  215646  ...             40   United-States   <=50K\n",
              "3   53            Private  234721  ...             40   United-States   <=50K\n",
              "4   28            Private  338409  ...             40            Cuba   <=50K\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
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        "id": "L0Vd32erT7lv",
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      "source": [
        "df.describe()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education_num</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>32561.000000</td>\n",
              "      <td>3.256100e+04</td>\n",
              "      <td>32561.000000</td>\n",
              "      <td>32561.000000</td>\n",
              "      <td>32561.000000</td>\n",
              "      <td>32561.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>38.581647</td>\n",
              "      <td>1.897784e+05</td>\n",
              "      <td>10.080679</td>\n",
              "      <td>1077.648844</td>\n",
              "      <td>87.303830</td>\n",
              "      <td>40.437456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13.640433</td>\n",
              "      <td>1.055500e+05</td>\n",
              "      <td>2.572720</td>\n",
              "      <td>7385.292085</td>\n",
              "      <td>402.960219</td>\n",
              "      <td>12.347429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>1.228500e+04</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>1.178270e+05</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>37.000000</td>\n",
              "      <td>1.783560e+05</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>2.370510e+05</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>90.000000</td>\n",
              "      <td>1.484705e+06</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>99999.000000</td>\n",
              "      <td>4356.000000</td>\n",
              "      <td>99.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                age        fnlwgt  ...  capital_loss  hours_per_week\n",
              "count  32561.000000  3.256100e+04  ...  32561.000000    32561.000000\n",
              "mean      38.581647  1.897784e+05  ...     87.303830       40.437456\n",
              "std       13.640433  1.055500e+05  ...    402.960219       12.347429\n",
              "min       17.000000  1.228500e+04  ...      0.000000        1.000000\n",
              "25%       28.000000  1.178270e+05  ...      0.000000       40.000000\n",
              "50%       37.000000  1.783560e+05  ...      0.000000       40.000000\n",
              "75%       48.000000  2.370510e+05  ...      0.000000       45.000000\n",
              "max       90.000000  1.484705e+06  ...   4356.000000       99.000000\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "avuhs4J1ZTfb"
      },
      "source": [
        "### Step 3) Descritize data for faster learning\n",
        "\n",
        "Make sure you dont have any NAs left in data before learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "lyCWPKPnR0YN",
        "outputId": "8dc1fa53-9585-4d47-a46a-179fae9ee831"
      },
      "source": [
        "# descritizing numeric data\n",
        "labeller = lambda x:str(x).replace('(','').replace(']','').replace(', ','to')\n",
        "struct_data = df.drop('fnlwgt',axis=1).copy(deep=True)\n",
        "struct_data['age'] = pd.cut(struct_data['age'], list(range(0,105,10))).apply(labeller)\n",
        "struct_data['education_num'] = pd.cut(struct_data['education_num'], list(range(0,40,10))).apply(labeller)\n",
        "struct_data['capital_gain'] = pd.cut(struct_data['capital_gain'], bins=5,include_lowest=True).apply(labeller)\n",
        "struct_data['capital_loss'] = pd.cut(struct_data['capital_loss'], bins=5,include_lowest=True).apply(labeller)\n",
        "struct_data['hours_per_week'] = pd.cut(struct_data['hours_per_week'], list(range(0,120,15))).apply(labeller)\n",
        "\n",
        "# storing colnames as we might need it laterBut bro do\n",
        "col_names = struct_data.columns.tolist()\n",
        "\n",
        "struct_data"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>education</th>\n",
              "      <th>education_num</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "      <th>country</th>\n",
              "      <th>target</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30to40</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>10to20</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>-100.0to19999.8</td>\n",
              "      <td>-4.357to871.2</td>\n",
              "      <td>30to45</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>40to50</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>10to20</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>-100.0to19999.8</td>\n",
              "      <td>-4.357to871.2</td>\n",
              "      <td>0to15</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>30to40</td>\n",
              "      <td>Private</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>0to10</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>-100.0to19999.8</td>\n",
              "      <td>-4.357to871.2</td>\n",
              "      <td>30to45</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50to60</td>\n",
              "      <td>Private</td>\n",
              "      <td>11th</td>\n",
              "      <td>0to10</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>-100.0to19999.8</td>\n",
              "      <td>-4.357to871.2</td>\n",
              "      <td>30to45</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20to30</td>\n",
              "      <td>Private</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>10to20</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>-100.0to19999.8</td>\n",
              "      <td>-4.357to871.2</td>\n",
              "      <td>30to45</td>\n",
              "      <td>Cuba</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32556</th>\n",
              "      <td>20to30</td>\n",
              "      <td>Private</td>\n",
              "      <td>Assoc-acdm</td>\n",
              "      <td>10to20</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Tech-support</td>\n",
              "      <td>Wife</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>-100.0to19999.8</td>\n",
              "      <td>-4.357to871.2</td>\n",
              "      <td>30to45</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32557</th>\n",
              "      <td>30to40</td>\n",
              "      <td>Private</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>0to10</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Machine-op-inspct</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>-100.0to19999.8</td>\n",
              "      <td>-4.357to871.2</td>\n",
              "      <td>30to45</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32558</th>\n",
              "      <td>50to60</td>\n",
              "      <td>Private</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>0to10</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>-100.0to19999.8</td>\n",
              "      <td>-4.357to871.2</td>\n",
              "      <td>30to45</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32559</th>\n",
              "      <td>20to30</td>\n",
              "      <td>Private</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>0to10</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>-100.0to19999.8</td>\n",
              "      <td>-4.357to871.2</td>\n",
              "      <td>15to30</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32560</th>\n",
              "      <td>50to60</td>\n",
              "      <td>Self-emp-inc</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>0to10</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Wife</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>-100.0to19999.8</td>\n",
              "      <td>-4.357to871.2</td>\n",
              "      <td>30to45</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>32561 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          age          workclass  ...         country  target\n",
              "0      30to40          State-gov  ...   United-States   <=50K\n",
              "1      40to50   Self-emp-not-inc  ...   United-States   <=50K\n",
              "2      30to40            Private  ...   United-States   <=50K\n",
              "3      50to60            Private  ...   United-States   <=50K\n",
              "4      20to30            Private  ...            Cuba   <=50K\n",
              "...       ...                ...  ...             ...     ...\n",
              "32556  20to30            Private  ...   United-States   <=50K\n",
              "32557  30to40            Private  ...   United-States    >50K\n",
              "32558  50to60            Private  ...   United-States   <=50K\n",
              "32559  20to30            Private  ...   United-States   <=50K\n",
              "32560  50to60       Self-emp-inc  ...   United-States    >50K\n",
              "\n",
              "[32561 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSyuz1SyVhGv",
        "outputId": "364033d3-40b1-4b67-b2e6-007d4d91bb8a"
      },
      "source": [
        "struct_data.isna().sum()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               0\n",
              "workclass         0\n",
              "education         0\n",
              "education_num     0\n",
              "marital_status    0\n",
              "occupation        0\n",
              "relationship      0\n",
              "race              0\n",
              "sex               0\n",
              "capital_gain      0\n",
              "capital_loss      0\n",
              "hours_per_week    0\n",
              "country           0\n",
              "target            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JX8apCWvadun"
      },
      "source": [
        "## Constructing a Bayesian Belief Network for the given data. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ouDk80rZaFec"
      },
      "source": [
        "### Using Pomegranate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHyPgVqOa8Ge"
      },
      "source": [
        "#### Learn and train your model on data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aKkkLngDUWSV",
        "outputId": "a83bd626-8820-4e5d-b7ac-e3654227a148"
      },
      "source": [
        "%%time\n",
        "import pomegranate as po\n",
        "# from pomegranate import BayesianNetwork\n",
        "\n",
        "# do some tweaking here to make a sparse learnign of structure\n",
        "model_po = po.BayesianNetwork.from_samples(X=struct_data, \n",
        "                                           algorithm='chow-liu', # most fastest alogrithm available \n",
        "                                           max_parents=3,  penalty=0.9, # defining how much to contraint the graph for learning\n",
        "                                           reduce_dataset=True, # drop duplicatesx to learn more coherent. If you dont want his just set it to False\n",
        "                                           state_names=col_names # give senseful name to node based on data itself\n",
        "                                           )"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 576 ms, sys: 5.22 ms, total: 581 ms\n",
            "Wall time: 581 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "33QJpTdeZseu"
      },
      "source": [
        "#### Make an inference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DMeMUxEhWavM",
        "outputId": "f5d09338-ff45-45d8-9868-276140b7d0ca"
      },
      "source": [
        "\n",
        "ques_inp = [['40to50', ' Self-emp-not-inc', ' Bachelors', '10to20',\n",
        "       ' Married-civ-spouse', ' Exec-managerial', ' Husband', ' White',\n",
        "       ' Male',None,None, '0to15',\n",
        "       ' United-States', None]]\n",
        "\n",
        "print('Making prediciton of unobserved variablesfrom models:\\n')\n",
        "preds = model_po.predict_proba(ques_inp)\n",
        "for p,nm in zip(preds[0],col_names):\n",
        "    if not isinstance(p,(str,int,list)):\n",
        "        s = '\\n'.join([f\"{str(k).rjust(30):30s} : {v:.4f}\" for k,v in p.to_dict()['parameters'][0].items()])\n",
        "        print(f\"\\nFor variable {nm} onboserved probabilities are:\")\n",
        "        print(s)"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making prediciton of unobserved variablesfrom models:\n",
            "\n",
            "\n",
            "For variable capital_gain onboserved probabilities are:\n",
            "               -100.0to19999.8 : 0.9858\n",
            "              19999.8to39999.6 : 0.0051\n",
            "              39999.6to59999.4 : 0.0000\n",
            "              79999.2to99999.0 : 0.0091\n",
            "\n",
            "For variable capital_loss onboserved probabilities are:\n",
            "                1742.4to2613.6 : 0.0465\n",
            "                3484.8to4356.0 : 0.0002\n",
            "                 -4.357to871.2 : 0.9398\n",
            "                 871.2to1742.4 : 0.0127\n",
            "                2613.6to3484.8 : 0.0007\n",
            "\n",
            "For variable target onboserved probabilities are:\n",
            "                          >50K : 0.4486\n",
            "                         <=50K : 0.5514\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "PcE4tpILWpgR",
        "outputId": "0950f62a-0b0b-456c-a4f4-f47369575dc4"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "model_po.plot()"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
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\" y=\"-7\"/>\n   </g>\n  </g>\n </g>\n <defs>\n  <clipPath id=\"pe36d2c2d56\">\n   <rect height=\"271.8\" width=\"207.847059\" x=\"7.2\" y=\"7.2\"/>\n  </clipPath>\n </defs>\n</svg>\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BjQ9wfLoYiGy"
      },
      "source": [
        "# from pomegranate.utils import plot_networkx\n",
        "# plot_networkx(model_po)"
      ],
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hVYe4bQaX0EZ"
      },
      "source": [
        "![image](plot_pomegranate.png)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zlm-uvy-XOrq",
        "outputId": "34e14620-8bdf-4ebd-abc6-622e113bb241",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Image(filename='plot_pomegranate.png',)\n",
        "!ls -lah"
      ],
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 984K\n",
            "drwxr-xr-x 1 root root 4.0K Sep 22 16:35 .\n",
            "drwxr-xr-x 1 root root 4.0K Sep 22 11:59 ..\n",
            "drwxr-xr-x 4 root root 4.0K Sep 16 13:39 .config\n",
            "-rw-r--r-- 1 root root  13K Sep 22 16:35 plot_pomegranate.png\n",
            "drwxr-xr-x 1 root root 4.0K Sep 16 13:40 sample_data\n",
            "-rw-r--r-- 1 root root 952K Sep 22 16:15 structure_data.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XaiglACteb7F"
      },
      "source": [
        "### Using Causalnex"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B1udMUPjebhL",
        "outputId": "d0f446d1-9805-4fa7-9191-19114c54b946",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "encoded_struct_data = struct_data.copy()\n",
        "non_numeric_columns = list(struct_data.select_dtypes(exclude=[np.number]).columns)\n",
        "\n",
        "\n",
        "for col in non_numeric_columns:\n",
        "    encoded_struct_data[col] = le.fit_transform(encoded_struct_data[col])\n",
        "\n",
        "encoded_struct_data.head(5)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>education</th>\n",
              "      <th>education_num</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "      <th>country</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  workclass  education  ...  hours_per_week  country  target\n",
              "0    2          7          9  ...               2       39       0\n",
              "1    3          6          9  ...               0       39       0\n",
              "2    2          4         11  ...               2       39       0\n",
              "3    4          4          1  ...               2       39       0\n",
              "4    1          4          9  ...               2        5       0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-uifENtEtn76",
        "outputId": "3daec8ba-645d-43d4-c627-98e7c0804852",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%%time\n",
        "from causalnex.structure.notears import from_pandas\n",
        "\n",
        "# sm = from_pandas(encoded_struct_data.drop(columns=['country','education_num','capital_gain','capital_loss'],axis=1), \n",
        "#                  tabu_edges=[(\"age\", \"sex\"),(\"age\",\"race\"),(\"marital_status\",\"sex\")], # Edges you dont want\n",
        "#                  tabu_parent_nodes=[\"target\"], # Nodes which cannot be parent\n",
        "#                  tabu_child_nodes=[\"age\", \"marital_status\"]) # nodes whihc cannot be a child\n",
        "\n",
        "selected_cols = ['age','target','marital_status','sex','education_num','race','country','relationship','education']\n",
        "sm = from_pandas(encoded_struct_data[selected_cols] ,\n",
        "                 tabu_edges = [],\n",
        "                 tabu_parent_nodes=[\"target\"],\n",
        "                 tabu_child_nodes=[\"age\", \"marital_status\",\"country\",\"sex\"])\n"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 7.15 s, sys: 7.89 s, total: 15 s\n",
            "Wall time: 7.68 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hryrro2Qtn3Y",
        "outputId": "e0fc5515-26a8-411a-903a-97779b8734cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 923
        }
      },
      "source": [
        "from IPython.display import Image\n",
        "from causalnex.plots import plot_structure, NODE_STYLE, EDGE_STYLE\n",
        "sm.remove_edges_below_threshold(0.02)\n",
        "graph_attributes = {\n",
        "    \"splines\": \"spline\",  # I use splies so that we have no overlap\n",
        "    \"ordering\": \"out\",\n",
        "    \"ratio\": \"fill\",  # This is necessary to control the size of the image\n",
        "    \"size\": \"5,4!\",  # Set the size of the final image. (this is a typical presentation size)\n",
        "    \"label\": \"A Simple learnt model from Causalnex\",\n",
        "    \"fontcolor\": \"#FFFFFFD9\",\n",
        "    \"fontname\": \"Helvetica\",\n",
        "    \"fontsize\": 50,\n",
        "    \"labeljust\": \"l\",\n",
        "    \"labelloc\": \"t\",\n",
        "    \"pad\": \"1,1\",\n",
        "    \"dpi\": 200,\n",
        "    \"nodesep\": 0.8,\n",
        "    \"ranksep\": \".5 equally\",\n",
        "}\n",
        "\n",
        "# Making all nodes hexagonal with black coloring\n",
        "node_attributes = {\n",
        "    node: {\n",
        "        \"shape\": \"hexagon\",\n",
        "        \"width\": 2.2,\n",
        "        \"height\": 2,\n",
        "        \"fillcolor\": \"#000000\",\n",
        "        \"penwidth\": \"10\",\n",
        "        \"color\": \"#4a90e2d9\",\n",
        "        \"fontsize\": 40,\n",
        "        \"labelloc\": \"c\",\n",
        "    }\n",
        "    for node in sm.nodes\n",
        "}\n",
        "\n",
        "# # Splitting two words with \"\\n\"\n",
        "# for node in sm.nodes:\n",
        "#     up_idx = [i for i, c in enumerate(node) if c.isupper()][-1]\n",
        "#     node_attributes[node][\"label\"] = node[:up_idx] + \"\\n\" + node[up_idx:]\n",
        "\n",
        "# Target nodes (ones with \"Cost\" in the name) are colored differently\n",
        "for node in sm.nodes:\n",
        "    if \"Cost\" in node:  # We color nodes with \"cost\" in the name with a orange colour.\n",
        "        node_attributes[node][\"fillcolor\"] = \"#DF5F00\"\n",
        "\n",
        "# Customising edges\n",
        "edge_attributes = {\n",
        "    (u, v): {\n",
        "        \"penwidth\": w * 20 + 2,  # Setting edge thickness\n",
        "        \"weight\": int(5 * w),  # Higher \"weight\"s mean shorter edges\n",
        "        \"arrowsize\": 2 - 2.0 * w,  # Avoid too large arrows\n",
        "        \"arrowtail\": \"dot\",\n",
        "    }\n",
        "    for u, v, w in sm.edges(data=\"weight\")\n",
        "}\n",
        "\n",
        "viz = plot_structure(\n",
        "    sm,\n",
        "    prog=\"dot\",\n",
        "    graph_attributes=graph_attributes,\n",
        "    node_attributes=node_attributes,\n",
        "    edge_attributes=edge_attributes,\n",
        ")\n",
        "Image(viz.draw(format='png'))"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/root/.local/lib/python3.7/site-packages/pygraphviz/agraph.py:1390: RuntimeWarning: Warning: node 'education_num', graph '' size too small for label\n",
            "Warning: node 'relationship', graph '' size too small for label\n",
            "Warning: node 'education', graph '' size too small for label\n",
            "Warning: node 'marital_status', graph '' size too small for label\n",
            "\n",
            "  warnings.warn(b\"\".join(errors).decode(self.encoding), RuntimeWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ww03CYxPtn0B",
        "outputId": "aac5ba85-2e38-4248-bb8c-2f3f6d847ca9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from causalnex.inference import InferenceEngine\n",
        "from causalnex.network import BayesianNetwork\n",
        "\n",
        " # Inititalise BN with defined structure model\n",
        "model_cnx = BayesianNetwork(sm)\n",
        "# First, learn all the possible states of the nodes using the whole dataset\n",
        "model_cnx.fit_node_states(encoded_struct_data[selected_cols])\n",
        "\n",
        "# Updating the model on the whole dataset\n",
        "model_cnx.fit_cpds(encoded_struct_data[selected_cols], \n",
        "            method=\"BayesianEstimator\", bayes_prior=\"K2\")\n"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<causalnex.network.network.BayesianNetwork at 0x7f507ae29710>"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UhnwD5rPzNe0",
        "outputId": "2723093a-4a97-4017-d87c-f53249923d66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "# printing CPD of Target\n",
        "model_cnx.cpds['target']"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <th colspan=\"40\" halign=\"left\">0</th>\n",
              "      <th>...</th>\n",
              "      <th colspan=\"40\" halign=\"left\">7</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_num</th>\n",
              "      <th colspan=\"40\" halign=\"left\">0</th>\n",
              "      <th>...</th>\n",
              "      <th colspan=\"40\" halign=\"left\">1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital_status</th>\n",
              "      <th colspan=\"12\" halign=\"left\">0</th>\n",
              "      <th colspan=\"12\" halign=\"left\">1</th>\n",
              "      <th colspan=\"12\" halign=\"left\">2</th>\n",
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              "      <th>...</th>\n",
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              "      <th colspan=\"12\" halign=\"left\">5</th>\n",
              "      <th colspan=\"12\" halign=\"left\">6</th>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <th colspan=\"2\" halign=\"left\">1</th>\n",
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              "      <th colspan=\"2\" halign=\"left\">4</th>\n",
              "      <th colspan=\"2\" halign=\"left\">5</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>0</th>\n",
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              "      <th>1</th>\n",
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              "      <th>1</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
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              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
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              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.969697</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.875</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.928571</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>...</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.030303</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 1344 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "age               0                           ...    7                         \n",
              "education_num     0                           ...    1                         \n",
              "marital_status    0                           ...    6                         \n",
              "relationship      0              1         2  ...    3    4              5     \n",
              "sex               0    1         0    1    0  ...    1    0         1    0    1\n",
              "target                                        ...                              \n",
              "0               0.5  0.5  0.916667  0.5  0.5  ...  0.5  0.5  0.666667  0.5  0.5\n",
              "1               0.5  0.5  0.083333  0.5  0.5  ...  0.5  0.5  0.333333  0.5  0.5\n",
              "\n",
              "[2 rows x 1344 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DaPAPUQKOD8v",
        "outputId": "81df50c2-f95f-41fd-f6a1-c054900b7a6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        }
      },
      "source": [
        "# building inference engine\n",
        "ie = InferenceEngine(model_cnx)\n",
        "#Querying all the marginal probabilities of the model's distribution\n",
        "marginals = ie.query({})"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-141-39ea0726ddd0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# building inference engine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mie\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mInferenceEngine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_cnx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m#Querying all the marginal probabilities of the model's distribution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mmarginals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mie\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/causalnex/inference/inference.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, bn)\u001b[0m\n\u001b[1;32m    121\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cpds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_cpds_dict_bn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    124\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_generate_domains_bn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_generate_bbn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/causalnex/inference/inference.py\u001b[0m in \u001b[0;36m_create_cpds_dict_bn\u001b[0;34m(self, bn)\u001b[0m\n\u001b[1;32m    304\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m             }\n\u001b[0;32m--> 306\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mvariable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcpd\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    307\u001b[0m         }\n\u001b[1;32m    308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/causalnex/inference/inference.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    304\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m             }\n\u001b[0;32m--> 306\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mvariable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcpd\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    307\u001b[0m         }\n\u001b[1;32m    308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/causalnex/inference/inference.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    302\u001b[0m                     \u001b[0;32mfor\u001b[0m \u001b[0mparent_value\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMultiIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcpd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m                 }\n\u001b[0;32m--> 304\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    305\u001b[0m             }\n\u001b[1;32m    306\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mvariable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcpd\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mfrom_frame\u001b[0;34m(cls, df, sortorder, names)\u001b[0m\n\u001b[1;32m    630\u001b[0m         \u001b[0mcolumn_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    631\u001b[0m         \u001b[0mnames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn_names\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnames\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 632\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msortorder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msortorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    634\u001b[0m     \u001b[0;31m# --------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mfrom_arrays\u001b[0;34m(cls, arrays, sortorder, names)\u001b[0m\n\u001b[1;32m    437\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"all arrays must be same length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m         \u001b[0mcodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfactorize_from_iterables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    440\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnames\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_default\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m             \u001b[0mnames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"name\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/arrays/categorical.py\u001b[0m in \u001b[0;36mfactorize_from_iterables\u001b[0;34m(iterables)\u001b[0m\n\u001b[1;32m   2723\u001b[0m         \u001b[0;31m# For consistency, it should return a list of 2 lists.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2724\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2725\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfactorize_from_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mit\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/arrays/categorical.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   2723\u001b[0m         \u001b[0;31m# For consistency, it should return a list of 2 lists.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2724\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2725\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfactorize_from_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mit\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/arrays/categorical.py\u001b[0m in \u001b[0;36mfactorize_from_iterable\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m   2695\u001b[0m         \u001b[0;31m# but only the resulting categories, the order of which is independent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2696\u001b[0m         \u001b[0;31m# from ordered. Set ordered to False as default. See GH #15457\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2697\u001b[0;31m         \u001b[0mcat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCategorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mordered\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2698\u001b[0m         \u001b[0mcategories\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcategories\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2699\u001b[0m         \u001b[0mcodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/arrays/categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, values, categories, ordered, dtype, fastpath)\u001b[0m\n\u001b[1;32m    360\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m             \u001b[0;31m# we're inferring from values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 362\u001b[0;31m             \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCategoricalDtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcategories\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mordered\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mis_categorical_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/dtypes/dtypes.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, categories, ordered)\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcategories\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mordered\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOrdered\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_finalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcategories\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mordered\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/dtypes/dtypes.py\u001b[0m in \u001b[0;36m_finalize\u001b[0;34m(self, categories, ordered, fastpath)\u001b[0m\n\u001b[1;32m    316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcategories\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 318\u001b[0;31m             \u001b[0mcategories\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_categories\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcategories\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfastpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_categories\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcategories\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/dtypes/dtypes.py\u001b[0m in \u001b[0;36mvalidate_categories\u001b[0;34m(categories, fastpath)\u001b[0m\n\u001b[1;32m    492\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Categorical categories cannot be null\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 494\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcategories\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    495\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Categorical categories must be unique\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    496\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/numeric.py\u001b[0m in \u001b[0;36mis_unique\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    426\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mcache_readonly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mis_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nan_idxs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mis_unique\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1649\u001b[0m         \u001b[0mReturn\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mindex\u001b[0m \u001b[0mhas\u001b[0m \u001b[0munique\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1650\u001b[0m         \"\"\"\n\u001b[0;32m-> 1651\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1652\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1653\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HTCXCyMyII_B"
      },
      "source": [
        "### Using PyArgum"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZW3hhDwGOSYs"
      },
      "source": [
        "%matplotlib inline\n",
        "from pylab import *\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import os\n",
        "\n",
        "import pyAgrum as gum\n",
        "import pyAgrum.lib.notebook as gnb"
      ],
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3OfJ_E47OTX6",
        "outputId": "1d121a3f-fd24-4353-c56e-5526b6cec54f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# storing the data in local\n",
        "# encoded_struct_data[selected_cols].to_csv('structure_data.csv')\n",
        "encoded_struct_data.to_csv('structure_data.csv',index=False)\n",
        "encoded_struct_data.isna().sum()\n"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               0\n",
              "workclass         0\n",
              "education         0\n",
              "education_num     0\n",
              "marital_status    0\n",
              "occupation        0\n",
              "relationship      0\n",
              "race              0\n",
              "sex               0\n",
              "capital_gain      0\n",
              "capital_loss      0\n",
              "hours_per_week    0\n",
              "country           0\n",
              "target            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BZCVyWDMOoo_",
        "outputId": "ac53ef31-eca3-4fcf-8517-342af5d9d5a8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sample_data  structure_data.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTlCwU9hIIfh",
        "outputId": "461f98aa-8d26-4588-ded0-11a8e99ab878",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%%time\n",
        "learner=gum.BNLearner('structure_data.csv',)\n",
        "learner.useLocalSearchWithTabuList()\n",
        "print(learner)"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filename       : structure_data.csv\n",
            "Size           : (32561,14)\n",
            "Variables      : age[8], workclass[9], education[16], education_num[2], marital_status[7], occupation[15], relationship[6], race[5], sex[2], capital_gain[4], capital_loss[5], hours_per_week[7], country[42], target[2]\n",
            "Induced types  : True\n",
            "Missing values : False\n",
            "Algorithm      : Local Search with Tabu List\n",
            "Tabu list size : 2\n",
            "Score          : BDeu\n",
            "Prior          : -\n",
            "\n",
            "CPU times: user 113 ms, sys: 6.52 ms, total: 120 ms\n",
            "Wall time: 121 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lY5_4aCQPEbo",
        "outputId": "d582118f-8d7d-4094-ce23-d7e9e2a56ad9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%%time\n",
        "model_argm=learner.learnBN()\n"
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 210 ms, sys: 7.59 ms, total: 218 ms\n",
            "Wall time: 217 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2GWZ2m9JQ_7h",
        "outputId": "144b0ff1-e8e6-490e-a645-6ab19931d4c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        }
      },
      "source": [
        "model_argm"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<svg height=\"360pt\" viewBox=\"0.00 0.00 277.75 360.00\" width=\"278pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
              "<g class=\"graph\" id=\"graph0\" transform=\"scale(.5202 .5202) rotate(0) translate(4 688)\">\n",
              "<title>G</title>\n",
              "<!-- age -->\n",
              "<g class=\"node\" id=\"node1\">\n",
              "<title>age</title>\n",
              "<g id=\"a_node1\"><a xlink:title=\"(0) age\">\n",
              "<ellipse cx=\"357\" cy=\"-594\" fill=\"#404040\" rx=\"27\" ry=\"18\" stroke=\"#000000\"/>\n",
              "<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"357\" y=\"-590.3\">age</text>\n",
              "</a>\n",
              "</g>\n",
              "</g>\n",
              "<!-- workclass -->\n",
              "<g class=\"node\" id=\"node6\">\n",
              "<title>workclass</title>\n",
              "<g id=\"a_node6\"><a xlink:title=\"(1) workclass\">\n",
              "<ellipse cx=\"357\" cy=\"-378\" fill=\"#404040\" rx=\"47.3916\" ry=\"18\" stroke=\"#000000\"/>\n",
              "<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"357\" y=\"-374.3\">workclass</text>\n",
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              "</g>\n",
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              "<!-- age&#45;&gt;workclass -->\n",
              "<g class=\"edge\" id=\"edge16\">\n",
              "<title>age-&gt;workclass</title>\n",
              "<g id=\"a_edge16\"><a xlink:title=\"(0, 1) : \">\n",
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              "</g>\n",
              "</g>\n",
              "<!-- relationship -->\n",
              "<g class=\"node\" id=\"node7\">\n",
              "<title>relationship</title>\n",
              "<g id=\"a_node7\"><a xlink:title=\"(6) relationship\">\n",
              "<ellipse cx=\"210\" cy=\"-522\" fill=\"#404040\" rx=\"53.8905\" ry=\"18\" stroke=\"#000000\"/>\n",
              "<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"210\" y=\"-518.3\">relationship</text>\n",
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              "<!-- age&#45;&gt;relationship -->\n",
              "<g class=\"edge\" id=\"edge19\">\n",
              "<title>age-&gt;relationship</title>\n",
              "<g id=\"a_edge19\"><a xlink:title=\"(0, 6) : \">\n",
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              "</a>\n",
              "</g>\n",
              "</g>\n",
              "<!-- capital_gain -->\n",
              "<g class=\"node\" id=\"node10\">\n",
              "<title>capital_gain</title>\n",
              "<g id=\"a_node10\"><a xlink:title=\"(9) capital_gain\">\n",
              "<ellipse cx=\"470\" cy=\"-90\" fill=\"#404040\" rx=\"55.7903\" ry=\"18\" stroke=\"#000000\"/>\n",
              "<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"470\" y=\"-86.3\">capital_gain</text>\n",
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              "<!-- age&#45;&gt;capital_gain -->\n",
              "<g class=\"edge\" id=\"edge21\">\n",
              "<title>age-&gt;capital_gain</title>\n",
              "<g id=\"a_edge21\"><a xlink:title=\"(0, 9) : \">\n",
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              "</g>\n",
              "</g>\n",
              "<!-- hours_per_week -->\n",
              "<g class=\"node\" id=\"node11\">\n",
              "<title>hours_per_week</title>\n",
              "<g id=\"a_node11\"><a xlink:title=\"(11) hours_per_week\">\n",
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              "</g>\n",
              "<!-- age&#45;&gt;hours_per_week -->\n",
              "<g class=\"edge\" id=\"edge10\">\n",
              "<title>age-&gt;hours_per_week</title>\n",
              "<g id=\"a_edge10\"><a xlink:title=\"(0, 11) : \">\n",
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              "</g>\n",
              "</g>\n",
              "<!-- sex -->\n",
              "<g class=\"node\" id=\"node2\">\n",
              "<title>sex</title>\n",
              "<g id=\"a_node2\"><a xlink:title=\"(8) sex\">\n",
              "<ellipse cx=\"282\" cy=\"-450\" fill=\"#404040\" rx=\"27\" ry=\"18\" stroke=\"#000000\"/>\n",
              "<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"282\" y=\"-446.3\">sex</text>\n",
              "</a>\n",
              "</g>\n",
              "</g>\n",
              "<!-- occupation -->\n",
              "<g class=\"node\" id=\"node4\">\n",
              "<title>occupation</title>\n",
              "<g id=\"a_node4\"><a xlink:title=\"(5) occupation\">\n",
              "<ellipse cx=\"244\" cy=\"-306\" fill=\"#404040\" rx=\"51.9908\" ry=\"18\" stroke=\"#000000\"/>\n",
              "<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"244\" y=\"-302.3\">occupation</text>\n",
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              "</g>\n",
              "</g>\n",
              "<!-- sex&#45;&gt;occupation -->\n",
              "<g class=\"edge\" id=\"edge4\">\n",
              "<title>sex-&gt;occupation</title>\n",
              "<g id=\"a_edge4\"><a xlink:title=\"(8, 5) : \">\n",
              "<path d=\"M277.2641,-432.0535C270.7787,-407.4774 259.0683,-363.1008 251.4146,-334.0974\" fill=\"none\" stroke=\"#4a4a4a\"/>\n",
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              "</g>\n",
              "</g>\n",
              "<!-- sex&#45;&gt;workclass -->\n",
              "<g class=\"edge\" id=\"edge7\">\n",
              "<title>sex-&gt;workclass</title>\n",
              "<g id=\"a_edge7\"><a xlink:title=\"(8, 1) : \">\n",
              "<path d=\"M297.54,-435.0816C307.4406,-425.577 320.4944,-413.0454 331.8482,-402.1457\" fill=\"none\" stroke=\"#4a4a4a\"/>\n",
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              "</g>\n",
              "<!-- sex&#45;&gt;hours_per_week -->\n",
              "<g class=\"edge\" id=\"edge12\">\n",
              "<title>sex-&gt;hours_per_week</title>\n",
              "<g id=\"a_edge12\"><a xlink:title=\"(8, 11) : \">\n",
              "<path d=\"M285.8633,-432.1473C290.9905,-407.8126 299.9785,-362.8127 305,-324 314.5425,-250.2423 319.6281,-162.7024 321.7612,-118.5315\" fill=\"none\" stroke=\"#4a4a4a\"/>\n",
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              "</g>\n",
              "</g>\n",
              "<!-- target -->\n",
              "<g class=\"node\" id=\"node3\">\n",
              "<title>target</title>\n",
              "<g id=\"a_node3\"><a xlink:title=\"(13) target\">\n",
              "<ellipse cx=\"222\" cy=\"-162\" fill=\"#404040\" rx=\"31.3957\" ry=\"18\" stroke=\"#000000\"/>\n",
              "<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"222\" y=\"-158.3\">target</text>\n",
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              "</g>\n",
              "</g>\n",
              "<!-- target&#45;&gt;capital_gain -->\n",
              "<g class=\"edge\" id=\"edge22\">\n",
              "<title>target-&gt;capital_gain</title>\n",
              "<g id=\"a_edge22\"><a xlink:title=\"(13, 9) : \">\n",
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              "</g>\n",
              "</g>\n",
              "<!-- target&#45;&gt;hours_per_week -->\n",
              "<g class=\"edge\" id=\"edge11\">\n",
              "<title>target-&gt;hours_per_week</title>\n",
              "<g id=\"a_edge11\"><a xlink:title=\"(13, 11) : \">\n",
              "<path d=\"M241.9543,-147.7751C255.7302,-137.9547 274.3637,-124.6715 290.2903,-113.3178\" fill=\"none\" stroke=\"#4a4a4a\"/>\n",
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              "</g>\n",
              "</g>\n",
              "<!-- capital_loss -->\n",
              "<g class=\"node\" id=\"node14\">\n",
              "<title>capital_loss</title>\n",
              "<g id=\"a_node14\"><a xlink:title=\"(10) capital_loss\">\n",
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              "<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"346\" y=\"-14.3\">capital_loss</text>\n",
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              "<!-- target&#45;&gt;capital_loss -->\n",
              "<g class=\"edge\" id=\"edge15\">\n",
              "<title>target-&gt;capital_loss</title>\n",
              "<g id=\"a_edge15\"><a xlink:title=\"(13, 10) : \">\n",
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              "</g>\n",
              "</g>\n",
              "<!-- education -->\n",
              "<g class=\"node\" id=\"node13\">\n",
              "<title>education</title>\n",
              "<g id=\"a_node13\"><a xlink:title=\"(2) education\">\n",
              "<ellipse cx=\"93\" cy=\"-234\" fill=\"#404040\" rx=\"46.5926\" ry=\"18\" stroke=\"#000000\"/>\n",
              "<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"93\" y=\"-230.3\">education</text>\n",
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              "</g>\n",
              "<!-- occupation&#45;&gt;education -->\n",
              "<g class=\"edge\" id=\"edge23\">\n",
              "<title>occupation-&gt;education</title>\n",
              "<g id=\"a_edge23\"><a xlink:title=\"(5, 2) : \">\n",
              "<path d=\"M213.4429,-291.4297C189.9071,-280.2074 157.2101,-264.6167 131.822,-252.5112\" fill=\"none\" stroke=\"#4a4a4a\"/>\n",
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              "</g>\n",
              "</g>\n",
              "<!-- education_num -->\n",
              "<g class=\"node\" id=\"node5\">\n",
              "<title>education_num</title>\n",
              "<g id=\"a_node5\"><a xlink:title=\"(3) education_num\">\n",
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              "<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"97\" y=\"-158.3\">education_num</text>\n",
              "</a>\n",
              "</g>\n",
              "</g>\n",
              "<!-- race -->\n",
              "<g class=\"node\" id=\"node9\">\n",
              "<title>race</title>\n",
              "<g id=\"a_node9\"><a xlink:title=\"(7) race\">\n",
              "<ellipse cx=\"49\" cy=\"-90\" fill=\"#404040\" rx=\"27\" ry=\"18\" stroke=\"#000000\"/>\n",
              "<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"49\" y=\"-86.3\">race</text>\n",
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              "</g>\n",
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              "<!-- education_num&#45;&gt;race -->\n",
              "<g class=\"edge\" id=\"edge3\">\n",
              "<title>education_num-&gt;race</title>\n",
              "<g id=\"a_edge3\"><a xlink:title=\"(3, 7) : \">\n",
              "<path d=\"M85.1348,-144.2022C79.2684,-135.4025 72.0892,-124.6338 65.6801,-115.0202\" fill=\"none\" stroke=\"#4a4a4a\"/>\n",
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              "</g>\n",
              "</g>\n",
              "<!-- workclass&#45;&gt;occupation -->\n",
              "<g class=\"edge\" id=\"edge8\">\n",
              "<title>workclass-&gt;occupation</title>\n",
              "<g id=\"a_edge8\"><a xlink:title=\"(1, 5) : \">\n",
              "<path d=\"M332.4807,-362.3771C316.4508,-352.1634 295.3,-338.6867 277.6974,-327.4709\" fill=\"none\" stroke=\"#4a4a4a\"/>\n",
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              "</g>\n",
              "</g>\n",
              "<!-- relationship&#45;&gt;sex -->\n",
              "<g class=\"edge\" id=\"edge18\">\n",
              "<title>relationship-&gt;sex</title>\n",
              "<g id=\"a_edge18\"><a xlink:title=\"(6, 8) : \">\n",
              "<path d=\"M227.4292,-504.5708C237.0742,-494.9258 249.1684,-482.8316 259.5184,-472.4816\" fill=\"none\" stroke=\"#4a4a4a\"/>\n",
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              "</g>\n",
              "</g>\n",
              "<!-- relationship&#45;&gt;target -->\n",
              "<g class=\"edge\" id=\"edge20\">\n",
              "<title>relationship-&gt;target</title>\n",
              "<g id=\"a_edge20\"><a xlink:title=\"(6, 13) : \">\n",
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              "</g>\n",
              "<!-- relationship&#45;&gt;race -->\n",
              "<g class=\"edge\" id=\"edge6\">\n",
              "<title>relationship-&gt;race</title>\n",
              "<g id=\"a_edge6\"><a xlink:title=\"(6, 7) : \">\n",
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              "</g>\n",
              "<!-- marital_status -->\n",
              "<g class=\"node\" id=\"node8\">\n",
              "<title>marital_status</title>\n",
              "<g id=\"a_node8\"><a xlink:title=\"(4) marital_status\">\n",
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              "<!-- marital_status&#45;&gt;age -->\n",
              "<g class=\"edge\" id=\"edge5\">\n",
              "<title>marital_status-&gt;age</title>\n",
              "<g id=\"a_edge5\"><a xlink:title=\"(4, 0) : \">\n",
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              "<!-- marital_status&#45;&gt;sex -->\n",
              "<g class=\"edge\" id=\"edge1\">\n",
              "<title>marital_status-&gt;sex</title>\n",
              "<g id=\"a_edge1\"><a xlink:title=\"(4, 8) : \">\n",
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              "<!-- marital_status&#45;&gt;relationship -->\n",
              "<g class=\"edge\" id=\"edge17\">\n",
              "<title>marital_status-&gt;relationship</title>\n",
              "<g id=\"a_edge17\"><a xlink:title=\"(4, 6) : \">\n",
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              "<!-- country -->\n",
              "<g class=\"node\" id=\"node12\">\n",
              "<title>country</title>\n",
              "<g id=\"a_node12\"><a xlink:title=\"(12) country\">\n",
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              "<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"49\" y=\"-14.3\">country</text>\n",
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              "<!-- race&#45;&gt;country -->\n",
              "<g class=\"edge\" id=\"edge2\">\n",
              "<title>race-&gt;country</title>\n",
              "<g id=\"a_edge2\"><a xlink:title=\"(7, 12) : \">\n",
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              "<!-- capital_gain&#45;&gt;capital_loss -->\n",
              "<g class=\"edge\" id=\"edge13\">\n",
              "<title>capital_gain-&gt;capital_loss</title>\n",
              "<g id=\"a_edge13\"><a xlink:title=\"(9, 10) : \">\n",
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              "<!-- education&#45;&gt;target -->\n",
              "<g class=\"edge\" id=\"edge9\">\n",
              "<title>education-&gt;target</title>\n",
              "<g id=\"a_edge9\"><a xlink:title=\"(2, 13) : \">\n",
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              "<!-- education&#45;&gt;education_num -->\n",
              "<g class=\"edge\" id=\"edge14\">\n",
              "<title>education-&gt;education_num</title>\n",
              "<g id=\"a_edge14\"><a xlink:title=\"(2, 3) : \">\n",
              "<path d=\"M94.0094,-215.8314C94.4372,-208.131 94.9459,-198.9743 95.4213,-190.4166\" fill=\"none\" stroke=\"#4a4a4a\"/>\n",
              "<polygon fill=\"#4a4a4a\" points=\"98.9169,-190.592 95.977,-180.4133 91.9276,-190.2037 98.9169,-190.592\" stroke=\"#4a4a4a\"/>\n",
              "</a>\n",
              "</g>\n",
              "</g>\n",
              "</g>\n",
              "</svg>"
            ],
            "text/plain": [
              "(pyAgrum.BayesNet<double>@0x5570e9c27800) BN{nodes: 14, arcs: 23, domainSize: 10^11.2322, dim: 1959}"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ZCKo7COWUei",
        "outputId": "d1e973db-a24f-427f-d77a-dd2c867f2d00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        }
      },
      "source": [
        "model_argm.cpt('capital_loss')"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table style=\"border:1px solid black;\">\n",
              "<tr><th colspan='2'></th>\n",
              "      <th colspan='5' style='border:1px solid black;color:black;background-color:#808080;'><center>capital_loss</center>\n",
              "      </th></tr>\n",
              "<tr><th style='border:1px solid black;color:black;background-color:#808080'><center>capital_gain</center></th><th style='border:1px solid black;color:black;background-color:#808080'><center>target</center></th><th style='border:1px solid black;border-bottom-style: double;color:black;background-color:#BBBBBB'>\n",
              "      <center>0</center></th><th style='border:1px solid black;border-bottom-style: double;color:black;background-color:#BBBBBB'>\n",
              "      <center>1</center></th><th style='border:1px solid black;border-bottom-style: double;color:black;background-color:#BBBBBB'>\n",
              "      <center>2</center></th><th style='border:1px solid black;border-bottom-style: double;color:black;background-color:#BBBBBB'>\n",
              "      <center>3</center></th><th style='border:1px solid black;border-bottom-style: double;color:black;background-color:#BBBBBB'>\n",
              "      <center>4</center></th></tr>\n",
              "<tr><th style='border:1px solid black;color:black;background-color:#BBBBBB;' rowspan = '2'>\n",
              "            <center>0</center></th><th style='border:1px solid black;color:black;background-color:#BBBBBB'><center>0</center></th><td style='color:black;background-color:#82fb64;text-align:right;'>0.9709</td><td style='color:black;background-color:#fd8064;text-align:right;'>0.0115</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0001</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0003</td><td style='color:black;background-color:#fc8164;text-align:right;'>0.0172</td></tr>\n",
              "<tr><th style='border:1px solid black;color:black;background-color:#BBBBBB'><center>1</center></th><td style='color:black;background-color:#8bf264;text-align:right;'>0.8985</td><td style='color:black;background-color:#f38a64;text-align:right;'>0.0924</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0016</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0001</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0074</td></tr>\n",
              "<tr><th style='border:1px solid black;color:black;background-color:#BBBBBB;' rowspan = '2'>\n",
              "            <center>1</center></th><th style='border:1px solid black;color:black;background-color:#BBBBBB'><center>0</center></th><td style='color:black;background-color:#81fc64;text-align:right;'>0.9837</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0041</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0041</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0041</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0041</td></tr>\n",
              "<tr><th style='border:1px solid black;color:black;background-color:#BBBBBB'><center>1</center></th><td style='color:black;background-color:#7ffe64;text-align:right;'>0.9988</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0003</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0003</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0003</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0003</td></tr>\n",
              "<tr><th style='border:1px solid black;color:black;background-color:#BBBBBB;' rowspan = '2'>\n",
              "            <center>2</center></th><th style='border:1px solid black;color:black;background-color:#BBBBBB'><center>0</center></th><td style='color:black;background-color:#85f864;text-align:right;'>0.9529</td><td style='color:black;background-color:#fd8064;text-align:right;'>0.0118</td><td style='color:black;background-color:#fd8064;text-align:right;'>0.0118</td><td style='color:black;background-color:#fd8064;text-align:right;'>0.0118</td><td style='color:black;background-color:#fd8064;text-align:right;'>0.0118</td></tr>\n",
              "<tr><th style='border:1px solid black;color:black;background-color:#BBBBBB'><center>1</center></th><td style='color:black;background-color:#e59864;text-align:right;'>0.2000</td><td style='color:black;background-color:#e59864;text-align:right;'>0.2000</td><td style='color:black;background-color:#e59864;text-align:right;'>0.2000</td><td style='color:black;background-color:#e59864;text-align:right;'>0.2000</td><td style='color:black;background-color:#e59864;text-align:right;'>0.2000</td></tr>\n",
              "<tr><th style='border:1px solid black;color:black;background-color:#BBBBBB;' rowspan = '2'>\n",
              "            <center>3</center></th><th style='border:1px solid black;color:black;background-color:#BBBBBB'><center>0</center></th><td style='color:black;background-color:#e59864;text-align:right;'>0.2000</td><td style='color:black;background-color:#e59864;text-align:right;'>0.2000</td><td style='color:black;background-color:#e59864;text-align:right;'>0.2000</td><td style='color:black;background-color:#e59864;text-align:right;'>0.2000</td><td style='color:black;background-color:#e59864;text-align:right;'>0.2000</td></tr>\n",
              "<tr><th style='border:1px solid black;color:black;background-color:#BBBBBB'><center>1</center></th><td style='color:black;background-color:#7ffe64;text-align:right;'>0.9994</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0002</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0002</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0002</td><td style='color:black;background-color:#fe7f64;text-align:right;'>0.0002</td></tr>\n",
              "</table>"
            ],
            "text/plain": [
              "(pyAgrum.Potential<double>@0x5570e9b491c0) \n",
              "             ||  capital_loss                                   |\n",
              "target|capita||0        |1        |2        |3        |4        |\n",
              "------|------||---------|---------|---------|---------|---------|\n",
              "0     |0     || 0.9709  | 0.0115  | 0.0001  | 0.0003  | 0.0172  |\n",
              "1     |0     || 0.8985  | 0.0924  | 0.0016  | 0.0001  | 0.0074  |\n",
              "0     |1     || 0.9837  | 0.0041  | 0.0041  | 0.0041  | 0.0041  |\n",
              "1     |1     || 0.9988  | 0.0003  | 0.0003  | 0.0003  | 0.0003  |\n",
              "0     |2     || 0.9529  | 0.0118  | 0.0118  | 0.0118  | 0.0118  |\n",
              "1     |2     || 0.2000  | 0.2000  | 0.2000  | 0.2000  | 0.2000  |\n",
              "0     |3     || 0.2000  | 0.2000  | 0.2000  | 0.2000  | 0.2000  |\n",
              "1     |3     || 0.9994  | 0.0002  | 0.0002  | 0.0002  | 0.0002  |"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rIRtH59JeNxe"
      },
      "source": [
        "# model_argm.eraseArc('marital_status','age')\n",
        "# model_argm.eraseArc('relationship','race')\n",
        "model_argm.eraseArc('race','country')\n",
        "model_argm.addArc('relationship','race')\n",
        "model_argm.addArc('country','occupation')"
      ],
      "execution_count": 216,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MCHmmT7iZg_P",
        "outputId": "f8f26053-7347-4fde-b8be-c58d3c9fd268",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847
        }
      },
      "source": [
        "gnb.showBN(model_argm,size=\"9\")"
      ],
      "execution_count": 217,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
            "image/svg+xml": "<svg height=\"620pt\" viewBox=\"0.00 0.00 463.90 620.00\" width=\"464pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 616)\">\n<title>G</title>\n<!-- age -->\n<g class=\"node\" id=\"node1\">\n<title>age</title>\n<g id=\"a_node1\"><a xlink:title=\"(0) age\">\n<ellipse cx=\"291\" cy=\"-594\" fill=\"#404040\" rx=\"27\" ry=\"18\" stroke=\"#000000\"/>\n<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"291\" y=\"-590.3\">age</text>\n</a>\n</g>\n</g>\n<!-- workclass -->\n<g class=\"node\" id=\"node6\">\n<title>workclass</title>\n<g id=\"a_node6\"><a xlink:title=\"(1) workclass\">\n<ellipse cx=\"249\" cy=\"-378\" fill=\"#404040\" rx=\"47.3916\" ry=\"18\" stroke=\"#000000\"/>\n<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"249\" y=\"-374.3\">workclass</text>\n</a>\n</g>\n</g>\n<!-- age&#45;&gt;workclass -->\n<g class=\"edge\" id=\"edge15\">\n<title>age-&gt;workclass</title>\n<g id=\"a_edge15\"><a xlink:title=\"(0, 1) : \">\n<path d=\"M287.4913,-575.9555C280.1722,-538.3144 263.2307,-451.1867 254.4687,-406.1246\" fill=\"none\" stroke=\"#4a4a4a\"/>\n<polygon fill=\"#4a4a4a\" points=\"257.8726,-405.2932 252.5282,-396.1451 251.0013,-406.6293 257.8726,-405.2932\" stroke=\"#4a4a4a\"/>\n</a>\n</g>\n</g>\n<!-- relationship -->\n<g class=\"node\" id=\"node7\">\n<title>relationship</title>\n<g id=\"a_node7\"><a xlink:title=\"(6) relationship\">\n<ellipse cx=\"181\" cy=\"-522\" fill=\"#404040\" rx=\"53.8905\" ry=\"18\" stroke=\"#000000\"/>\n<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"181\" y=\"-518.3\">relationship</text>\n</a>\n</g>\n</g>\n<!-- age&#45;&gt;relationship -->\n<g class=\"edge\" id=\"edge18\">\n<title>age-&gt;relationship</title>\n<g id=\"a_edge18\"><a xlink:title=\"(0, 6) : \">\n<path d=\"M271.5874,-581.2936C255.7204,-570.9079 232.9284,-555.9895 214.1763,-543.7154\" fill=\"none\" stroke=\"#4a4a4a\"/>\n<polygon fill=\"#4a4a4a\" points=\"215.8983,-540.6595 205.6145,-538.1113 212.0647,-546.5164 215.8983,-540.6595\" stroke=\"#4a4a4a\"/>\n</a>\n</g>\n</g>\n<!-- capital_gain -->\n<g class=\"node\" id=\"node10\">\n<title>capital_gain</title>\n<g id=\"a_node10\"><a xlink:title=\"(9) capital_gain\">\n<ellipse cx=\"400\" cy=\"-90\" fill=\"#404040\" rx=\"55.7903\" ry=\"18\" stroke=\"#000000\"/>\n<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"400\" y=\"-86.3\">capital_gain</text>\n</a>\n</g>\n</g>\n<!-- age&#45;&gt;capital_gain -->\n<g class=\"edge\" id=\"edge20\">\n<title>age-&gt;capital_gain</title>\n<g id=\"a_edge20\"><a xlink:title=\"(0, 9) : \">\n<path d=\"M310.2361,-581.2148C341.5278,-558.7093 400,-508.7896 400,-450 400,-450 400,-450 400,-234 400,-193.876 400,-147.4631 400,-118.4177\" fill=\"none\" stroke=\"#4a4a4a\"/>\n<polygon fill=\"#4a4a4a\" points=\"403.5001,-118.1853 400,-108.1854 396.5001,-118.1854 403.5001,-118.1853\" stroke=\"#4a4a4a\"/>\n</a>\n</g>\n</g>\n<!-- hours_per_week -->\n<g class=\"node\" id=\"node11\">\n<title>hours_per_week</title>\n<g id=\"a_node11\"><a xlink:title=\"(11) hours_per_week\">\n<ellipse cx=\"253\" cy=\"-90\" fill=\"#404040\" rx=\"72.5877\" ry=\"18\" stroke=\"#000000\"/>\n<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"253\" y=\"-86.3\">hours_per_week</text>\n</a>\n</g>\n</g>\n<!-- age&#45;&gt;hours_per_week -->\n<g class=\"edge\" id=\"edge9\">\n<title>age-&gt;hours_per_week</title>\n<g id=\"a_edge9\"><a xlink:title=\"(0, 11) : \">\n<path d=\"M304.6895,-578.4061C325.3213,-553.2398 362,-501.2875 362,-450 362,-450 362,-450 362,-234 362,-183.199 318.3392,-139.0212 286.2831,-113.4318\" fill=\"none\" stroke=\"#4a4a4a\"/>\n<polygon fill=\"#4a4a4a\" points=\"288.057,-110.3788 278.0068,-107.0255 283.7722,-115.9143 288.057,-110.3788\" stroke=\"#4a4a4a\"/>\n</a>\n</g>\n</g>\n<!-- sex -->\n<g class=\"node\" id=\"node2\">\n<title>sex</title>\n<g id=\"a_node2\"><a xlink:title=\"(8) sex\">\n<ellipse cx=\"99\" cy=\"-450\" fill=\"#404040\" rx=\"27\" ry=\"18\" stroke=\"#000000\"/>\n<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"99\" y=\"-446.3\">sex</text>\n</a>\n</g>\n</g>\n<!-- occupation -->\n<g class=\"node\" id=\"node4\">\n<title>occupation</title>\n<g id=\"a_node4\"><a xlink:title=\"(5) occupation\">\n<ellipse cx=\"144\" cy=\"-306\" fill=\"#404040\" rx=\"51.9908\" ry=\"18\" stroke=\"#000000\"/>\n<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"144\" y=\"-302.3\">occupation</text>\n</a>\n</g>\n</g>\n<!-- sex&#45;&gt;occupation -->\n<g class=\"edge\" id=\"edge4\">\n<title>sex-&gt;occupation</title>\n<g id=\"a_edge4\"><a xlink:title=\"(8, 5) : \">\n<path d=\"M94.087,-432.0687C89.9001,-413.3991 85.8743,-383.637 95,-360 99.3728,-348.6737 107.3464,-338.2549 115.6091,-329.6533\" fill=\"none\" stroke=\"#4a4a4a\"/>\n<polygon fill=\"#4a4a4a\" points=\"118.1429,-332.0711 122.8614,-322.585 113.2572,-327.0582 118.1429,-332.0711\" stroke=\"#4a4a4a\"/>\n</a>\n</g>\n</g>\n<!-- sex&#45;&gt;workclass -->\n<g class=\"edge\" id=\"edge6\">\n<title>sex-&gt;workclass</title>\n<g id=\"a_edge6\"><a xlink:title=\"(8, 1) : \">\n<path d=\"M121.133,-439.3762C144.5626,-428.13 181.9972,-410.1613 210.321,-396.5659\" fill=\"none\" stroke=\"#4a4a4a\"/>\n<polygon fill=\"#4a4a4a\" points=\"212.0431,-399.6217 219.5438,-392.139 209.014,-393.311 212.0431,-399.6217\" stroke=\"#4a4a4a\"/>\n</a>\n</g>\n</g>\n<!-- sex&#45;&gt;hours_per_week -->\n<g class=\"edge\" id=\"edge11\">\n<title>sex-&gt;hours_per_week</title>\n<g id=\"a_edge11\"><a xlink:title=\"(8, 11) : \">\n<path d=\"M86.9484,-433.6706C69.1711,-407.9205 38,-355.6856 38,-306 38,-306 38,-306 38,-234 38,-193.1184 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id=\"edge14\">\n<title>target-&gt;capital_loss</title>\n<g id=\"a_edge14\"><a xlink:title=\"(13, 10) : \">\n<path d=\"M229.3719,-149.7551C207.5034,-137.8714 177.428,-119.9298 171,-108 163.4105,-93.9146 162.6658,-85.658 171,-72 182.5682,-53.0422 203.1511,-40.4589 223.0465,-32.2501\" fill=\"none\" stroke=\"#4a4a4a\"/>\n<polygon fill=\"#4a4a4a\" points=\"224.3177,-35.5112 232.4087,-28.6712 221.8182,-28.9727 224.3177,-35.5112\" stroke=\"#4a4a4a\"/>\n</a>\n</g>\n</g>\n<!-- education -->\n<g class=\"node\" id=\"node13\">\n<title>education</title>\n<g id=\"a_node13\"><a xlink:title=\"(2) education\">\n<ellipse cx=\"144\" cy=\"-234\" fill=\"#404040\" rx=\"46.5926\" ry=\"18\" stroke=\"#000000\"/>\n<text fill=\"#ffffff\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"144\" y=\"-230.3\">education</text>\n</a>\n</g>\n</g>\n<!-- occupation&#45;&gt;education -->\n<g class=\"edge\" id=\"edge22\">\n<title>occupation-&gt;education</title>\n<g id=\"a_edge22\"><a 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lvijpH6Zw3S",
        "outputId": "8dcb0668-e521-4c91-d456-bf3491645662",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%%time\n",
        "ie=gum.LazyPropagation(model_argm)\n",
        "ie.makeInference()\n",
        "print(ie.posterior(model_argm.idFromName(\"target\")))"
      ],
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "  target           |\n",
            "0        |1        |\n",
            "---------|---------|\n",
            " 0.5759  | 0.4241  |\n",
            "\n",
            "CPU times: user 11.1 ms, sys: 972 µs, total: 12.1 ms\n",
            "Wall time: 11.9 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z7qWgax0Z8rg",
        "outputId": "d539b96d-6f24-494f-d41b-5eda5c9682d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "gnb.showPosterior(model_argm,evs={},target='target')"
      ],
      "execution_count": 219,
      "outputs": [
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AbPhj79kcrAJ"
      },
      "source": [
        "**The Wooooowwwwwwwwww Factor**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mw5jKLS5aFSY",
        "outputId": "b85553ac-c9a3-47f1-f3d8-e8a45d3187b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# gnb.flow.row(gnb.showInference(model_argm,evs={},size=\"14\"),\n",
        "gnb.showInference(model_argm,evs={\"target\":1},size=\"20\")"
      ],
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lBVLUa0pZwa1"
      },
      "source": [
        "## Plotting the network without Graphviz\n",
        "This implementation dosent need pygraphviz so shlud be runnable on all environments."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3IRrjdDczss"
      },
      "source": [
        "def get_graph_from_model(model,pos=None):\n",
        "    graph = nx.DiGraph()\n",
        "    for state in model.states:\n",
        "        if pos is None:\n",
        "            graph.add_node(state.name,pos=(1,1))\n",
        "        else:\n",
        "            graph.add_node(state.name,pos=pos[state.name])\n",
        "\n",
        "    for parent, child in model.edges:\n",
        "        graph.add_edge(parent.name,child.name,weight=1)\n",
        "    \n",
        "    return graph"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-xnk8GsWadun"
      },
      "source": [
        "# taken from gist https://gist.github.com/jkapila/7c103c35e942e7d9eb5a4c195ab9717e\n",
        "# function to plot bayesian network\n",
        "def plot_bn_graph(G,title='',label_fontsize = 14,node_color='b',node_size=10,figsize=(12,10),position=False,layout='circular',cmap='YlOrRd'):\n",
        "    \"\"\"\n",
        "    Plotting Network diagram using Networkx and Matplotlib only.\n",
        "    \n",
        "    :param G: A Networkx Digraph havinf nodes as variables and edges and relationship\n",
        "    :param title: Title for plot. Default to \"Structure Plot\"\n",
        "    :param label_fontsize: Fontzise of the variable lables on the plot.\n",
        "    :param node_color: Color of the node, Defaults to \"blue\"\n",
        "    :param node_size: multipler for size of node. Defaults to 10.\n",
        "    :param figsize: Figure size fo the Plot shoudl be passed as tuple (w,h)\n",
        "    :param position: If 'pos' is an attribute of graphs then use it to specify coordinate of node on the graph.\n",
        "                    Default to false. If set to True, 'layout' has no impact.\n",
        "    :param layout: 3 type of layouts avaialble \"circular\", \"spring\" and \"spectral. Defaults to \"Circular\"\n",
        "    :param cmap: A color gradient used to draw edges and is based on weights. Can take any matplotlib colormap.\n",
        "    \n",
        "    Example Usage:\n",
        "    G = nx.DiGraph()\n",
        "    nodes = ['Intelligence','Course','Grades','SAT','Job']\n",
        "    positions = [(4,3),(2,3),(3,2),(5,2),(3,1)]\n",
        "    for nm,pos in zip(nodes,positions):\n",
        "        G.add_node(nm,pos=pos)\n",
        "    G.add_edge('Course','Grades',weight=0.5)\n",
        "    G.add_edge('Intelligence','Grades',weight=0.5)\n",
        "    G.add_edge('Intelligence','SAT',weight=1)\n",
        "    G.add_edge('Grades','Job',weight=1.5)\n",
        "\n",
        "    plot_bn_graph(G,title='A basic Student Bayesian Network plot',position=True,node_size=200,label_fontsize=16,node_color='skyblue',cmap='Wistia')\n",
        "    \n",
        "    \"\"\"\n",
        "    \n",
        "    fig, ax = plt.subplots(figsize = figsize)\n",
        "    cmap = plt.get_cmap(cmap)\n",
        "    bg_color = '#183861'\n",
        "    fg_color = 'white'\n",
        "    \n",
        "    # set title plus title color\n",
        "    ax.set_title('Structure Plot' if len(title)==0 else title, color=fg_color, fontsize=16)\n",
        "    # set figure facecolor\n",
        "    ax.patch.set_facecolor(bg_color)\n",
        "    \n",
        "    # layouts\n",
        "    layouts = {'spring':nx.layout.spring_layout,\n",
        "               'circular':nx.layout.circular_layout,\n",
        "               'spectral':nx.layout.spectral_layout}\n",
        "    if position:\n",
        "        pos=nx.get_node_attributes(G,'pos')\n",
        "    elif layout not in layouts.keys():\n",
        "        pos = nx.layout.planar_layout(G)\n",
        "    else:\n",
        "        pos = layouts[layout](G)\n",
        "    \n",
        "    node_sizes = [3 + node_size * len(G) for _ in range(len(G))]\n",
        "    edge_weights = G.edges(data=\"weight\")\n",
        "    M = len(edge_weights)\n",
        "    if sum([x[2] is None for x in edge_weights]) == M:\n",
        "        edge_colors = [5 for _ in edge_weights]\n",
        "        edge_alphas = [1 for _ in edge_weights]\n",
        "        print('No weights in edges')\n",
        "    else:\n",
        "        edge_colors = [np.nan if x[2] is None else abs(x[2]) for x in edge_weights]\n",
        "        edge_weights = [0.85 for _ in edge_weights]\n",
        "        minw, maxw = min(edge_weights),max(edge_weights)\n",
        "        edge_alphas = [ (x - minw + 0.01) / (maxw - minw + 0.01) for x in edge_weights]\n",
        "        # M = G.number_of_edges()\n",
        "        # edge_colors = range(2, M + 2)\n",
        "        # edge_alphas = [(5 + i) / (M + 4) for i in range(M)]\n",
        "\n",
        "    nodes = nx.draw_networkx_nodes(G, pos, ax=ax, node_size=node_sizes, \n",
        "                                   node_color=node_color)\n",
        "    edges = nx.draw_networkx_edges(G, pos, ax=ax, node_size=node_sizes, arrowstyle=\"->\",\n",
        "                                   arrowsize=12, edge_color=edge_colors, edge_cmap=cmap, width=3,)\n",
        "    labels = nx.draw_networkx_labels(G, pos, ax=ax, font_size=label_fontsize, font_color=fg_color,\n",
        "                                    verticalalignment ='bottom')\n",
        "    # set alpha value for each edge\n",
        "    for i in range(M):\n",
        "        edges[i].set_alpha(edge_alphas[i])\n",
        "    \n",
        "    # the colorbar and its properties\n",
        "    pc = mpl.collections.PatchCollection(edges, cmap=cmap)\n",
        "    pc.set_array(edge_colors)\n",
        "    cb = plt.colorbar(pc,ax=ax)\n",
        "    # set colorbar label plus label color\n",
        "    cb.set_label(label='Weights',weight='bold', color=fg_color)\n",
        "    # set colorbar tick color\n",
        "    cb.ax.yaxis.set_tick_params(color=fg_color)\n",
        "    # set colorbar edgecolor \n",
        "    cb.outline.set_edgecolor(fg_color)\n",
        "    # set colorbar ticklabels\n",
        "    plt.setp(plt.getp(cb.ax.axes, 'yticklabels'), color=fg_color)\n",
        "\n",
        "    gc = plt.gca()\n",
        "    gc.set_axis_off()\n",
        "    fig.set_facecolor(bg_color)\n",
        "    plt.show()"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "y96c1gcJWa2Z",
        "outputId": "db4de129-9f3c-4a23-be89-842b362452b7"
      },
      "source": [
        "model_graph = get_graph_from_model(model_po)\n",
        "plot_bn_graph(model_graph,title='A Bayesian Network plot for Adult Dta',node_size=5,label_fontsize=14,node_color='skyblue',cmap='Wistia',figsize=(15,10))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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