Benchmarking_experiments_on_the_Adult_dataset_and_interpretability.ipynb

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      "name": "Benchmarking experiments on the Adult dataset and interpretability.ipynb",
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      "cell_type": "markdown",
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      "source": [
        "## Introduction\n",
        "\n",
        "The purpose of this notebook is to - \n",
        "- Benchmark machine learning models on the [Adult dataset](https://archive.ics.uci.edu/ml/datasets/adult) using `fastai`, `h2o` and `interpret` libraries\n",
        "- Use decision tree surrogates to explain a `h2o` model\n",
        "- Show how easy it is to interpret blackbox models using `interpret`\n",
        "\n",
        "**Note**: I am not going to explain the inner mechanics of the predictive models I have used here. Rather, I will briefly explain the key points that are related to Machine Learning Explainability."
      ]
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      "source": [
        "## About the dataset\n",
        "\n",
        "[Adult dataset](https://archive.ics.uci.edu/ml/datasets/adult) is one of the most popular datasets which is a representative of the **Finance** domain. The dataset contains 32561 records of individuals including several features like occupation, age, marital status and so on (14 features). The prediction task is to determine whether a person makes over 50K a year. "
      ]
    },
    {
      "cell_type": "markdown",
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      },
      "source": [
        "## Simple data explorations"
      ]
    },
    {
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        "id": "dXnrBDrPCIBJ",
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      },
      "source": [
        "import pandas as pd"
      ],
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      "outputs": []
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      "source": [
        "columns = [\"Age\", \"WorkClass\", \"fnlwgt\", \"Education\", \"EducationNum\",\n",
        "        \"MaritalStatus\", \"Occupation\", \"Relationship\", \"Race\", \"Gender\",\n",
        "        \"CapitalGain\", \"CapitalLoss\", \"HoursPerWeek\", \"NativeCountry\", \"Income\"]\n",
        "\n",
        "data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data',\n",
        "                    header=None,\n",
        "                    names=columns)\n",
        "\n",
        "data.head()"
      ],
      "execution_count": 3,
      "outputs": [
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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>EducationNum</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Relationship</th>\n",
              "      <th>Race</th>\n",
              "      <th>Gender</th>\n",
              "      <th>CapitalGain</th>\n",
              "      <th>CapitalLoss</th>\n",
              "      <th>HoursPerWeek</th>\n",
              "      <th>NativeCountry</th>\n",
              "      <th>Income</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",
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            ],
            "text/plain": [
              "   Age          WorkClass  fnlwgt  ... HoursPerWeek   NativeCountry  Income\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]"
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      "source": [
        "data.describe(include='all').T"
      ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>32561</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.5816</td>\n",
              "      <td>13.6404</td>\n",
              "      <td>17</td>\n",
              "      <td>28</td>\n",
              "      <td>37</td>\n",
              "      <td>48</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WorkClass</th>\n",
              "      <td>32561</td>\n",
              "      <td>9</td>\n",
              "      <td>Private</td>\n",
              "      <td>22696</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fnlwgt</th>\n",
              "      <td>32561</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>189778</td>\n",
              "      <td>105550</td>\n",
              "      <td>12285</td>\n",
              "      <td>117827</td>\n",
              "      <td>178356</td>\n",
              "      <td>237051</td>\n",
              "      <td>1.48470e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Education</th>\n",
              "      <td>32561</td>\n",
              "      <td>16</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>10501</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EducationNum</th>\n",
              "      <td>32561</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0807</td>\n",
              "      <td>2.57272</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>12</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MaritalStatus</th>\n",
              "      <td>32561</td>\n",
              "      <td>7</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>14976</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Occupation</th>\n",
              "      <td>32561</td>\n",
              "      <td>15</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>4140</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Relationship</th>\n",
              "      <td>32561</td>\n",
              "      <td>6</td>\n",
              "      <td>Husband</td>\n",
              "      <td>13193</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Race</th>\n",
              "      <td>32561</td>\n",
              "      <td>5</td>\n",
              "      <td>White</td>\n",
              "      <td>27816</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>32561</td>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21790</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CapitalGain</th>\n",
              "      <td>32561</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1077.65</td>\n",
              "      <td>7385.29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>99999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CapitalLoss</th>\n",
              "      <td>32561</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>87.3038</td>\n",
              "      <td>402.96</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HoursPerWeek</th>\n",
              "      <td>32561</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.4375</td>\n",
              "      <td>12.3474</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>40</td>\n",
              "      <td>45</td>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NativeCountry</th>\n",
              "      <td>32561</td>\n",
              "      <td>42</td>\n",
              "      <td>United-States</td>\n",
              "      <td>29170</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <td>32561</td>\n",
              "      <td>2</td>\n",
              "      <td>&lt;=50K</td>\n",
              "      <td>24720</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               count unique                  top  ...     50%     75%          max\n",
              "Age            32561    NaN                  NaN  ...      37      48           90\n",
              "WorkClass      32561      9              Private  ...     NaN     NaN          NaN\n",
              "fnlwgt         32561    NaN                  NaN  ...  178356  237051  1.48470e+06\n",
              "Education      32561     16              HS-grad  ...     NaN     NaN          NaN\n",
              "EducationNum   32561    NaN                  NaN  ...      10      12           16\n",
              "MaritalStatus  32561      7   Married-civ-spouse  ...     NaN     NaN          NaN\n",
              "Occupation     32561     15       Prof-specialty  ...     NaN     NaN          NaN\n",
              "Relationship   32561      6              Husband  ...     NaN     NaN          NaN\n",
              "Race           32561      5                White  ...     NaN     NaN          NaN\n",
              "Gender         32561      2                 Male  ...     NaN     NaN          NaN\n",
              "CapitalGain    32561    NaN                  NaN  ...       0       0        99999\n",
              "CapitalLoss    32561    NaN                  NaN  ...       0       0         4356\n",
              "HoursPerWeek   32561    NaN                  NaN  ...      40      45           99\n",
              "NativeCountry  32561     42        United-States  ...     NaN     NaN          NaN\n",
              "Income         32561      2                <=50K  ...     NaN     NaN          NaN\n",
              "\n",
              "[15 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 6
        }
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      "source": [
        "### Some of the key observations\n",
        "\n",
        "- The individuals mostly belong to **Private** _WorkClass_\n",
        "- Most of the individuals are **HS-grad**\n",
        "- The topmost marital status is **Married-civ-spouse**\n",
        "- Most of the individuals are from **United-States **natively\n",
        "- **White** is the dominant _Race_ (in the dataset)\n"
      ]
    },
    {
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      "source": [
        "data.shape"
      ],
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            "text/plain": [
              "(32561, 15)"
            ]
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      },
      "source": [
        "data.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 32561 entries, 0 to 32560\n",
            "Data columns (total 15 columns):\n",
            "Age              32561 non-null int64\n",
            "WorkClass        32561 non-null object\n",
            "fnlwgt           32561 non-null int64\n",
            "Education        32561 non-null object\n",
            "EducationNum     32561 non-null int64\n",
            "MaritalStatus    32561 non-null object\n",
            "Occupation       32561 non-null object\n",
            "Relationship     32561 non-null object\n",
            "Race             32561 non-null object\n",
            "Gender           32561 non-null object\n",
            "CapitalGain      32561 non-null int64\n",
            "CapitalLoss      32561 non-null int64\n",
            "HoursPerWeek     32561 non-null int64\n",
            "NativeCountry    32561 non-null object\n",
            "Income           32561 non-null object\n",
            "dtypes: int64(6), object(9)\n",
            "memory usage: 3.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3zyfSHRAfjb9",
        "colab_type": "text"
      },
      "source": [
        "The labels are present in the **Income** column. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "drX0DTMVDRnW",
        "outputId": "0b685366-c684-4ba9-e6d0-4a0d027b9221",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "# Checking for missing values\n",
        "data.isna().sum()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age              0\n",
              "WorkClass        0\n",
              "fnlwgt           0\n",
              "Education        0\n",
              "EducationNum     0\n",
              "MaritalStatus    0\n",
              "Occupation       0\n",
              "Relationship     0\n",
              "Race             0\n",
              "Gender           0\n",
              "CapitalGain      0\n",
              "CapitalLoss      0\n",
              "HoursPerWeek     0\n",
              "NativeCountry    0\n",
              "Income           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0XSMjWUmgxlA",
        "colab_type": "text"
      },
      "source": [
        "### Class distribution in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "723l0WvrDoae",
        "outputId": "487dc925-acb5-4bb4-b8af-179f1425aefa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "from collections import Counter\n",
        "\n",
        "classes = Counter(data['Income'].values)\n",
        "class_dist = pd.DataFrame(classes.most_common(), columns=['Class', 'Num_Observations'])\n",
        "class_dist"
      ],
      "execution_count": 0,
      "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>Class</th>\n",
              "      <th>Num_Observations</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>&lt;=50K</td>\n",
              "      <td>24720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>&gt;50K</td>\n",
              "      <td>7841</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Class  Num_Observations\n",
              "0   <=50K             24720\n",
              "1    >50K              7841"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K7A-GoDqg2p1",
        "colab_type": "text"
      },
      "source": [
        "### How are the features correlated?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lugcmxbdFIlu",
        "colab": {}
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.set(style=\"white\")\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "FabuN86hEw7A",
        "outputId": "31a6cae9-1252-4f04-db49-1048351b4f6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "source": [
        "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
        "\n",
        "sns.heatmap(data.corr(), cmap=cmap, square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2196c18470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gZG5mVgFO5mZmFeBkbmZWAU7mZmYV4GRuZlYBTuZmZhXgZG5mVgFO5mZmFeBk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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1PuZp4EbhMUg",
        "colab_type": "text"
      },
      "source": [
        "## Start of machine learning modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hMZuWQXfHnhR"
      },
      "source": [
        "## `fastai`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "MGQ0-qf9Hp_K",
        "colab": {}
      },
      "source": [
        "from fastai.tabular import * \n",
        "import torch\n",
        "\n",
        "torch.cuda.manual_seed_all(7)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ayKL0iDmDVCz",
        "colab": {}
      },
      "source": [
        "preprocessing = [FillMissing, Categorify, Normalize]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "C-XDkBWnD5HD",
        "colab": {}
      },
      "source": [
        "dep_var = 'Income'\n",
        "cat_names = ['WorkClass', 'Education', 'MaritalStatus', 'Occupation', 'Relationship', 'Race', 'Gender', 'NativeCountry']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XMrkroLshYWc",
        "colab_type": "text"
      },
      "source": [
        "**I am not using a randomized validation set. Instead I am using the last 2000 entries for the purpose.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "h0NDfN_mGLGy",
        "colab": {}
      },
      "source": [
        "valid_idx = range(len(data)-2000, len(data))\n",
        "\n",
        "path=\".\"\n",
        "\n",
        "databunch = TabularDataBunch.from_df(path, data, dep_var, valid_idx=valid_idx, procs=preprocessing, cat_names=cat_names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KnnOvdtYGdAL",
        "outputId": "eafcaf4c-361a-4f07-9acb-309fbaedc6e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        }
      },
      "source": [
        "databunch.show_batch()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>WorkClass</th>\n",
              "      <th>Education</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Relationship</th>\n",
              "      <th>Race</th>\n",
              "      <th>Gender</th>\n",
              "      <th>NativeCountry</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>HoursPerWeek</th>\n",
              "      <th>Age</th>\n",
              "      <th>EducationNum</th>\n",
              "      <th>CapitalGain</th>\n",
              "      <th>CapitalLoss</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>Private</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Other-service</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>United-States</td>\n",
              "      <td>-0.2373</td>\n",
              "      <td>-0.0338</td>\n",
              "      <td>-1.2140</td>\n",
              "      <td>-0.4186</td>\n",
              "      <td>-0.1462</td>\n",
              "      <td>-0.2161</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Private</td>\n",
              "      <td>12th</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Craft-repair</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>United-States</td>\n",
              "      <td>0.3247</td>\n",
              "      <td>-0.0338</td>\n",
              "      <td>-1.3606</td>\n",
              "      <td>-0.8076</td>\n",
              "      <td>-0.1462</td>\n",
              "      <td>-0.2161</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Private</td>\n",
              "      <td>9th</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Craft-repair</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>United-States</td>\n",
              "      <td>0.0693</td>\n",
              "      <td>-0.0338</td>\n",
              "      <td>1.6442</td>\n",
              "      <td>-1.9748</td>\n",
              "      <td>-0.1462</td>\n",
              "      <td>-0.2161</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>State-gov</td>\n",
              "      <td>Doctorate</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>Germany</td>\n",
              "      <td>0.6094</td>\n",
              "      <td>2.4012</td>\n",
              "      <td>0.9846</td>\n",
              "      <td>2.3047</td>\n",
              "      <td>-0.1462</td>\n",
              "      <td>4.4776</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>State-gov</td>\n",
              "      <td>12th</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>United-States</td>\n",
              "      <td>-0.0316</td>\n",
              "      <td>0.7779</td>\n",
              "      <td>-1.2140</td>\n",
              "      <td>-0.8076</td>\n",
              "      <td>-0.1462</td>\n",
              "      <td>-0.2161</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o8UJYr5Khioc",
        "colab_type": "text"
      },
      "source": [
        "### Class distribution in the train and test set after splitting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RUjpd-59hOrJ",
        "outputId": "4acb7eb8-c27a-4fac-f692-f6f07b4c5d2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "train_dist = Counter(databunch.train_ds.y)\n",
        "pd.DataFrame(train_dist.most_common(), columns=['Classes', '#'])"
      ],
      "execution_count": 0,
      "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>Classes</th>\n",
              "      <th>#</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>&lt;=50K</td>\n",
              "      <td>23235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>&gt;50K</td>\n",
              "      <td>7326</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Classes      #\n",
              "0   <=50K  23235\n",
              "1    >50K   7326"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QWXJQMENhlA6",
        "outputId": "826876d2-49c8-48e9-bc52-21131a9817e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "valid_dist = Counter(databunch.valid_ds.y)\n",
        "pd.DataFrame(valid_dist.most_common(), columns=['Classes', '#'])"
      ],
      "execution_count": 0,
      "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>Classes</th>\n",
              "      <th>#</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>&lt;=50K</td>\n",
              "      <td>1485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>&gt;50K</td>\n",
              "      <td>515</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Classes     #\n",
              "0   <=50K  1485\n",
              "1    >50K   515"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mdME7s5_Ha9c",
        "outputId": "4b461734-92b3-43f3-ae0e-e6e80988aa26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        }
      },
      "source": [
        "learn = tabular_learner(databunch, layers=[200,100], emb_szs={'NativeCountry': 10}, metrics=accuracy)\n",
        "learn.lr_find()\n",
        "learn.recorder.plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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elwF478vMbFpm+cxy220KfGVmrwKNgEeAq733AegITCq37eTM40VEUuc23gLX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\nIiKSGoWQiIik5v8BF3QbBLXyye4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "YyV4dCJDIlfx",
        "outputId": "008acd62-48d6-47ff-df3c-fa3fe7dd1017",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "learn.fit_one_cycle(5, max_lr=slice(1e-03))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.365516</td>\n",
              "      <td>0.350557</td>\n",
              "      <td>0.838500</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.321920</td>\n",
              "      <td>0.329586</td>\n",
              "      <td>0.842000</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.320433</td>\n",
              "      <td>0.323776</td>\n",
              "      <td>0.852000</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.325805</td>\n",
              "      <td>0.325966</td>\n",
              "      <td>0.847000</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.313125</td>\n",
              "      <td>0.323612</td>\n",
              "      <td>0.851000</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3petUkohq21",
        "colab_type": "text"
      },
      "source": [
        "**Takes seconds to train.** We get an accuracy of **85.1%**. We just supplied a good learning rate and trained the network with 1cycle policy. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "kSi3FVqrI7bc",
        "outputId": "3cfd931f-93b7-4332-d21b-b764c1c20bdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        }
      },
      "source": [
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vZDsUEZGcpMTSBmanCTB/Ln79umyHIiKSc5RY2sCMGAs+hIUfZjsUEZGco8TSFsNGQSKP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\nxCIiImmlxCIiImmlxCIiImn1/7AQi8c9W2ayAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oKiRu38bdu7i",
        "outputId": "9d1b9879-9cbc-4573-d529-a90d54b93603",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "interp = ClassificationInterpretation.from_learner(learn)\n",
        "interp.plot_confusion_matrix()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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npXpuo/bZdZtm69W/0Mnlu1h1jqcJ0AO4e732JiJC4SOed/KWFwE3u/tT1VyPiJSAQoPn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\nj4gEp+ARkeAUPCISnIJHRIJT8IhIcP8fMn+s/CmkoLAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x8xlfEjniehH",
        "colab_type": "text"
      },
      "source": [
        "**The model confuses in identifying the individuals that actually earn more than 50K a year.** "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "VuyMVuUxJ8ZU"
      },
      "source": [
        "## `h20`\n",
        "\n",
        "I will be using the [`automl`](http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html) module that comes with `h2o`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iAR6XzuHJ7Mh",
        "colab": {}
      },
      "source": [
        "!pip install -f http://h2o-release.s3.amazonaws.com/h2o/latest_stable_Py.html h2o"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AX48Po0cKR2v",
        "colab": {}
      },
      "source": [
        "import h2o\n",
        "from h2o.automl import H2OAutoML\n",
        "\n",
        "h2o.init()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kDwFfZFyjG38",
        "colab_type": "text"
      },
      "source": [
        "`h2o` models require the data to be in [H2OFrame](http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/frame.html) format. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BrxOP0q1KXcb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "a1accf8d-a4d7-446e-df68-b5f00c9e05d2"
      },
      "source": [
        "# Following the same train/test split\n",
        "train = data[:30561]\n",
        "test = data[30561:]\n",
        "\n",
        "train_frame = h2o.H2OFrame(train)\n",
        "test_frame = h2o.h2o.H2OFrame(test)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UI1-IFcYmhH",
        "colab_type": "code",
        "outputId": "ca5593b7-711c-45e0-cd6e-7482ebcb8d0a",
        "colab": {}
      },
      "source": [
        "# Verify the distribution is same\n",
        "print('In the training set', Counter(train['Income']).most_common())\n",
        "print('\\nIn the testing set', Counter(test['Income']).most_common())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "In the training set [(' <=50K', 23235), (' >50K', 7326)]\n",
            "\n",
            "In the testing set [(' <=50K', 1485), (' >50K', 515)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_13n9Gt0LY8i",
        "outputId": "c3b5a4b5-0ac3-4f10-c4aa-1c005831c99a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 93
        }
      },
      "source": [
        "x = data.columns[0:-1]\n",
        "y = data.columns[-1]\n",
        "\n",
        "x, y"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Index([u'Age', u'WorkClass', u'fnlwgt', u'Education', u'EducationNum',\n",
              "        u'MaritalStatus', u'Occupation', u'Relationship', u'Race', u'Gender',\n",
              "        u'CapitalGain', u'CapitalLoss', u'HoursPerWeek', u'NativeCountry'],\n",
              "       dtype='object'), 'Income')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wEoSGgDOMB9N",
        "colab": {}
      },
      "source": [
        "# The targets have to be factorized before they are fed to the models in h2o\n",
        "train_frame[y] = train_frame[y].asfactor()\n",
        "test_frame[y] = test_frame[y].asfactor()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yQ0bDABLMtQM",
        "outputId": "152d07d7-2ea4-40b3-c66d-66029b7ecb16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "import time\n",
        "\n",
        "aml = H2OAutoML(max_models=20, seed=1)\n",
        "start = time.time()\n",
        "aml.train(x=x.tolist(), y=y, training_frame=train_frame, validation_frame=test_frame)\n",
        "print('AutoML took {} seconds'.format(time.time()-start))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AutoML progress: |████████████████████████████████████████████████████████| 100%\n",
            "AutoML took 1899.14495087 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Y-BcfhakIVv",
        "colab_type": "text"
      },
      "source": [
        "**Takes more than half an hour of time.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RRPyJGv5XTpG",
        "outputId": "9f6eafd1-517d-4d65-aec1-892be6b6355d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        }
      },
      "source": [
        "# An overview of how the models did within automl\n",
        "lb = aml.leaderboard\n",
        "lb.head(rows=lb.nrows)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th>model_id                                           </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">      mse</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td>StackedEnsemble_BestOfFamily_AutoML_20190517_051139</td><td style=\"text-align: right;\">0.927794</td><td style=\"text-align: right;\"> 0.294853</td><td style=\"text-align: right;\">              0.173797</td><td style=\"text-align: right;\">0.301101</td><td style=\"text-align: right;\">0.0906617</td></tr>\n",
              "<tr><td>StackedEnsemble_AllModels_AutoML_20190517_051139   </td><td style=\"text-align: right;\">0.927341</td><td style=\"text-align: right;\"> 0.293938</td><td style=\"text-align: right;\">              0.177802</td><td style=\"text-align: right;\">0.301006</td><td style=\"text-align: right;\">0.0906044</td></tr>\n",
              "<tr><td>GBM_1_AutoML_20190517_051139                       </td><td style=\"text-align: right;\">0.927287</td><td style=\"text-align: right;\"> 0.280554</td><td style=\"text-align: right;\">              0.172945</td><td style=\"text-align: right;\">0.297822</td><td style=\"text-align: right;\">0.0886982</td></tr>\n",
              "<tr><td>GBM_2_AutoML_20190517_051139                       </td><td style=\"text-align: right;\">0.926877</td><td style=\"text-align: right;\"> 0.28149 </td><td style=\"text-align: right;\">              0.174273</td><td style=\"text-align: right;\">0.298392</td><td style=\"text-align: right;\">0.0890378</td></tr>\n",
              "<tr><td>XGBoost_grid_1_AutoML_20190517_051139_model_1      </td><td style=\"text-align: right;\">0.926558</td><td style=\"text-align: right;\"> 0.281426</td><td style=\"text-align: right;\">              0.180692</td><td style=\"text-align: right;\">0.298438</td><td style=\"text-align: right;\">0.0890653</td></tr>\n",
              "<tr><td>GBM_3_AutoML_20190517_051139                       </td><td style=\"text-align: right;\">0.926515</td><td style=\"text-align: right;\"> 0.281957</td><td style=\"text-align: right;\">              0.169478</td><td style=\"text-align: right;\">0.298677</td><td style=\"text-align: right;\">0.0892077</td></tr>\n",
              "<tr><td>XGBoost_grid_1_AutoML_20190517_051139_model_4      </td><td style=\"text-align: right;\">0.926337</td><td style=\"text-align: right;\"> 0.281744</td><td style=\"text-align: right;\">              0.176406</td><td style=\"text-align: right;\">0.298589</td><td style=\"text-align: right;\">0.0891556</td></tr>\n",
              "<tr><td>XGBoost_1_AutoML_20190517_051139                   </td><td style=\"text-align: right;\">0.925989</td><td style=\"text-align: right;\"> 0.282082</td><td style=\"text-align: right;\">              0.181502</td><td style=\"text-align: right;\">0.298905</td><td style=\"text-align: right;\">0.0893445</td></tr>\n",
              "<tr><td>XGBoost_grid_1_AutoML_20190517_051139_model_3      </td><td style=\"text-align: right;\">0.924797</td><td style=\"text-align: right;\"> 0.285085</td><td style=\"text-align: right;\">              0.180002</td><td style=\"text-align: right;\">0.300316</td><td style=\"text-align: right;\">0.0901899</td></tr>\n",
              "<tr><td>GBM_4_AutoML_20190517_051139                       </td><td style=\"text-align: right;\">0.924704</td><td style=\"text-align: right;\"> 0.285485</td><td style=\"text-align: right;\">              0.17366 </td><td style=\"text-align: right;\">0.300621</td><td style=\"text-align: right;\">0.0903731</td></tr>\n",
              "<tr><td>GBM_5_AutoML_20190517_051139                       </td><td style=\"text-align: right;\">0.923928</td><td style=\"text-align: right;\"> 0.286686</td><td style=\"text-align: right;\">              0.170585</td><td style=\"text-align: right;\">0.301495</td><td style=\"text-align: right;\">0.0908994</td></tr>\n",
              "<tr><td>XGBoost_2_AutoML_20190517_051139                   </td><td style=\"text-align: right;\">0.923395</td><td style=\"text-align: right;\"> 0.287352</td><td style=\"text-align: right;\">              0.176231</td><td style=\"text-align: right;\">0.301707</td><td style=\"text-align: right;\">0.0910271</td></tr>\n",
              "<tr><td>XGBoost_3_AutoML_20190517_051139                   </td><td style=\"text-align: right;\">0.922526</td><td style=\"text-align: right;\"> 0.29146 </td><td style=\"text-align: right;\">              0.17291 </td><td style=\"text-align: right;\">0.302874</td><td style=\"text-align: right;\">0.0917328</td></tr>\n",
              "<tr><td>GBM_grid_1_AutoML_20190517_051139_model_1          </td><td style=\"text-align: right;\">0.919806</td><td style=\"text-align: right;\"> 0.296376</td><td style=\"text-align: right;\">              0.177414</td><td style=\"text-align: right;\">0.305892</td><td style=\"text-align: right;\">0.0935697</td></tr>\n",
              "<tr><td>XGBoost_grid_1_AutoML_20190517_051139_model_2      </td><td style=\"text-align: right;\">0.919488</td><td style=\"text-align: right;\"> 0.306043</td><td style=\"text-align: right;\">              0.180484</td><td style=\"text-align: right;\">0.30894 </td><td style=\"text-align: right;\">0.095444 </td></tr>\n",
              "<tr><td>DRF_1_AutoML_20190517_051139                       </td><td style=\"text-align: right;\">0.914053</td><td style=\"text-align: right;\"> 0.318848</td><td style=\"text-align: right;\">              0.186924</td><td style=\"text-align: right;\">0.309512</td><td style=\"text-align: right;\">0.0957977</td></tr>\n",
              "<tr><td>XRT_1_AutoML_20190517_051139                       </td><td style=\"text-align: right;\">0.913135</td><td style=\"text-align: right;\"> 0.306373</td><td style=\"text-align: right;\">              0.190739</td><td style=\"text-align: right;\">0.310776</td><td style=\"text-align: right;\">0.0965818</td></tr>\n",
              "<tr><td>DeepLearning_1_AutoML_20190517_051139              </td><td style=\"text-align: right;\">0.910358</td><td style=\"text-align: right;\"> 0.312872</td><td style=\"text-align: right;\">              0.18745 </td><td style=\"text-align: right;\">0.316064</td><td style=\"text-align: right;\">0.0998963</td></tr>\n",
              "<tr><td>DeepLearning_grid_1_AutoML_20190517_051139_model_1 </td><td style=\"text-align: right;\">0.908405</td><td style=\"text-align: right;\"> 0.323896</td><td style=\"text-align: right;\">              0.186483</td><td style=\"text-align: right;\">0.317481</td><td style=\"text-align: right;\">0.100794 </td></tr>\n",
              "<tr><td>GBM_grid_1_AutoML_20190517_051139_model_2          </td><td style=\"text-align: right;\">0.907593</td><td style=\"text-align: right;\"> 0.340761</td><td style=\"text-align: right;\">              0.189399</td><td style=\"text-align: right;\">0.32227 </td><td style=\"text-align: right;\">0.103858 </td></tr>\n",
              "<tr><td>GLM_grid_1_AutoML_20190517_051139_model_1          </td><td style=\"text-align: right;\">0.906696</td><td style=\"text-align: right;\"> 0.318681</td><td style=\"text-align: right;\">              0.193507</td><td style=\"text-align: right;\">0.319155</td><td style=\"text-align: right;\">0.10186  </td></tr>\n",
              "<tr><td>DeepLearning_grid_1_AutoML_20190517_051139_model_2 </td><td style=\"text-align: right;\">0.895188</td><td style=\"text-align: right;\"> 0.34834 </td><td style=\"text-align: right;\">              0.189348</td><td style=\"text-align: right;\">0.329126</td><td style=\"text-align: right;\">0.108324 </td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1vKufTQ4k6NQ",
        "colab_type": "text"
      },
      "source": [
        "As we can see the best performing model is a StackedEnsemble of different models. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Fjw_V2RkRYm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3407
        },
        "outputId": "4506d2d4-a65c-48d5-8dbe-5d36a026bacf"
      },
      "source": [
        "# An extremely detailed overview of the best performing model from automl\n",
        "aml.leader"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model Details\n",
            "=============\n",
            "H2OStackedEnsembleEstimator :  Stacked Ensemble\n",
            "Model Key:  StackedEnsemble_BestOfFamily_AutoML_20190517_105916\n",
            "No model summary for this model\n",
            "\n",
            "\n",
            "ModelMetricsBinomialGLM: stackedensemble\n",
            "** Reported on train data. **\n",
            "\n",
            "MSE: 0.0818098670582\n",
            "RMSE: 0.286024242081\n",
            "LogLoss: 0.268486688876\n",
            "Null degrees of freedom: 30560\n",
            "Residual degrees of freedom: 30558\n",
            "Null deviance: 33663.1756673\n",
            "Residual deviance: 16410.4433975\n",
            "AIC: 16416.4433975\n",
            "AUC: 0.942033840872\n",
            "pr_auc: 0.810480622707\n",
            "Gini: 0.884067681744\n",
            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.346604175412: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        <=50K     >50K    Error    Rate\n",
              "-----  --------  -------  -------  ----------------\n",
              "<=50K  21370     1865     0.0803   (1865.0/23235.0)\n",
              ">50K   1748      5578     0.2386   (1748.0/7326.0)\n",
              "Total  23118     7443     0.1182   (3613.0/30561.0)"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
              "<td><b> <=50K</b></td>\n",
              "<td><b> >50K</b></td>\n",
              "<td><b>Error</b></td>\n",
              "<td><b>Rate</b></td></tr>\n",
              "<tr><td> <=50K</td>\n",
              "<td>21370.0</td>\n",
              "<td>1865.0</td>\n",
              "<td>0.0803</td>\n",
              "<td> (1865.0/23235.0)</td></tr>\n",
              "<tr><td> >50K</td>\n",
              "<td>1748.0</td>\n",
              "<td>5578.0</td>\n",
              "<td>0.2386</td>\n",
              "<td> (1748.0/7326.0)</td></tr>\n",
              "<tr><td>Total</td>\n",
              "<td>23118.0</td>\n",
              "<td>7443.0</td>\n",
              "<td>0.1182</td>\n",
              "<td> (3613.0/30561.0)</td></tr></table></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "metric                       threshold    value     idx\n",
              "---------------------------  -----------  --------  -----\n",
              "max f1                       0.346604     0.755366  220\n",
              "max f2                       0.120096     0.82887   316\n",
              "max f0point5                 0.704123     0.791169  103\n",
              "max accuracy                 0.473638     0.8859    176\n",
              "max precision                0.967757     1         0\n",
              "max recall                   0.0350344    1         398\n",
              "max specificity              0.967757     1         0\n",
              "max absolute_mcc             0.390067     0.678378  204\n",
              "max min_per_class_accuracy   0.198559     0.858176  279\n",
              "max mean_per_class_accuracy  0.130849     0.862058  310"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n",
              "<td><b>threshold</b></td>\n",
              "<td><b>value</b></td>\n",
              "<td><b>idx</b></td></tr>\n",
              "<tr><td>max f1</td>\n",
              "<td>0.3466042</td>\n",
              "<td>0.7553660</td>\n",
              "<td>220.0</td></tr>\n",
              "<tr><td>max f2</td>\n",
              "<td>0.1200957</td>\n",
              "<td>0.8288704</td>\n",
              "<td>316.0</td></tr>\n",
              "<tr><td>max f0point5</td>\n",
              "<td>0.7041231</td>\n",
              "<td>0.7911685</td>\n",
              "<td>103.0</td></tr>\n",
              "<tr><td>max accuracy</td>\n",
              "<td>0.4736383</td>\n",
              "<td>0.8859003</td>\n",
              "<td>176.0</td></tr>\n",
              "<tr><td>max precision</td>\n",
              "<td>0.9677568</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max recall</td>\n",
              "<td>0.0350344</td>\n",
              "<td>1.0</td>\n",
              "<td>398.0</td></tr>\n",
              "<tr><td>max specificity</td>\n",
              "<td>0.9677568</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max absolute_mcc</td>\n",
              "<td>0.3900667</td>\n",
              "<td>0.6783781</td>\n",
              "<td>204.0</td></tr>\n",
              "<tr><td>max min_per_class_accuracy</td>\n",
              "<td>0.1985594</td>\n",
              "<td>0.8581764</td>\n",
              "<td>279.0</td></tr>\n",
              "<tr><td>max mean_per_class_accuracy</td>\n",
              "<td>0.1308493</td>\n",
              "<td>0.8620582</td>\n",
              "<td>310.0</td></tr></table></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Gains/Lift Table: Avg response rate: 23.97 %, avg score: 24.05 %\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    group    cumulative_data_fraction    lower_threshold    lift        cumulative_lift    response_rate    score      cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain\n",
              "--  -------  --------------------------  -----------------  ----------  -----------------  ---------------  ---------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------\n",
              "    1        0.0100128                   0.967533           4.17158     4.17158            1                0.967802   1                           0.967802            0.041769        0.041769                   317.158   317.158\n",
              "    2        0.0200255                   0.966835           4.17158     4.17158            1                0.967221   1                           0.967512            0.041769        0.0835381                  317.158   317.158\n",
              "    3        0.0300056                   0.965724           4.17158     4.17158            1                0.966318   1                           0.967114            0.0416325       0.125171                   317.158   317.158\n",
              "    4        0.0400183                   0.963484           4.17158     4.17158            1                0.964766   1                           0.966527            0.041769        0.16694                    317.158   317.158\n",
              "    5        0.0500311                   0.95929            4.15795     4.16885            0.996732         0.961576   0.999346                    0.965536            0.0416325       0.208572                   315.795   316.885\n",
              "    6        0.100029                    0.860734           3.80302     3.986              0.911649         0.912121   0.955512                    0.938837            0.190145        0.398717                   280.302   298.6\n",
              "    7        0.150028                    0.711234           3.10138     3.69119            0.743455         0.794251   0.884842                    0.890652            0.155064        0.553781                   210.138   269.119\n",
              "    8        0.200026                    0.500437           2.46527     3.38476            0.590969         0.608321   0.811386                    0.820081            0.12326         0.677041                   146.527   238.476\n",
              "    9        0.300056                    0.217432           1.66072     2.81002            0.398103         0.339304   0.67361                     0.659804            0.166121        0.843161                   66.0718   181.002\n",
              "    10       0.40002                     0.0992922          0.949018    2.34496            0.227496         0.149342   0.562127                    0.532241            0.0948676       0.938029                   -5.09825  134.496\n",
              "    11       0.500016                    0.0586165          0.404054    1.9568             0.0968586        0.075774   0.469079                    0.440954            0.040404        0.978433                   -59.5946  95.6802\n",
              "    12       0.600013                    0.0443723          0.14606     1.65503            0.0350131        0.0503125  0.396739                    0.37585             0.0146055       0.993038                   -85.394   65.5028\n",
              "    13       0.70001                     0.0391201          0.0559669   1.4266             0.0134162        0.0413709  0.341981                    0.32807             0.00559651      0.998635                   -94.4033  42.6601\n",
              "    14       0.800007                    0.0366853          0.0122854   1.24982            0.00294503       0.0377974  0.299603                    0.291787            0.0012285       0.999863                   -98.7715  24.9819\n",
              "    15       0.900003                    0.0351215          0           1.11096            0                0.0358155  0.266315                    0.263347            0               0.999863                   -100      11.0955\n",
              "    16       1                           0.0344806          0.00136505  1                  0.000327225      0.0348374  0.239717                    0.240497            0.0001365       1                          -99.8635  0"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
              "<td><b>group</b></td>\n",
              "<td><b>cumulative_data_fraction</b></td>\n",
              "<td><b>lower_threshold</b></td>\n",
              "<td><b>lift</b></td>\n",
              "<td><b>cumulative_lift</b></td>\n",
              "<td><b>response_rate</b></td>\n",
              "<td><b>score</b></td>\n",
              "<td><b>cumulative_response_rate</b></td>\n",
              "<td><b>cumulative_score</b></td>\n",
              "<td><b>capture_rate</b></td>\n",
              "<td><b>cumulative_capture_rate</b></td>\n",
              "<td><b>gain</b></td>\n",
              "<td><b>cumulative_gain</b></td></tr>\n",
              "<tr><td></td>\n",
              "<td>1</td>\n",
              "<td>0.0100128</td>\n",
              "<td>0.9675329</td>\n",
              "<td>4.1715807</td>\n",
              "<td>4.1715807</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9678024</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9678024</td>\n",
              "<td>0.0417690</td>\n",
              "<td>0.0417690</td>\n",
              "<td>317.1580672</td>\n",
              "<td>317.1580672</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2</td>\n",
              "<td>0.0200255</td>\n",
              "<td>0.9668349</td>\n",
              "<td>4.1715807</td>\n",
              "<td>4.1715807</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9672209</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9675116</td>\n",
              "<td>0.0417690</td>\n",
              "<td>0.0835381</td>\n",
              "<td>317.1580672</td>\n",
              "<td>317.1580672</td></tr>\n",
              "<tr><td></td>\n",
              "<td>3</td>\n",
              "<td>0.0300056</td>\n",
              "<td>0.9657240</td>\n",
              "<td>4.1715807</td>\n",
              "<td>4.1715807</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9663176</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9671145</td>\n",
              "<td>0.0416325</td>\n",
              "<td>0.1251706</td>\n",
              "<td>317.1580672</td>\n",
              "<td>317.1580672</td></tr>\n",
              "<tr><td></td>\n",
              "<td>4</td>\n",
              "<td>0.0400183</td>\n",
              "<td>0.9634842</td>\n",
              "<td>4.1715807</td>\n",
              "<td>4.1715807</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9647655</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9665268</td>\n",
              "<td>0.0417690</td>\n",
              "<td>0.1669397</td>\n",
              "<td>317.1580672</td>\n",
              "<td>317.1580672</td></tr>\n",
              "<tr><td></td>\n",
              "<td>5</td>\n",
              "<td>0.0500311</td>\n",
              "<td>0.9592896</td>\n",
              "<td>4.1579481</td>\n",
              "<td>4.1688524</td>\n",
              "<td>0.9967320</td>\n",
              "<td>0.9615759</td>\n",
              "<td>0.9993460</td>\n",
              "<td>0.9655359</td>\n",
              "<td>0.0416325</td>\n",
              "<td>0.2085722</td>\n",
              "<td>315.7948055</td>\n",
              "<td>316.8852365</td></tr>\n",
              "<tr><td></td>\n",
              "<td>6</td>\n",
              "<td>0.1000294</td>\n",
              "<td>0.8607339</td>\n",
              "<td>3.8030182</td>\n",
              "<td>3.9859951</td>\n",
              "<td>0.9116492</td>\n",
              "<td>0.9121215</td>\n",
              "<td>0.9555119</td>\n",
              "<td>0.9388374</td>\n",
              "<td>0.1901447</td>\n",
              "<td>0.3987169</td>\n",
              "<td>280.3018243</td>\n",
              "<td>298.5995140</td></tr>\n",
              "<tr><td></td>\n",
              "<td>7</td>\n",
              "<td>0.1500278</td>\n",
              "<td>0.7112337</td>\n",
              "<td>3.1013846</td>\n",
              "<td>3.6911893</td>\n",
              "<td>0.7434555</td>\n",
              "<td>0.7942509</td>\n",
              "<td>0.8848419</td>\n",
              "<td>0.8906524</td>\n",
              "<td>0.1550642</td>\n",
              "<td>0.5537811</td>\n",
              "<td>210.1384583</td>\n",
              "<td>269.1189266</td></tr>\n",
              "<tr><td></td>\n",
              "<td>8</td>\n",
              "<td>0.2000262</td>\n",
              "<td>0.5004366</td>\n",
              "<td>2.4652731</td>\n",
              "<td>3.3847604</td>\n",
              "<td>0.5909686</td>\n",
              "<td>0.6083210</td>\n",
              "<td>0.8113856</td>\n",
              "<td>0.8200811</td>\n",
              "<td>0.1232596</td>\n",
              "<td>0.6770407</td>\n",
              "<td>146.5273132</td>\n",
              "<td>238.4760368</td></tr>\n",
              "<tr><td></td>\n",
              "<td>9</td>\n",
              "<td>0.3000556</td>\n",
              "<td>0.2174322</td>\n",
              "<td>1.6607176</td>\n",
              "<td>2.8100168</td>\n",
              "<td>0.3981027</td>\n",
              "<td>0.3393037</td>\n",
              "<td>0.6736096</td>\n",
              "<td>0.6598045</td>\n",
              "<td>0.1661207</td>\n",
              "<td>0.8431613</td>\n",
              "<td>66.0717592</td>\n",
              "<td>181.0016773</td></tr>\n",
              "<tr><td></td>\n",
              "<td>10</td>\n",
              "<td>0.4000196</td>\n",
              "<td>0.0992922</td>\n",
              "<td>0.9490175</td>\n",
              "<td>2.3449572</td>\n",
              "<td>0.2274959</td>\n",
              "<td>0.1493421</td>\n",
              "<td>0.5621268</td>\n",
              "<td>0.5322411</td>\n",
              "<td>0.0948676</td>\n",
              "<td>0.9380289</td>\n",
              "<td>-5.0982466</td>\n",
              "<td>134.4957250</td></tr>\n",
              "<tr><td></td>\n",
              "<td>11</td>\n",
              "<td>0.5000164</td>\n",
              "<td>0.0586165</td>\n",
              "<td>0.4040536</td>\n",
              "<td>1.9568019</td>\n",
              "<td>0.0968586</td>\n",
              "<td>0.0757740</td>\n",
              "<td>0.4690792</td>\n",
              "<td>0.4409536</td>\n",
              "<td>0.0404040</td>\n",
              "<td>0.9784330</td>\n",
              "<td>-59.5946375</td>\n",
              "<td>95.6801927</td></tr>\n",
              "<tr><td></td>\n",
              "<td>12</td>\n",
              "<td>0.6000131</td>\n",
              "<td>0.0443723</td>\n",
              "<td>0.1460599</td>\n",
              "<td>1.6550281</td>\n",
              "<td>0.0350131</td>\n",
              "<td>0.0503125</td>\n",
              "<td>0.3967388</td>\n",
              "<td>0.3758503</td>\n",
              "<td>0.0146055</td>\n",
              "<td>0.9930385</td>\n",
              "<td>-85.3940075</td>\n",
              "<td>65.5028052</td></tr>\n",
              "<tr><td></td>\n",
              "<td>13</td>\n",
              "<td>0.7000098</td>\n",
              "<td>0.0391201</td>\n",
              "<td>0.0559669</td>\n",
              "<td>1.4266014</td>\n",
              "<td>0.0134162</td>\n",
              "<td>0.0413709</td>\n",
              "<td>0.3419810</td>\n",
              "<td>0.3280698</td>\n",
              "<td>0.0055965</td>\n",
              "<td>0.9986350</td>\n",
              "<td>-94.4033113</td>\n",
              "<td>42.6601421</td></tr>\n",
              "<tr><td></td>\n",
              "<td>14</td>\n",
              "<td>0.8000065</td>\n",
              "<td>0.0366853</td>\n",
              "<td>0.0122854</td>\n",
              "<td>1.2498192</td>\n",
              "<td>0.0029450</td>\n",
              "<td>0.0377974</td>\n",
              "<td>0.2996033</td>\n",
              "<td>0.2917872</td>\n",
              "<td>0.0012285</td>\n",
              "<td>0.9998635</td>\n",
              "<td>-98.7714586</td>\n",
              "<td>24.9819151</td></tr>\n",
              "<tr><td></td>\n",
              "<td>15</td>\n",
              "<td>0.9000033</td>\n",
              "<td>0.0351215</td>\n",
              "<td>0.0</td>\n",
              "<td>1.1109554</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0358155</td>\n",
              "<td>0.2663152</td>\n",
              "<td>0.2633469</td>\n",
              "<td>0.0</td>\n",
              "<td>0.9998635</td>\n",
              "<td>-100.0</td>\n",
              "<td>11.0955405</td></tr>\n",
              "<tr><td></td>\n",
              "<td>16</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0344806</td>\n",
              "<td>0.0013650</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0003272</td>\n",
              "<td>0.0348374</td>\n",
              "<td>0.2397173</td>\n",
              "<td>0.2404967</td>\n",
              "<td>0.0001365</td>\n",
              "<td>1.0</td>\n",
              "<td>-99.8634954</td>\n",
              "<td>0.0</td></tr></table></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "ModelMetricsBinomialGLM: stackedensemble\n",
            "** Reported on validation data. **\n",
            "\n",
            "MSE: 0.0947953984135\n",
            "RMSE: 0.307888613647\n",
            "LogLoss: 0.303313212553\n",
            "Null degrees of freedom: 1999\n",
            "Residual degrees of freedom: 1997\n",
            "Null deviance: 2285.11669608\n",
            "Residual deviance: 1213.25285021\n",
            "AIC: 1219.25285021\n",
            "AUC: 0.929094831813\n",
            "pr_auc: 0.781822138869\n",
            "Gini: 0.858189663627\n",
            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.295499539002: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        <=50K     >50K    Error    Rate\n",
              "-----  --------  -------  -------  --------------\n",
              "<=50K  1330      155      0.1044   (155.0/1485.0)\n",
              ">50K   114       401      0.2214   (114.0/515.0)\n",
              "Total  1444      556      0.1345   (269.0/2000.0)"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
              "<td><b> <=50K</b></td>\n",
              "<td><b> >50K</b></td>\n",
              "<td><b>Error</b></td>\n",
              "<td><b>Rate</b></td></tr>\n",
              "<tr><td> <=50K</td>\n",
              "<td>1330.0</td>\n",
              "<td>155.0</td>\n",
              "<td>0.1044</td>\n",
              "<td> (155.0/1485.0)</td></tr>\n",
              "<tr><td> >50K</td>\n",
              "<td>114.0</td>\n",
              "<td>401.0</td>\n",
              "<td>0.2214</td>\n",
              "<td> (114.0/515.0)</td></tr>\n",
              "<tr><td>Total</td>\n",
              "<td>1444.0</td>\n",
              "<td>556.0</td>\n",
              "<td>0.1345</td>\n",
              "<td> (269.0/2000.0)</td></tr></table></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "metric                       threshold    value     idx\n",
              "---------------------------  -----------  --------  -----\n",
              "max f1                       0.2955       0.748833  232\n",
              "max f2                       0.0864712    0.834185  332\n",
              "max f0point5                 0.748605     0.764499  93\n",
              "max accuracy                 0.459507     0.87      181\n",
              "max precision                0.967771     1         0\n",
              "max recall                   0.0349058    1         398\n",
              "max specificity              0.967771     1         0\n",
              "max absolute_mcc             0.2955       0.658075  232\n",
              "max min_per_class_accuracy   0.20751      0.848544  273\n",
              "max mean_per_class_accuracy  0.159652     0.853947  292"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n",
              "<td><b>threshold</b></td>\n",
              "<td><b>value</b></td>\n",
              "<td><b>idx</b></td></tr>\n",
              "<tr><td>max f1</td>\n",
              "<td>0.2954995</td>\n",
              "<td>0.7488329</td>\n",
              "<td>232.0</td></tr>\n",
              "<tr><td>max f2</td>\n",
              "<td>0.0864712</td>\n",
              "<td>0.8341845</td>\n",
              "<td>332.0</td></tr>\n",
              "<tr><td>max f0point5</td>\n",
              "<td>0.7486049</td>\n",
              "<td>0.7644991</td>\n",
              "<td>93.0</td></tr>\n",
              "<tr><td>max accuracy</td>\n",
              "<td>0.4595073</td>\n",
              "<td>0.87</td>\n",
              "<td>181.0</td></tr>\n",
              "<tr><td>max precision</td>\n",
              "<td>0.9677708</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max recall</td>\n",
              "<td>0.0349058</td>\n",
              "<td>1.0</td>\n",
              "<td>398.0</td></tr>\n",
              "<tr><td>max specificity</td>\n",
              "<td>0.9677708</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max absolute_mcc</td>\n",
              "<td>0.2954995</td>\n",
              "<td>0.6580753</td>\n",
              "<td>232.0</td></tr>\n",
              "<tr><td>max min_per_class_accuracy</td>\n",
              "<td>0.2075100</td>\n",
              "<td>0.8485437</td>\n",
              "<td>273.0</td></tr>\n",
              "<tr><td>max mean_per_class_accuracy</td>\n",
              "<td>0.1596518</td>\n",
              "<td>0.8539472</td>\n",
              "<td>292.0</td></tr></table></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Gains/Lift Table: Avg response rate: 25.75 %, avg score: 25.37 %\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    group    cumulative_data_fraction    lower_threshold    lift       cumulative_lift    response_rate    score      cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain\n",
              "--  -------  --------------------------  -----------------  ---------  -----------------  ---------------  ---------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------\n",
              "    1        0.01                        0.967638           3.8835     3.8835             1                0.967878   1                           0.967878            0.038835        0.038835                   288.35    288.35\n",
              "    2        0.02                        0.966983           3.8835     3.8835             1                0.967311   1                           0.967594            0.038835        0.0776699                  288.35    288.35\n",
              "    3        0.03                        0.965473           3.8835     3.8835             1                0.966394   1                           0.967194            0.038835        0.116505                   288.35    288.35\n",
              "    4        0.04                        0.963767           3.8835     3.8835             1                0.964792   1                           0.966594            0.038835        0.15534                    288.35    288.35\n",
              "    5        0.05                        0.958823           3.8835     3.8835             1                0.961701   1                           0.965615            0.038835        0.194175                   288.35    288.35\n",
              "    6        0.1                         0.873821           3.41748    3.65049            0.88             0.919495   0.94                        0.942555            0.170874        0.365049                   241.748   265.049\n",
              "    7        0.15                        0.74358            2.87379    3.39159            0.74             0.81562    0.873333                    0.900243            0.143689        0.508738                   187.379   239.159\n",
              "    8        0.2                         0.562911           2.05825    3.05825            0.53             0.656918   0.7875                      0.839412            0.102913        0.61165                    105.825   205.825\n",
              "    9        0.3                         0.260968           1.8835     2.66667            0.485            0.399564   0.686667                    0.692796            0.18835         0.8                        88.3495   166.667\n",
              "    10       0.4                         0.110561           1.16505    2.29126            0.3              0.174648   0.59                        0.563259            0.116505        0.916505                   16.5049   129.126\n",
              "    11       0.5                         0.0603267          0.466019   1.92621            0.12             0.0809472  0.496                       0.466797            0.0466019       0.963107                   -53.3981  92.6214\n",
              "    12       0.6                         0.0457118          0.213592   1.64078            0.055            0.0521295  0.4225                      0.397685            0.0213592       0.984466                   -78.6408  64.0777\n",
              "    13       0.7                         0.0396289          0.0970874  1.42025            0.025            0.0422908  0.365714                    0.346915            0.00970874      0.994175                   -90.2913  42.025\n",
              "    14       0.8                         0.036989           0.038835   1.24757            0.01             0.0381749  0.32125                     0.308322            0.0038835       0.998058                   -96.1165  24.7573\n",
              "    15       0.9                         0.0351912          0          1.10895            0                0.0359679  0.285556                    0.278061            0               0.998058                   -100      10.8954\n",
              "    16       1                           0.0345331          0.0194175  1                  0.005            0.0348527  0.2575                      0.25374             0.00194175      1                          -98.0583  0"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
              "<td><b>group</b></td>\n",
              "<td><b>cumulative_data_fraction</b></td>\n",
              "<td><b>lower_threshold</b></td>\n",
              "<td><b>lift</b></td>\n",
              "<td><b>cumulative_lift</b></td>\n",
              "<td><b>response_rate</b></td>\n",
              "<td><b>score</b></td>\n",
              "<td><b>cumulative_response_rate</b></td>\n",
              "<td><b>cumulative_score</b></td>\n",
              "<td><b>capture_rate</b></td>\n",
              "<td><b>cumulative_capture_rate</b></td>\n",
              "<td><b>gain</b></td>\n",
              "<td><b>cumulative_gain</b></td></tr>\n",
              "<tr><td></td>\n",
              "<td>1</td>\n",
              "<td>0.01</td>\n",
              "<td>0.9676379</td>\n",
              "<td>3.8834951</td>\n",
              "<td>3.8834951</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9678777</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9678777</td>\n",
              "<td>0.0388350</td>\n",
              "<td>0.0388350</td>\n",
              "<td>288.3495146</td>\n",
              "<td>288.3495146</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2</td>\n",
              "<td>0.02</td>\n",
              "<td>0.9669828</td>\n",
              "<td>3.8834951</td>\n",
              "<td>3.8834951</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9673111</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9675944</td>\n",
              "<td>0.0388350</td>\n",
              "<td>0.0776699</td>\n",
              "<td>288.3495146</td>\n",
              "<td>288.3495146</td></tr>\n",
              "<tr><td></td>\n",
              "<td>3</td>\n",
              "<td>0.03</td>\n",
              "<td>0.9654732</td>\n",
              "<td>3.8834951</td>\n",
              "<td>3.8834951</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9663936</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9671942</td>\n",
              "<td>0.0388350</td>\n",
              "<td>0.1165049</td>\n",
              "<td>288.3495146</td>\n",
              "<td>288.3495146</td></tr>\n",
              "<tr><td></td>\n",
              "<td>4</td>\n",
              "<td>0.04</td>\n",
              "<td>0.9637669</td>\n",
              "<td>3.8834951</td>\n",
              "<td>3.8834951</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9647921</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9665936</td>\n",
              "<td>0.0388350</td>\n",
              "<td>0.1553398</td>\n",
              "<td>288.3495146</td>\n",
              "<td>288.3495146</td></tr>\n",
              "<tr><td></td>\n",
              "<td>5</td>\n",
              "<td>0.05</td>\n",
              "<td>0.9588234</td>\n",
              "<td>3.8834951</td>\n",
              "<td>3.8834951</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9617010</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9656151</td>\n",
              "<td>0.0388350</td>\n",
              "<td>0.1941748</td>\n",
              "<td>288.3495146</td>\n",
              "<td>288.3495146</td></tr>\n",
              "<tr><td></td>\n",
              "<td>6</td>\n",
              "<td>0.1</td>\n",
              "<td>0.8738211</td>\n",
              "<td>3.4174757</td>\n",
              "<td>3.6504854</td>\n",
              "<td>0.88</td>\n",
              "<td>0.9194948</td>\n",
              "<td>0.94</td>\n",
              "<td>0.9425549</td>\n",
              "<td>0.1708738</td>\n",
              "<td>0.3650485</td>\n",
              "<td>241.7475728</td>\n",
              "<td>265.0485437</td></tr>\n",
              "<tr><td></td>\n",
              "<td>7</td>\n",
              "<td>0.15</td>\n",
              "<td>0.7435795</td>\n",
              "<td>2.8737864</td>\n",
              "<td>3.3915858</td>\n",
              "<td>0.74</td>\n",
              "<td>0.8156203</td>\n",
              "<td>0.8733333</td>\n",
              "<td>0.9002434</td>\n",
              "<td>0.1436893</td>\n",
              "<td>0.5087379</td>\n",
              "<td>187.3786408</td>\n",
              "<td>239.1585761</td></tr>\n",
              "<tr><td></td>\n",
              "<td>8</td>\n",
              "<td>0.2</td>\n",
              "<td>0.5629105</td>\n",
              "<td>2.0582524</td>\n",
              "<td>3.0582524</td>\n",
              "<td>0.53</td>\n",
              "<td>0.6569183</td>\n",
              "<td>0.7875</td>\n",
              "<td>0.8394121</td>\n",
              "<td>0.1029126</td>\n",
              "<td>0.6116505</td>\n",
              "<td>105.8252427</td>\n",
              "<td>205.8252427</td></tr>\n",
              "<tr><td></td>\n",
              "<td>9</td>\n",
              "<td>0.3</td>\n",
              "<td>0.2609683</td>\n",
              "<td>1.8834951</td>\n",
              "<td>2.6666667</td>\n",
              "<td>0.485</td>\n",
              "<td>0.3995635</td>\n",
              "<td>0.6866667</td>\n",
              "<td>0.6927959</td>\n",
              "<td>0.1883495</td>\n",
              "<td>0.8</td>\n",
              "<td>88.3495146</td>\n",
              "<td>166.6666667</td></tr>\n",
              "<tr><td></td>\n",
              "<td>10</td>\n",
              "<td>0.4</td>\n",
              "<td>0.1105605</td>\n",
              "<td>1.1650485</td>\n",
              "<td>2.2912621</td>\n",
              "<td>0.3</td>\n",
              "<td>0.1746481</td>\n",
              "<td>0.59</td>\n",
              "<td>0.5632590</td>\n",
              "<td>0.1165049</td>\n",
              "<td>0.9165049</td>\n",
              "<td>16.5048544</td>\n",
              "<td>129.1262136</td></tr>\n",
              "<tr><td></td>\n",
              "<td>11</td>\n",
              "<td>0.5</td>\n",
              "<td>0.0603267</td>\n",
              "<td>0.4660194</td>\n",
              "<td>1.9262136</td>\n",
              "<td>0.12</td>\n",
              "<td>0.0809472</td>\n",
              "<td>0.496</td>\n",
              "<td>0.4667966</td>\n",
              "<td>0.0466019</td>\n",
              "<td>0.9631068</td>\n",
              "<td>-53.3980583</td>\n",
              "<td>92.6213592</td></tr>\n",
              "<tr><td></td>\n",
              "<td>12</td>\n",
              "<td>0.6</td>\n",
              "<td>0.0457118</td>\n",
              "<td>0.2135922</td>\n",
              "<td>1.6407767</td>\n",
              "<td>0.055</td>\n",
              "<td>0.0521295</td>\n",
              "<td>0.4225</td>\n",
              "<td>0.3976854</td>\n",
              "<td>0.0213592</td>\n",
              "<td>0.9844660</td>\n",
              "<td>-78.6407767</td>\n",
              "<td>64.0776699</td></tr>\n",
              "<tr><td></td>\n",
              "<td>13</td>\n",
              "<td>0.7</td>\n",
              "<td>0.0396289</td>\n",
              "<td>0.0970874</td>\n",
              "<td>1.4202497</td>\n",
              "<td>0.025</td>\n",
              "<td>0.0422908</td>\n",
              "<td>0.3657143</td>\n",
              "<td>0.3469148</td>\n",
              "<td>0.0097087</td>\n",
              "<td>0.9941748</td>\n",
              "<td>-90.2912621</td>\n",
              "<td>42.0249653</td></tr>\n",
              "<tr><td></td>\n",
              "<td>14</td>\n",
              "<td>0.8</td>\n",
              "<td>0.0369890</td>\n",
              "<td>0.0388350</td>\n",
              "<td>1.2475728</td>\n",
              "<td>0.01</td>\n",
              "<td>0.0381749</td>\n",
              "<td>0.32125</td>\n",
              "<td>0.3083223</td>\n",
              "<td>0.0038835</td>\n",
              "<td>0.9980583</td>\n",
              "<td>-96.1165049</td>\n",
              "<td>24.7572816</td></tr>\n",
              "<tr><td></td>\n",
              "<td>15</td>\n",
              "<td>0.9</td>\n",
              "<td>0.0351912</td>\n",
              "<td>0.0</td>\n",
              "<td>1.1089536</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0359679</td>\n",
              "<td>0.2855556</td>\n",
              "<td>0.2780607</td>\n",
              "<td>0.0</td>\n",
              "<td>0.9980583</td>\n",
              "<td>-100.0</td>\n",
              "<td>10.8953614</td></tr>\n",
              "<tr><td></td>\n",
              "<td>16</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0345331</td>\n",
              "<td>0.0194175</td>\n",
              "<td>1.0</td>\n",
              "<td>0.005</td>\n",
              "<td>0.0348527</td>\n",
              "<td>0.2575</td>\n",
              "<td>0.2537399</td>\n",
              "<td>0.0019417</td>\n",
              "<td>1.0</td>\n",
              "<td>-98.0582524</td>\n",
              "<td>0.0</td></tr></table></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "ModelMetricsBinomialGLM: stackedensemble\n",
            "** Reported on cross-validation data. **\n",
            "\n",
            "MSE: 0.0906647233136\n",
            "RMSE: 0.301105834074\n",
            "LogLoss: 0.29484706714\n",
            "Null degrees of freedom: 30560\n",
            "Residual degrees of freedom: 30557\n",
            "Null deviance: 33664.409594\n",
            "Residual deviance: 18021.6424377\n",
            "AIC: 18029.6424377\n",
            "AUC: 0.927758382245\n",
            "pr_auc: 0.805903025954\n",
            "Gini: 0.85551676449\n",
            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.332247543874: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        <=50K     >50K    Error    Rate\n",
              "-----  --------  -------  -------  ----------------\n",
              "<=50K  21107     2128     0.0916   (2128.0/23235.0)\n",
              ">50K   1882      5444     0.2569   (1882.0/7326.0)\n",
              "Total  22989     7572     0.1312   (4010.0/30561.0)"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
              "<td><b> <=50K</b></td>\n",
              "<td><b> >50K</b></td>\n",
              "<td><b>Error</b></td>\n",
              "<td><b>Rate</b></td></tr>\n",
              "<tr><td> <=50K</td>\n",
              "<td>21107.0</td>\n",
              "<td>2128.0</td>\n",
              "<td>0.0916</td>\n",
              "<td> (2128.0/23235.0)</td></tr>\n",
              "<tr><td> >50K</td>\n",
              "<td>1882.0</td>\n",
              "<td>5444.0</td>\n",
              "<td>0.2569</td>\n",
              "<td> (1882.0/7326.0)</td></tr>\n",
              "<tr><td>Total</td>\n",
              "<td>22989.0</td>\n",
              "<td>7572.0</td>\n",
              "<td>0.1312</td>\n",
              "<td> (4010.0/30561.0)</td></tr></table></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "metric                       threshold    value     idx\n",
              "---------------------------  -----------  --------  -----\n",
              "max f1                       0.332248     0.730836  228\n",
              "max f2                       0.0956723    0.80803   336\n",
              "max f0point5                 0.671594     0.766306  117\n",
              "max accuracy                 0.451948     0.874153  186\n",
              "max precision                0.968287     1         0\n",
              "max recall                   0.0348767    1         398\n",
              "max specificity              0.968287     1         0\n",
              "max absolute_mcc             0.383061     0.646954  209\n",
              "max min_per_class_accuracy   0.182085     0.840158  292\n",
              "max mean_per_class_accuracy  0.143445     0.844092  310"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n",
              "<td><b>threshold</b></td>\n",
              "<td><b>value</b></td>\n",
              "<td><b>idx</b></td></tr>\n",
              "<tr><td>max f1</td>\n",
              "<td>0.3322475</td>\n",
              "<td>0.7308364</td>\n",
              "<td>228.0</td></tr>\n",
              "<tr><td>max f2</td>\n",
              "<td>0.0956723</td>\n",
              "<td>0.8080301</td>\n",
              "<td>336.0</td></tr>\n",
              "<tr><td>max f0point5</td>\n",
              "<td>0.6715937</td>\n",
              "<td>0.7663056</td>\n",
              "<td>117.0</td></tr>\n",
              "<tr><td>max accuracy</td>\n",
              "<td>0.4519481</td>\n",
              "<td>0.8741533</td>\n",
              "<td>186.0</td></tr>\n",
              "<tr><td>max precision</td>\n",
              "<td>0.9682873</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max recall</td>\n",
              "<td>0.0348767</td>\n",
              "<td>1.0</td>\n",
              "<td>398.0</td></tr>\n",
              "<tr><td>max specificity</td>\n",
              "<td>0.9682873</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max absolute_mcc</td>\n",
              "<td>0.3830608</td>\n",
              "<td>0.6469536</td>\n",
              "<td>209.0</td></tr>\n",
              "<tr><td>max min_per_class_accuracy</td>\n",
              "<td>0.1820847</td>\n",
              "<td>0.8401583</td>\n",
              "<td>292.0</td></tr>\n",
              "<tr><td>max mean_per_class_accuracy</td>\n",
              "<td>0.1434449</td>\n",
              "<td>0.8440924</td>\n",
              "<td>310.0</td></tr></table></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Gains/Lift Table: Avg response rate: 23.97 %, avg score: 23.97 %\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    group    cumulative_data_fraction    lower_threshold    lift        cumulative_lift    response_rate    score      cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain\n",
              "--  -------  --------------------------  -----------------  ----------  -----------------  ---------------  ---------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------\n",
              "    1        0.0100128                   0.967435           4.17158     4.17158            1                0.967982   1                           0.967982            0.041769        0.041769                   317.158   317.158\n",
              "    2        0.0200255                   0.966557           4.15795     4.16476            0.996732         0.967018   0.998366                    0.9675              0.0416325       0.0834016                  315.795   316.476\n",
              "    3        0.0300056                   0.965411           4.1579      4.16248            0.996721         0.966068   0.997819                    0.967024            0.041496        0.124898                   315.79    316.248\n",
              "    4        0.0400183                   0.962999           4.14432     4.15794            0.993464         0.964301   0.996729                    0.966342            0.041496        0.166394                   314.432   315.794\n",
              "    5        0.0500311                   0.957903           4.11705     4.14975            0.986928         0.960811   0.994768                    0.965235            0.041223        0.207617                   311.705   314.975\n",
              "    6        0.100029                    0.860286           3.60918     3.87956            0.865183         0.910742   0.929997                    0.937998            0.180453        0.38807                    260.918   287.956\n",
              "    7        0.150028                    0.706885           2.94304     3.56745            0.705497         0.791028   0.85518                     0.889019            0.147147        0.535217                   194.304   256.745\n",
              "    8        0.200026                    0.493341           2.35607     3.26466            0.564791         0.603784   0.782594                    0.817722            0.1178          0.653017                   135.607   226.466\n",
              "    9        0.300023                    0.216285           1.60256     2.71069            0.384162         0.335169   0.649798                    0.656888            0.160251        0.813268                   60.2564   171.069\n",
              "    10       0.40002                     0.099797           1.02788     2.29002            0.246401         0.149001   0.548957                    0.529927            0.102785        0.916052                   2.78797   129.002\n",
              "    11       0.500016                    0.0591725          0.476401    1.92732            0.114202         0.0762984  0.462012                    0.439207            0.0476385       0.963691                   -52.3599  92.7319\n",
              "    12       0.600013                    0.0447003          0.233423    1.64502            0.0559555        0.0507838  0.394339                    0.374474            0.0233415       0.987032                   -76.6577  64.5018\n",
              "    13       0.70001                     0.0393333          0.0819028   1.42173            0.0196335        0.0415942  0.340812                    0.326922            0.00819001      0.995222                   -91.8097  42.1726\n",
              "    14       0.800007                    0.0367796          0.0368562   1.24862            0.00883508       0.0379411  0.299317                    0.2908              0.0036855       0.998908                   -96.3144  24.8625\n",
              "    15       0.900003                    0.035345           0.00955532  1.11096            0.00229058       0.0359554  0.266315                    0.262485            0.000955501     0.999863                   -99.0445  11.0955\n",
              "    16       1                           0.0341124          0.00136505  1                  0.000327225      0.034837   0.239717                    0.239721            0.0001365       1                          -99.8635  0"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
              "<td><b>group</b></td>\n",
              "<td><b>cumulative_data_fraction</b></td>\n",
              "<td><b>lower_threshold</b></td>\n",
              "<td><b>lift</b></td>\n",
              "<td><b>cumulative_lift</b></td>\n",
              "<td><b>response_rate</b></td>\n",
              "<td><b>score</b></td>\n",
              "<td><b>cumulative_response_rate</b></td>\n",
              "<td><b>cumulative_score</b></td>\n",
              "<td><b>capture_rate</b></td>\n",
              "<td><b>cumulative_capture_rate</b></td>\n",
              "<td><b>gain</b></td>\n",
              "<td><b>cumulative_gain</b></td></tr>\n",
              "<tr><td></td>\n",
              "<td>1</td>\n",
              "<td>0.0100128</td>\n",
              "<td>0.9674346</td>\n",
              "<td>4.1715807</td>\n",
              "<td>4.1715807</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9679818</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9679818</td>\n",
              "<td>0.0417690</td>\n",
              "<td>0.0417690</td>\n",
              "<td>317.1580672</td>\n",
              "<td>317.1580672</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2</td>\n",
              "<td>0.0200255</td>\n",
              "<td>0.9665569</td>\n",
              "<td>4.1579481</td>\n",
              "<td>4.1647644</td>\n",
              "<td>0.9967320</td>\n",
              "<td>0.9670175</td>\n",
              "<td>0.9983660</td>\n",
              "<td>0.9674997</td>\n",
              "<td>0.0416325</td>\n",
              "<td>0.0834016</td>\n",
              "<td>315.7948055</td>\n",
              "<td>316.4764363</td></tr>\n",
              "<tr><td></td>\n",
              "<td>3</td>\n",
              "<td>0.0300056</td>\n",
              "<td>0.9654108</td>\n",
              "<td>4.1579034</td>\n",
              "<td>4.1624823</td>\n",
              "<td>0.9967213</td>\n",
              "<td>0.9660683</td>\n",
              "<td>0.9978190</td>\n",
              "<td>0.9670236</td>\n",
              "<td>0.0414960</td>\n",
              "<td>0.1248976</td>\n",
              "<td>315.7903358</td>\n",
              "<td>316.2482350</td></tr>\n",
              "<tr><td></td>\n",
              "<td>4</td>\n",
              "<td>0.0400183</td>\n",
              "<td>0.9629986</td>\n",
              "<td>4.1443154</td>\n",
              "<td>4.1579369</td>\n",
              "<td>0.9934641</td>\n",
              "<td>0.9643011</td>\n",
              "<td>0.9967294</td>\n",
              "<td>0.9663424</td>\n",
              "<td>0.0414960</td>\n",
              "<td>0.1663937</td>\n",
              "<td>314.4315438</td>\n",
              "<td>315.7936908</td></tr>\n",
              "<tr><td></td>\n",
              "<td>5</td>\n",
              "<td>0.0500311</td>\n",
              "<td>0.9579025</td>\n",
              "<td>4.1170502</td>\n",
              "<td>4.1497542</td>\n",
              "<td>0.9869281</td>\n",
              "<td>0.9608111</td>\n",
              "<td>0.9947678</td>\n",
              "<td>0.9652354</td>\n",
              "<td>0.0412230</td>\n",
              "<td>0.2076167</td>\n",
              "<td>311.7050205</td>\n",
              "<td>314.9754219</td></tr>\n",
              "<tr><td></td>\n",
              "<td>6</td>\n",
              "<td>0.1000294</td>\n",
              "<td>0.8602862</td>\n",
              "<td>3.6091817</td>\n",
              "<td>3.8795564</td>\n",
              "<td>0.8651832</td>\n",
              "<td>0.9107425</td>\n",
              "<td>0.9299967</td>\n",
              "<td>0.9379979</td>\n",
              "<td>0.1804532</td>\n",
              "<td>0.3880699</td>\n",
              "<td>260.9181707</td>\n",
              "<td>287.9556379</td></tr>\n",
              "<tr><td></td>\n",
              "<td>7</td>\n",
              "<td>0.1500278</td>\n",
              "<td>0.7068853</td>\n",
              "<td>2.9430392</td>\n",
              "<td>3.5674521</td>\n",
              "<td>0.7054974</td>\n",
              "<td>0.7910283</td>\n",
              "<td>0.8551799</td>\n",
              "<td>0.8890187</td>\n",
              "<td>0.1471471</td>\n",
              "<td>0.5352170</td>\n",
              "<td>194.3039243</td>\n",
              "<td>256.7452086</td></tr>\n",
              "<tr><td></td>\n",
              "<td>8</td>\n",
              "<td>0.2000262</td>\n",
              "<td>0.4933410</td>\n",
              "<td>2.3560694</td>\n",
              "<td>3.2646560</td>\n",
              "<td>0.5647906</td>\n",
              "<td>0.6037839</td>\n",
              "<td>0.7825945</td>\n",
              "<td>0.8177217</td>\n",
              "<td>0.1177996</td>\n",
              "<td>0.6530167</td>\n",
              "<td>135.6069450</td>\n",
              "<td>226.4655968</td></tr>\n",
              "<tr><td></td>\n",
              "<td>9</td>\n",
              "<td>0.3000229</td>\n",
              "<td>0.2162848</td>\n",
              "<td>1.6025640</td>\n",
              "<td>2.7106857</td>\n",
              "<td>0.3841623</td>\n",
              "<td>0.3351693</td>\n",
              "<td>0.6497982</td>\n",
              "<td>0.6568884</td>\n",
              "<td>0.1602512</td>\n",
              "<td>0.8132678</td>\n",
              "<td>60.2564041</td>\n",
              "<td>171.0685750</td></tr>\n",
              "<tr><td></td>\n",
              "<td>10</td>\n",
              "<td>0.4000196</td>\n",
              "<td>0.0997970</td>\n",
              "<td>1.0278797</td>\n",
              "<td>2.2900186</td>\n",
              "<td>0.2464005</td>\n",
              "<td>0.1490013</td>\n",
              "<td>0.5489571</td>\n",
              "<td>0.5299270</td>\n",
              "<td>0.1027846</td>\n",
              "<td>0.9160524</td>\n",
              "<td>2.7879662</td>\n",
              "<td>129.0018641</td></tr>\n",
              "<tr><td></td>\n",
              "<td>11</td>\n",
              "<td>0.5000164</td>\n",
              "<td>0.0591725</td>\n",
              "<td>0.4764011</td>\n",
              "<td>1.9273189</td>\n",
              "<td>0.1142016</td>\n",
              "<td>0.0762984</td>\n",
              "<td>0.4620116</td>\n",
              "<td>0.4392072</td>\n",
              "<td>0.0476385</td>\n",
              "<td>0.9636910</td>\n",
              "<td>-52.3598935</td>\n",
              "<td>92.7318863</td></tr>\n",
              "<tr><td></td>\n",
              "<td>12</td>\n",
              "<td>0.6000131</td>\n",
              "<td>0.0447003</td>\n",
              "<td>0.2334229</td>\n",
              "<td>1.6450183</td>\n",
              "<td>0.0559555</td>\n",
              "<td>0.0507838</td>\n",
              "<td>0.3943393</td>\n",
              "<td>0.3744735</td>\n",
              "<td>0.0233415</td>\n",
              "<td>0.9870325</td>\n",
              "<td>-76.6577129</td>\n",
              "<td>64.5018260</td></tr>\n",
              "<tr><td></td>\n",
              "<td>13</td>\n",
              "<td>0.7000098</td>\n",
              "<td>0.0393333</td>\n",
              "<td>0.0819028</td>\n",
              "<td>1.4217265</td>\n",
              "<td>0.0196335</td>\n",
              "<td>0.0415942</td>\n",
              "<td>0.3408124</td>\n",
              "<td>0.3269215</td>\n",
              "<td>0.0081900</td>\n",
              "<td>0.9952225</td>\n",
              "<td>-91.8097238</td>\n",
              "<td>42.1726484</td></tr>\n",
              "<tr><td></td>\n",
              "<td>14</td>\n",
              "<td>0.8000065</td>\n",
              "<td>0.0367796</td>\n",
              "<td>0.0368562</td>\n",
              "<td>1.2486248</td>\n",
              "<td>0.0088351</td>\n",
              "<td>0.0379411</td>\n",
              "<td>0.2993169</td>\n",
              "<td>0.2908005</td>\n",
              "<td>0.0036855</td>\n",
              "<td>0.9989080</td>\n",
              "<td>-96.3143757</td>\n",
              "<td>24.8624784</td></tr>\n",
              "<tr><td></td>\n",
              "<td>15</td>\n",
              "<td>0.9000033</td>\n",
              "<td>0.0353450</td>\n",
              "<td>0.0095553</td>\n",
              "<td>1.1109554</td>\n",
              "<td>0.0022906</td>\n",
              "<td>0.0359554</td>\n",
              "<td>0.2663152</td>\n",
              "<td>0.2624854</td>\n",
              "<td>0.0009555</td>\n",
              "<td>0.9998635</td>\n",
              "<td>-99.0444678</td>\n",
              "<td>11.0955405</td></tr>\n",
              "<tr><td></td>\n",
              "<td>16</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0341124</td>\n",
              "<td>0.0013650</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0003272</td>\n",
              "<td>0.0348370</td>\n",
              "<td>0.2397173</td>\n",
              "<td>0.2397213</td>\n",
              "<td>0.0001365</td>\n",
              "<td>1.0</td>\n",
              "<td>-99.8634954</td>\n",
              "<td>0.0</td></tr></table></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "z0wT3h6fXlpn",
        "outputId": "44330b2b-6f2e-40e4-8c1a-d78f3d6b87bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "# Get the best model to make predictions\n",
        "preds = aml.leader.predict(test_frame)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "stackedensemble prediction progress: |████████████████████████████████████| 100%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "a8h3va96X2bx",
        "colab": {}
      },
      "source": [
        "preds = h2o.as_list(preds[\"predict\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9wGohTIYbcns",
        "outputId": "a87746b4-28d5-4a78-a50e-b5e82c9cfc5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "\n",
        "print(\"Accuracy score\", accuracy_score(test['Income'].values, preds))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy score 0.865\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fI8vlUwhiK25",
        "colab": {}
      },
      "source": [
        "!pip install scikit-plot"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yR4-fOoIiMBx",
        "outputId": "f0c434bb-ee9d-4eca-eeb1-13c3de77db2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "import scikitplot as skplt\n",
        "\n",
        "skplt.metrics.plot_confusion_matrix(test['Income'].values, preds)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DUGlm28VNhwP3xI+nFKgr7XmtyskiIi2tiRZrfEu8Uuxw4Dozm0GUYZ/dUF2p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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGNGmAL1juWW",
        "colab_type": "text"
      },
      "source": [
        "**Much improved performance than the previous one but comes at the cost of computation.** \n",
        "**Accuracy achieved: 86.5%**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IBHzS8AFt2ew",
        "colab_type": "text"
      },
      "source": [
        "### Coming to explaining H2O models\n",
        "\n",
        "This section mostly comes from this notebook: https://github.com/h2oai/mli-resources/blob/master/notebooks/dt_surrogate.ipynb\n",
        "\n",
        "Check out [this repository](https://github.com/h2oai/mli-resources) to discover more resources on Machine Learning Interpretability. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7UXKQK-nSz2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "1102bc50-1b00-4607-9cbd-21cb0d66774c"
      },
      "source": [
        "# Get the predictions on the training frame\n",
        "train_preds = aml.predict(train_frame)['predict']\n",
        "# Bind the predictions to the training frame\n",
        "frame_yhat = train_frame.cbind(train_preds)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "stackedensemble prediction progress: |████████████████████████████████████| 100%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ex5fpv70p3mh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1421
        },
        "outputId": "0090c030-5589-4b2d-b365-66bcdad9cbd8"
      },
      "source": [
        "from h2o.estimators.gbm import H2OGradientBoostingEstimator\n",
        "\n",
        "yhat = 'predict'\n",
        "model_id = 'dt_surrogate_mojo'\n",
        "\n",
        "# Train single tree surrogate model\n",
        "surrogate = H2OGradientBoostingEstimator(ntrees=1,\n",
        "                                         sample_rate=1,\n",
        "                                         col_sample_rate=1,\n",
        "                                         max_depth=3,\n",
        "                                         seed=12345,\n",
        "                                         model_id=model_id)\n",
        "\n",
        "_ = surrogate.train(x=x.tolist(), y=yhat, training_frame=frame_yhat)\n",
        "\n",
        "mojo_path = surrogate.download_mojo(path='.')\n",
        "\n",
        "print(surrogate)\n",
        "print('Generated MOJO path:\\n', mojo_path)\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
            "Model Details\n",
            "=============\n",
            "H2OGradientBoostingEstimator :  Gradient Boosting Machine\n",
            "Model Key:  dt_surrogate_mojo\n",
            "\n",
            "\n",
            "ModelMetricsBinomial: gbm\n",
            "** Reported on train data. **\n",
            "\n",
            "MSE: 0.170880287639\n",
            "RMSE: 0.413376689762\n",
            "LogLoss: 0.51935709929\n",
            "Mean Per-Class Error: 0.132987744457\n",
            "AUC: 0.932587883927\n",
            "pr_auc: 0.836870182741\n",
            "Gini: 0.865175767853\n",
            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.286452829926: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        <=50K     >50K    Error    Rate\n",
              "-----  --------  -------  -------  ----------------\n",
              "<=50K  20828     1695     0.0753   (1695.0/22523.0)\n",
              ">50K   1533      6505     0.1907   (1533.0/8038.0)\n",
              "Total  22361     8200     0.1056   (3228.0/30561.0)"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
              "<td><b> <=50K</b></td>\n",
              "<td><b> >50K</b></td>\n",
              "<td><b>Error</b></td>\n",
              "<td><b>Rate</b></td></tr>\n",
              "<tr><td> <=50K</td>\n",
              "<td>20828.0</td>\n",
              "<td>1695.0</td>\n",
              "<td>0.0753</td>\n",
              "<td> (1695.0/22523.0)</td></tr>\n",
              "<tr><td> >50K</td>\n",
              "<td>1533.0</td>\n",
              "<td>6505.0</td>\n",
              "<td>0.1907</td>\n",
              "<td> (1533.0/8038.0)</td></tr>\n",
              "<tr><td>Total</td>\n",
              "<td>22361.0</td>\n",
              "<td>8200.0</td>\n",
              "<td>0.1056</td>\n",
              "<td> (3228.0/30561.0)</td></tr></table></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "metric                       threshold    value     idx\n",
              "---------------------------  -----------  --------  -----\n",
              "max f1                       0.286453     0.801207  5\n",
              "max f2                       0.257015     0.833423  6\n",
              "max f0point5                 0.307599     0.840217  3\n",
              "max accuracy                 0.307599     0.895488  3\n",
              "max precision                0.342956     1         0\n",
              "max recall                   0.239219     1         7\n",
              "max specificity              0.342956     1         0\n",
              "max absolute_mcc             0.286453     0.729365  5\n",
              "max min_per_class_accuracy   0.286453     0.809281  5\n",
              "max mean_per_class_accuracy  0.286453     0.867012  5"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n",
              "<td><b>threshold</b></td>\n",
              "<td><b>value</b></td>\n",
              "<td><b>idx</b></td></tr>\n",
              "<tr><td>max f1</td>\n",
              "<td>0.2864528</td>\n",
              "<td>0.8012070</td>\n",
              "<td>5.0</td></tr>\n",
              "<tr><td>max f2</td>\n",
              "<td>0.2570147</td>\n",
              "<td>0.8334229</td>\n",
              "<td>6.0</td></tr>\n",
              "<tr><td>max f0point5</td>\n",
              "<td>0.3075992</td>\n",
              "<td>0.8402175</td>\n",
              "<td>3.0</td></tr>\n",
              "<tr><td>max accuracy</td>\n",
              "<td>0.3075992</td>\n",
              "<td>0.8954877</td>\n",
              "<td>3.0</td></tr>\n",
              "<tr><td>max precision</td>\n",
              "<td>0.3429557</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max recall</td>\n",
              "<td>0.2392185</td>\n",
              "<td>1.0</td>\n",
              "<td>7.0</td></tr>\n",
              "<tr><td>max specificity</td>\n",
              "<td>0.3429557</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max absolute_mcc</td>\n",
              "<td>0.2864528</td>\n",
              "<td>0.7293654</td>\n",
              "<td>5.0</td></tr>\n",
              "<tr><td>max min_per_class_accuracy</td>\n",
              "<td>0.2864528</td>\n",
              "<td>0.8092809</td>\n",
              "<td>5.0</td></tr>\n",
              "<tr><td>max mean_per_class_accuracy</td>\n",
              "<td>0.2864528</td>\n",
              "<td>0.8670123</td>\n",
              "<td>5.0</td></tr></table></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Gains/Lift Table: Avg response rate: 26.30 %, avg score: 26.44 %\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    group    cumulative_data_fraction    lower_threshold    lift       cumulative_lift    response_rate    score     cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain\n",
              "--  -------  --------------------------  -----------------  ---------  -----------------  ---------------  --------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------\n",
              "    1        0.0179968                   0.342082           3.78824    3.78824            0.996364         0.342533  0.996364                    0.342533            0.0681762       0.0681762                  278.824   278.824\n",
              "    2        0.203069                    0.329078           3.34361    3.38302            0.87942          0.329078  0.889784                    0.330271            0.618811        0.686987                   234.361   238.302\n",
              "    3        0.469913                    0.257015           1.04015    2.0526             0.273574         0.264261  0.539865                    0.292787            0.277557        0.964543                   4.01481   105.26\n",
              "    4        1                           0.239219           0.0668882  1                  0.0175926        0.239219  0.263015                    0.264391            0.0354566       1                          -93.3112  0"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
              "<td><b>group</b></td>\n",
              "<td><b>cumulative_data_fraction</b></td>\n",
              "<td><b>lower_threshold</b></td>\n",
              "<td><b>lift</b></td>\n",
              "<td><b>cumulative_lift</b></td>\n",
              "<td><b>response_rate</b></td>\n",
              "<td><b>score</b></td>\n",
              "<td><b>cumulative_response_rate</b></td>\n",
              "<td><b>cumulative_score</b></td>\n",
              "<td><b>capture_rate</b></td>\n",
              "<td><b>cumulative_capture_rate</b></td>\n",
              "<td><b>gain</b></td>\n",
              "<td><b>cumulative_gain</b></td></tr>\n",
              "<tr><td></td>\n",
              "<td>1</td>\n",
              "<td>0.0179968</td>\n",
              "<td>0.3420822</td>\n",
              "<td>3.7882395</td>\n",
              "<td>3.7882395</td>\n",
              "<td>0.9963636</td>\n",
              "<td>0.3425332</td>\n",
              "<td>0.9963636</td>\n",
              "<td>0.3425332</td>\n",
              "<td>0.0681762</td>\n",
              "<td>0.0681762</td>\n",
              "<td>278.8239499</td>\n",
              "<td>278.8239499</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2</td>\n",
              "<td>0.2030693</td>\n",
              "<td>0.3290783</td>\n",
              "<td>3.3436125</td>\n",
              "<td>3.3830171</td>\n",
              "<td>0.8794201</td>\n",
              "<td>0.3290783</td>\n",
              "<td>0.8897841</td>\n",
              "<td>0.3302707</td>\n",
              "<td>0.6188106</td>\n",
              "<td>0.6869868</td>\n",
              "<td>234.3612492</td>\n",
              "<td>238.3017077</td></tr>\n",
              "<tr><td></td>\n",
              "<td>3</td>\n",
              "<td>0.4699126</td>\n",
              "<td>0.2570147</td>\n",
              "<td>1.0401481</td>\n",
              "<td>2.0526016</td>\n",
              "<td>0.2735745</td>\n",
              "<td>0.2642613</td>\n",
              "<td>0.5398649</td>\n",
              "<td>0.2927868</td>\n",
              "<td>0.2775566</td>\n",
              "<td>0.9645434</td>\n",
              "<td>4.0148061</td>\n",
              "<td>105.2601589</td></tr>\n",
              "<tr><td></td>\n",
              "<td>4</td>\n",
              "<td>1.0</td>\n",
              "<td>0.2392185</td>\n",
              "<td>0.0668882</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0175926</td>\n",
              "<td>0.2392185</td>\n",
              "<td>0.2630150</td>\n",
              "<td>0.2643909</td>\n",
              "<td>0.0354566</td>\n",
              "<td>1.0</td>\n",
              "<td>-93.3111816</td>\n",
              "<td>0.0</td></tr></table></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Scoring History: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    timestamp            duration    number_of_trees    training_rmse    training_logloss    training_auc    training_pr_auc    training_lift    training_classification_error\n",
              "--  -------------------  ----------  -----------------  ---------------  ------------------  --------------  -----------------  ---------------  -------------------------------\n",
              "    2019-05-17 12:00:21  0.083 sec   0                  0.44027          0.576187            0.5             0                  1                0.736985\n",
              "    2019-05-17 12:00:21  0.151 sec   1                  0.413377         0.519357            0.932588        0.83687            3.78824          0.105625"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
              "<td><b>timestamp</b></td>\n",
              "<td><b>duration</b></td>\n",
              "<td><b>number_of_trees</b></td>\n",
              "<td><b>training_rmse</b></td>\n",
              "<td><b>training_logloss</b></td>\n",
              "<td><b>training_auc</b></td>\n",
              "<td><b>training_pr_auc</b></td>\n",
              "<td><b>training_lift</b></td>\n",
              "<td><b>training_classification_error</b></td></tr>\n",
              "<tr><td></td>\n",
              "<td>2019-05-17 12:00:21</td>\n",
              "<td> 0.083 sec</td>\n",
              "<td>0.0</td>\n",
              "<td>0.4402705</td>\n",
              "<td>0.5761869</td>\n",
              "<td>0.5</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.7369850</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2019-05-17 12:00:21</td>\n",
              "<td> 0.151 sec</td>\n",
              "<td>1.0</td>\n",
              "<td>0.4133767</td>\n",
              "<td>0.5193571</td>\n",
              "<td>0.9325879</td>\n",
              "<td>0.8368702</td>\n",
              "<td>3.7882395</td>\n",
              "<td>0.1056248</td></tr></table></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Variable Importances: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "variable       relative_importance    scaled_importance    percentage\n",
              "-------------  ---------------------  -------------------  ------------\n",
              "Relationship   1814.48                1                    0.511242\n",
              "Occupation     1050.29                0.578836             0.295925\n",
              "CapitalGain    430.092                0.237033             0.121181\n",
              "Education      252.824                0.139337             0.0712348\n",
              "HoursPerWeek   1.47916                0.0008152            0.000416765\n",
              "Age            0                      0                    0\n",
              "WorkClass      0                      0                    0\n",
              "fnlwgt         0                      0                    0\n",
              "EducationNum   0                      0                    0\n",
              "MaritalStatus  0                      0                    0\n",
              "Race           0                      0                    0\n",
              "Gender         0                      0                    0\n",
              "CapitalLoss    0                      0                    0\n",
              "NativeCountry  0                      0                    0"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>variable</b></td>\n",
              "<td><b>relative_importance</b></td>\n",
              "<td><b>scaled_importance</b></td>\n",
              "<td><b>percentage</b></td></tr>\n",
              "<tr><td>Relationship</td>\n",
              "<td>1814.4801025</td>\n",
              "<td>1.0</td>\n",
              "<td>0.5112421</td></tr>\n",
              "<tr><td>Occupation</td>\n",
              "<td>1050.2857666</td>\n",
              "<td>0.5788356</td>\n",
              "<td>0.2959251</td></tr>\n",
              "<tr><td>CapitalGain</td>\n",
              "<td>430.0915527</td>\n",
              "<td>0.2370329</td>\n",
              "<td>0.1211812</td></tr>\n",
              "<tr><td>Education</td>\n",
              "<td>252.8236847</td>\n",
              "<td>0.1393367</td>\n",
              "<td>0.0712348</td></tr>\n",
              "<tr><td>HoursPerWeek</td>\n",
              "<td>1.4791644</td>\n",
              "<td>0.0008152</td>\n",
              "<td>0.0004168</td></tr>\n",
              "<tr><td>Age</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>WorkClass</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>fnlwgt</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>EducationNum</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>MaritalStatus</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>Race</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>Gender</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>CapitalLoss</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>NativeCountry</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr></table></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "('Generated MOJO path:\\n', '/content/dt_surrogate_mojo.zip')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fFIubxv-qnZ7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "c3665803-1e92-4292-e4d0-361eceb52e3a"
      },
      "source": [
        "h2o.h2o.H2OFrame(surrogate.varimp(), column_names=['Feature', 'relative_importance',\t'scaled_importance', 'Percentage'])"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th>Feature      </th><th style=\"text-align: right;\">  relative_importance</th><th style=\"text-align: right;\">  scaled_importance</th><th style=\"text-align: right;\">  Percentage</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td>Relationship </td><td style=\"text-align: right;\">           1814.48   </td><td style=\"text-align: right;\">          1        </td><td style=\"text-align: right;\"> 0.511242   </td></tr>\n",
              "<tr><td>Occupation   </td><td style=\"text-align: right;\">           1050.29   </td><td style=\"text-align: right;\">          0.578836 </td><td style=\"text-align: right;\"> 0.295925   </td></tr>\n",
              "<tr><td>CapitalGain  </td><td style=\"text-align: right;\">            430.092  </td><td style=\"text-align: right;\">          0.237033 </td><td style=\"text-align: right;\"> 0.121181   </td></tr>\n",
              "<tr><td>Education    </td><td style=\"text-align: right;\">            252.824  </td><td style=\"text-align: right;\">          0.139337 </td><td style=\"text-align: right;\"> 0.0712348  </td></tr>\n",
              "<tr><td>HoursPerWeek </td><td style=\"text-align: right;\">              1.47916</td><td style=\"text-align: right;\">          0.0008152</td><td style=\"text-align: right;\"> 0.000416765</td></tr>\n",
              "<tr><td>Age          </td><td style=\"text-align: right;\">              0      </td><td style=\"text-align: right;\">          0        </td><td style=\"text-align: right;\"> 0          </td></tr>\n",
              "<tr><td>WorkClass    </td><td style=\"text-align: right;\">              0      </td><td style=\"text-align: right;\">          0        </td><td style=\"text-align: right;\"> 0          </td></tr>\n",
              "<tr><td>fnlwgt       </td><td style=\"text-align: right;\">              0      </td><td style=\"text-align: right;\">          0        </td><td style=\"text-align: right;\"> 0          </td></tr>\n",
              "<tr><td>EducationNum </td><td style=\"text-align: right;\">              0      </td><td style=\"text-align: right;\">          0        </td><td style=\"text-align: right;\"> 0          </td></tr>\n",
              "<tr><td>MaritalStatus</td><td style=\"text-align: right;\">              0      </td><td style=\"text-align: right;\">          0        </td><td style=\"text-align: right;\"> 0          </td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6G1rU4kspuj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from IPython.display import Image\n",
        "from IPython.display import display\n",
        "\n",
        "import os\n",
        "import re\n",
        "import subprocess\n",
        "from subprocess import CalledProcessError\n",
        "import time\n",
        "\n",
        "from h2o.backend import H2OLocalServer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRL7XRKlskAZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113
        },
        "outputId": "f272cf84-2589-4d6c-b72a-a3e58ad5c69b"
      },
      "source": [
        "details = False\n",
        "title = 'Adult dataset Decision Tree Surrogate'\n",
        "\n",
        "hs = H2OLocalServer()\n",
        "h2o_jar_path = hs._find_jar()\n",
        "print('Discovered H2O jar path:\\n', h2o_jar_path)\n",
        "\n",
        "gv_file_name = model_id + '.gv'\n",
        "gv_args = str('-cp ' + h2o_jar_path +\n",
        "              ' hex.genmodel.tools.PrintMojo --tree 0 -i '\n",
        "              + mojo_path + ' -o').split()\n",
        "gv_args.insert(0, 'java')\n",
        "gv_args.append(gv_file_name)\n",
        "\n",
        "if details:\n",
        "    gv_args.append('--detail')\n",
        "\n",
        "if title is not None:\n",
        "    gv_args = gv_args + ['--title', title]\n",
        "    \n",
        "print()\n",
        "print('Calling external process ...')\n",
        "print(' '.join(gv_args))\n",
        "    \n",
        "_ = subprocess.call(gv_args)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('Discovered H2O jar path:\\n', u'/usr/local/lib/python2.7/dist-packages/h2o/backend/bin/h2o.jar')\n",
            "()\n",
            "Calling external process ...\n",
            "java -cp /usr/local/lib/python2.7/dist-packages/h2o/backend/bin/h2o.jar hex.genmodel.tools.PrintMojo --tree 0 -i /content/dt_surrogate_mojo.zip -o dt_surrogate_mojo.gv --title Adult dataset Decision Tree Surrogate\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gRyttQ6rtFtn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "335ad719-e7b1-40c1-e834-b4acf7956dfa"
      },
      "source": [
        "png_file_name = model_id + '.png'\n",
        "png_args = str('dot -Tpng ' + gv_file_name + ' -o ' + png_file_name)\n",
        "png_args = png_args.split()\n",
        "\n",
        "print('Calling external process ...')\n",
        "print(' '.join(png_args))\n",
        "\n",
        "_ = subprocess.call(png_args)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Calling external process ...\n",
            "dot -Tpng dt_surrogate_mojo.gv -o dt_surrogate_mojo.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nH-cuZvtaCD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1108
        },
        "outputId": "981ce498-1b6a-483d-a33b-f103b56794dc"
      },
      "source": [
        "display(Image((png_file_name)))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kAAAAFBuZmZl65513NHjw4GK9K/mvJkyYoBo1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UWNjIwAACdOnMD79+8RERGR6x6hmZmZePPmDdLT0xEWFgYnJycYGRkVuJ+vnp4eoqOjERkZicTE\nxOxtDwoa70MdOnTAjh07cOXKFfTq1QvHjh3Ds2fPkJ6ejkePHmHHjh3Ze7xmKezcyoOysjLGjRuH\n7du3F9g2NjYWv/zyC2bOnFmiMW/fvo3Vq1fD29sbysrK2XtKZ32tXbs2u23nzp3x6NEjpKenIzIy\nErNnz8aJEyewdevW7D1hAWDhwoXQ1tbOUTBcsGABYmNjsWjRIiQlJeH8+fNYs2YN7O3t0axZsxLN\ngcqGhYUFVq5cie3bt8Pe3h7Xr1/H+/fvkZCQgICAAPTu3Rtv3rzBvn37oK2tDeDfdz58++23CAoK\nwowZM/D06VNkZmYiMTERt2/fLvA8ADRp0gSvX7/GwYMHkZaWhhcvXnz0VHuWgtacwtzfH64/uRWy\n5XI5Zs2ahQMHDmDnzp1ISEjAjRs3MGXKFBgYGMDBwaG0vu1ERERE+RMlYG1tLaytrUvSBREREVVy\nJXk9EBUVJWbPni3MzMyEhoaGUFRUFDo6OuKTTz4R48ePF2fPns1uO3fuXKGnpyd0dHSEjY2N8PT0\nFABE48aNRVRUlHBwcBDKysrC0NBQKCkpCS0tLTFs2DBx//797D5+/PFHUbduXQFAqKmpCUtLSyGE\nEKGhoaJhw4ZCVVVV9OjRQ8TExOQ7npOTk9DX1xcAhLq6urCyssoxr4cPHwonJyfRqlUroa6uLuRy\nuWjUqJEwNzcXrq6uIigoKEf7/MaaM2fOR5k3btwo1NTUBABhamoq7t+/L3766SehpaUlAIiGDRuK\nf/75p1g/k8JydnYWdnZ2+bY5f/686N69uzAwMBAABABRt25d0a1bN3HmzBkhhBA3btzIPpfb15o1\na7L7s7CwEDo6OkJJSUno6uqKQYMGiUuXLn007oIFC4SmpqY4fvx4juNnzpwRnTt3FioqKsLAwEC4\nuLiI9+/fF3nuPj4+ooQvo6udkqwT58+fF6NGjRJGRkaiRo0aQl1dXbRu3VrMmjVLPHnyJNdrPD09\nhZmZmZDL5UIul4tPPvlEbNy4sVDnX716JXr37p19306fPl24uLgIAKJJkyYiKipKCCEKteYIUfDa\n9eH6M3/+/FzXqczMTLFmzRphamoqlJWVha6urhg+fLgIDw/PHqss1gYAwsfHp0jXEBERUeFUwv/O\n+sqEEKK4hWgbGxsAgJ+fX3G7ICIiokquorwemDx5Mvz8/PDq1StJc1DV5+vri5EjR6IEL6OrnYqy\nTpSm6rLmyGQy+Pj4wNbWVuooREREVU4l/O+sH7e5ICIioiojIyND6ghEVI1wzSEiIqLqhsVkIiIi\nIiIiIiIiIioQi8lERERU6X377bfYtm0b4uPj0ahRI+zbt0/qSERUhXHNISIioupKSeoARERERCW1\nYsUKrFixQuoYRFRNcM0hIiKi6opPJhMRERERERERERFRgVhMJiIiIiIiIiIiIqICsZhMRERERERE\nRERERAViMZmIiIiIiIiIiIiICsRiMhEREREREREREREViMVkIiIiIiIiypOHhweOHDmCjIwMqaMQ\nERGRxFhMJiIiIiIiojzFx8djyJAhMDIygqurK+7evSt1JCIiIpIIi8lERERERESUp0WLFuHRo0eY\nMWMG/Pz80KJFC3Ts2BHr16/Hq1evpI5HRERE5YjFZCIiIiIiIspXgwYNMHfuXERERCA4OBgdOnTA\nd999h/r168PW1hb+/v7cBoOIiKgaYDGZiIiIiIiICkVBQQE9evTAli1b8OTJE7i7uyMqKgqWlpZo\n3LgxFi9ejIcPH0odk4iIiMoIi8lERERERERUZDo6Opg8eTIuXLiAO3fuwN7eHtu2bUOTJk1gYWEB\nPz8/Pq1MRERUxbCYTERERERERCXSvHlzLF68GPfv34efnx8yMzMxcuRINGvWDGvXrsXr16+ljkhE\nRESlgMVkIiIiIiIiKhVKSkqwsrLCyZMnER4eDmtra6xcuRL16tWDra0tzp07J3VEIiIiKgElqQMQ\nERFR5bdv3z7IZDKpYxBRBcZ1ovoxNTXFqlWrsHjxYvj6+mLdunXo3r07OnTogEmTJsHOzg5qampS\nxyQiIqIikAkhRHEvtrGxAQD4+fmVWiAiIiKqXM6fP4/Hjx9LHaPCEEJgy5YtCAkJgYuLC9q2bVtm\nY40cORJOTk7o2rVrmY1BebO1tZU6QqVRldeJ8+fPw8PDAz4+PlJHKTPdunVD/fr1S6WvK1euYP36\n9di7dy/U1dUxevRoODk5oVGjRqXSPxERUWUik8ng4+NTmV5X+rGYTERERFRKMjIyMH78eOzduxe+\nvr6wtLQs0/Eq4YtPoirH19cXI0eORAn+rKqWnj17hp9//hlbtmzBs2fPYGlpCRcXF3Tr1k3qaERE\nROWmEr6e9+OeyURERESlICMjA2PHjoWvry/8/f3LvJBMRFSZGRgYYMGCBYiMjISvry9iY2PRvXt3\nmJubw9/fn8V5IiKiCorFZCIiIqISysjIwJgxY7B//3788ccfsLCwkDoSEVGloKSkhBEjRuDcuXMI\nDg6GgYEBhg0bhqZNm2L9+vV4//691BGJiIjoP1hMJiIiIiqB1NRU2Nra4uDBg/D390e/fv2kjkRE\nVCn16NEDvr6+CA8Px+effw5XV1cYGxtj8eLFiIuLkzoeERERgcVkIiIiomJLTU3FyJEjcfz4cRw+\nfBh9+vSROhIRUaXXpEkTrF+/Hg8fPsTkyZOxfv16GBkZwdHREU+fPpU6HhERUbXGYjIRERFRMaSk\npMDGxgaBgYE4ceIEevXqJXUkIqIqpW7duli8eDGioqKwbNky7N+/HyYmJhg9ejTu3LkjdTwiIqJq\nicVkIiIioiJKTk7GkCFDEBQUhICAAHTp0kXqSEREVZampiYcHR1x//59eHp64uLFi2jdujWsra0R\nFhYmdTwiIqJqhcVkIiIioiJITk6GpaUlLl++jICAAHTu3FnqSERE1YKKigomTpyIO3fuwM/PDw8e\nPEC7du0wYsQIFpWJiIjKCYvJRERERIWUlJSEwYMHIywsDKdPn0anTp2kjkREVO0oKCjAysoKoaGh\nCAgIQFRUFNq1a4chQ4bgypUrUscjIiKq0lhMJiIiIiqE+Ph49O/fH7dv38bJkyfRpk0bqSMREVV7\n/fr1w99//40DBw7gyZMn6NSpE6ytrXHr1i2poxEREVVJLCYTERERFSA+Ph4DBw7EgwcPcPLkSZiZ\nmUkdiYiI/j+ZTIZhw4YhNDQUBw4cQEREBNq2bQt7e3s8evRI6nhERERVCovJRERERPmIi4tD//79\n8fTpUwQFBaFVq1ZSRyIiolxkFZWvXbuGPXv24OzZszA1NYWDgwNiYmKkjkdERFQlsJhMRERElIc3\nb97AwsICMTExCAwMhKmpqdSRiIioADKZDDY2Nrh9+zY8PT3h7++PJk2awNXVFfHx8VLHIyIiqtRY\nTCYiIiLKxfPnz9GrVy+8ePECgYGBaNy4sdSRiIioCJSVlTFp0iTcu3cPCxYswJYtW9C4cWOsX78e\n6enpUscjIiKqlFhMJiIiIvpAbGws+vbti4SEBJw+fRomJiZSRyIiomJSU1PD3Llzcf/+fXz99ddw\ncXFBu3btcOzYMamjERERVTosJhMRERH9R0xMDPr06YPU1FSEhITA2NhY6khERFQK9PT04O7ujlu3\nbqFly5b47LPPYGFhgbCwMKmjERERVRosJhMRERH9f48fP4a5uTkyMzMRGBgIQ0NDqSMREVEpMzU1\nha+vLwIDA/H69Wu0b98eDg4OePHihdTRiIiIKjwWk4mIiIgAREVFoXfv3lBWVsapU6dQr149qSMR\nEVEZ6tWrFy5duoStW7fiyJEjaNasGby8vJCRkSF1NCIiogqLxWQiIiKq9h49eoTevXtDU1MTQUFB\nMDAwkDoSERGVAwUFBYwZMwb//PMPZsyYAWdnZ3Ts2BFnz56VOhoREVGFxGIyERERVWsRERHo0aMH\ntLW1ceLECdSqVUvqSEREVM7U1NSwePFihIWFQV9fH+bm5hg9ejRiY2OljkZERFShsJhMRERE1VZ4\neDh69+4NfX19nDhxAjVr1pQ6EhERSahp06Y4duwY9u7di8DAQLRo0QKbNm1CZmam1NGIiIgqBBaT\niYiIqFq6e/cu+vTpg4YNG+LUqVPQ09OTOhIREVUQtra2uHPnDiZOnAhHR0f07NkTd+7ckToWERGR\n5FhMJiIiomrnzp076NOnD0xMTHDs2DFoaWlJHYmIiCoYDQ0NuLm54fLly0hLS0Pbtm3h6uqKlJQU\nqaMRERFJhsVkIiIiqlauXbuGnj17wtTUFEePHoWmpqbUkYiIqAJr06YNzp8/D09PT2zcuBFmZmY4\nffq01LGIiIgkwWIyERERVRuhoaHo168fWrVqhSNHjkBDQ0PqSEREVAkoKChg0qRJuHHjBkxMTNC3\nb19Mnz4dSUlJUkcjIiIqVywmExERUbVw5coVWFhYoFOnTjh69CgLyUREVGTGxsY4duwYtm/fjj17\n9qBdu3Y4d+6c1LGIiIjKDYvJREREVOWdO3cOffr0wf/+9z/8/vvvUFVVlToSERFVYnZ2drh16xZa\ntmwJc3NzODo6ci9lIiKqFlhMJiIioiotJCQEAwcORM+ePfH7779DLpdLHYmIiKoAfX19HDp0CNu2\nbcO2bdvQoUMHhIaGSh2LiIioTLGYTERERFVWUFAQPvvsMwwYMAAHDhyAioqK1JGIiKiKGT16NK5f\nv46aNWuia9euWLFiBTIyMqSORUREVCZYTCYiIqIqKSAgAAMHDsTnn3+OPXv2QFlZWepIRERURTVq\n1AiBgYFYsWIFli1bhn79+uHp06dSxyIiIip1LCYTERFRlXPs2DEMHToUQ4cOxa5du6CkpCR1JCIi\nquIUFBQwa9YsXL58GS9fvkS7du1w+PBhqWMRERGVKhaTiYiIqEr5888/MXz4cIwaNYqFZCIiKnet\nWrXC33//jVGjRsHS0hKOjo5ITU2VOhYREVGpYDGZiIiIqozDhw/DysoKX3/9Nby9vaGgwJc6RERU\n/lRVVbF+/Xr8+uuv+OWXX9CtWzfcu3dP6lhEREQlxr+wiIiIqErw8/ODlZUV7O3tsXnzZhaSiYhI\ncqNHj8bly5eRkZGBDh064NChQ1JHIiIiKhH+lUVERESVno+PD0aNGoUJEyZg06ZNLCQTEVGF0axZ\nM5w/fx62trYYPnw4Fi5ciMzMTKljERERFQv/0iIiIqJKbc+ePbCzs4OjoyO8vLwgk8mkjkRERJSD\nXC6Ht7c3fv31V6xduxaDBw/GmzdvpI5FRERUZCwmExERUaW1detW2NnZwdnZGWvXrpU6DhERUb5G\njx6N4OBg3L59G506dcKNGzekjkRERFQkLCYTERFRpeTt7Y1JkybBxcUFbm5uUschIiIqlA4dOuDC\nhQswMDBA165d4efnJ3UkIiKiQmMxmYiIiCqdLVu2wMHBAYsWLcKqVaukjkNERFQkdevWxalTp2Bv\nb4+RI0dixYoVUkciIiIqFCWpAxAREREVhbu7O2bNmoWlS5di/vz5UschIiIqFmVlZXh6eqJt27aY\nOnUqIiIisGXLFtSoUUPqaERERHliMZmIiIgqjbVr12LOnDn44YcfMHPmTKnjEBERldjEiRPRsGFD\n2NjYICoqCvv374eOjo7UsYiIiHLFbS6IiIioUnBzc8OcOXPg4eHBQjIREVUp/fv3R0hICCIiItC9\ne3dERkZKHYmIiChXLCYTERFRhefm5oZ58+Zhw4YNmDFjhtRxiIiISp2ZmRnOnTsHZWVldOnSBVeu\nXJE6EhER0UdYTCYiIqIKbdGiRZg3bx48PT0xbdo0qeMQERGVmfr16yM4OBht27ZFnz59EBwcLHUk\nIiKiHFhMJiIiogpr/vz5+P777/HLL79g6tSpUschIiIqc5qamvD398eAAQNgYWGBgwcPSh2JiIgo\nG4vJREREVOEIITBz5kysWrUK27Ztg729vdSRbatWYgAAIABJREFUiIiIyk2NGjWwZ88e2NnZYeTI\nkfD19ZU6EhEREQBASeoARERERP8lhICTkxM2btyI7du346uvvpI6EhERUblTVFSEt7c3tLW1MWrU\nKMTHx2PixIlSxyIiomqOxWQiIiKqMIQQmD59On766Sf4+vrCyspK6khERESSkclk+OGHH6ChoQEH\nBwckJSXByclJ6lhERFSNsZhMREREFUJmZiYmTpyInTt3wtfXF8OGDZM6EhERUYWwZMkSaGpqwtnZ\nGQoKCpgxY4bUkYiIqJpiMZmIiIgkl5GRgfHjx2Pv3r3w8/ODpaWl1JGIiIgqlNmzZ0NRURFOTk5Q\nUFDAtGnTpI5ERETVED+Aj4iIiCSVkZGBsWPHwtfXF/7+/iwkExFVQZmZmRg+fDiMjIwgl8thaGiI\noUOHIiwsTOpolcrMmTOxZs0azJgxA15eXlLHISKiaojFZCIiIpJMRkYGxowZg/379+OPP/6AhYWF\n1JGIiKgMZGZmIjg4GLt378br168REhKCd+/eoWfPnoiOjpY6XqUya9YsuLm5Ydq0adi0aZPUcYiI\nqJphMZmIiIgkkZqaCltbWxw8eBD+/v7o16+f1JGIiKgMde3aFT169ICamhoaNWqE77//HvHx8fj1\n11+ljlbpuLi4YMGCBfjmm2+wZcsWqeMQEVE1wj2TiYiIqNylpqZi5MiROHHiBA4fPoxevXpJHYmI\niMqQkpIS/P39cxwzMTEBANy/f1+KSJXekiVLkJGRgalTp0JdXR12dnZSRyIiomqAxWQiIiIqVykp\nKbCxsUFQUBD++usvdOnSRepIREQkgXfv3gEAtLS0JE5SeS1fvhwpKSkYN24cdHR0MHjwYKkjERFR\nFcdiMhEREZWb5ORkDBs2DJcuXUJAQAA6d+4sdSQiIpLI33//DQDo0aOHxEkqt9WrVyM+Ph62trY4\nduwYevbsKXUkIiKqwlhMJiIionKRnJwMS0tLhIaGIiAgAJ06dZI6EhERSSA2NhaBgYFwcXFB165d\nYWlpKXWkSk0mk2HTpk2Ii4vDkCFDEBgYiPbt20sdi4iIqih+AB8RERGVuaSkJAwePBhhYWE4ffo0\nC8lERNVY165d4ejoiGHDhuHo0aNQVlaWOlKlp6ioiJ07d6Jr164YOHAg7t69K3UkIiKqolhMJiIi\nojIVHx+P/v374/bt2zh58iTatGkjdSQiIpJQnTp1cOrUKfz444/Q1taWOk6VUaNGDRw4cACmpqbo\n378/oqKipI5ERERVEIvJREREVGbi4+MxcOBAPHjwACdPnoSZmZnUkYiISGK1a9eGjo6O1DGqJDU1\nNfzxxx/Q1NTE559/jtevX0sdiYiIqhgWk4mIiKhMxMXFoX///nj69CmCgoLQqlUrqSMREVEF4O/v\nD0NDQ6ljVFk1a9bE8ePHkZiYiOHDhyMlJUXqSEREVIWwmExERESl7s2bN7CwsEBMTAwCAwNhamoq\ndSQiIqoA7t27B319fYwcOVLqKFVa/fr1cfToUdy4cQOjR49GZmam1JGIiKiKYDGZiIiIStXz58/R\nq1cvvHjxAoGBgWjcuLHUkYiIqIIQQkgdodpo2bIlfv/9dxw6dAjfffed1HGIiKiKUJI6ABEREVUd\nsbGx6NevH96+fYvTp0/D2NhY6khERFSBmJqaIjY2VuoY1cann36KX3/9FaNGjYKhoSGmTZsmdSQi\nIqrkWEwmIiKiUhETE4O+ffsiPT0dISEh3A+TiIioAvjiiy8QEREBJycnNGjQAEOHDpU6EhERVWLc\n5oKIiIhK7PHjxzA3N0dmZiYCAwNZSCYiIqpAFixYAHt7e9jZ2eHKlStSxyEiokqMxWQiIiIqkaio\nKPTu3RvKyso4deoU6tWrJ3UkIiIi+sCmTZvQvXt3WFpaIjo6Wuo4RERUSbGYTERERMX26NEj9O7d\nG5qamggKCoKBgYHUkYiIiCgXysrK8PPzg66uLoYMGYLk5GSpIxERUSXEYjIREREVS0REBHr06AFt\nbW2cOHECtWrVkjoSERER5UNTUxP+/v6IiorCmDFjIISQOhIREVUyLCYTERFRkYWHh6N3797Q19fH\niRMnULNmTakjERERUSE0atQIe/bswcGDB7Fy5Uqp4xARUSXDYjIREREVyd27d9GnTx80bNgQp06d\ngp6entSRiIiqjd27d0Mmk6Fbt25SRynQhAkToKmpCZlMhmvXruXbdv/+/TAxMYFMJsvzy9jYuHyC\nVwP9+vWDu7s75s+fDz8/P6njkMQmT56c416zs7P76J78+uuvP7quf//+0NTUhKKiIlq1aoXQ0NBc\n+y/MunXw4MEcGfiON6KKi8VkIiIiKrQ7d+6gT58+MDExwbFjx6ClpSV1JCKiamX37t1o3Lgxzp8/\nj3v37kkdJ18///wzvL29C9V2xIgRePDgARo3bgxtbW0IISCEQHp6OpKTkxEbGws1NbUyTly9TJs2\nDQ4ODhg3bhzCwsKkjkMS09PTw9GjRxEeHo6tW7fmuCdr1qyJnTt34siRIzmuCQgIgJ+fH4YMGYJb\nt26hffv2ufZdmHVr6NChePLkCYKCgvD555+X+vyIqPSwmExERESFcu3aNfTs2ROmpqY4evQoNDU1\npY5ERFStvHr1Crdv38aSJUsAAL/99pvEicqeoqIiVFVVUadOHTRt2lTqOFXOhg0b0KFDBwwdOhQv\nX76UOg5JSFVVFQMHDkTTpk2hoqKS49yGDRugoKAABwcHxMfHF6nfwq5bMpkMhoaGMDc3h6mpafEm\nQUTlgsVkIiIiKlBoaCj69euHVq1a4ciRI9DQ0JA6EhFRtePr64tBgwbB0tIScrkcO3bsqPAfoCaT\nyUqtr4MHD5ZaX/QvZWVl7Nu3D0IIjBo1ChkZGVJHogqoW7ducHJywtOnTzF79uwiXVsZ1y0iyh+L\nyURERJSvK1euwMLCAp06dcLRo0dZSCYiksju3bthZWUFTU1N9O/fH5GRkQgODv6o3erVq6GmpgZN\nTU08f/4cs2bNgqGhIaZMmQJ1dXUoKCigQ4cO0NfXh7KyMtTV1dG+fXuYm5ujQYMGkMvl0NHRwZw5\nc3L0m5GRgYULF8LIyAiqqqpo06YNfHx8ss8LIbBmzRo0a9YMKioq0NbWhouLS5l8L4QQWLduHVq0\naAEVFRXo6upi2LBhuHv3boHfh/DwcADAjh070LFjR8jlcqirq8PY2BjLli0r1FzPnDmDzp07Q01N\nDVpaWjAzM0NCQkKZzLWs1apVCwcPHkRISAgWLlwodRyqoJYvX46mTZvi559/xokTJwp9XWHXLSKq\nPFhMJiIiojydO3cOffr0QZcuXfD7779DVVVV6khERNVSVFQUwsPD0bNnTwCAjY0NgNzfMj5nzhw4\nOzvj7du3WLFiBRo1aoQuXbrA0dERLi4uEEJg06ZNePjwIWJiYtCzZ09cvXoV8+bNw9WrV/H69WuM\nGTMGa9aswfXr17P7dXV1xerVq+Hu7o5nz55hyJAhGDVqFC5fvgwAWLBgAebOnQsHBwfExsYiJiYG\nrq6uJZ77qVOnsHbt2hzHFi9ejHnz5uG7777D8+fPERQUhMePH8Pc3ByxsbH5fh+EEPDw8MDo0aNh\nbW2N6OhoPHnyBN9++212oTm/uSYlJcHS0hLW1tZ4/fo1IiIi0LRpU6SmppZ4rlJp164dtmzZgpUr\nV2L//v1Sx6EKSFVVFb/++isUFBQwceJEJCUlFXhNUdYtIqo8WEwmIiKiXIWEhGDgwIHo2bMnDhw4\nALlcLnUkIqJqa/fu3Rg8eDAUFRUBAJaWllBRUYGfnx/evXuX53WrVq3CtGnTsH//fjRv3jz7eMuW\nLaGmpoaaNWviyy+/BAAYGRmhVq1aUFNTg52dHQBkP+n7/v17eHl5Yfjw4RgxYgR0dHQwf/58KCsr\nY9u2bXj37h3c3d3Rr18/ODs7Q0dHB6qqqtDT0yvyXOPj4yGTybK/+vbtm+P8u3fvsG7dOlhZWcHO\nzg7a2towMzPD5s2b8fLlS/z000/5fh8aN26MJUuWoHfv3nB1dYWenh50dXUxfvx4dOrUqcC5RkZG\nIiEhAa1atYJcLoe+vj7279+PWrVqFXmuFcnXX3+N8ePHY+zYsbhz547UcagC6tq1K2bOnInIyMhC\n/Y+i4q5bRFSxsZhMREREHwkKCsJnn32GAQMG4MCBAx99EAsREZWvrLeKZ9HS0kL//v2RkJCAQ4cO\nlajvGjVqAADS09OzjykrKwMA0tLSAADh4eFITk5G69ats9uoqqqibt26uHv3Lu7du4fk5OSPCr/F\noa2tDSFE9ldgYGCO87du3cLbt2/RsWPHHMc7deqEGjVq4OLFi/n2HxYWhri4OAwYMCDHcUVFRTg6\nOhY4VxMTE9SpUwd2dnZYvHgxIiMjSzbhCsTT0xPNmzeHlZVVpd22g8rW8uXL0axZM2zcuBEhISH5\nti3LdYuIpMNiMhEREeUQEBCAgQMH4vPPP8eePXuyCwpERCSNmzdv4saNGxgyZEiOJ3b9/f0BlM9b\nxrPe0j5//vwcGR49eoTk5GQ8efIEAFC7du18+zl8+HCO62UyWfZT0Hnp1atXjg/9iouLA4Bc9/DX\n0dFBYmJivv1lFUl1dHRyPV/QXFVVVXHq1Cn06NED33//PUxMTPDFF19UiSctVVRUsH//frx69QqT\nJk2SOg5VQHK5HNu2bYNMJsO4cePy/L2vCOsWEZUNFpOJiIgo27FjxzB06FAMHToUu3btgpKSktSR\niIiqvV27duHLL7/M8bSuEAKvX7+GqqoqAgICEBMTU6YZsorE7u7uH+U4f/589lZIKSkp+fYzePDg\nj67fuXNnkbJkFYFzKxrHxcWhfv36+V5fr149AMDLly9zPV/QXAGgVatW8Pf3R3R0NObOnQsfH5+P\n9nWurBo0aIBdu3Zh37598PT0lDoOVUBdu3aFs7MzIiIisj+08kMVYd0iorLBYjIREREBAP78808M\nHz4co0aNYiGZiKiCEEJg7969+Oabbz46p6urCxsbG2RkZGD37t1lmqNBgwaQy+W4du1arudbt24N\nBQUFnDlzpkxzZI2loaGR/cF/WS5evIjU1FR06NAh3+uNjY2hp6eHgICAXM8XNNfo6Gjcvn0bwL+F\n55UrV6J9+/bZx6oCCwsLLFiwALNnz0ZoaKjUcagCWrZsGZo3b46rV69+dK6irFtEVDZYTCYiIiIc\nPnwYVlZW+Prrr+Ht7Q0FBb5EICKqCM6dOwctLS1079491/NTpkwBUPZvGZfL5Rg7diz27NkDLy8v\nJCQkICMjA0+ePMGzZ89Qu3ZtjBgxAvv27cPWrVuRkJCAsLCwXD8MrzSyzJo1CwcOHMDOnTuRkJCA\nGzduYMqUKTAwMICDg0O+16uoqODbb79FUFAQZsyYgadPnyIzMxOJiYm4fft2gXONjo7G5MmTcffu\nXaSmpuLq1at49OgRunTpUupzldKCBQtgbm4OW1tb7p9MH8na7iLrw/X+q6KsW0RURkQJWFtbC2tr\n65J0QURERBLz9fUVysrKwsHBQWRkZEgdh4oAgPDx8ZE6BlG15uPjI0r4Z1Wexo8fL9TV1YWSkpJo\n27atCA0NzXF+2bJlwsDAQAAQAIShoaHYuHGjcHNzE6qqqgKAaNCggdixY4cQQggPDw+hpqYmAAhj\nY2MRHBwsVq1aJbS1tQUAoa+vL3bt2iX27t0r9PX1BQChq6sr9uzZI4QQIiUlRcydO1cYGRkJJSUl\nUbt2bTFixAhx69YtIYQQiYmJYsKECaJmzZpCQ0ND9OjRQyxcuFAAEPXr1xfXr1/Pc65nz54VTZs2\nzZ5L3bp1Rd++ffNsn5mZKdasWSNMTU2FsrKy0NXVFcOHDxfh4eHZbfL6PmTx9PQUZmZmQi6XC7lc\nLj755BOxcePGAucaGRkpunXrJnR1dYWioqKoV6+e+O6770R6enoRfrqVQ0xMjDAwMBC2trZSR6Ey\n5ODgIAwNDXMcO3DggGjcuLEAIGrVqiWmTZuW67UuLi5i6NCh2f8u7rr1X46OjqJmzZqlNDuiiq0S\nvp73lQkhRHEL0TY2NgAAPz+/YheziYiISDo+Pj6ws7PDxIkTsXHjRshkMqkjURHIZDL4+PjA1tZW\n6ihE1Zavry9GjhyJEvxZRVShBQYGwsLCAps2bcLEiROljkNlYPLkyTh8+HD2B2lKzcnJCTt37sxz\nX3OiqqQSvp7343tYiYiIqqk9e/bAzs4OTk5O8PLyYiGZiIiIPtK7d2/MnTsXM2bMyHMfaar83r17\nh+PHjyMiIgKpqanlPr4QAtHR0QgJCcG9e/fKfXwiKjwWk4mIiKqhrVu3ws7ODs7OzlizZo3UcYiI\niKgCW7p0Kbp16wZbW1skJiZKHYfKwOvXrzFw4EA0bdoU48aNK/fxDx06BENDQ5ibm+PIkSPlPj4R\nFR6LyURERNWMt7c3Jk2aBBcXF7i5uUkdh4iIiCo4RUVFbN++HW/evMHMmTOljkOlbPPmzRBCZH/t\n3Lmz3DMMGzYsRwZucUFUcbGYTEREVI1s2bIFDg4OWLRoEVatWiV1HCIiIqok6tevj59//hlbt26F\nr6+v1HGIiEgiLCYTERFVE+7u7pgyZQqWLl2KhQsXSh2HiIiIKpmhQ4di4sSJmDx5Mh4/fix1HCIi\nkgCLyURERNXA2rVrMWvWLPzwww+YP3++1HGIiIioknJ3d0edOnUwfvx4ZGZmSh2HiIjKGYvJRERE\nVZybmxvmzJkDDw8P7nNIRFQNhIeHY/r06WjVqhU0NTWhpKQEbW1tNG3aFIMGDcL58+dLZZyUlBQ4\nOjqibt26UFNTw7Fjx0ql3/Ly559/QltbG/7+/lJHqVTU1dWxa9cunDlzBuvXr5c6TrWxe/duyGQy\ndOvWrUjXTZgwAZqampDJZLh27VoZpSvY2rVrUadOHchkMmzevLnE/ZX2/Vva+YiqMhaTiYiIqjA3\nNzfMmzcPGzZswIwZM6SOQ0REZWzr1q0wMzNDWFgY1q1bh8ePHyMpKQlXr17FsmXLEBcXhxs3bpTK\nWD/88AOOHTuGu3fvwsPDA2/fvi2VfsuLEELqCJVWhw4dsHDhQsybNw/Xr1+XOk61sHv3bjRu3Bjn\nz5/HvXv3Cn3dzz//DG9v7zJMVjizZ8/GuXPnSq2/0r5/SzsfUVWmJHUAIiIiKhuLFi3CsmXL4Onp\nialTp0odh4iIytiFCxfg4OCATz/9FMePH4eS0v/9uWdiYgITExPo6OggIiKiVMY7ePAgOnbsCB0d\nHUyaNAkA8O7dO/Tt27dSFGUGDRqE+Ph4qWNUWq6urggICICdnR0uXboEuVwudaQq69WrV7h9+zaW\nLVsGOzs7/Pbbb1i6dKnUsSTF+5dIOnwymYiIqAqaP38+vv/+e/zyyy8sJBMRVRPLly9HRkYGVq5c\nmaOQ/F8DBgzAtGnTSmW8J0+eQFlZOcexrVu34vnz56XSP1VsioqK+O233/D48WN89913Usep0nx9\nfTFo0CBYWlpCLpdjx44dRXoyVyaTlWE6IqpuWEwmIiKqQoQQmDlzJlatWoVt27bB3t5e6khERFQO\nUlNTcfLkSdSsWROdO3cu1DWrV6+GmpoaNDU18fz5c8yaNQuGhoYIDw9HcHAwWrZsCW1tbcjlcpiZ\nmeH48eMAgL/++gtNmjTBs2fPsH37dshkMmhoaMDJyQmzZs3C/fv3IZPJ0KRJk2KNnZGRgYULF8LI\nyAiqqqpo06YNfHx8AAAbNmyAXC5HnTp1MHnyZBgYGEAul6Nbt264ePFijjHym0NISAiMjIwgk8ng\n6elZYCbKXcOGDeHh4QEPDw+cOXNG6jhV1u7du2FlZQVNTU30798fkZGRCA4OzrWtEAJr1qxBs2bN\noKKiAm1tbbi4uORo4+HhAXV1dSgoKKBDhw7Q19eHsrIy1NXV0b59e5ibm6NBgwaQy+XQ0dHBnDlz\nCp11x44d6NixI+RyOdTV1WFsbIxly5bl2T6/+z2ve3Lr1q0f3b+FGT+/NYGICo/FZCIioipCCAEn\nJyf8+OOP2L59O77++mupIxERUTl59OgR3r9/D1NT00JfM2fOHDg7O+Pt27dYsWIFGjVqhC5dukAI\ngdjYWIwcORKRkZGIjo6GhoYGvvrqKwCAhYUF7t27B319fYwZMwZCCLx9+xYeHh4YMmQIGjduDCFE\nvvu65je2q6srVq9eDXd3dzx79gxDhgzBqFGjcPnyZcyYMQP29vZITk6Go6MjIiMjERoaivT0dFhY\nWODx48fZY+Q3hx49eny0FUd+mShv9vb2GDp0KMaOHVvp9s2uDKKiohAeHo6ePXsCAGxsbAAAv/32\nW67tFyxYgLlz58LBwQGxsbGIiYmBq6trjjZOTk5wcXGBEAKbNm3Cw4cPERMTg549e+Lq1auYN28e\nrl69itevX2PMmDFYs2ZNofbG9vDwwOjRo2FtbY3o6Gg8efIE3377bb7/Qya/+z2ve7J79+65bqVT\n0Pj5rQlEVHgsJhMREVUBQghMnz4dmzZtgq+vL18YExFVMwkJCQAADQ2NYl2/atUqTJs2Dfv370fz\n5s1hbW2NRYsWQVdXF3p6erC0tMSrV6/w4sWL0oz90djGxsbw8vLC8OHDMWLECOjo6GD+/PlQVlbG\ntm3bsq9RUlJCixYtoKKigpYtW8LLywuJiYk52pRkDh9+Pyh/GzduREJCwkdFSyq53bt3Y/DgwVBU\nVAQAWFpaQkVFBX5+fnj37l2Otu/evYO7uzv69esHZ2dn6OjoQFVVFXp6enn237JlS6ipqaFmzZr4\n8ssvAQBGRkaoVasW1NTUYGdnBwC4e/duvjnT0tKwZMkS9O7dG66urtDT04Ouri7Gjx+PTp065XrN\n+/fvC3W/AwXfk4UZvzzXNaKqjMVkIiKiSi4zMxMTJkyAt7c3fH19YWVlJXUkIiIqZ1lF5OTk5DLp\nP2tv5IyMjDLpP0t4eDiSk5PRunXr7GOqqqqoW7duvsWsjh07Qk1NLd825TWH6sjAwAA//vgjvLy8\nuG1AKcva4iKLlpYW+vfvj4SEBBw6dChH23v37iE5ORl9+/Yt1lg1atQAAKSnp2cfy7pv0tLS8r02\nLCwMcXFxGDBgQI7jioqKcHR0zPWa4t7vpTU+1wSi4mExmYiIqBLLyMjAuHHjsGvXLvj5+WHYsGFS\nRyIiIgkYGxtDLpfjn3/+KZX+jhw5gl69eqF27dpQUVEp0p6p/3X48GHIZLIcX1lPOuYmKSkJwL8f\nJPvfax49elRgoVxFRSXHE4alNQcqnC+//BLW1tYYP3484uLipI5TJdy8eRM3btzAkCFDctwP/v7+\nAD7e6uLJkycAgNq1a5dZprzu6ax3R+jo6BS6r5Lc7x8qzPhcE4hKB4vJRERElVRGRgbGjh0LX19f\n+Pv7w9LSUupIREQkERUVFQwYMAAvX77E2bNn82z3+vVrTJgwId++oqKiMHz4cNStWxcXL15EfHw8\n3NzcipVr8ODBEELk+Nq5c2ee7bOKYO7u7h9dd/78+TyvS0tLQ1xcHOrXr1/qc6DC8/LyQnp6OmbP\nni11lCph165d+PLLLz+6F16/fg1VVVUEBAQgJiYmu71cLgcApKSklFmmvO7pevXqAQBevnxZ6L6K\ne7/npqDxuSYQlR4Wk4mIiCqhjIwMjBkzBvv378cff/wBCwsLqSMREZHEFi9eDBUVFTg7O3+0l2qW\nmzdvQklJKd9+bty4gbS0NEydOhUmJiaQy+WQyWRlEfkjDRo0gFwux7Vr14p03enTpyGEQJcuXQBI\nO4fqrFatWvDy8sLWrVsREBAgdZxKTQiBvXv34ptvvvnonK6uLmxsbJCRkYHdu3dnH2/dujUUFBRw\n5syZ8owK4N93R+jp6RXp517c+70443NNICo9LCYTERFVMqmpqbC1tcXBgwfh7++Pfv36SR2JiIgq\ngHbt2mHXrl24efMmzM3N8eeffyI+Ph5paWl4+PAhvL29MX78+Ox9QvNiZGQEADhx4gTev3+PiIgI\nXLx4sVAZ9PT0EB0djcjISCQmJha4z+qH5HI5xo4diz179sDLywsJCQnIyMjAkydP8OzZs+x2mZmZ\nePPmDdLT0xEWFgYnJycYGRnB3t6+xHOgkrGysoKNjQ0mTZqEt2/fSh2n0jp37hy0tLTQvXv3XM9P\nmTIFQM6tLmrXro0RI0Zg37592Lp1KxISEhAWFoaffvqpzPOqqKjg22+/RVBQEGbMmIGnT58iMzMT\niYmJuH37dq7XFPZ+L43xuSYQlSJRAtbW1sLa2rokXRAREVERpKSkiGHDhgkNDQ0RGBgodRySGADh\n4+MjdQyias3Hx0eU8M+qUhcVFSVmz54tzMzMhIaGhlBUVBQ6Ojrik08+EePHjxdnz54VQgjh5uYm\nVFVVBQDRoEEDsWPHjuw+5s6dK/T09ISOjo6wsbERnp6eAoBo3LixCA4OFp988okAIJSUlET79u3F\nvn37hBBChIaGioYNGwpVVVXRo0cPERMTk2vG/MZOSUkRc+fOFUZGRkJJSUnUrl1bjBgxQty6dUsI\nIYSDg4NQVlYWhoaGQklJSWhpaYlhw4aJ+/fv5xgjvznMmTNH1K1bVwAQampqwtLSMt9MVDQvXrwQ\ntWvXFo6OjlJHqZTGjx8v1NXVhZKSkmjbtq0IDQ3NcX7ZsmXCwMBAABAAhKGhodi4caMQQojExEQx\nYcIEUbNmTaGhoSF69OghFi5cKACI+vXri+vXrwsPDw+hpqYmAAhjY2MRHBwsVq1aJbS1tQUAoa+v\nL3bt2iX27t0r9PX1BQChq6sr9uzZU2B2T09PYWZmJuRyuZDL5eKTTz4RGzduFD/88EN2X+rq6sLK\nykoIkf/9ntc9+eOPP350/xY0vhD5rwlOTk655iMqa5Xw9byvTAghiluItrGxAQD4+fkVtwsiIiIq\npJSUFNjY2CAoKAjHjh3LfisvVV8ymQzgOkItAAAgAElEQVQ+Pj6wtbWVOgpRteXr64uRI0eiBH9W\nURFNnjwZfn5+ePXqldRRKB/bt2/HuHHjEBQUlOfTtURE1V0lfD3vx20uiIiIKoHk5GQM+X/s3Xlc\nVPX+P/DXsDgLywDKomzKqDdF67pe82pfpdQsUYFBuWqlaaJUgkKCSy6ZGmaCgUulUonaAJpesYzc\nQrQ0S3NBCTVEQsVUQPbt/fvDn3MbgRmWgTPA+/l4zB+e8zmf8zpnzvsw8/HMOZ6eOH78OJKSkngg\nmTHGWJtWWVkpdASmw2uvvYYRI0ZgxowZKCkpEToOY4wxPeHBZMYYY8zAFRUVYezYsThz5gySkpIw\ncOBAoSMxxhhjjOn0ySefICsrCx988IHQURhjjOkJDyYzxhhjBqywsBBjxozBhQsXcOzYMQwYMEDo\nSIwxxphgFi5ciJiYGOTl5aFLly5ISEgQOhLTwtXVFStWrMDq1atx8eJFoeMwxhjTAx5MZowxxgxU\nXl4eRo4cidTUVBw+fBhPP/200JEYY4wxQa1atQqlpaUgIvzxxx9QKpVCR2I6vP322+jbty9mzpyJ\nqqoqoeMwxhhrJB5MZowxxgxQXl4eXnzxRVy/fh2HDx9Gr169hI7EGGOMMVZvxsbG+Oyzz3DmzBls\n2bJF6DiMMcYaiQeTGWOMMQOTm5uLkSNH4s8//0RycjLc3d2FjsQYY4wx1mC9evVCYGAgwsLCkJOT\nI3QcxhhjjcCDyYwxxpgBefDgAUaMGIHbt2/j6NGj6Natm9CRGGOMNaHdu3fDzc0NIpGo1lfnzp0F\nzfjNN99ALpdj//79gqy/tLQUgYGBcHBwgEwmw8GDB+uVadq0aZBIJBCJRCgpKdHaVuhtbc2WL18O\nuVyOkJAQoaM0m5ZQ36y6+p4H1q5dCzs7O4hEImzevLmJ0zEmPB5MZowxxgxETk4Ohg0bhrt37+Lo\n0aNQKBRCR2KMMdbEfHx8cP36dSgUCsjlchARiAgVFRUoKirCnTt3IJPJBM1IRIKu/6OPPsLBgwdx\n5coVREZGoqCgoF6ZYmJi6jyAKfS2tmYymQwbNmzA9u3bcfjwYaHjNIuWUN+suvqeB0JCQnDy5Mkm\nSsOY4TEROgBjjDHGgDt37uCFF15AQUEBjh07xlepMMZYG2dsbAypVAqpVIru3bsLmuXll19GXl6e\nYOvfu3cv+vfvDysrK8ycOVM9vSkyCb2trd1LL72EcePGYfbs2Th//jwkEonQkQRhSPXNquPzAGPa\n8ZXJjDHGmMBu374NDw8PlJWVISUlhQeSGWOMadi7d6/QEfSKiBAfH49PP/20Tu2zsrJgamqql3WL\nRCK99FNX9d3WtiAqKgq3bt3Chx9+KHQUg9Da6lsoXGuMNR8eTGaMMcYEdPPmTQwdOhRVVVU4evQo\nHB0dhY7EGGPMgB0/fhw9e/aEXC6HRCJB79698d133wEA1qxZA5lMBgsLC+Tk5CA4OBiOjo6YPXs2\nzMzMYGRkhH79+sHe3h6mpqYwMzND3759MXToUDg7O0MikcDKygrz589Xry8lJQUuLi4QiUSIjo4G\nAGzcuBFmZmaQyWTYt28fRo8eDUtLSzg5OWHXrl0aeSsrK7Fq1Sr84x//gFQqRYcOHdClSxesWrUK\nEyZM0Lqt33//Pbp27Ypbt27hiy++gEgkgrm5eY2ZAOCHH37AwIEDIZPJYGlpid69eyM/P18938jI\nCAcOHMDo0aMhl8vRsWNHbNu2zSC2tS1xdnbG0qVLsWrVKly7dk3oOAbl888/h7m5OUQiEaytrbF3\n716cOXMGrq6uMDY2xqRJk9RtKysrsWTJEri4uEAqleLpp5+GSqXS6G/79u3o378/JBIJzMzM0Llz\nZ6xYsaLW9UdGRjb4XAFoPz/Vp5a09fN42+tSa9r2UW3ny61bt9Z4ftGVibE2hRpBqVSSUqlsTBeM\nMcZYm3Xjxg1SKBTUo0cPys7OFjoOa4EAkEqlEjoGY22aSqWiRn6tIiIihUJBcrlcY9rhw4fpww8/\n1JgWHx9Py5Yto/v379O9e/do0KBB1L59e/X8RYsWEQAKDAykqKgo8vb2psuXL9PSpUsJAJ06dYoK\nCwvpr7/+ohdffJEA0IEDB+ju3btUWFhIc+bMIQB07tw5dZ83b94kABQVFVVtPYcPH6a8vDzKycmh\noUOHkpmZGZWVlanbrVy5koyNjWnfvn1UVFREv/zyC9nb29OwYcPqvG/s7e3ptdde05j2ZKaCggKy\ntLSk8PBwKi4uptu3b5O3tzfdvXu3Wt7c3Fy6f/8+vfTSSyQWi6mwsNBgtrWtKC8vJ3d3dxozZozQ\nUZpFTfUdGBhIFy5cqNY2NTWVZDKZxjG/YMEC2rJli0a7kJAQEovFlJCQQA8ePKCFCxeSkZER/fzz\nz0REFBERQQBo9erVdO/ePbp//z598sknNHnyZK1ZG3OuqOv5SVct6eqnrrWmax/Vdr6s6TygK1N6\nejoBoE2bNmndv4w9qQV+no/jK5MZY4wxAdy4cQPDhg2DhYUFkpOT0bFjR6EjMcYYE1heXh5EIpH6\n9fzzz1dro1QqsXTpUlhbW8PGxgZjx47FvXv3cPfuXY12H3zwAd566y3s3r0bTz31lHp6z549IZPJ\n0L59e/znP/8BALi4uKBDhw6QyWSYMmUKAODKlSt1yjx48GBYWlrC1tYWfn5+KCwsRGZmpnr+3r17\n0a9fP4wdOxZSqRR9+/bFuHHjkJycjLKysnrvo9pkZGQgPz8f7u7ukEgksLe3x+7du9GhQ4dqeeVy\nOaytreHn54fS0lL88ccfLWpbWwMTExNs2rQJBw4cwIEDB4SO0yyerO/169fX2K5Hjx6IiIjAF198\ngR07dmDXrl0oLS3F9OnT1W1KSkqwceNGeHl5wcfHB1ZWVli8eDFMTU0RExOD8vJyLF++HMOHD0dY\nWBhsbGxgbW2N6dOnY8CAAXXK25BzRV3PT7pqSVc/dak1Xfvo72o7X/5dXbeNsbaAB5MZY4yxZpae\nno4hQ4bAysoKhw4dqvZFlzHGWNskl8tBROrX0aNHdS7z+F7ClZWV9V5fu3btAAAVFRXV+isvL29w\nf39ftqSkBESk0a6yshKmpqYwNjau9zpq4+bmBjs7O0yZMgXLli1DRkaGzmVa6ra2FkOHDoWvry8C\nAwNRUlIidJwm92R9BwYG1tp25syZUCqVmDVrFuLi4rBmzRqN+WlpaSgqKkKvXr3U06RSKRwcHHDl\nyhWcP38eubm5GDVqlMZyxsbGWtdbm4aeK+pyfqqplnT1U5da07WPGqsx517GWjoeTGaMMcaaUVpa\nGoYPHw57e3scOnQI7du3FzoSY4wxAzVs2DCEhIRoTDtw4ACGDRsGW1tbiMXiavcsNTQvvfQSfvnl\nF+zbtw/FxcU4c+YM9u7dizFjxqgHfRITEzWu2BSJROqrHutKKpXiyJEjGDJkCFauXAk3Nzf4+fmh\nuLi4KTarRnXZVqbpo48+wu3bt7Fu3TqhozS7yMhIjYHOJ61cuRIFBQXIycmpNq+wsBAAsHjxYo26\nuXHjBoqKitT3Creysqqxb33UXE30dX7S1U9dak3XPhJq2xhrDXgwmTHGGGsmV65cgYeHB1xdXXHk\nyBHY2NgIHYkxxlgLkpmZCS8vLzg4OODUqVPIy8tDeHi40LG0WrZsGTw8PDB16lRYWlrC29sbEyZM\nwGeffaZuM2bMGI0rNokIsbGx9V6Xu7s79u/fj+zsbISGhkKlUmHt2rX63Byt6rKtTJOTkxMWLFiA\nlStX4saNG0LHMRjl5eUIDAzEunXr8OOPP+L999/XmG9rawsAiIiIqFY7P/74Izp16gQA+Ouvv2rs\nX18193f6Oj/VpZ+61JqufSTEtjHWWvBgMmOMMdYMLl++DA8PD7i5ueHgwYOwtLQUOpJBmjVrVrWr\nZHbv3g03Nzf1tFdeeaXaciNHjoSFhQWMjY3h7u6OX3/9tcb+d+7cCZFIhMGDB9eaYe/evRoZ+DYk\njDFDceHCBZSXlyMgIABubm6QSCQQiURCx9Lq0qVLuHbtGu7evYvy8nJkZmZi48aNsLa21ut6srOz\nkZqaCuDRINLq1avRt29f9bTm0Fzb2tq88847cHJyarNXet66dQvTpk3TmPb222/jjTfewNy5czFv\n3jysWLFCYwDU2dkZEokE586dq7HPzp07w8bGBklJSU2a/e/0dX6qSz91qTVd+0iIbWOsteDBZMYY\nY6yJnTt3Ds899xy6deuGb7/9FhYWFkJHMmg2Njb49ttvkZaWhq1bt8LHxwfXr1+HQqFA+/btERsb\nW+1hPUlJSYiPj4enpycuXbqEvn371tj3zp07oVAo8OOPP+Lq1as1thk3bhyysrKQnJyMl156Se/b\nxxhjDeXi4gIAOHToEEpKSpCeno5Tp04JnEq7t956Cy4uLigoKGjS9WRnZ2PWrFm4cuUKysrKcPbs\nWdy4cQODBg1q0vX+XXNta2vTrl07rFu3DnFxcThy5IjQcZoNEaG4uBi7d+/WuMhgw4YNcHR0hLe3\nNwBg1apV6NmzJyZPnqy+fYVEIsG0adOwa9cubNy4Efn5+aisrERWVhZu3boFsViMhQsXIjk5GXPm\nzMGff/6JqqoqPHz4sMn+g0Vf56e69FOXWtO1j/SdibE2hRpBqVSSUqlsTBeMMcZYq/bLL79Q+/bt\n6f/+7//o4cOHQscxeP7+/uTo6FjjPIVCQTt27CAjIyNydHSk3NxcjfnffvstjRs3rta+//rrL+rS\npQvFxsYSAHr33Xd15gkMDKT27dvXbyOaEQBSqVRCx2CsTVOpVNSYr1UnTpyg7t27EwACQA4ODvT8\n88/X2j40NJRsbGzIysqKfH19KTo6mgCQQqGgt956i6RSKQEgZ2dn2r59OxERRUZGkkwmIwDUuXNn\nOn78OH3wwQckl8sJANnb29OOHTvoq6++Int7ewJA1tbWtGvXLoqKiiIHBwcCQDKZjMaOHUsbNmxQ\n99etWze6du0affrpp2RpaUkAyNXVlX7//XciIjpy5Ai1b99evX0AyNTUlHr06EG7d+/Wum8yMjKo\nT58+BIBMTEyob9++lJCQUGOmjIwMGjx4MFlbW5OxsTF16tSJFi1aRBUVFRQeHq7eL4/zxsbGkrW1\nNQEgJycnunjxoqDbyohefvll6t27N1VUVAgdRW/27NlDCoVC45io6bV48WIiIvL09CSRSEQ2NjZ0\n8uRJIiKaO3cuGRkZEQCSy+V05swZIiIqLS2l0NBQcnFxIRMTE7K1tSUfHx+6dOmSev3R0dHUu3dv\nkkgkJJFIqE+fPrRhw4Za8zbmXEGk/fwUFhZW51rS1k9mZmada03bPvr7eeHv58uazgO6MgUFBan3\nh5mZGXl7e+vrEGJtQAv8PB8nInriEZj14OvrCwCIj49vaBeMMcZYq/XLL79g5MiRGDhwIPbs2QOp\nVCp0JIM3a9YsJCYmIisrq9q8rl274tChQ4iKisK6deswY8YMjXvjHTx4EJs3b8bevXtr7HvTpk1I\nTU3FqlWrYGdnBwcHB1y/fl3rzxSDgoIQGxtb6z0HhSYSiaBSqTBhwgShozDWZsXFxWHixIloxNeq\nVm3jxo1IT09HRESEelpZWRnCwsKwceNGPHjwoNX8fWxL29oUrl69Cnd3d3z88cfw9/cXOg4zYFxr\nrDVpgZ/n4/k2F4wxxlgTOHHiBDw8PDBo0CB8/fXX/IFWj95//310794dW7ZswaFDh+q83M6dO+Ht\n7Q0LCwuMHDkSGRkZOH78eBMmZYyxtu327duYM2cOpk+frjG9Xbt2cHFxQXl5OcrLywVKp19taVub\nSteuXTF79my8++67yMvLEzoOM1Bca4wJjweTGWOMMT1LSUnB6NGj8dxzz2HPnj2QSCRCR2pVpFIp\nPv/8cxgZGeGNN95AYWGhzmUyMzORlpaG5557DsD/fl315ZdfNmlWxhhry6RSKUxNTbF161bcuXMH\n5eXlyM7OxpYtW7BkyRL4+fm1mgfStqVtbUpLly4FEWHlypVCR2EGimuNMeHxYDJjjDGmR8nJyRg9\nejRGjRqFPXv2QCwWCx2pVXr22Wcxd+5cZGRkICwsTGf7nTt3YsyYMTA2NgYAjB07FmKxGPHx8Sgu\nLm7quIwx1ibJ5XIkJSXh4sWL6N69O6RSKXr27ImYmBh88MEH+OKLL4SOqDdtaVubkrW1NZYsWYL1\n69cjPT1d6DjMAHGtMSY8E6EDMMYYY61FUlISxo8fD09PT+zYsQMmJvxntim9//77SExMxIYNGzBx\n4kStbXfu3IlVq1ap/21paYmRI0di//792LdvH/z8/Jo6LmOMtUlDhw7F999/L3SMZtGWtrUpzZ49\nG59++inCwsKwe/duoeMwA8S1xpiw+MpkxhhjTA8OHjyIcePGYdy4cTyQ3EwkEgliYmIgEonw+uuv\n13qF8cWLF3HhwgV4enpCJBKpX/v37wfAt7pgjDHGDImJiQnWrFmDPXv21OvZCIwxxpoHDyYzxhhj\njfTNN9/Ay8sLkyZN4oHkZvbss89i3rx5SE9Px4oVK2pss2PHDvznP/8BEWm87t+/D6lUiqSkJNy+\nfbuZkzPGGGOsNqNHj8aLL76IsLAwEJHQcRhjjP0NDyYzxhhjjZCYmAhvb2+88sor+Oyzz2BkxH9a\nm9uKFSvw1FNP4ezZs9XmERG++uorvPnmm9XmWVtbw9fXF5WVldi5c2dzRGWMtXG7d++Gm5ub+hcS\nzs7O2Lp1q3r+9OnTYW1tDZFIBFNTU/Tp0weZmZlNmmnGjBmwsLCASCTCuXPnAABr166FnZ0dRCIR\nNm/e3KTrbwhDz8f0Y82aNTh79izi4uKEjtIsnjw/PD4PODo6YvLkybh8+bLe11laWorAwEA4ODhA\nJpPh4MGDel+HPn3zzTeQy+XqX5fpwucKxpoGf+NljDHGGig+Ph7e3t6YOnUqNm/ezAPJAnl8u4vH\nD9f7u5MnT8LS0hL//ve/a1x29uzZAPhWF4yx5uHj44Pr169DoVBALpfj5s2bmD59unr+1q1b8e23\n3wIA3nzzTZw9exYuLi5NmmnLli347LPPNKaFhITg5MmTTbrexjD0fEw/evfujcmTJ2PBggUoKysT\nOk6Te/L8QETIzc3F5s2bkZKSgoEDByItLU2v6/zoo49w8OBBXLlyBZGRkSgoKNBr//pW36vU+VzB\nWNPgb72MMcZYA6hUKkyaNAkzZszApk2beCC5CX399dfo2rUrrl27hgEDBuDtt9+u1mbQoEGYN2+e\nxrQZM2Zg1KhRSE1NxT//+c9qVy6///778Pb2BgD89ttvcHJywsaNG5tuQ5pIeXk5nn/+eRw5ckTo\nKIwxxpherVixAtnZ2RpX8LclZmZm8PT0xPr161FQUICoqCi99r937170798fVlZWmDlzJpRKpV77\n17eXX34ZeXl58PT0bFQ/xcXFGDx4sJ5SMdb28DdfxhhjrJ527dqFKVOmICgoCBs3boRIJBI6Uqvm\n5eWFq1evgohw9+7dWr9IrVmzBnv37lX/e8uWLSgoKEB5eTnOnTuHPn36aLRfvHgxsrOz1fdQzsrK\nQkBAQJNuS1MwNTXF2rVrsWnTJgwZMgQHDhwQOhJjrIXhv2PMULm6umL27NlYtmwZHj58KHQcwQwc\nOBDAo4cK61NWVhZMTU312mdLsHXrVuTk5Agdg7EWiweTGWOMsXrYunUrpkyZgnnz5uHDDz8UOk6r\nVFxcjO+++w7p6emC/KyViJCdnY2UlBRcvXq12dffEH369EF8fDy2bduG3bt341//+hcSEhJQVVWl\n0a6goEDjXow1vQYNGiTQVjDGtDl+/Dh69uwJuVwOiUSC3r1747vvvgMAbNy4EWZmZpDJZNi3bx9G\njx4NS0tLODk5YdeuXRr9EBE+/PBD/OMf/4BYLIZcLsc777xTpwyVlZVYsmQJXFxcIJVK8fTTT0Ol\nUgF49B96MpkMFhYWyMnJQXBwMBwdHZGWloYffvgBAwcOhEwmg6WlJXr37o38/Hyd69u+fTv69+8P\niUQCMzMzdO7cudaHrerKp8992Nz7oa1btGgRSkpKsH79eqGjCKaiogIAIBaLAWg/zogI69atQ48e\nPSAWi2FtbY3x48fjypUr6v6+//57dO3aFbdu3cIXX3wBkUgEc3NzrRlqO34//vhjSCQS2NnZYdas\nWejYsSMkEgkGDx6MU6dOafShq0aB2us+JSUFLi4uEIlEiI6OVrfXVtc1CQoKQnBwMK5duwaRSISu\nXbtixowZ6s9BCoVC/Wu2adOmQSaTQS6X47///W8d3inG2gYeTGaMMcbq6LPPPsPMmTPxzjvvIDw8\nXOg4rdb9+/fx4osvonv37nj99debff379u2Do6Mjhg4d2uKu8u3evTu2bduGhIQEHD9+HAMHDsT2\n7dvVX0LNzc3VV2LX9vrpp58E3grGWE3u3LmDiRMnIiMjA9nZ2TA3N8fkyZMBAAEBAZg7dy6Ki4th\nYWEBlUqFa9euwc3NDW+88QbKy8vV/bz77rsIDQ2Fv78/7ty5g9u3byMsLKxOGcLCwrBmzRpERETg\n1q1b8PT0xKRJk3DmzBnMnz8f8+bNQ0FBAVatWoUuXbpg0KBBKCgowNixY6FUKnH//n2kp6eje/fu\nOv+zMDIyEq+++iqUSiWys7ORlZWFhQsXar1nrLZ8+tyHzbkfGNChQweEhIRgzZo1bfZq0uTkZADA\nM888AwC1HmdEhGXLlmHBggVYtGgRcnJykJycjJs3b2Lo0KG4c+cOAGDEiBG4evUq7O3t8dprr4GI\ntN4vubCwsNbjd86cOZg6dSqKiooQGBiIjIwM/Prrr6ioqMCIESNw8+ZNdT+6alRb3Q8ZMqTG+x9r\nq+uaREZGwtPTEwqFAkSEq1evYsuWLfDx8YGxsTGOHz+u/jVbTEwMvLy8EBsbi7Fjx9bzXWOsFaNG\nUCqVpFQqG9MFY4wx1iJs3ryZRCIRLV++XOgojKkBIJVKVev81NRU6tSpE/n6+jZjKsbaFpVKRfX9\nWqVQKAiA1ldgYKDWPlatWkUAKCcnh4iIFi1aRACouLhY3WbDhg0EgK5evUpEREVFRSSTyWjEiBEa\nfe3atYsA0NmzZ9XT0tPTCQBt2rSJiIiKi4tJJpORn5+fuk1RURGJxWIKCAioNcPFixcJACUmJtZ5\n/5SVlZGVlRUNHz5cY3pFRQVFRkY2ON+TGrIPm3M/sP8pKCggBwcHmjt3rtBRmpxCoSC5XE5Ej7Y7\nISGB7O3tyc7OjrKystTtajrOioqKyNzcXOP4JCI6ffo0AaD33ntPY7q9vT299tprOjPpOn79/f3V\nmR/7+eefCYD6s7Ou2qlL3d+8eZMAUFRUVK1Zn6zrJ88VREQ+Pj6kUCg0ljt06BABoPfff189LS8v\nj7p160YVFRW1ro+xxtL1ed4AxfGVyYwxxpgOERERmD17Nt577z0sWbJE6DiM6ZSVlYWgoCC88sor\nWL16NXbu3Cl0JMbYE+RyeY2/Dvjxxx/rtPzj+5xWVlbW2qZdu3YAoL6q9urVqygqKsLzzz9f77xp\naWkoKipCr1691NOkUikcHBw0fj7/JDc3N9jZ2WHKlClYtmwZMjIydK7r/PnzyM3NxahRozSmGxsb\nIzAwUG/5GrIPm3M/sP8xMzPD4sWLsWHDBly/fl3oOE0uLy8PIpEIcrkcgYGBeOmll3D69Gk4Ojpq\nXe7SpUsoKChA//79NaYPGDAA7dq1q3bbibpqyPHbv39/yGQydV3oqp2G1H1N6lLXNfHw8FD/wouI\nAABfffUV/Pz8YGxsXK++GGvteDCZMcYY02Lt2rUIDg7GRx99hMWLFwsdhzGtfv/9d0yfPh0+Pj74\n97//jdOnT+PVV1+FiYkJAL5nMmMt2YEDBzBs2DDY2tpCLBZj/vz59e4jKysLAGBra1vvZQsLCwE8\nenjp388ZN27cQFFRUa3LSaVSHDlyBEOGDMHKlSvh5uYGPz8/FBcXIzExsdo5aMqUKer7CFtZWek1\nnz72YVPsB1Y3M2fOhIuLC5YvXy50lCb3+D+bKioqkJWVhW3btsHV1VXncrm5uQBQ4/2PraysdD7E\nsLaabOjxKxaLcffuXQC6a6chdQ/op66BRw8inTVrFq5fv47Dhw8DAL788ktMnz69Qf0x1prxYDJj\njDFWi/DwcMyfPx+RkZGYO3eu0HEYq9XZs2cxceJEvP766/Dy8sKpU6fg6+sLIyPNj3p8z2TGWqbM\nzEx4eXnBwcEBp06dQl5eXoPu3S+RSAAApaWl9V728QB0REREva+mdnd3x/79+5GdnY3Q0FCoVCqs\nXbsWY8aMqdZXbGwsOnXqBAD466+/9JZPX/uwKfYDqxtTU1OsWLECsbGxOHfunNBxDNLjgdiaBo1z\nc3Ph5OSkdfnaahKo//FbXl6usU5dtdOQutdXXT82depUSCQSbNmyBWlpabC0tKzTID5jbQ0PJjPG\nGGM1CA8Px4IFC/Dxxx9jzpw5QsdhrFbl5eUIDg7GzJkzkZKSgjFjxggdiTGmZxcuXEB5eTkCAgLg\n5uYGiUQCkUhU73569eoFIyMj/PDDD/Ve1tnZGRKJpN6DeNnZ2UhNTQXwaDBp9erV6Nu3r3paTTp3\n7gwbGxskJSXpLZ++9mFz7gdW3cSJE9GnTx/+tVgtevXqBXNzc/UD7R47deoUysrK0K9fvwb125Dj\n99ixYyAi9S+edNVOQ+peX3X9mLW1NSZOnIi9e/di7dq1eOONNxrcF2OtGQ8mM8YYY09YunQpFixY\ngOjoaLz11ltCx2FMK1NTUxw5cqRB90BljLUMLi4uAIBDhw6hpKQE6enpDbr3qa2tLXx8fJCQkICt\nW7ciPz8f58+fx6effqpzWYlEgmnTpmHXrl3YuHEj8vPzUVlZiaysLNy6davW5bKzszFr1ixcuXIF\nZWVlOHv2LG7cuKH1ljpisRgLFzHMRXkAACAASURBVC5EcnIy5syZgz///BNVVVV4+PBhrYNXuvLp\nax82535g1YlEIqxevRoHDhzA0aNHhY5jcCQSCYKDg7Fnzx7ExsYiPz8fFy5cwOzZs9GxY0f4+/s3\nqN+6HL9VVVV48OABKioqcP78eQQFBcHFxQVTp05VZ9NWOw2p+4bWtY2NDbKzs5GRkYGHDx+q74kO\nALNnz0ZpaSkSExPh6enZoP3FWKvXmMf3KZVKUiqVjemCMcYYMyiLFi0iY2NjiomJEToK05OysjLa\ntm0bvfrqq0JH0Tu0vKc/M9bqqFQqquvXqj179pBCoSAABIBcXV01/t688cYbZG1tTQDI1NSU+vXr\nR5mZmUREFBoaSjY2NmRlZUW+vr4UHR1NAEihUFBYWBjJZDICQN26daNr167Rp59+SpaWlur1/P77\n70RE9PDhQ5oxYwa1b9+ezM3NaciQIbRkyRICQE5OTvTbb7/RRx99RPb29gSAzMzMyNvbm4iISktL\nKTQ0lFxcXMjExIRsbW3Jx8eHLl26ROHh4SSVSgkAOTs70/bt24mIKCMjgwYPHkzW1tZkbGxMnTp1\nokWLFlFFRYXO/RUdHU29e/cmiURCEomE+vTpQxs2bGhQPn3uw+beD6y6F154gQYOHEhVVVVCR9Gb\nEydOUPfu3dXnh44dO5Kvr2+NbWs7zoiIqqqq6MMPP6Ru3bqRqakpWVtbk5eXF6WlpanbZGRkUJ8+\nfQgAmZiYUN++fSkhIaHWbLqOX39/fzI1NSVHR0cyMTEhS0tLGj9+PF27dk2jH101SlR73UdFRZGD\ngwMBIJlMRmPHjiUi7XUdFBRU47ni119/JVdXV5JKpTRkyBC6ffu2Rs4+ffrQggUL6vK2MdZoLfDz\nfJyI6P8/prIBfH19AQDx8fEN7YIxxhgzCESEefPmISoqCjExMXjllVeEjsQaqbS0FNu2bcPGjRsx\natQoBAcHo2PHjkLH0iuRSASVSoUJEyYIHYWxNisuLg4TJ05EI75WMcbq6cyZMxg4cCD27NmD8ePH\nCx2nzZs1axbi4+Nx7949oaPoxcsvv4zo6Gh06dJF6CisDWiBn+fj+TYXjDHG2jwiQlBQEKKiovDF\nF1/wQHILV1hYiHXr1qF///7IycnBDz/8gLVr17a6gWTGGGOsrerfvz+USiWWLFmCqqoqoeMwAJWV\nlUJHaLC/3+bi/PnzkEgkPJDMmBY8mMwYY6xNIyK8/fbb2LRpE+Li4jB58mShI7EGysvLw8qVKzF4\n8GAQEX766ScsXboUNjY2QkdjjDHGmJ4tW7YMly5dwtdffy10FNbChYaGIj09Hb///jumTZuGFStW\nCB2JMYNmInQAxhhjTChVVVV44403EBsbi7i4OP6ZZAtWXl6OZ555Bq6urkhJSYGFhYXQkRhjjDHW\nhHr27AkfHx8sXboUXl5eMDLia+WEsHDhQsTExKCsrAxdunTBhx9+CKVSKXSsepHJZHjqqafg6OiI\nDRs2oGfPnkJHYsyg8dmWMcZYm1RZWYnXX38dO3bsQHx8PA8kt3Cmpqb47bffMGLECAwdOhQREREo\nLCwUOhZjjDHGmtDSpUtx+fJl7N27V+gobdaqVatQWloKIsIff/zR4gaSAeD9999HZWUlMjMz4enp\nKXQcxgweDyYzxhhrcyorKzFt2jTExcVh//79GDt2rNCRmB7I5XIsXrwYJ06cQFVVFQYNGoT33nsP\nDx48EDoaY4wxxpqAu7s7vL29sXTpUr53MmOMNRMeTGaMMdamVFZW4rXXXsPu3bvx3//+FyNGjBA6\nEtMzMzMzBAcH4+eff0aHDh3w3HPP4Z133sGtW7eEjsYYY4wxPVu2bBlSU1Oxb98+oaMwxlibwIPJ\njDHG2oyysjJMmDABe/fuxf79+/HCCy8IHYk1IYlEgoCAAPz666/o0aMHwsLChI7EGGtDdu/eDTc3\nN4hEIohEIrz77rta269btw4ikQhGRkZ46qmnkJyc3OgM06ZNg0QigUgkQklJida233zzDeRyOfbv\n39/o9Rqa1rxt7NHVyV5eXliyZEmLujr5yXOESCSCqakpHB0dMXnyZFy+fFnoiA3C9cZY68eDyYwx\nxtqEsrIyTJw4EUlJSUhMTISHh4fQkVgzMTU1xeuvv44vvvhC6CiMsTbEx8cH169fh0KhAABs2bIF\n5eXlNbatrKzExx9/DADw8PDAlStX8NxzzzU6Q0xMDEJCQurUlogavT5D1Zq3jT2yfPlypKam4r//\n/a/QUers7+cIuVwOIkJubi42b96MlJQUDBw4EGlpaULHrDeuN8ZaPx5MZowx1uqVlpZCqVTi6NGj\n+P777zFs2DChIzHGGGtD+vXrh9u3b9f6kLDdu3fD0dGxmVNpevnll5GXl9cqHz5l6NtWXFyMwYMH\nCx2jRXN3d8f48eOxfPnyFj2YaWZmBk9PT6xfvx4FBQWIiorSW99NcZzV1Keh1xtjrPF4MJkxxlir\nVlRUBE9PTxw/fhxJSUkYNGiQ0JEYY4y1MQEBAQCATZs21Th/3bp1CA4ObtIMIpGoSftnDbd161bk\n5OQIHaPFW758Oc6fP9+irk6uzcCBAwEAFy9e1FufTXGc8bHLWNvEg8mMMcZaraKiIowdOxZnzpxB\nUlKS+oM5Y4wx1pw8PDzQo0cPHD16tNrP1k+cOIGioiKMHDmyxmWPHz+Onj17Qi6XQyKRoHfv3vju\nu+802mzfvh39+/eHRCKBmZkZOnfujBUrVqjnGxkZ4cCBAxg9ejTkcjk6duyIbdu2qeenpKTAxcUF\nIpEI0dHRAICNGzfCzMwMMpkM+/btw+jRo2FpaQknJyfs2rVLY/2VlZVYsmQJXFxcIJVK8fTTT0Ol\nUuncL0SEdevWoUePHhCLxbC2tsb48eNx5coVdZuPP/4YEokEdnZ2mDVrFjp27AiJRILBgwfj1KlT\nOtfR2G374YcfMHDgQMhkMlhaWqJ3797Iz8+vd67a3qOgoCAEBwfj2rVrEIlE6Nq1q85tYjXr1asX\nxo0b1+KvTgaAiooKAIBYLAYArFmzBjKZDBYWFsjJyUFwcDAcHR2RlpZWpzqq7TirS+3W59itqd56\n9Oihvh98v379UFRUBACYP3+++rz2+eef1zlPTWqrU1191rZfjYyM6pyZsTaJGkGpVJJSqWxMF4wx\nxliTKCgooOHDh5OdnR399ttvQsdhrEkAIJVKJXQMxto0lUpF2r5WKRQK+uOPP2j9+vUEgIKCgjTm\ne3l5UUxMDD18+JAA0PPPP68xPz4+npYtW0b379+ne/fu0aBBg6h9+/bq+REREQSAVq9eTffu3aP7\n9+/TJ598QpMnTyYiokWLFhEAOnz4MOXm5tL9+/fppZdeIrFYTIWFhep+bt68SQAoKipKPe3vy+bl\n5VFOTg4NHTqUzMzMqKysTN0uJCSExGIxJSQk0IMHD2jhwoVkZGREP//8s9Z9t2TJEmrXrh1t376d\ncnNz6fz589S3b1/q0KED3b59W93O39+fzMzMKDU1lUpKSujSpUs0YMAAsrCwoMzMTK3raMy2FRQU\nkKWlJYWHh1NxcTHdvn2bvL296e7du/XKpes98vHxIYVCoXM7mG5nz54lkUhE+/btEzpKnSkUCpLL\n5RrTtm/fTgDonXfeUU97fMwGBgZSVFQUeXt70+XLl+tcRzUdZ7pqtyHH7pP1VlFRQZ07dyYXFxeq\nqKjQaDt37lyKiIioc56a6KpTXX3WtF8vXrxY58yMNVYL/Dwfx4PJjDHGWp3c3Fx69tlnyd7eni5c\nuCB0HMaaTAv88MlYq1PXweTc3FwyMzMja2trKioqIiKia9eukZOTE5WWltY6mPykVatWEQDKycmh\nsrIysrKyouHDh2u0qaiooMjISCL630BJcXGxev6XX35JAOjixYvqadoGXP++7IYNGwgAXb16lYiI\niouLSSaTkZ+fn7pNUVERicViCggIqHU7ioqKyNzcXGM5IqLTp08TAHrvvffU0/z9/asNtv38888E\ngJYvX177zmrktl28eJEAUGJiYo391iVXXd4jHkzWr3HjxlGfPn2oqqpK6Ch18vfB5IKCAkpISCB7\ne3uys7OjrKwsdbuajtn61NGTx5mu2m3osVtTvT0elI6Li1NPKywsJBcXF8rLy6tTntpoq9O69FnT\nfq1rZsb0oQV+no/j21wwxhhrVfLy8vDiiy/ijz/+wOHDh9GrVy+hIzHGGGOQy+WYNGkSHjx4gK++\n+goAEBERgYCAALRr167O/ZiamgJ49NPt8+fPIzc3F6NGjdJoY2xsjMDAQJ19lJeX13cz1FkfL5uW\nloaioiKNv7dSqRQODg4aP7N/0qVLl1BQUID+/ftrTB8wYADatWun8xYW/fv3h0wm07qO+npy29zc\n3GBnZ4cpU6Zg2bJlyMjI0NnHk7ka+h6xhlu6dCnOnTuHxMREoaPUWV5eHkQiEeRyOQIDA/HSSy/h\n9OnTOh/M2Zg60lW7+jx2Z8yYAblcjsjISPW02NhYjB8/HpaWlnXKUxttddrQPuuambG2igeTGWOM\ntRq5ubkYOXIk/vzzTyQnJ8Pd3V3oSIwxxpja4wfxbd68Gbm5uYiPj8esWbO0LnPgwAEMGzYMtra2\nEIvFmD9/vnre43uCWllZNV1oHQoLCwEAixcvhkgkUr9u3LiBoqIiJCYmakwXiUSYMmUKcnNzAQDm\n5ubV+rSyssLDhw91rlssFuPu3bsAUOt6GkMqleLIkSMYMmQIVq5cCTc3N/j5+aG4uLjOuQzhPWpr\n+vTpg7Fjx+K9994TOkqdyeVyEBEqKiqQlZWFbdu2wdXVVedyjakjXbWrz2PX3NwcM2fOxMmTJ3H6\n9GkAjx5IOmfOnDrnqa3GtdWprj4bm5mxtooHkxljjLUKDx48wIgRI3D79m0cPXoU3bp1EzoSY4wx\npuGf//wnBg0ahNOnT8Pf3x++vr6wtrautX1mZia8vLzg4OCAU6dOIS8vD+Hh4er5nTp1AgD89ddf\nTZ69Nra2tgAeXWVNRBqvH3/8EWPGjKk2PTY2Vj1AVdNgV25uLpycnLSut7y8XKNdbetpLHd3d+zf\nvx/Z2dkIDQ2FSqXC2rVr65zLEN6jtujdd99VP4C5NWtMHemqXX0fu3PmzIGpqSkiIiKQnJwMZ2dn\nKBSKOufRVuO11amuPhubmbG2igeTGWOMtXg5OTkYNmwY7t69i6NHj/KHPMYYYwbr8dXJCQkJmDt3\nrta2Fy5cQHl5OQICAuDm5gaJRAKRSKSe37lzZ9jY2Ag6YObs7AyJRIJz587Va7levXrB3NwcZ86c\n0Zh+6tQplJWVoV+/flqXP3bsGIgIgwYNqnfmusrOzkZqaiqARwNdq1evRt++fdXT6pLLEN6jtqhf\nv3544YUXNP7zpTVqTB3pql19H7tOTk6YMGECEhIS8O677yIoKKheeWqjrU4b2mddMzPWVvFgMmOM\nsRbtzp07eP7555Gfn49jx47Bzc1N6EiMMcZYrSZMmIAOHTrAy8tL598sFxcXAMChQ4dQUlKC9PR0\njXugisViLFy4EMnJyZgzZw7+/PNPVFVV4eHDh1oHPPVJIpFg2rRp2LVrFzZu3Ij8/HxUVlYiKysL\nt27d0rpccHAw9uzZg9jYWOTn5+PChQuYPXs2OnbsCH9/f432VVVVePDgASoqKnD+/HkEBQXBxcUF\nU6dObbJty87OxqxZs3DlyhWUlZXh7NmzuHHjhsYAtq5cdXmPbGxskJ2djYyMDDx8+LBB97Jm1YWG\nhuLIkSM677/dktWnjp48zoyNjbXWblMcu8HBwaioqMCDBw/g4eFRbVsaci7RVqcN7bOumRlrsxrz\n+D6lUklKpbIxXTDGGGMNduvWLerZsyd1795d42nXjLUVaHlPf2as1VGpVFTT16o9e/aQQqEgANSh\nQwd666231PPmz59PJ0+eVP978eLF5ODgQADIyMiIevbsScePHyciotDQULKxsSErKyvy9fWl6Oho\nAkAKhYIyMzOJiCg6Opp69+5NEomEJBIJ9enThzZs2EDh4eEklUoJAHXr1o2uXbtGsbGxZG1tTQDI\nycmJLl68SFFRUer1y2QyGjt2LG3YsIFkMpnGsp9++ilZWloSAHJ1daXff/+diIhKS0spNDSUXFxc\nyMTEhGxtbcnHx4cuXbqkdd9VVVXRhx9+SN26dSNTU1OytrYmLy8vSktL02jn7+9Ppqam5OjoSCYm\nJmRpaUnjx4+na9eu6Xx/GrNtGRkZNHjwYLK2tiZjY2Pq1KkTLVq0iCoqKuqdq7b3iIjo119/JVdX\nV5JKpTRkyBC6ffu2zu1iddOvXz/y9fUVOkaNTpw4Qd27dycABIA6duxYa9a/17KzszNt375dPa+u\ndVTTcVaX2q3Psfv3c9njenvS8OHDacuWLTVuZ0POJbrqVFuf2vZrXTMz1lgt8PN8nIiIqKED0b6+\nvgCA+Pj4hnbBGGOMNcjNmzfh4eEBExMTHD58WH1fN8baEpFIBJVKhQkTJggdhbE2Ky4uDhMnTkQj\nvlYxHWbNmoX4+Hjcu3dP6CgaDDUX+x+VSoXJkyfj8uXL/DwNxphBaoGf5+P5NheMMcZanMzMTAwf\nPhympqY4cuQIDyQzxhhjrVxlZaXQEWpkqLnYI0qlEl26dMG6deuEjsIYY60GDyYzxhhrUW7cuIFh\nw4bBwsICycnJ6Nixo9CRGGOMMcaYATI2NsbcuXMRExNT53vkMsYY044HkxljjLUY6enpGDJkCKys\nrHDo0CF06NBB6EiMMcYYa0ILFy5ETEwM8vLy0KVLFyQkJAgdCYDh5mLVvf7667C2tkZ0dLTQURhj\nrFXgwWTGGGMtQlpaGoYPHw57e3scOnQI7du3FzoSY4wxxprYqlWrUFpaCiLCH3/8AaVSKXQkAIab\ni1UnkUjw5ptvYsOGDcjLyxM6DmOMtXg8mMwYY8zgXblyBR4eHnB1dcWRI0dgY2MjdCTGGGOMMdZC\nvPnmm6isrMTWrVuFjsIYYy0eDyYzxhgzaJcvX4aHhwfc3Nxw8OBBWFpaCh2JMcYYY4y1INbW1pgx\nYwbWrVuHsrIyoeMwxliLxoPJjDHGDNa5c+fw3HPPoVu3bvj2229hYWEhdCTGGGPMYBw6dAhKpRLO\nzs4Qi8UwNzeHu7s75s6dixs3buhcftq0aZBIJBCJRCgpKWmGxIbhm2++gVwux/79+4WOwppRcHAw\n7t69i507dwodxeD99ttv8PPzQ5cuXSAWi9GhQwc888wzeP/99+vcx9q1a2FnZweRSITNmzc3YVrG\nWHPjwWTGGGMG6ddff8ULL7wAd3d3HDhwAObm5kJHYowxxgxGWFgYRowYAUtLS+zfvx95eXnIzs7G\nunXrcPz4cTz99NM4cuSI1j5iYmIQEhLSTIkNBxEJHYEJwMnJCX5+fggPD0dVVZXQcQzWhQsXMHjw\nYDg4OODo0aPIy8vDyZMn8eKLL+LYsWN17ickJAQnT55suqCMMcHwYDJjjDGD88svv2DEiBEYMGAA\nvv32Wx5IZowxxv5m3759CA8Px8yZM7Ft2zb885//hEQigaWlJUaNGoVjx47BwcEBEyZMwL1799TL\nFRcXY/DgwQImNwwvv/wy8vLy4OnpKXQU1szCwsLw+++/48CBA0JHMVhr166FlZUVIiMj0blzZ0gk\nEnTv3h0rVqyAVCoVOh5jzADwYDJjjDGDcuLECXh4eGDQoEH4+uuv+UMrY4wx9oS1a9cCABYvXlzj\nfHNzc8ybNw/37t3Dli1b1NO3bt2KnJycGpcRiUT6D8qYgenRowdGjx6NNWvWCB3FYN27dw95eXm4\nf/++xvR27drxrWEYYwB4MJkxxpgBSUlJwejRo/Hcc89hz549kEgkQkdijDHGDEpRURF++uknuLi4\nwNnZudZ2zz77LADg+++/BwAEBQUhODgY165dg0gkQteuXdVtjYyMcODAAYwePRpyuRwdO3bEtm3b\nNPqrrKzEkiVL4OLiAqlUiqeffhoqlQoAsGbNGshkMlhYWCAnJwfBwcFwdHREWlparfl++OEHDBw4\nEDKZDJaWlujduzfy8/MbvC4jIyOIRCIYGRmhX79+KCoqAgDMnz8fcrkcEokEn3/+OVJSUuDi4gKR\nSITo6GiNTNu3b0f//v0hkUhgZmaGzp07Y8WKFTozsZYlJCQEKSkpOHPmjNBRDNKAAQNQWFgIDw8P\nnDhxQmvb48ePo2fPnuoa6927N7777juty+iqJW3nBsaYYeDBZMYYYwYhOTkZo0ePxqhRo7Bnzx6I\nxWKhIzHGGGMGJzMzExUVFbCzs9PazsHBAQBw/fp1AEBkZCQ8PT2hUChARLh69aq6bVVVFaysrPDV\nV18hIyMDffv2RUBAgHpAFnh0e4A1a9YgIiICt27dgqenJyZNmoQzZ85g/vz5mDdvHgoKCrBq1Sp0\n6dIFgwYNqvXexIWFhRg7diyUSiXu37+P9PR0dO/eHWVlZQ1e14ULF9C5c2c4Ozvj9OnTkMlkAB4N\nPk+fPh0ffPABpk6diiFDhtR4H9fIyEi8+uqrUCqVyM7ORlZWFhYuXKgeENeWibUsw4YNwzPPPIOP\nP/5Y6CgGaf78+ejfvz9+++03DBkyBO7u7lizZk21K5UB4M6dO5g4cSIyMjKQnZ0Nc3NzTJ48WWv/\n2mpJ17mBMWYgqBGUSiUplcrGdMEYY4zRd999R1KplCZMmEDl5eVCx2GsxQBAKpVK6BiMtWkqlYoa\n+bWqXn7++WcCQB4eHlrblZaWEgDq0KGDepqPjw8pFAqNdosWLSIAVFxcrJ725ZdfEgC6ePEiEREV\nFxeTTCYjPz8/dZuioiISi8UUEBBQaz+1uXjxIgGgxMTEavMas66IiAgCQHFxcepphYWF5OLiQnl5\neeppN2/eJAAUFRVFRERlZWVkZWVFw4cP1+ivoqKCIiMj65SJtSxbt26ldu3a0a1bt4SOYpDKyspo\n/fr19NRTTxEAAkB2dnZ07NgxrcutWrWKAFBOTg4REaWnpxMA2rRpExHprm9t5wbGWqsW+Hk+jq9M\nZowxJqiDBw9i3LhxGD9+PHbs2AETExOhIzHGGGMGy8LCAgCQm5urtd3jqwgtLS3rvQ5TU1MAQHl5\nOQAgLS0NRUVF6NWrl7qNVCqFg4MDrly5Uu/+3dzcYGdnhylTpmDZsmXIyMhQz2vMumbMmAG5XI7I\nyEj1tNjYWIwfP17rfjh//jxyc3MxatQojenGxsYIDAzU+/Yz4U2ePBnW1tbYvHmz0FEMkqmpKebM\nmYPLly/jp59+wvjx45GTkwNfX188ePBA63LAo1tZ1ERXLWk7NzDGDAcPJjPGGBPMN998Ay8vL0ya\nNAmxsbE8kMwYY4zp4OrqClNTU9y5c0dru9u3bwMAunXr1uh1FhYWAnj0wD+RSKR+3bhxQ+NWGE9K\nTEzUaC8SiTBlyhRIpVIcOXIEQ4YMwcqVK+Hm5gY/Pz8UFxc3eF3AowcPzpw5EydPnsTp06cBAJs2\nbcKcOXO0Lvf4fqxWVlZ63X5muMRiMWbMmIFNmzahpKRE6DgG7V//+he+/vprzJ49G3fv3sXRo0fV\n8w4cOIBhw4bB1tYWYrEY8+fP19qXrlrSdm5gjBkOHkxmjDEmiMTERHh7e+OVV17BZ599BiMj/pPE\nGGOM6SKRSDB06FD8+eef+OOPP2ptl5KSAgDVrrZtCFtbWwBAREQEiEjj9eOPP9a63JgxY6q1j42N\nBQC4u7tj//79yM7ORmhoKFQqFdauXdvgdT02Z84cmJqaIiIiAsnJyXB2doZCodC6TKdOnQAAf/31\nl163nxm2gIAA5ObmIi4uTugoBsXHxwcVFRXVpr/yyisAoP4PlMzMTHh5ecHBwQGnTp1CXl4ewsPD\ntfZdl1qq7dzAGDMc/M2dMcZYs4uPj4e3tzemTp2KzZs380AyY4wxVg9hYWEAgPfee6/G+fn5+YiI\niICdnR1ef/31Rq/P2dkZEokE586da3RfAJCdnY3U1FQAjwaXVq9ejb59+yI1NbXR63JycsKECROQ\nkJCAd999F0FBQTqX6dy5M2xsbJCUlFTjfH1vPzMMnTp1go+Pj8ZtURhQWlqqrs+/e/wwyqeffhoA\ncOHCBZSXlyMgIABubm6QSCQQiURa+9ZVS9rODYwxw8Hf3hljjDUrlUqFSZMmqX9ayAPJjDHGWP2M\nGDECq1evxhdffIGpU6fit99+Q0lJCfLz85GUlIThw4fjwYMHSEhIgFwuVy9nY2OD7OxsZGRk4OHD\nh+p7IusikUgwbdo07Nq1Cxs3bkR+fj4qKyuRlZWFW7du1Tt/dnY2Zs2ahStXrqCsrAxnz57FjRs3\nMGjQIL2sKzg4GBUVFXjw4AE8PDx0theLxVi4cCGSk5MxZ84c/Pnnn6iqqsLDhw+Rmpqq9+1nhmPe\nvHk4e/as+kp+9oiXlxfi4uKQm5uLvLw87Nu3D2FhYRg3bpx6MNnFxQUAcOjQIZSUlCA9PR2nTp3S\n2q+uWtJ2bmCMGZDGPL5PqVSSUqlsTBeMMcbakJ07d5KJiQmFhIQIHYWxVgEt7+nPjLU6KpWKGvm1\nqsF+/PFHmjRpErm4uFC7du3IzMyMevXqRcHBwZSVlVWt/a+//kqurq4klUppyJAhNG/ePJJKpQSA\nunXrRteuXaPY2FiytrYmAOTk5EQXL14kIqLS0lIKDQ0lFxcXMjExIVtbW/Lx8aFLly5ReHi4uh9n\nZ2favn271twZGRk0ePBgsra2JmNjY+rUqRMtWrSIKioq9Lau4cOH05YtW6pNj4qKIgcHBwJAMpmM\nxo4dq54XHR1NvXv3JolEQhKJhPr06UMbNmzQmYm1bP/617/I19dX6BgGIykpiSZOnEgKhYLEYjG1\na9eO/vGPf9CyZcuopKREo21oaCjZ2NiQlZUV+fr6UnR0NAEghUJBQUFBZG9vTwDIzMyMvL29iUh7\nLek6NzDWGrXAz/NxIiKi7KAHGwAAIABJREFUhg5E+/r6Anj0c2XGGGNMm61bt2LmzJkICQnReT81\nxljdiEQiqFQqTJgwQegojLVZcXFxmDhxIhrxtYoxJqBdu3bhlVdewdWrV9G5c2eh4zDG2pgW+Hk+\nnn9bzBhjrMl99tlnmDlzJt555x0eSGaMMcYYYwZDqVSiY8eO2Lx5s9BRGGOsReDBZMYYY03qk08+\ngb+/P5YuXYoPPvhA6DiMMcYYY4ypmZqawt/fH59++ikKCwuFjsMYYwaPB5MZY4w1mXXr1mH27Nl4\n7733sGTJEqHjMMYYY4wxVo2/vz+Ki4uxY8cOoaMwxpjB48FkxhhjTWLt2rUICQnBunXrsHjxYqHj\nMMYYY4wxViNbW1v85z//wfr16/n+54wxpgMPJjPGGNO78PBwzJ8/H5GRkQgKChI6DmOMMcYYY1oF\nBgYiNTUVR48eFToKY4wZNB5MZowxplfh4eFYsGABPv74Y8yZM0foOIwxxhhjjOn0zDPPYPDgwfjk\nk0+EjsIYYwaNB5MZY4zpzdKlS7FgwQJER0fjrbfeEjoOY4wx1mLs3r0bbm5uEIlE6pepqSkcHR0x\nefJkXL58uU79fPPNN5DL5di/f79eck2bNg0SiQQikQglJSV66ZMxQ+Xv74+vv/4a2dnZgmXw8/PT\nOA9oeyUmJjZqXVzfjLGG4MFkxhhjerF48WKsXLkS27ZtQ0BAgNBxGGOMsRbFx8cH169fh0KhgFwu\nBxEhNzcXmzdvRkpKCgYOHIi0tDSd/ej7fq8xMTEICQnRa5+MGaoJEybA0tISn3/+uaA5kpKSkJub\ni/Lycty6dQsA/h97dx4e09n/D/w9SSaZ7AtBkB2laGv9aoRHtKgtiEjSUqJoVFu0ihZFFyrla6mt\nqupp8WhiLakSa1OqSlFbqeYRSUQSRfYwWT6/P/rLfI1sk/Vkeb+ua66rPec+536fY86ZOZ+cuQ98\nfX2h1WqRmZmJ5ORkTJgwocL98PgmovJgMZmIiCpERPDWW29h0aJF2LhxI4KDg5WOREREVCdYWlpi\n8ODBWLFiBTIyMrBy5cpSlxk4cCBSU1MxePDgakhIVLdoNBqMHj0a69atQ15eniIZVCoVunfvDltb\nW5iYmOhNV6vVsLCwgKOjIzp16qRIvvogOzsbXl5eSscgqrFYTCYionITEUydOhUrV67E119/jZdf\nflnpSERERHVO165dAQCXLl1SNIdKpVK0f6LqEBISgri4OERGRirS/9atW2FhYVFqu5CQEAwaNKjS\n+uXx/X82bNiA5ORkpWMQ1VgsJhMRUbmICN58802sXbsW4eHhGDlypNKRiIiI6qTc3FwAgJmZGQDg\n008/hYWFBaytrZGcnIxp06ahWbNm2LBhA1xcXKBSqbBq1SoAQJs2baBSqWBkZIROnTohKysLADBj\nxgzY2tpCo9EY9JN+IyMjfP/99+jfvz9sbW3h5OSEr776Sq+NiGDp0qVo06YNzMzMYG9vj6FDh+Lq\n1au6NpMnT4apqSmaNGmim/b666/D0tISKpUKf//9t276jz/+iK5du8LCwgI2NjZo37490tLSAAB5\neXmYO3cuXFxcYG5ujqeeegphYWGlbkd511ncPjcyMqqU/Us1xxNPPIFevXrVmgfxGXIsbNq0CZ07\nd4ZGo4GlpSXc3Nzw0Ucf6eYbcnwXp7hjytBj/bPPPoNGo0GjRo0wceJEODk5QaPRwMvLC6dOndIt\na2g7wLBzUXHHdP/+/TFt2jRER0dDpVKhRYsWhv9jENUXUgH+/v7i7+9fkVUQEVEtlJeXJ6+88oqY\nmprKrl27lI5DVG8BkLCwMKVjENVrYWFhUsHLKj2enp5ia2urN23Tpk0CQKZPn66bNnv2bAEgU6ZM\nkZUrV4qfn5/88ccfEhcXJwBk5cqVIiKSm5srbm5u4uLiIrm5uXrrfeutt2TZsmWlZiro6/Dhw5KS\nkiL37t2TAQMGiJmZmWRmZurazZ07V0xNTWXTpk2SkpIiFy5ckI4dO0rDhg0lMTFR127kyJHSuHFj\nvT4WL14sAOTOnTsiIpKRkSE2NjYSGhoq2dnZkpiYKH5+frr577zzjpiZmcn27dvl/v37MmvWLDEy\nMpLTp08Xux0VXWdR+/zSpUsV3r9U83z77bdibGwsMTExSkeR27dvCwAZMmRIkfNLe98uW7ZMAMgn\nn3wid+/elXv37sm6detk5MiRImL48V2U0o4pQ451EZGQkBCxtLSUK1euyIMHD+Ty5cvSpUsXsba2\nltjY2DK3M/RcVNx5dPjw4eLp6WnIPw9RhdXC7/PhLCYTEVGZ5ObmypgxY8TMzEy+++47peMQ1Wu1\n8MsnUZ1TlcXkjIwM2b59uzRu3FgaNWok8fHxunYFRZDs7Gy95R8vJov8XzEpPDxcNy0zM1NcXFwk\nNTW11ExF9fXNN98IALl06ZKIiGRlZYmVlZUEBQXpLfvrr78KAPnwww910wwpMF26dEkASERERKE8\n2dnZYmFhoddXVlaWmJmZyaRJk4rdjoqus7h9XtH9SzWPVqsVJycnmTdvntJRSiwml/a+1Wq1Ymdn\nJz4+PnrL5ebmyvLly0XEsOO7OCUdUyJlKyY//ke006dPCwD54IMPytSuLOei4o5pFpOpOtXC7/Ph\nHOaCiIgMlpeXh7FjxyI8PBx79+6Fr6+v0pGIiIjqnNTUVKhUKtja2mLKlCkYMGAAfv31VzRr1qxc\n6xs/fjxsbW2xfPly3bTNmzdj6NChsLGxKdc61Wo1ACAnJwcAcPnyZWRkZKBz58567bp06QJTU9NC\nP0MvjYeHBxo1aoRRo0Zh/vz5iImJ0c27du0asrKy0K5dO900c3NzNGnSRO9n7NWxTqBq9i8pS61W\nIzg4GF988YXuPV4Tlfa+vXDhAlJSUtCvXz+95YyNjTFlypRi1/v48V2cko6piurcuTMsLCxKPf4e\nb1fZ5yIiKozFZCIiMkheXh7GjBmDHTt2YM+ePejTp4/SkYiIiOokW1tbiAhyc3MRHx+Pr776Cq6u\nruVen5WVFV599VX8/PPP+PXXXwEAa9euxeTJk3VtIiIioFKp9F6jRo0yuI+UlBRdX4+zs7NDenp6\nmTKbm5vjyJEj8Pb2xoIFC+Dh4YGgoCBkZ2cjMzMTADBnzhy9vDdv3kRWVlax21KRdZbEkP1Ltc/E\niRORnJyMiIgIpaMUq7T3bcF44HZ2dhXqpzzHVGUwMzPDnTt3ytSuss9FRFQYi8lERFQqrVaLgIAA\n7N69G3v37sXzzz+vdCQiIiIqg8mTJ0OtVmPZsmWIioqCs7MzPD09dfMHDRoEEdF7bd682eD1FxSr\niirUpKSkoHnz5mXO3LZtW+zduxcJCQmYOXMmwsLCsGTJEjg6OgIAli1bVijzyZMnS9yW8q6zNKXt\nX6p9XFxc0KdPnxr9IL7S3rdNmzYFAL0HW5ZHeY6pisrJyTHo3PF4u6o4FxGRPhaTiYioRFqtFoGB\ngYiMjERERAR69+6tdCQiIiIqo+bNmyMgIADbt2/H+++/j6lTp1bq+tu1awcrKyucOXNGb/qpU6eg\n1WrRqVMn3TQTE5NSfz6fkJCAK1euAPinYPbJJ5+gY8eOuHLlCpydnaHRaHD+/PkyZayKdRao6v1L\nyggJCUFkZCSuX7+udJQilfa+dXNzg4ODAyIjI6uk/5KOKcCwY704x44dg4igW7duZWpXlnMREZUP\ni8lERFSshw8fwt/fH0ePHsXBgwfRq1cvpSMRERFROU2bNg25ubm4f/9+pf9xWKPRYNq0adi5cyc2\nb96MtLQ0XLx4Ea+99hqcnJwQEhKia9uiRQvcu3cPu3fvRk5ODu7cuYObN2/qrS8hIQETJ07E1atX\nodVqce7cOdy8eRPdunWDRqPB2LFjsXXrVqxZswZpaWnIy8tDfHw8bt++XWzGqljno6py/5IyBg8e\nDGdnZ2zYsEHpKEUq7X1rZmaGWbNmISoqCpMnT8atW7eQn5+P9PR0XcG3Iko6pgDDjvUC+fn5uH//\nPnJzc3HhwgVMnToVLi4uCA4OLlO7spyLiuPg4ICEhATExMQgPT29Ro+bTaSIijy+z9/fX/z9/Suy\nCiIiqqEyMzOlT58+YmdnJ6dOnVI6DhEVAbXv6c9EdU5YWJhU8LJKREROnDghrVq1EgACQJycnGTE\niBFFtg0NDRVzc3MBIM7OzrJp0yYREVm5cqU0adJEAIiFhYX4+voWWtbHx0e+/PJLg3M92lfLli0l\nOjpaNm/eLPb29gJAmjdvLpcuXRIRkfz8fFm8eLG0bNlS1Gq12Nvby7Bhw+TatWt667x79674+PiI\nRqMRd3d3efPNN2X69OkCQFq0aCGxsbESExMjXl5eYm9vL8bGxtK0aVOZPXu25ObmiojIw4cPZebM\nmeLi4iImJibi6Ogow4cPl8uXLxe7LRVZZ3H7vKL7l2q++fPni6Ojozx8+LBa+01LS5OePXuKg4OD\nABAjIyNp0aKFLFiwQK+dIcfCqlWrpH379qLRaESj0UiHDh1k9erVZTq+i1LaMWXIsS4iEhISImq1\nWpo1ayYmJiZiY2MjQ4cOlejoaL3+DG1nyLmopGP67Nmz4urqKubm5uLt7S2JiYll/NcjMlwt/D4f\nrhIRKW8hesSIEQCAbdu2lbuYTURENU9WVhZ8fX1x9uxZHDhwAF26dFE6EhEVQaVSISwsDAEBAUpH\nIaq3wsPDERgYiApcVhFRDRYfHw83NzeEh4fDz89P6Th10sSJE7Ft2zbcvXu3UtoR1Sa18Pv8Ng5z\nQUREejIzMzFo0CBcvHgRx44dYyGZiIiIiOqt5s2b47nnnsPGjRuVjlKn5eXlVWo7Iqo6LCYTEZFO\namoq+vTpgytXruDw4cN46qmnlI5ERERERKSosWPHYv/+/UhISFA6ChGR4lhMJiIiAP8Ukl944QXc\nuHEDhw8fRrt27ZSORERERESkOD8/P9jZ2eGbb75ROkqdM2vWLGzcuBGpqalwd3fH9u3bK9SOiKoe\ni8lERISUlBT07dsXt27dQlRUFNq2bat0JCIiIiKiGsHU1BRBQUHYuHEjx0evZAsXLsTDhw8hIrhx\n4wb8/f0r1I6Iqh6LyURE9dz9+/fRp08fJCYm4ujRo2jZsqXSkYiIiIiIapSxY8fizz//xM8//6x0\nFCIiRbGYTERUjyUnJ6NXr164c+cOjh49Ck9PT6UjERERERHVOB07dsQzzzzDB/ERUb3HYjIRUT2V\nlJSE5557DmlpaTh27Bg8PDyUjkRERFTnBAUFQaVSGfSKiIhQOi6VYN++fbC1tcXevXuVjkIKCQ4O\nRlhYGNLT05WOQkSkGBaTiYjqocTERPTu3RtarRbHjx+Hm5ub0pGIiIjqrMjISKSkpCAnJwe3b98G\nAPj6+kKr1SIzMxPJycmYMGGCwimpNBwrl0aNGoWcnBzs2LFD6ShERIphMZmIqJ6Ji4tDjx49kJ+f\nj6NHj6JZs2ZKRyIiIqqzVCoVunfvDltbW5iYmOhNV6vVsLCwgKOjIzp16qRgyuqVnZ0NLy8vpWOU\n2cCBA5GamorBgwcrHYUU0qBBA/j6+nKoCyKq10xKb0JERHVFbGwsevfuDVNTUxw+fBhOTk5KRyIi\nIqrTtm7dalC7kJCQKk5Sc2zYsAHJyclKx1CciGD79u24f/8+Xn31VaXjkIHGjh2LAQMG4OrVq2jd\nurXScYiIqh3vTCYiqidu3ryJXr16wdraGlFRUSwkExER1TCffvopLCwsYG1tjeTkZEybNg3NmjXD\ntWvX8NNPP+HJJ5+Era0tNBoN2rdvjwMHDgAA1qxZA0tLS1hYWOC7775D//79YWNjg+bNmxcqZv/4\n44/o2rUrLCwsYGNjg/bt2yMtLQ2fffYZNBoNGjVqhIkTJ8LJyQkajQZeXl44deqU3jpEBEuXLkWb\nNm1gZmYGe3t7DB06FFevXi11W/r3749p06YhOjoaKpUKLVq0KHZ/LF++HJaWljAyMkKnTp3QuHFj\nqNVqWFpaomPHjujRowecnZ2h0WhgZ2eHGTNmlDlnmzZtoFKpdH1kZWUBAGbMmKHb1//+979x/Phx\nuLi4QKVSYdWqVWXe73l5eVi4cCGeeOIJmJubo2HDhnB3d8fChQsREBBg6FuEaoB+/frB2dkZmzZt\nUjoKEZEiWEwmIqoHrl+/Dm9vb9jZ2eHQoUNo2LCh0pGIiIjoMTNmzMDbb7+NjIwMLFy4EO7u7ujW\nrRtEBElJSQgMDERMTAwSEhJgZWWFkSNHAgAmTZqEt956C9nZ2bC2tkZYWBiio6Ph4eGBCRMmICcn\nBwCQmZkJX19f+Pv74969e7h+/TpatWoFrVaLyZMnIzg4GFlZWZgyZQpiYmJw9uxZ5Obmok+fPoiL\ni9PlnD9/Pt577z3Mnj0bycnJiIqK0g2jlZSUVOK2LFu2DIMHD4anpydEBH/99Vex+2Pq1KmYPn06\nRARr167FjRs3kJiYiJ49e+LcuXN47733cO7cOdy7dw9jxozB4sWL8fvvv5cp56VLl+Dm5gZnZ2f8\n+uuvsLCwAPBPMXzcuHFYtGgRgoOD4e3tjZ9//lkvn6H7HQBCQ0Mxd+5cLF68GPfu3UNkZCQePHgA\nOzs72NnZVeRtQ9XMyMgIL7/8Mr7++mvk5eUpHYeIqNqxmExEVMddu3YNPj4+aNy4MQ4dOoQGDRoo\nHYmIiIhKsWjRIrzxxhvYsWMHWrduDX9/f8ybNw/29vZwcHCAr68v7t69izt37ugt5+XlBRsbGzg6\nOiIoKAiZmZmIjY0FAMTExCAtLQ1t27aFRqNB48aNsWPHDr0/MpuYmOju5H3yySexZs0apKen68aI\nzc7OxtKlS+Hn54dRo0bB1tYW7du3x+eff46///4bX3zxRanbUh5PPvkkLCws0KBBA7z44osAABcX\nFzRs2BAWFhYYNWoUAOjuOjY0p7GxMaZMmYLY2Fjs3LlT119WVhZ27NiBV155xaB8Je13ANi9ezc6\ndeoEX19fmJubo2PHjhgyZAiioqKg1WrLtU9IOWPHjkVCQgIOHjyodBQiomrHYjIRUR129epV9O7d\nG66urjhy5AgcHByUjkRERESVQK1WA0CJd0aampoCgO4OWQ8PDzRq1AijRo3C/PnzERMTU2o/nTt3\nhoWFha5Ie/nyZWRkZKBz58567bp06QJTU9NCQ2JUhYLtys3N1U0r2B8F21qWnOPHj4etrS2WL1+u\nm7Z582YMHToUNjY25c736J3JDx48gIjotcvLy4NarYaxsXGZ+yBltWjRAt26dcOWLVuUjkJEVO1Y\nTCYiqqP++OMP+Pj4wMPDA/v37y/XxRARERHVDN9//z169eoFR0dHmJmZFRof2BDm5uY4cuQIvL29\nsWDBAnh4eCAoKAjZ2dklLmdmZqa7AzolJQUAYGVlVaidnZ0d0tPTy5wrIiICKpVK71Vwp3F5lSWn\nlZUVXn31Vfz888/49ddfAQBr167F5MmTK5ThUQMGDMBvv/2G7777DtnZ2Thz5gx2796NQYMGsZhc\nS40cORK7du1CRkaG0lGIiKoVi8lERHXQ+fPn0bNnT7Rq1Qo//PADrK2tlY5ERERE5RQbG4thw4ah\nSZMmOHXqFFJTUxEaGlqudbVt2xZ79+5FQkICZs6cibCwMCxZsqTY9jk5OUhJSUHz5s0BQDe+b1FF\n40fblcWgQYMgInqvzZs3l3k9jyprzsmTJ0OtVmPZsmWIioqCs7MzPD09K5ThUfPnz0fv3r0RHBwM\nGxsb+Pn5ISAgAOvXr6+0Pqh6vfjii8jJycHu3buVjkJEVK1MlA5ARESV6+zZs+jbty/atWuHiIiI\nIu/IISIiotrj4sWLyMnJwaRJk+Dh4QEAUKlUZV5PQkICUlJS8OSTT8LR0RGffPIJIiMjceXKlWKX\nOXbsGEQE3bp1AwC0a9cOVlZWOHPmjF67U6dOQavVolOnTmXOVRXKmrN58+YICAhAWFgYEhISMG/e\nvErNc/nyZURHR+POnTswMeFleF3g4OCAF154AVu2bKnwnfRERLUJ70wmIqpDfvvtN/Tp0wddunTB\nDz/8wEIyERFRHeDi4gIAOHToEB48eIDr16+Xa2zihIQETJw4EVevXoVWq8W5c+dw8+ZNXaEYAPLz\n83H//n3k5ubiwoULmDp1KlxcXBAcHAwA0Gg0mDZtGnbu3InNmzcjLS0NFy9exGuvvQYnJyeEhISU\nmsPBwQEJCQmIiYlBenq63tjClaU8OadNm4bc3Fzcv38fvXv3rtQ8b7zxBlxcXDgkQh0zcuRIHDx4\nEImJiUpHISKqPlIB/v7+4u/vX5FVEBFRJTl+/LjY2NjIgAEDJDs7W+k4RFQNAEhYWJjSMYjqtbCw\nMDHksiotLU169uwpDg4OAkCMjIykRYsWsmDBAl2b0NBQMTc3FwDi7OwsmzZt0s2bOXOmODg4iJ2d\nnYwYMUJWrVolAMTT01PeffddsbCwEADSsmVLiY6Oli+++EJsbGwEgLi6usqff/4pMTEx4uXlJfb2\n9mJsbCxNmzaV2bNnS25uroiIhISEiFqtlmbNmomJiYnY2NjI0KFDJTo6Wm9b8vPzZfHixdKyZUtR\nq9Vib28vw4YNk2vXrhm0LWfPnhVXV1cxNzcXb29vSUxMLHKfLV++XLddbm5u8tNPP8miRYvE1tZW\nAEjjxo1ly5Yt8u2330rjxo0FgNjb28vWrVsNzvk4Hx8f+fLLLwtNX7lypTRp0kQAiIWFhfj6+srq\n1asN2u8iIkeOHJEGDRoIAN1LrVZLmzZtZMeOHcXmoZotOztb7OzsZNmyZUpHIaJaqhZ+nw9XiTz2\nSNkyGDFiBABg27Zt5V0FERFVguPHj2PAgAH417/+he3bt8PMzEzpSERUDVQqFcLCwhAQEKB0FKJ6\nKzw8HIGBgajAZVWNMXHiRGzbtg13795VOkqds2bNGly/fh3Lli3TTdNqtXj33XexZs0a3L9/H+bm\n5gompPIaN24cLly4gNOnTysdhYhqoVr4fX4bh7kgIqrloqKi0L9/f/Tr1w87d+5kIZmIiIjKLS8v\nT+kIdU5iYiImT56McePG6U03NTWFi4sLcnJyqmSoD6oeI0eOxJkzZ3D58mWloxARVQsWk4mIarHI\nyEi88MILGDBgALZu3Qq1Wq10JCIiIiJ6hLm5OdRqNTZs2ICkpCTk5OQgISEBX375JebOnYugoCDY\n2NgoHZPKqVevXnB2dsa3336rdBQiomrBYjIRUS21f/9+DBkyBEOHDsWWLVv4ZHAiIiIqt1mzZmHj\nxo1ITU2Fu7s7tm/frnSkOsPW1haRkZG4dOkSWrVqBXNzczz55JPYuHEjFi1ahK+//lrpiFQBRkZG\nePHFF7Fp06Y6MdwNEVFpWHkgIqqF9u3bh+HDh+Oll17C+vXrYWTEvw0SERFR+S1cuBALFy5UOkad\n1aNHDxw8eFDpGFRFRo8ejU8//RQnTpyAt7e30nGIiKoUqw9ERLVMREQE/Pz88PLLL7OQTERERESk\nsLZt2+Kpp57Cli1blI5CRFTlWIEgIqpFtm3bBj8/PwQHB+Pzzz9nIZmIiIiIqAYYNWoUwsPDodVq\nlY5CRFSlWIUgIqolwsLC8NJLL2H8+PFYu3YtC8lERERERDVEUFAQ7t+/z+FMiKjOYyWCiKgW2Lp1\nK0aNGoWpU6dizZo1UKlUSkciIiIiIqL/z9nZGc8++yzCwsKUjkJEVKVYTCYiquE2bNiAUaNG4e23\n38bixYuVjkNEREREREUIDAzE7t27kZ2drXQUIqIqw2IyEVENtn79erz66quYPn06QkNDlY5DRERE\nRETFCAgIQFZWFg4cOKB0FCKiKsNiMhFRDbVu3TqEhIRg3rx5WLRokdJxiIiIiIioBE2aNIG3tzeH\nuiCiOo3FZCKiGmjp0qV47bXX8OGHH2Lu3LlKxyEiIiIiIgMEBgZiz549yMzMVDoKEVGVYDGZiKiG\nWbJkCd555x0sXboUc+bMUToOEREREREZyN/fH1qtFvv27VM6ChFRlWAxmYioBgkNDcWMGTOwfPly\nTJ06Vek4RERERERUBo6OjujVqxeHuiCiOovFZCKiGiI0NBTvvfcePvvsM0yePFnpOEREREREVA6B\ngYH4/vvvkZaWpnQUIqJKx2IyEVENMG/ePLz33ntYtWoV3njjDaXjEBERERFROQ0fPhz5+fmIiIhQ\nOgoRUaVjMZmISGFz5szBggULsHHjRkyaNEnpOEREREREVAH29vZ47rnnONQFEdVJLCYTESlERPDW\nW29h0aJF2LhxI8aMGaN0JCIiIiIiqgSBgYE4cOAAUlNTlY5CRFSpWEwmIlKAiGDq1KlYuXIlvv76\na7z88stKRyIiIiIiokoyZMgQiAj27t2rdBQiokrFYjIRUTUTEbz55ptYu3YtwsPDMXLkSKUjERER\nERFRJbKzs0Pv3r2xa9cupaMQEVUqFpOJiKpRfn4+xo8fj/Xr1yM8PBx+fn5KRyIiIiIioiowbNgw\n7N+/H5mZmUpHISKqNCwmExFVk7y8PLzyyivYsmULtm3bhqFDhyodiYiIiIiIqoifnx+0Wi3279+v\ndBQiokrDYjIRUTXIy8vD2LFjER4ejr1798LX11fpSEREREREVIUaNmyI7t27c6gLIqpTWEwmIqpi\neXl5GDNmDHbs2IE9e/agT58+SkciIiIiIqJqMGzYMOzZswcPHjxQOgoRUaVgMZmIqApptVoEBARg\n9+7d2Lt3L55//nmlIxERERERUTUZPnw4MjIycOTIEaWjEBFVChaTiYiqSEEhOTIyEhEREejdu7fS\nkYiIiIiIqBo1b94cXbp04VAXRFRnsJhMRFQFHj58CH9/fxw7dgwHDx5Er169lI5EREREREQKGDZs\nGHbv3o3c3FyloxARVRiLyURElSwrKwuDBw/GTz/9hMjISHTr1k3pSEREREREpJDhw4fj77//xk8/\n/aR0FCKiCmMxmYhkPIj7AAAgAElEQVSoEmVlZcHX1xdnzpxBZGQkunbtqnQkIiIiIiJSUMuWLdGu\nXTsOdUFEdQKLyURElSQzMxODBg3CxYsXcezYMXTp0kXpSEREREREVAP4+flh165dEBGloxARVQiL\nyURElSA1NRV9+vTBlStXcPjwYTz11FNKRyIiIiIiohpiyJAhiI+Px9mzZ5WOQkRUISwmExFVUGpq\nKl544QXcuHEDhw8fRrt27ZSORERERERENUjHjh3h6uqKPXv2KB2FiKhCWEwmIqqAlJQU9O3bF7du\n3UJUVBTatm2rdCQiIiIiIqqBBg4cyGIyEdV6LCYTEZXT/fv30adPHyQmJuLo0aNo2bKl0pGIiIiI\niKiG8vX1xfnz5xETE6N0FCKicmMxmYioHJKTk/Gvf/0Ld+7cwdGjR+Hp6al0JCIiIiIiqsF8fHxg\nY2ODiIgIpaMQEZUbi8lERGWUlJSE5557Dunp6Th27Bg8PDyUjkRERERERDWcqakp+vXrx6EuiKhW\nYzGZiKgMEhMT0bt3b2i1Whw/fhxubm5KRyIiIiIiolpi8ODBOHbsGFJSUpSOQkRULiwmExEZKC4u\nDj169EB+fj6OHj2KZs2aKR2JiIiIiIhqkYEDB0JEcODAAaWjEBGVC4vJREQGiI2NhY+PD9RqNY4c\nOYKmTZsqHYmIiIiIiGoZBwcHeHt76w118ddff2HlypVYuHChgsmIiAxjonQAIqKaICkpCTk5OWje\nvHmheTdv3oSPjw9sbW1x8OBBNGzYUIGERFTfTZw4EVevXtWb5uTkhE8//RRr1qzRTTM2NsY333zD\nX08QVYFbt25h9OjRyMvL003LyMiAk5MTevXqpde2devW+Pzzz6s5IRHVBv3798eHH36IN998E3v3\n7sXNmzcBAEFBQQonIyIqHYvJREQAPv30U+zatQvHjx/Xu+v4+vXr6N27NxwdHXHw4EE0aNBAwZRE\nVJ81atQI69atKzT99u3bev/v4eHBQjJRFWnWrBlu3ryJ6OjoQvMePxZ79uxZXbGIqBa4ceMGDh48\niAMHDuCHH35AdnY21q1bh5ycHACAiYkJbG1tFU5JRFQ6DnNBRPVecnIyVq9ejZiYGPTs2ROJiYkA\ngGvXrsHHxweNGzfGoUOHWEgmIkW99NJLpbZRq9UIDg6u+jBE9djo0aOhVqtLbcc7DImowKBBg+Dh\n4YHXX38du3fvRnZ2NgDoCsnAP78ssrGxUSoiEZHBWEwmonpvyZIlyM/Ph4ggNjYW3bt3x/Hjx9G7\nd2+4urriyJEjcHBwUDomEdVzrVu3Rrt27aBSqYptk5OTwwIWURUbOXIkcnNzi52vUqnQvn17PPnk\nk9WYiohqsvfffx9GRkbIzc1Ffn5+se1YTCai2oDFZCKq1+7evYtVq1bp7grIyclBfHw8hgwZAldX\nV+zfv59f6oioxhg9ejSMjY2LnKdSqfDMM8+gZcuW1ZyKqH7x9PTE008/XewfdkxMTDB69OhqTkVE\nNdn//M//YN68eTAyKr4EIyIc5oKIagUWk4moXluyZEmhu4u0Wi3S09ORkpICrVarUDIiosKCgoL0\nHvz1KGNjY4wZM6aaExHVTyX9YSc3NxcjRoyo5kREVNPNmTMHzz77bLHD5OTl5bGYTES1AovJRFRv\n3bt3DytWrNAbq6xATk4O/vrrL/Tq1Qv37t1TIB0RUWHOzs7o1q1bkXc25eXlsYBFVE2CgoKK/Km6\nkZERunXrBldXVwVSEVFNZmRkhE2bNkGtVhf5y4a8vDz+IpKIagUWk4mo3lq6dGmRheQCOTk5uHr1\nKvr27Yu0tLRqTEZEVLyXX3650EWokZERvL290axZM4VSEdUvTk5O6N69e6E/7BgZGXGICyIqlru7\nO9auXQsRKXI+70wmotqAxWQiqpfu37+PZcuWlfgAHeCfMUh///13fPPNN9WUjIioZIGBgYWKySqV\nigUsomr28ssvF5omIvD391cgDRHVFqNHj0ZAQECRw12wmExEtQGLyURULy1fvrzE8ZBNTEyg0Wjw\n2muvITo6Gm+88UY1piMiKp6DgwOee+45mJiY6KapVCr4+fkpmIqo/vH399cbN9nY2BjPP/88GjZs\nqGAqIqoN1q1bh4YNGxYae53DXBBRbcBiMhHVO6mpqcXelWxsbAwLCwtMmjQJN27cwIoVK+Di4qJA\nSiKi4o0aNUo3XquJiQleeOEFODg4KJyKqH6xt7dHv379dH/YEZEi71YmInqcnZ0dwsLCCg13wTuT\niag2YDGZiOqdFStWIDs7W/f/KpUKRkZGaNCgAebMmYOEhASsWLECTZo0UTAlEVHxhg0bBlNTUwD/\nPLBn1KhRCiciqp9GjhyJvLw8AIBarcaQIUMUTkREtUWPHj3wzjvv6N2dzDuTiag2YDGZiOqVtLQ0\n/O///i9yc3NhZGQElUoFDw8PbNiwAQkJCZg/fz7vCCCiGs/S0hKDBw8GAGg0Gt1/E1H18vX1hUaj\n0f23lZWVwomIqDb56KOP0LZtW6hUKqjVapiZmSkdiYioVCalNyGimi4+Ph4///yz0jFqhV27diEt\nLQ0A4OrqCj8/P3Tp0gUqlQq7d++u1L4CAgIqdX1ENc3JkycRFxendIx6y83NDQDQqVMnREREKBum\nnqsp5/vw8HClI9RLHTt2xIkTJ+Dm5sZ/AwU4Ozvj2WefVToG1TC16fooODgYM2fOhKmpaZ0/h9SU\nz0siqhiVPD5ITxmMGDECALBt27ZKC0REZRceHo7AwEClY9BjKnB6JaoVRowYge3btysdg0hxNeV8\nr1KplI5AVO38/f15PUqF8PqoZqopn5dENYlKpUJYWFht+mPLNg5zQVSHiAhfJbx+++03nD17tsr7\nCQsLU/qtQFRt/P39FT+26/NrxowZePjwoeI56uurJp7vCx7oxFf1vbRaLd59913Fc9THl7+/v9KH\nHNVwSr9HDX3l5+dj/vz5iueoqldN/LwkovLjMBdEVG907NhR6QhERJXqww8/1D2Ij4iUoVarMX/+\nfKVjEFEtplKp8P777ysdg4jIILwzmYiIiKiW4oN6iGoGHotEVFFGRizPEFHtwLMVERERERERERER\nEZWKxWQiIiIiIiIiIiIiKhWLyURERERERERERERUKhaTiYiIiIiIiIiIiKhULCYTERERERERERER\nUalYTCYiIiIiIiIiIiKiUrGYTERERERERERERESlYjGZiIiIiIiIiIiIiErFYjIRERERERERERER\nlYrFZCIiIiIiIiIiIiIqFYvJRERERERERERERFQqFpOJiIiIiIiIiIiIqFQsJhPVQzt27ICHhwdU\nKlWxLzc3txLXMX78eFhbW0OlUuH8+fPVE7wU+/btg62tLfbu3at0FCIqwuPnHmdnZ2zYsEE3f9y4\ncbC3t4dKpYJarUaHDh0QGxurYOL/U9R5U61Wo1mzZhg5ciT++OOPCvfRsWNH3bqdnJwwZcqUUpf5\n888/0aVLF1hZWcHIyAgvvPBChTJ88sknsLW1VezcHhoaitatW8Pc3ByWlpZo3bo13n//faSlpZW4\n3IMHD9C6dWvMmTPHoH6OHz+O7t27w8LCAk5OTpg5cyYePnxYGZtQK127dg1vvvkm2rZtC2tra5iY\nmMDW1hatWrXCwIEDcfLkyUrvszI+s5csWYJGjRpBpVLh888/LzT/zz//1G2XjY0NTE1N4ejoiNat\nW8PPzw+7du2q9syV6eOPPy7yO1y7du0KtTXkPd+rV69ivxdaWVnptf3Pf/6DLl26wNraGq6urhg7\ndiwSExMrNTNRdeL1ERGR4VhMJqqHhg8fjv/+97/w9PSEra0tRAQigtzcXGRlZSEpKQkWFhYlruPL\nL7/E+vXrqymxYURE6QhEVILHzz1xcXEYN26cbv6GDRvwww8/AABef/11nDt3Di4uLkrF1VPUeTMl\nJQWff/45jh8/jq5du+LatWsV6uPs2bPo27cvVCoVzp07hxUrVujNz8vLQ+/evfWmtWrVCqdPn8bC\nhQvx0ksvYf/+/RXK8N5772HdunUVWkdF/PTTT5gwYQJiY2ORlJSEjz76CKGhofD39y9xudmzZxu8\n/y9fvoy+ffviueeew507d7Bz50589dVXeO211ypjE2qdDRs2oH379rhw4QKWLl2KuLg4ZGZm4ty5\nc/joo4+QkpKCixcvVnq/lfGZ/c477+Dnn38uct6///1vtG/fHufPn8fSpUsRGxuL9PR0nDp1CkuW\nLIFWq8WBAweqPbMSKuM97+3trfvvsLAwjBw5EiNGjEB8fDy+++47REVFoX///sjNza2KTSCqcrw+\nIiIyHIvJRKRjbGwMc3NzNGrUCK1atVI6Tomys7Ph5eWlN23gwIFITU3F4MGDFUpFRPWJpaUlBg8e\njBUrViAjIwMrV66s8DqDgoIgIoiIiCg07+jRozh69CiuXLlSaN6+ffsQGBhY4f6rSk5ODjZu3Igx\nY8aU2M7U1BSvv/46HB0dYWVlhREjRmDo0KE4ePAgbt++XeQyP//8My5dumRwlo8++ghNmjTBBx98\nAEtLSzz77LOYOXMm/v3vf+Pq1atl2q7a7pdffkFISAh69OiBw4cPo1+/frCzs4OZmRk8PDwQGBiI\nuXPnQqvVVnrfRX1mF/XZXh6//PILxo8fDy8vLxw9erTQdg0aNAifffZZpWSuCoYeLwCwadMmXdGr\n4PX48WDoe16j0SAtLa3Q+kJCQjBjxgxdu3Xr1qFp06aYPn06bG1t8cwzz+Dtt9/G+fPncerUqUrJ\nTFRT8PqIiKgwFpOJqEi7d+8utY1KpaqGJEXbsGEDkpOTFeufiKhA165dAaBSiiF+fn4wNTXFnj17\nCs2LjIxE06ZNsX37dr3p2dnZ+P3339GvX78K91/ZHj58iLVr16Jjx464fPkyFi1aVGL7nTt3QqPR\n6E1r1qwZACAjI6NQ++zsbEyfPh3Lly83KE9ubi6+//57/Otf/9L7DOvfvz9EBN99951B66krPv74\nY+Tl5eGTTz6BiYlJkW369euHN954o1ryVNZn+4IFC0rdLg8PjyKHxlBSWY8XQ5TlPb9//35YW1vr\nLR8XF4dLly7p/SoiLi4OTk5OeutzdnYGANy8ebPCmYlqKl4fERH9g8VkIjKIiGDx4sV44oknYGZm\nBltbW0yfPl2vzeTJk2FqaoomTZropr3++uuwtLSESqXC33//rdd+06ZN6Ny5MzQaDSwtLeHm5oaP\nPvoIwD8/dX7yySdha2sLjUaD9u3b636OOnXqVEybNg3R0dFQqVRo0aIFjh8/DhcXF6hUKqxatUov\n99KlS9GmTRuYmZnB3t4eQ4cO1bsTZ82aNbC0tISFhQW+++479O/fHzY2NmjevDm2bt1a6fuSiMrO\nkGPZ0HPQp59+CgsLC1hbWyM5ORnTpk1Ds2bNcO3aNfz444/o2rUrLCwsYGNjg/bt25c6Xm/Bz7rN\nzMx00/Ly8jB37ly4uLjA3NwcTz31FMLCwkrt39bWFv369cOhQ4eQnZ2tW19OTg4AwN/fv1AxueCO\nUlNT01L7NmT+45KSkuDm5gYTExODx2TOzMzE0qVL0blzZyQnJ+PHH3/EkiVL4OTkZNDyj7p+/Trs\n7Ozg6upaaN7s2bN1dzIb4r///S8yMjIKDZ/i6ekJALhw4UKZ89VWWq0Whw8fRoMGDXR/EDFESZ/P\nn332GTQaDRo1aoSJEyfCyckJGo0GXl5eenesFvWZXdRne2n9Fbddhw4dgoODA7p161amfVJSX0Vl\nrqzvD5V5vDyuou/5RYsWFRq/3cPDo1DBqmC8ZA8Pj4pGJqo1eH1ERPUVi8lEpGfq1KlF3l33/vvv\nY+bMmQgJCUFSUhISExPx7rvv6rX57LPPEBAQoDdt9erV+OCDDwqtb/ny5Rg9ejT8/f2RkJCA+Ph4\nzJo1SzfmZVJSEgIDAxETE4OEhARYWVlh5MiRumUHDx4MT09PiAj++usveHt7Fzlu4vz58/Hee+9h\n9uzZSE5ORlRUFOLi4tCjRw8kJSUBACZNmoS33noL2dnZsLa2RlhYGKKjo+Hh4YEJEyboijhEpBxD\njmVDz0EzZszA22+/jYyMDCxcuBDu7u7o1q0bMjIy4OvrC39/f9y7dw/Xr19Hq1atSv2Jf1RUFADg\n6aef1k1799138emnn2LZsmW4ffs2Bg8ejJdeeglnzpwptv+CcQ2DgoKQnZ2NQ4cO6dZ38OBBPP/8\n8xgxYgQuXryIP//8Uzfv8SEuSurbkPmPc3BwQOfOnbFz585Sx2ROTU3FggUL4OXlBRHBL7/8gnnz\n5sHBwaHE5R6Xk5ODW7duYdWqVTh06BBWrlypK5YXOHHiBKKjo/HSSy8ZvN6Cgtfjd19qNBqYm5vr\n3kv1wc2bN/HgwQO0bNmyTMuV9Pk8efJkBAcHIysrC1OmTEFMTAzOnj2L3Nxc9OnTB3FxcQBQ5Gd2\nUZ/tpfVX0naV5+foJfVVVOaKfn+ojOPlvffeg729PUxNTeHu7o6hQ4fi9OnTuvkVec/funULx44d\nw/Dhw/Wmz5o1C4mJiVi5ciXS09Nx+fJlLF++HP369TOogF9aZqKahtdHvD4iosdIBfj7+4u/v39F\nVkFElSAsLEzKczh7enoKgEKvixcv6rXLysoSCwsL6dOnj970rVu3CgA5d+6cbtrIkSOlcePGeu0W\nL14sAOTOnTsiIqLVasXOzk58fHz02uXm5sry5cuLzLpw4UIBIMnJySIiMnz4cPH09NRrExcXJwBk\n5cqVutxWVlYSFBSk1+7XX38VAPLhhx/qps2ePVsASHZ2tm7a6tWrBYD89ddfRWYqTnn/PYhqm/J+\nDyju3PPoa8qUKbr2ZTmWDTkHiRR9zF+6dEkASERERInZbW1tRUQkIyNDtm/fLo0bN5ZGjRpJfHy8\niIhkZ2eLhYWFXt6srCwxMzOTSZMmFdt/gfT0dDE3N5fx48frpk2bNk1ycnIkPz9fmjZtKh9//LFu\nXseOHUWr1RrUtyHZHj235+TkyIsvvig//PBDsfukgFarFVdXV+nZs6ekpaWV2r4kjRs3FgDSoEED\nWbFihW77Hs3cuXNn3T6/c+eOAJDZs2eXuN7IyEgBIEuXLi00z8bGRry8vMqUs6ad7wFIWFiYQW3P\nnDkjAOT555+vUJ+Pfz6HhITojpECp0+fFgDywQcf6KY9/pktUvRne2n9Xb9+XQDI2rVrK3W7iuqr\nqMzl/f5QGcdLbGysnD17VtLT0+Xhw4dy8uRJ6dChg5ibm8ulS5dEpGLv+TfeeEO3Xx83Z84cvXN2\n8+bNJS4urlIylwWvR6k4vD76B6+PiGq+snx/qyHCeWcyUT336NOKRaTQTxkB4K+//kJWVhaee+65\nSunzwoULSElJKTS+p7GxcZH9A4BarQbwz8+zDXX58mVkZGSgc+fOetO7dOkCU1PTUh8SU3AXHP/y\nTlT5Hj/3FLxOnjxZqG1Fj2VDeXh4oFGjRhg1ahTmz5+PmJiYItulpqZCpVLB1tYWU6ZMwYABA/Dr\nr7/qxva9du0asrKy0K5dO90y5ubmaNKkiUEPeLOyssLAgQMREREBEcHDhw9hYmICExMTqFQqDB8+\nXDfUxZUrV9ChQwfdObK0vsuSLS8vDy+99BIaNWpk0PAWarUav//+O/r06YMePXpg2bJlyMzMLHW5\nosTFxSE5ORn/+c9/8PXXX6NDhw56P6ufNWsWXn31Vd0+N1TBeMwFQ5M8SqvVwtzcvFx5ayMrKysA\nQFZWVoXWY8jnc+fOnWFhYVEpDzgsrb+C7SruvRceHg53d3eoVCqoVCq0adOm2DFGy/PdAzDs+0Nl\nHC/Ozs7o0KEDrKysYGpqim7dumHjxo3Izs7G6tWrAZT/PZ+QkIA9e/YgODi40LzZs2fjiy++wOHD\nh5GRkYH//ve/8PLywrPPPqu7+7wimYmUxuuj4vH6iIgADnNBRI9Zvny5XpEBAOLj4wHA4DEpS1Mw\n/qidnV2xbb7//nv06tULjo6OMDMz03uKuKFSUlIA/N+F5aPs7OyQnp5e5nUSUfWrrmPZ3NwcR44c\ngbe3NxYsWAAPDw/dkBOPKrjIzM3NRXx8PL766iu98XwLCkJz5szRFaxUKhVu3rxpcOEuKCgIiYmJ\nOH36NPbt26dXzPX398f58+cRHR1daIiL0vouS7Y33ngD169fx+eff44rV64YlNvW1hZz5szBiRMn\nkJ+fj27duuHDDz/E/fv3DVq+gFqthqOjI/r27Ytvv/0Wly9fxsKFCwH8M3btxYsXMX78+DKtE4Bu\nzMrHx8HOysrCgwcPKmWM2trCzc0NGo1Gb8gUQ5T389nMzAx37twpc86y9ufq6gozMzPdMBmPCwgI\nwI0bN+Dq6orGjRvjjz/+QKNGjcrVV0VV1vHyqPbt28PY2Fj371re93xoaCgmTJhQ6IGYt2/fRmho\nKF599VX07t0blpaWcHd3x/r165GQkIDFixdXODNRTcPrIyIifSwmE1GpCi4kHj58WCnra9q0KQAU\neuBEgdjYWAwbNgxNmjTBqVOnkJqaitDQ0DL3U/BlrKgvRSkpKWjevHmZ10lE1a86j+W2bdti7969\nSEhIwMyZMxEWFoYlS5aUaR0FF5bLli0z6M7rogwcOBDW1tbYs2cPoqKi0LNnT908b29vODk5Yfv2\n7Thz5gx8fHwM7rss2QICAnDw4EHY2dlh9OjRRd7ZWBxLS0tMmzYNp0+fRsOGDdGzZ09Mnz4dt2/f\nNngdBVq0aAFjY2NcvnwZwD9Pqz98+DCMjIx0xfCC7VqwYAFUKlWx4z+7u7vD2toaN2/e1JteUHh8\n6qmnypyvtjIzM0O/fv3w999/48SJE8W2u3fvnq5wX97P55ycnHIdq+XpT6PR4Pnnn8edO3fwyy+/\nVGlflaUyj5f8/Hzk5+frHghanvd8YmIi/vOf/2DSpEmF5l2/fh15eXm673IFbGxs4ODgoDtOK5KZ\nqDbg9RER1WcsJhNRkW7fvo2xY8cCANq1awcjIyP8+OOPpS5nYmJS6s+e3Nzc4ODggMjIyCLnX7x4\nETk5OZg0aRI8PDyg0WigUqnKvA3t2rWDlZVVoaLCqVOnoNVq0alTpzKvk4iqX1mOZUPOQcVJSEjQ\n3YHr6OiITz75BB07djT4rtwCzs7O0Gg0OH/+fLlyAP9cpPr6+mL79u0wNzeHkdH/fWUzMjKCn58f\nvvnmGzRq1AgmJiYG912WbD4+PmjYsCG++OIL/Pbbb/j444/LtR2TJk3C2bNn0aZNm0IPJnrU3bt3\ni3yYXkHxytnZGQCwcePGQoXwgjteZ8+eDREp9PPdAiYmJhgwYACioqKQn5+vm/7DDz9ApVLB19e3\nzNtYm82fPx9mZmZ4++23C92BX+DSpUu691h5P5+PHTsGETHo4WyPKm9/H3zwAdRqNaZPn27w+aCy\nvntURFmOFwCFfg4PAKdPn4aI4NlnnwVQvvd8aGgoRo0aVeSDAAsKTY8XutPT03Hv3j3dcVqRzEQ1\nFa+PiIj+wWIyEekREWRnZ2PHjh2wsbEB8E9RpWCMzg0bNiAtLQ0XLlzAF198UWj5Fi1a4N69e9i9\nezdycnJw586dQnfDmJmZYdasWYiKisLkyZNx69Yt5OfnIz09HVeuXIGLiwsA4NChQ3jw4AGuX79e\naPwuBwcHJCQkICYmBunp6UV+QdNoNJg2bRp27tyJzZs3Iy0tDRcvXsRrr70GJycnhISEVNZuI6Iq\nVJZj2ZBzUHESEhIwceJEXL16FVqtFufOncPNmzfLXADTaDQYO3Ystm7dijVr1iAtLQ15eXmIj48v\n052GQUFBuHbtGgYNGlRo3ogRI3DlyhUMGzasTH2XJ5uvry+Cg4OxYMEC/Pbbb2XaFwXUajVeeeUV\nfP3118W2sbS0RGRkJI4cOYK0tDTk5OTg3LlzGDNmDCwtLfH222+Xud+5c+fC1tZW7+L8/fffR1JS\nEubNm4fMzEycPHkSixcvRnBwMJ544olybV9t9cwzz2DLli24dOkSevTogX379iE1NRU5OTm4ceMG\n1q9fj3HjxunG5TTk8xn4507T+/fvIzc3FxcuXMDUqVPh4uJS5Pi7j3r8s71gCIbS+ntcp06dsGnT\nJvz222/o1asX9u/fj9u3byM3Nxc3b97Epk2bcO/ePb1lDN226mDI8QIAt27dwrfffouUlBTk5OTg\n5MmTGD9+PFxcXPDaa6/p2pXlPZ+UlISvvvoKb731VpF9uru7w8fHB+vXr0dUVBSys7MRFxenOw+P\nGzdO17ao48/QzEQ1Ca+PiIgeU5HH9/HpuUQ1Q1mfjrtz585in1T86GvOnDm6ZdLT02X8+PHSoEED\nsbKyEm9vb5k7d67uCd6///67iIjcvXtXfHx8RKPRiLu7u7z55psyffp0ASAtWrSQ2NhY3TpXrVol\n7du3F41GIxqNRjp06CCrV68WEZGZM2eKg4OD2NnZyYgRI2TVqlUCQDw9PXVPAnd1dRVzc3Px9vaW\nOXPmSJMmTQSAWFhYiK+vr4iI5Ofny+LFi6Vly5aiVqvF3t5ehg0bJteuXdPlWL16tVhYWAgAadmy\npURHR8sXX3whNjY2AkBcXV3lzz//rLJ/D6LaqqzfAx4/97i6usrGjRt18ydMmCD29vYCQNRqtXTq\n1El3zjDkWBYx7BwUGhoq5ubmAkCcnZ1l06ZNIiISExMjXl5eYm9vL8bGxtK0aVOZPXu25ObmyokT\nJ6RVq1a67E5OTjJixIhit/Xhw4cyc+ZMcXFxERMTE3F0dJThw4fL5cuXi+3/cVqtVp5++mnJz88v\nNC8vL0+efvppycvLK1Pfpc3fsWOH7t/Azc1NkpOTJS0tTZydnQWAWFlZyTfffFPyP3QF+Pr6iru7\nu1hZWYmZmZl4enpKUFCQXLx4scTl7ty5IwBk9uzZetPff/99sba2lgMHDuhN//HHH6Vr165iZmYm\nTk5OMn36dH79n+oAACAASURBVHnw4EGZ89a08z3K+TTw2NhYeeedd6R9+/ZiZWUlxsbGYmdnJx06\ndJBx48bJiRMndG1L+3wOCQkRtVotzZo1ExMTE7GxsZGhQ4dKdHS0bh0rV64s8jP78c/2xMTEEvub\nOnWqNG7cWACIpaWl+Pn56W3XjRs3ZOrUqdK2bVuxtLTUnRd69Ogh7777rkRFRem1L6mvGTNmFMpc\n2d8fymratGni6ekplpaWYmJiIs2bN5cJEyZIQkJCobaGvufffvttGTVqVIn9/v333zJ16lRp0aKF\nmJmZiZWVlXTv3l127dql166o468smQ3B61EqDq+PeH1EVFuU9/ubgsJVIiLlLUSPGDECALBt27by\nroKIKkF4eDgCAwNRgcOZKhH/Pai+4PcAqu9q2vlepVIhLCwMAQEBimWYOHEitm3bhrt37yqWgeoP\nfg5RcWra+bm+478HUfFqwve3MtrGYS6IiIiIiKjS5OXlKR2BiIiIiKoIi8lEREREREREREREVCoW\nk4mIiIiIqMJmzZqFjRs3IjU1Fe7u7ti+fbvSkYiIiIiokpkoHYCIiIiIiGq/hQsXYuHChUrHICIi\nIqIqxDuTiYiIiIiIiIiIiKhULCYTERERERERERERUalYTCYiIiIiIiIiIiKiUrGYTERERERERERE\nRESlYjGZiIiIiIiIiIiIiErFYjIRERERERERERERlYrFZCIiIiIiIiIiIiIqFYvJRERERERERERE\nRFQqFpOJiIiIiIiIiIiIqFQsJhMRERERERERERFRqVhMJiIiIiIiIiIiIqJSsZhMRERERERERERE\nRKViMZmIiIiIiIiIiIiISmWidAAiqjzh4eFKRyAAJ0+eVDoCUbWJj4/nuYfqrZp4vq+JmYiqSnx8\nPJo3b650DKrB+B2lZuBnE1HdwmIyUR0SGBiodAQiqmd++eUXnnuIapDly5dj+fLlSscgqjb+/v5K\nR6AajN9RiIgqH4e5IKoDAgICICKV8tq3bx8sLS0xaNAgZGdnV9p6DXmFhYUBQLX2WZUvorpu27Zt\nih9nde3l5+eHLl26lGmZKVOmoHv37opnr8+vmkLp/VDfXq+++ip69+5d7uW9vLzQt29fxbejtr+2\nbdum9KFHNVBlXh8p9froo4/QqlUrxXOU5ZWXl4c333wTKpUKvr6+SEhI0JtPRHUDi8lEpLNlyxYM\nGTIEfn5+2LlzJzQajdKRiIjqjT///BO7d+/GjBkzyrScra0tUlNTqygVERUnKSkJjRs3Lvfy/4+9\nO42Lsm7fBn4MiOwzgEBiZgZmuaak3YiGkYlaQkUqmrbgDrggiyzuiSggiIpbKVoqoahlZqmZmlrm\nXalomv7dplRiUNEE2Zl5XnjDE4nEMjO/WY7v5+OLmGuu6xhNhINzzmvRokXYt28fDhw4oMZURGQo\nysvL0bx5c9ExGsTExATLli3DkSNHcP78eXTs2BEffvih6FhEpGYsk4kIALBs2TK8++67CAoKwscf\nfwwzMzPRkYiIjEpycjLatm2LN998s0HPk0qluHfvnoZSEdGjNLVMfvHFFzFgwABER0dzYo+IHlJa\nWqp3ZXKV3r1749SpU5gwYQKCgoLw6quv4vr166JjEZGasEwmMnIqlQpz585FaGgoFi1ahKVLl0Ii\nkYiORURkVPLy8rBx40ZMnz4dpqamDXouJ5OJxGhqmQwACQkJ+OWXX7Bz5041pSIiQ1FWVqa3ZTIA\nWFpaYtGiRThy5AguX76Mzp07c0qZyECwTCYyYpWVlRg9ejTi4+OxceNGREZGio5ERGSUli5dCltb\nW7z77rsNfq5MJkNBQQGUSqUGkhHRo+Tl5TW5TH7uuecwZMgQREdHo6KiQk3JiMgQ6HuZXMXT0xOn\nTp3CxIkTERQUBH9/f9y6dUt0LCJqApbJREaqrKwMw4cPx9atW7Fz506MHDlSdCQiIqN0//59rFmz\nBlOmTIGlpWWDny+VSqFUKlFYWKiBdERUm8LCQty/f7/JZTIAxMXF4fLly9i8ebMakhGRoTCUMhn4\n/1PK+/fvx88//4znnnsO33zzjehYRNRILJOJjFBpaSmGDRuGPXv24IsvvsCgQYNERyIiMlpr1qxB\nSUkJJk6c2Kjny2QyAODeZCItUigUAKCWMvnpp59GYGAg5syZg9LS0iafj4gMgyGVyVW8vb1x5swZ\neHt7Y8CAAZgwYQKKiopExyKiBmKZTGRkCgsL8dprr+Hw4cP45ptv0K9fP9GRiIiMVnl5OZYuXYrx\n48ejRYsWjTqHVCoFAO5NJtIidZbJADB37lzcvHkTa9asUcv5iEj/lZWVwdzcXHQMtZPJZNi0aRO2\nbNmCrKws9OzZE6dOnRIdi4gagGUykRG5c+cO+vfvj19//RWHDh2Ch4eH6EhEREbt008/xZ9//omp\nU6c2+hxVk8ksk4m0R6FQQCKRwMnJSS3na9WqFYKDgxEXF4eCggK1nJOI9JshTib/3dChQ3Hy5Ek4\nOjqiV69eSEhI4P0fiPQEy2QiI5GbmwsvLy/k5ubi6NGj6Nq1q+hIRERGTaVSYcmSJRg+fDiefPLJ\nRp+Hay6ItE+hUMDOzk6tU4OxsbGoqKhAamqq2s5JRPrL0MtkAHjyySdx4MABxMbGYubMmXj11Vdx\n8+ZN0bGI6F+wTCYyAjk5OXjppZdQXl6Ow4cPo127dqIjEREZva+++gqnTp1CWFhYk85jbW0NU1NT\nTiYTaZFCoVDbiosq9vb2mDZtGpKSkpCXl6fWcxOR/jGGMhkATE1NMWvWLBw9ehQXLlxA9+7dcfTo\nUdGxiKgOLJOJDNz169fx0ksvAQAOHDiAJ554QmwgIiICACQlJWHQoEHo1q1bk84jkUhga2vLyWQi\nLdJEmQwA06ZNg5WVFZKSktR+biLSL8ZSJlf5z3/+g+zsbPTq1Qve3t6YO3cu114Q6SiWyUQG7Nq1\na/D29oapqSkOHjyIVq1aiY5EREQAfvrpJ3z33XeIjIxUy/lkMhknk4m0SFNlso2NDWJjY5GWloZr\n166p/fxEpD+MrUwGHtxUeOvWrVi8eDEWLlwIPz8/5Ofni45FRP/AMpnIQP3xxx/w9vaGmZkZDh48\nCBcXF9GRiIjofxISEtCjRw94e3ur5XwymYyTyURapKkyGQAmTpyIVq1aYf78+Ro5PxHpB2Msk4EH\n77iaOnUqjh49inPnzqFbt2744YcfRMcior9hmUxkgH7//Xd4e3vD1tYWhw8fRsuWLUVHIiKi/7ly\n5Qo+//xzTJ8+XW3nlEqlnEwm0iJNlsnNmzfH7NmzkZ6ejt9++00j1yAi3WesZXKVnj174qeffkKX\nLl3g7e2N1NRUqFQq0bGICCyTiQzOlStX4OXlBTs7O3z77bdwdHQUHYmIiP4mMTERTz75JPz9/dV2\nTq65INIuTZbJADBq1Cg8++yzmDt3rsauQUS6raysDGZmZqJjCNWiRQt8+eWXSExMxPTp0zFixAgU\nFRWJjkVk9FgmExmQP/74Ay+//DKcnZ2xf/9+ODg4iI5ERER/k5eXh08++QSRkZEwNTVV23mlUinX\nXBBpSXFxMQoKCjRaJpuamiIuLg5ZWVn473//q7HrEJHuKi0tNerJ5CpVay/279+PgwcPonfv3pDL\n5aJjERk1lslEBkKhUGDAgAGQSqXYs2cP7O3tRUciIqJ/WLZsGWxtbfHee++p9bycTCbSHoVCAQAa\nLZMB4I033oCHhwdmz56t0esQkW4y9jUX/+Tl5YVjx46hoqICPXv2xKFDh0RHIjJaLJOJDMDNmzfx\n8ssvQ6lUYt++fWjRooXoSERE9A/379/H6tWrMXnyZFhaWqr13LwBH5H2aKtMBoBFixZh7969OHDg\ngMavRUS6paysDObm5qJj6BRXV1ccO3YMXl5e6N+/P5YvXy46EpFRYplMpOfu3r2LgQMHoqysDAcP\nHuTN9oiIdNSHH36IkpISBAUFqf3cvAEfkfZUlcnOzs4av5aXlxd8fHwQExPDG08RGZnKyko0a9ZM\ndAydY2Njg23btiEuLg6hoaGYMGECysvLRcciMiosk4n02L179+Dj44ObN2/im2++QatWrURHIiKi\nWpSXl2Pp0qUYN26cRt49wjUXRNqjUCgglUrV/g6DR4mPj8dPP/2EL774QivXIyLdUFlZCYlEIjqG\nTpJIJIiKikJWVhYyMjLQv39/3Lx5U3QsIqPBMplITxUWFmLQoEHIycnBwYMH0bZtW9GRiIjoETIz\nM3Hjxg2EhoZq5Py8AR+R9igUCq2suKjy/PPPY8iQIZgxYwYqKyu1dl0iEkulUsHEhJVNXfz9/fHD\nDz/gjz/+QK9evXDx4kXRkYiMAj8zEemhsrIy+Pv74/Lly9i/fz/c3NxERyIiojqkpKRg+PDhePLJ\nJzVyfplMhpKSEpSWlmrk/ET0/2m7TAaAuLg4XLhwAZs3b9bqdYlIHJVKxcnkeujSpQt++ukntGzZ\nEh4eHjh69KjoSEQGj2UykZ5RKpV49913cfz4cXz11Vd49tlnRUciIqI6fP311zh16hTCwsI0dg2p\nVAoAnE4m0gIRZXL79u3x3nvvYd68edwNSmQkWCbXX4sWLbBv3z707t0b/fv3R1ZWluhIRAaNZTKR\nngkLC8Nnn32Gbdu2wd3dXXQcIiL6F4mJiRg4cCC6d++usWvIZDIA4N5kIi0QUSYDwOzZs3Hjxg1s\n2LBB69cmIu1jmdwwVlZW+OyzzzB69GiMGDECK1asEB2JyGCxTCbSI3PnzkVaWho2b96M/v37i45D\nRET/4qeffsKhQ4cQGRmp0etUlcmcTCbSPFFlcps2bTB69GjExcVxpQ2REWCZ3HCmpqZYsWIFkpOT\nMWXKFEydOhVKpVJ0LCKDwzKZSE+sXr0aH3zwAVatWoUhQ4aIjkNERPWQmJiIHj164OWXX9bodarW\nXHAymUjzRJXJADBr1izcvHkTa9euFXJ9ItIepVLJMrmRpk6dig0bNmD16tUICAhASUmJ6EhEBoVl\nMpEe+PzzzzFp0iQsWLAA48aNEx2HiIjq4cqVK/jss880PpUMcDKZSFtKS0vx119/CSuTXVxcMH78\neCxYsADFxcVCMhCRdqhUKpiYsLJprHfeeQdffvkl9u3bBz8/PxQVFYmORGQw+JmJSMcdOnQIAQEB\nmDRpEmJiYkTHISKiekpKSkKbNm3g7++v8WuZmZnB0tKSk8lEGpaXlweVSiWsTAaA6Oho/PXXX1iz\nZo2wDESkeVxz0XT9+/fHoUOHcOrUKQwaNAgFBQWiIxEZBJbJRDrswoULeOutt/D6668jJSVFdBwi\nIqqnvLw8fPzxx4iIiECzZs20ck2ZTMYymUjDFAoFAAgtk1u2bIng4GDEx8ejsLBQWA4i0iyWyerR\nvXt3HD58GJcuXcLLL7+M/Px80ZGI9B7LZCIddfv2bfj6+sLV1RUbNmzgW5yIiPTI8uXLYWtri8DA\nQK1dUyqVcs0FkYbpQpkMAFFRUSgpKcHKlSuF5iAizWGZrD7PPvssDh48iNzcXPTv3x+3b98WHYlI\nr7GdItJBZWVlGDJkCMrLy/Hll1/CyspKdCQiIqqn+/fvY9WqVZg0aRIsLS21dl1OJhNpnkKhgLW1\nNaytrYXmcHR0xOTJk5GYmMgfIhEZKJbJ6tW+fXscOXIEd+/exSuvvIKbN2+KjkSkt1gmE+kYlUqF\nMWPG4MSJE/jiiy+ET74QEVHDfPTRRyguLkZQUJBWr8vJZCLNUygUOvO1WWRkJCorK7Fs2TLRUYhI\nA5RKJd+dqmZt27bFwYMHUVhYCC8vL+Tk5IiORKSX+JmJSMfMnj0bW7ZswbZt29ClSxfRcYiIqAEq\nKiqQmpqKcePGwdHRUavX5mQykebpUplsZ2eHqVOnIjk5GXfu3BEdh4jUjJPJmtGmTRscOXIEJiYm\n8Pb2xvXr10VHItI7LJOJdMgnn3yCBQsWYOXKlejfv7/oOERE1ECZmZm4fv06pkyZovVrs0wm0jxd\nKpMBYNq0aTAxMUFqaqroKESkZiyTNadly5b49ttv0axZM/Tv358rL4gaiGUykY746aefMGHCBERE\nRGDs2LGi4xARUSOkpKRg+PDhcHV11fq1ueaCSPN0rUyWyWQIDw9HSkoKyxAiA8QyWXNatmyJgwcP\nQqlUon///rh7967oSER6g2UykQ7Iy8uDv78/+vTpg4ULF4qOQ0REjbBnzx6cPHkS4eHhQq7PyWQi\nzdO1MhkApk6dCisrK6SkpIiOQkRqolKpALBM1jRnZ2d8/fXXyMvLw5tvvomSkhLRkYj0AstkIsEq\nKioQEBCAZs2a4dNPP4WpqanoSERE1AiJiYkYMGAAunfvLuT6nEwm0jxdLJOtra0RERGB5cuXQ6FQ\niI5DRGqgVCoBsEzWBldXV+zbtw+nT59GQEAAKioqREci0nksk4kECw8Px/Hjx7F9+3at36yJiIjU\n4+eff8bBgwcRGRkpLAMnk4k0q6KiAnfu3NG5MhkAJk2aBJlMhqSkJNFRiEgNqiaTTUxY2WhD586d\n8dVXX+HAgQMIDAysLvOJqHb8zEQk0ObNm7F8+XKkp6fD3d1ddBwiImqkxMREPPfcc3j55ZeFZZDJ\nZCgoKOA3QEQakpeXB6VSqZNlsqWlJaZPn44VK1bgxo0bouMQURNxzYX2/ec//8Fnn32GrKwsTJ06\nVXQcIp3GMplIkOzsbIwfPx7h4eEYPny46DhERNRIV65cwY4dOxATEyP0mz6pVAqlUonCwkJhGYgM\nWdUKCV0skwFgwoQJcHR0REJCgugoRNRELJPFeOWVV5CRkYFVq1YhPj5edBwincUymUiAO3fu4I03\n3kCvXr14wz0iIj23ePFitGnTBm+99ZbQHDKZDAC4N5lIQ3S9TLawsEBsbCzWrFkDuVwuOg4RNQHL\nZHH8/f2xbNkyzJw5E5s2bRIdh0gnsUwmEmDcuHEoLy/Hp59+imbNmomOQ0REjZSXl4cNGzYgPDxc\n+OdzqVQKANybTKQhCoUCFhYW1X/XdNHYsWPx+OOPY9GiRaKjEFETsEwWKzg4GOHh4RgzZgwOHTok\nOg6RzmGZTKRla9aswWeffYaPP/4YTk5OouMQEVETpKWlwcrKCu+//77oKNWTySyTiTRDoVDo7FRy\nFTMzM8TGxiI9PR1XrlwRHYeImqiqVCbtS0hIwKuvvoohQ4bg0qVLouMQ6RSWyURadO7cOYSFhSEm\nJgb9+vUTHYeIiJqgqKgIq1atwpQpU2BtbS06DtdcEGmYPpTJABAYGIinnnoKCxYsEB2FiBrJ1NQU\nAHhTXYFMTEywadMmtGnTBq+//joKCgpERyLSGSyTibSktLQUb7/9Njp37ow5c+aIjkNERE300Ucf\noaioCMHBwaKjAACsra1hamrKyWQiDdGXMtnU1BQzZszAxx9/jAsXLoiOQ0SNUFUmV1ZWCk5i3Kyt\nrfHFF18gPz8f77//PifFif6HZTKRloSHh0MulyMzMxNmZmai4xARURNUVFRgyZIlGDt2LBwdHUXH\nAfBgr6KtrS0nk4k0RF/KZAAYOXIk2rdvj7i4ONFRiKgRTExMIJFIWCbrgNatW2P79u348ssvuY+e\n6H9YJhNpwVdffYWVK1di1apVeOqpp0THISKiJtqyZQuuX7+OKVOmiI5Sg0wm42QykYboU5lsamqK\nWbNm4dNPP8Vvv/0mOg4RNYKJiQnLZB3h6emJhIQEzJw5E3v27BEdh0g4lslEGpabm4t3330Xo0eP\nxogRI0THISIiNUhOTkZAQADc3NxER6lBJpNxMplIQ/SpTAaAgIAAdOjQAfPmzRMdhYgawdTUlGWy\nDgkNDcXw4cPxzjvv4Pr166LjEAnFMplIw4KCgiCTyZCamio6ChERqcHevXtx8uRJhIeHi47yEKlU\nyslkIg2orKzE7du39apMNjExwaxZs5CVlYVff/1VdBwiaiCWybrnww8/hJOTE95++21UVFSIjkMk\nDMtkIg3avHkzvvjiC6Snp8PGxkZ0HCIiUoOkpCT4+PjA3d1ddJSHcM0FkWbcunULlZWVelUmA8CQ\nIUPQsWNH7vkk0kMsk3WPtbU1tm7dip9//pnv+iCjxjKZSENyc3MxZcoUBAcHo2/fvqLjEBGRGmRn\nZ+PAgQOIjIwUHaVWUqmUay6INCAvLw8A9K5MNjExQUxMDDIzM3HhwgXRcYioAQyhTFYqlViyZAk8\nPT0feczRo0fRu3dvWFlZwcXFBVFRUSgtLa3zvIGBgbCwsIBEIkFJSYm6Y9epc+fOSElJQXx8PL75\n5hutXptIV7BMJtKQkJAQyGQyLFy4UHQUIiJSk/j4eHTt2hX9+vUTHaVWnEwm0gyFQgFA/8pk4MHu\n5Hbt2vFrUiI9o+9l8sWLF+Hl5YWwsDAUFRXVeszZs2fh4+ODfv364ebNm9ixYwfS09MRFBRU57nX\nr1+PiIgITcSul4kTJyIgIADvvPMOcnNzheUgEoVlMpEGbNq0CZ9//jnWr1/P9RZERAbi6tWr2LFj\nB6KjoyGRSETHqRVvwEekGQqFAs2bN4ednZ3oKA1mamqK2NhYbNq0CRcvXhQdh4jqycTEBEqlUnSM\nRsnOzkZ0dDSCgoLQrVu3Rx43f/58tGzZEvPmzYO1tTV69eqFqKgobNiwAefPn68+rri4uM7pZhFW\nrVoFKysrvP3223r750TUWCyTidQsNzcXU6dORUhICNdbEBEZkMWLF+OJJ57AkCFDREd5JN6Aj0gz\nFAoFnJ2ddfYHSf9m5MiRcHNzQ0JCgugoRFRP+jyZ/Nxzz2H79u0YOXIkzM3Naz2moqICu3fvRt++\nfWt8bh00aBBUKhV27txZ/bF169ZVrxv6J1Gfl2UyGTIzM3H06FEkJSUJyUAkCstkIjWrWm8RHx8v\nOgoREanJ7du38fHHHyMsLAzNmjUTHeeRuOaCSDMUCoVerrioYmpqiqioKHzyySe4evWq6DhEVA/6\nXCbXx5UrV1BYWIg2bdrU+LibmxsA4PTp0wCA0NBQhIeH4/Lly5BIJGjXrl31sSYmJti9ezcGDRoE\nmUwGFxcXpKena+01vPDCC1iwYAFmz56NEydOaO26RKKxTCZSI663ICIyTMuWLYO5uTkCAwNFR6kT\nb8BHpBn6XiYDwDvvvIPWrVtzOplIT5iZmaGiokJ0DI2p2jVsa2tb4+MWFhawtLSs3lWfmpoKX19f\nuLm5QaVS4dKlS9XHKpVK2NnZITMzE3K5HO7u7ggODn7kjmZNCA8PR58+fTBq1CgUFxdr7bpEIrFM\nJlKTP//8k+stiIgMUFFREVauXInJkyfD2tpadJw6yWQylJSU/Otd0ImoYQyhTDYzM0N0dDTS09Px\n+++/i45DRP/CzMwMZWVlomNoTNXXKqampg89ZmZmVu9i1tPTEzKZDPb29hg+fDhKS0u1+g4MExMT\nfPzxx8jNzUVUVJTWrkskEstkIjXhegsiIsO0du1aFBUVISQkRHSUfyWVSgGA08lEamYIZTIABAYG\n4vHHH+d+TyI90Lx5c4Muky0sLACg1unrsrIyWFpaNvicZmZmAIDy8vKmhWug1q1bY/ny5UhLS8NX\nX32l1WsTicAymUgNNm7ciJ07d3K9BRGRgamsrMSyZcswevRoODk5iY7zr2QyGQBwbzKRmhlKmWxm\nZobIyEisXbsWN27cEB2HiOrQvHlzrZei2tSyZUsAD/8AvKioCCUlJXBxcRERq9FGjhyJgIAAjBs3\nDrdv3xYdh0ijWCYTNdGff/6J0NBQrrcgIjJAW7ZsgVwuR2hoqOgo9VJVJnMymUh9VCoVbt68aRBl\nMgCMGTMGTk5OnE4m0nGGPpn81FNPwdbW9qG1O1U7kbt27SoiVpOsWrUKzZo1w7hx40RHIdIolslE\nTTRx4kTY2dlh4cKFoqMQEZGaJScnY9iwYdV3Ftd1VWsuOJlMpD75+fkoLy83mDLZ3NwckZGRWLNm\nDXJyckTHIaJHaN68uUHfA6FZs2Z49dVXcfjwYSiVyuqPf/3115BIJPDz8xOYrnHs7OyQnp6OnTt3\nYvPmzaLjUB0mTpwIiURS/WvUqFHYvn07XF1dqz/2zjvvPPQ8Hx8f2NrawtTUFJ06dcKJEydqPX9G\nRgYkEgk8PT0fmeHzzz+HRCIBAAQEBMDR0VE9L04LWCYTNcH27duxa9curF27VudvykRERA2zb98+\nnDhxAhEREaKj1Bsnk4nUT6FQAIDBlMkAMH78eDg4OCAlJUV0FCJ6BEOfTAaAWbNmQaFQYM6cObh/\n/z6OHTuGpKQkvP/++3jmmWeqj3NwcEBOTg7kcjkKCgp0ev1Hv379EBQUhKlTpyI3N1d0HKqDg4MD\nvv76a1y4cAHr1q3DW2+9hStXrsDNzQ0tWrTApk2bsHv37hrP2bdvH7KysuDr64uzZ8/C3d291nNn\nZGTAzc0Nx44dq562/6fXX38d169fBwB0795dvS9Ow1gmEzVSQUEBQkND8d5778Hb21t0HCIiUrOk\npCT079//kV8k6iIzMzNYWlpyMplIjQyxTLawsEB4eDhWrVqFvLw80XGIqBb6XCb/+OOP6NOnD1q1\naoXjx48jOzsbLi4u6N27Nw4fPlx9XKdOnbB3717s27cPLVq0wFtvvYXRo0dj1apVNc4XFBQEZ2dn\ndOzYEa+++iqio6OrfxjWtWtXXLlyBZs3b0ZwcDAAVBd9oiQkJMDOzg4TJkwQloH+naWlJQYOHIj2\n7dvD3Ny8xmPLli2DiYkJJkyY0OCvq2/fvo1z585h3rx5AIBPPvmk1uMkEgkef/xxANC7HeEsk4ka\naebMmSgqKkJiYqLoKEREpGbZ2dn49ttvERkZKTpKg8lkMpbJRGqkUCjQrFkzODg4iI6iVsHBwZDJ\nZJxOJtJR5ubmelsme3h44OjRo8jJyYFKpYJKpcKff/6J77//Hl5eXjWO9fLywvHjx1FSUoKcnBwk\nJiY+WKSh1QAAIABJREFUVOx1794dcrkcRUVFOHLkCJKTk1FUVASVSoX/+7//g6urK0aOHIn8/Hyo\nVCpcu3YNnTp10uZLrsHa2hofffQRdu3ahczMTGE5qPE8PT0RGhqKGzduNPhdilu3bsVrr70GPz8/\nWFhYYOPGjVCpVBpKKgbLZKJGOH36NFauXImkpCQ4OTmJjkNERGq2aNEidO3aFa+88oroKA0mlUq5\n5oJIjRQKBZycnGBiYljfOllYWGDatGlIS0vDzZs3Rcchon/Q58lkAry9vTFx4kRMmjSp+h0upF/i\n4uLQvn17rF27Fvv376/38zIyMuDv7w9bW1v4+PhALpfjyJEjGkyqfYb1FRGRFiiVSkyYMAEeHh4I\nDAwUHYeIiNTs6tWr2LZtG6KioqpviqFPOJlMpF4KhcKgVlz8XXBwMKytrbF06VLRUYjoH1gm67+k\npCSuu9BjlpaW2LBhA0xMTDBu3Djcv3//X5/zxx9/4MKFC9UT+EOHDgXw6FUX+oplMlEDrVixAidO\nnMCaNWv0smQgIqK6JScno3Xr1tVf/OkbTiYTqZchl8nW1tYIDQ3F8uXLkZ+fLzoOEf0Ny2T9Z21t\njdWrV+OLL77Atm3bRMehRujVqxemTZsGuVyO6Ojofz0+IyMDgwcPhqmpKQDAz88P5ubmyMrKQnFx\nsabjag3LZKIGyM3NxezZsxEREYGOHTuKjkNERGp2+/ZtbNiwAWFhYWjWrJnoOI3CyWQi9TLkMhkA\nJk+ejObNm2PZsmWioxDR37BMNgyvvPIKxo4di8mTJ+POnTui41AjxMXF4ZlnnsGKFStw9OjROo+t\nWnFRRSqVwsfHB/fu3cPOnTs1HVVrWCYTNcCUKVMgk8kQGxsrOgoREWnA8uXLYW5urtdrjFgmE6mX\noZfJNjY2mDJlClJTU3H37l3RcYjof1gmG46kpCSYmJjUa7KVdI+FhQXWr18PiUSC0aNHP3LC+Ndf\nf8WZM2fg6+sLiURS/WvXrl0ADGvVBctkonrau3cvsrKysGLFClhbW4uOQ0REalZUVIQVK1Zg8uTJ\nsLGxER2n0bjmgki9DL1MBoCpU6fCxMQEy5cvFx2FiP7H3NwcJSUlomOQGshkMqSkpOCjjz7CwYMH\nRcehRujVqxfCwsJw8eJFzJ8/v9ZjNm/ejBEjRkClUtX4lZ+fD0tLS+zbtw+5ublaTq4ZLJOJ6qG4\nuBjBwcEYNmwYXnvtNdFxiIhIA9atW4eioiKEhISIjtIknEwmUq+8vDyDL5OlUmn1dHJBQYHoOESE\nB9OQhlAmK5VKLFmyBJ6enk065u8CAwNhYWEBiUSiN79HAQEBeO211xAUFITS0lLRcagR5s+fj2ef\nfRYnT5586DGVSoXMzMxav4+wt7fH0KFDUVlZiYyMDG1E1TiWyUT1MH/+fNy6dQspKSmioxARkQZU\nVlZi6dKlCAwMhJOTk+g4TcLJZCL1uXv3LkpLSw2+TAaAadOmQalUIi0tTXQUIgJgaWmJoqIi0TGa\n5OLFi/Dy8kJYWNgjX0t9jvmn9evXIyIiQp1RtSItLQ03btxAYmKi6CjUCFXrLqpurvd3P/zwA6RS\nKXr37l3rc4OCggAYzqoLlslE/+Ls2bNITk5GfHw8Hn/8cdFxiIhIA7Zu3Qq5XI7Q0FDRUZqMk8lE\n6qNQKAAAzs7OgpNonkwmQ0hICJKTkzmdTKQDrKysHrmbVR9kZ2cjOjoaQUFB6NatW6OPAR68U7i+\nU8u67Mknn8Ts2bOxYMEC/Pbbb6Lj0D989tlnaNeuHS5fvoyePXti8uTJDx3j4eGBsLCwGh8bO3Ys\nBgwYgHPnzqFbt24PTS7HxcVV35QvOzsbrVu3xsqVKzX3QrSAZTJRHVQqFSZPnoyuXbti4sSJouMQ\nEZGGLF68GEOHDkW7du1ER2kymUyGgoICKJVK0VGI9F5VmWwMk8nAg+nksrIyrF69WnQUIqNnaWmp\n12Xyc889h+3bt2PkyJEwNzdv9DHAg1VkeXl5tT4mkUjUkldbpk2bhg4dOiAkJAQqlUp0HPqbN998\nE5cuXYJKpcLNmzcfeR+BxMREfP7559X/vXbtWhQWFqK8vBynTp1C9+7daxw/c+ZM5OTkVO9Qvn79\nOoKDgzX6WjSNZTJRHdavX4/Dhw9jzZo1tb6VgYiI9N8333yDEydO6OXbJWsjlUqhVCpRWFgoOgqR\n3lMoFDAxMYGjo6PoKFrRokULBAcHIzk5We/fXk+k76ysrPj3EEBoaCjCw8Nx+fJlSCSSGj/4NzEx\nwe7duzFo0CDIZDK4uLggPT1dYNp/16xZM6xZswbfffcdPv30U9FxjFpxcTH27t2LixcvoqysTOvX\nV6lUyMnJAQC9uzEfy2SiR/jrr78QExODkJAQuLu7i45DREQakpSUhFdeeQXPP/+86ChqIZPJAIB7\nk4nUQKFQoEWLFmjWrJnoKFoTERGB+/fvY82aNaKjEBk1S0tLlJaWGv07jVJTU+Hr6ws3NzeoVCpc\nunSp+jGlUgk7OztkZmZCLpfD3d0dwcHBOl/Cv/DCCxg9ejTCw8O5mkyg/Px8DBw4EO3bt8fo0aO1\nfv2dO3dWr1I9ceKE1q/fFCyTiR5h3rx5qKiowOzZs0VHISIiDcnOzsb+/fsRGRkpOoraSKVSAOA3\nJ0RqoFAojGbFRRVHR0dMmDABSUlJKCkpER2HyGhZWVkBgF6vutAGT09PyGQy2NvbY/jw4SgtLcXV\nq1dFx/pXCQkJqKioQFxcnOgoRmn16tXVaydUKhU2bdqk9QxvvPFG9aqTLVu24NatW1rP0Fgsk4lq\ncf78eaSlpWHhwoVo0aKF6DhERKQhCQkJ6NKlC/r37y86itpUTSazTCZqOmMsk4EH08l3797FunXr\nREchMlqWlpYAWCY3hJmZGQCgvLxccJJ/5+DggPnz5yM1NRVnzpwRHYeoQVgmE9UiLCwMnTp1wpgx\nY0RHISIiDZHL5cjKykJUVJTe3bylLlxzQaQ+xlomt2zZEmPGjEFCQoKQPZJE9P8nk3V9ZQM13vjx\n4+Hu7s6b8ZHeYZlM9A+7du3C119/jdTUVN50j4jIgCUnJ8PFxQVDhw4VHUWtrK2tYWpqyslkIjUw\n1jIZAKZPnw6FQiHkrb9ExMlkY2BiYoIVK1bg+++/x9atW0XHIao3lslEf1NWVoaIiAgEBASgb9++\nouMQEZGG5OfnY/369YiIiKh+S6ShkEgksLW15WQykRoYc5n8xBNP4N1330V8fDwqKipExyEyOtyZ\nbBx69OhRfTO+wsJC0XF0VkZGBiQSCTw9PUVH0Qljx46Fra0tJBIJTp06pfXrs0wm+pvU1FRcu3YN\nCQkJoqMQEZEGLVu2DObm5kLu3KwNMpmMk8lEapCXl2e0ZTIAxMTE4Pfff+fEHJEAVZPJXHPxYL9w\nTk4O5HI5CgoK9GInckPEx8fj/v377CHqkJGRATc3Nxw7dgyXLl0SHUe4tWvX4qOPPhJ2fZbJRP+j\nUCgQHx+PqKgoPPnkk6LjEBGRhhQVFWHlypWYNGkSbGxsRMfRCJlMxslkoiYqKChAUVGRUZfJrq6u\nCAgIQFxcHJRKpeg4REZF3yeTf/zxR/Tp0wetWrXC8ePHkZ2dDRcXF/Tu3RuHDx+u9zEAEBQUBGdn\nZ3Ts2BGvvvoqoqOjkZKSAgDo2rUrrly5gs2bNyM4OBgA4Ovri7Nnz2r/RTeSk5MTZs2ahcWLF0Mu\nl4uOo3Nu376Nc+fOYd68eQCATz75RHAiaiY6AJGuiIqKglQqRUREhOgoRESkQenp6SgoKEBQUJDo\nKBojlUo5mUzURAqFAgCMukwGgBkzZqBz587YuXMn3nzzTdFxiIyGvu9M9vDwwNGjR5t8DAB07979\noZI1OTm5xn+7urpi5MiRDc6pKyZPnow1a9YgJiYGn376qeg4OmXr1q147bXX4OfnBwsLC2zcuBHz\n5s0zqBtoN4bI18/JZCIAv/zyCzZu3IjFixfD2tpadBwiItKQyspKpKamYvTo0WjZsqXoOBrDNRdE\nTccy+YEOHTrgjTfewPz586FSqUTHITIaFhYWMDU1xf3790VHIS0wMzNDUlISMjMzceTIEdFxdEpG\nRgb8/f1ha2sLHx8fyOXyR/4efffdd3jhhRdgZWUFqVSKLl26VL9br67HVCoVUlJS0KFDB5ibm8Pe\n3h5vvPEGzp8//9A1Nm7ciB49esDCwgLW1tZo27Yt5s+fX+drOHLkCDp27AiZTAYLCwt06dIFe/fu\nrfd5VSoVkpKS8Mwzz8Dc3BwymQyRkZE1np+amgpra2uYmJjg+eefx2OPPQYzMzNYW1vD3d0dL774\nIp544glYWFjAzs4O06dPr98fQC1YJpPRU6lUCA0NRa9evTB06FDRcYiISIOysrIgl8sxbdo00VE0\nSiqVcs0FURMpFApIJBI4OTmJjiLcrFmzcOrUKezZs0d0FCKjYmNjg4KCAtExSEv8/Pzg4+ODqVOn\ncrXQ//zxxx+4cOECvLy8AKC6s6lt1cX9+/fh5+eHIUOGID8/HxcvXkT79u1RVlZW52MAMHfuXMTE\nxGDGjBnIy8vD4cOHce3aNbz44ovVP1wGHhS27777LoYMGYKcnBxcv34dsbGxuHDhQp2vQ6FQICAg\nAHK5HDk5ObCxsakxSf/VV1/Ved5Zs2YhKioKEyZMgEKhQG5uLqKjo2tcIzQ0FJGRkVCpVFi1ahWu\nXr2K3NxceHl54eTJk4iJicHJkyeRn5+P9957D0lJScjOzm7EnwrLZCJs3LgRP/zwA5YuXWr0b5Mg\nIjJ0ixcvxltvvYV27dqJjqJRnEwmajqFQgF7e3s0b95cdBThunXrhkGDBuGDDz4QHYXIqNja2rJM\nNjJLlizBmTNnsHHjRtFRdEJGRgYGDx4MU1NTAA8Kd3Nzc2RlZT20AkYul+PevXvo1KkTLCws8Nhj\nj2H79u1wdHSs87Hi4mKkpKTA398fo0aNgkwmQ5cuXbB69WrcunULH374IQCgvLwc8+bNg7e3N6Kj\no+Hg4AB7e3uMGTMGPXv2rPN1DBkyBHPmzIG9vT0cHBzg5+eH27dv4+bNmwCAbdu2PfK8xcXFWLJk\nCV555RWEhYXBzs4OlpaWcHBweOT1OnbsCCsrK7Ro0QIjRowAALRp0waOjo6wsrLCqFGjAKDWyev6\nYJlMRq2wsBAxMTEYP348nn/+edFxiIhIg/bv349ffvkFYWFhoqNoHG/AR9R0CoXC6Fdc/N2sWbPw\n448/4tChQ6KjEBkNqVTKMtnIdOzYEWPHjkVsbCxXnOD/r7ioIpVK4ePjg3v37mHnzp01jnV1dYWz\nszNGjRqFuXPn1tizXddjZ8+eRWFhIXr06FHjfD179kTz5s1x/PhxAMDp06dx9+5dDBgwoMZxpqam\nmDp1aoNel5mZGYAHK/iAB1PVjzrvpUuXUFRUhH79+jXoGlWqfiheUVHx0PXLy8sbdU6WyWTUFixY\ngOLiYk5ZEBEZgaSkJPTr1w//+c9/REfRON6Aj6jpWCbX5OHhAW9vbyxYsEB0FCKjwclk4zRv3jwU\nFhYiJSVFdBShfv31V5w5cwa+vr6QSCTVv3bt2gXg4VUXlpaWOHDgAPr06YMFCxbA1dUVw4cPR3Fx\ncZ2P3b17F8CDtTL/ZGdnV/13sGpQw87Orta8X375ZY2cEomkegJ49+7deOmll+Dk5ARzc/Na9xU/\n6rzXr18HAJ1au8UymYzW77//jtTUVMyePVun/lISEZH6nT59Gt98881DN6owVFxzQdR0LJMfNmPG\nDOzfvx/ff/+96ChERsHW1pbvNDJCzs7OiIyMRGJiInJzc0XHEWbz5s0YMWIEVCpVjV/5+fmwtLTE\nvn37Hvr96dSpE3bt2oWcnBxERUVhy5YtWLx4cZ2PVZW4tf3g5u7du2jdujUAoFWrVgCAW7du1Zp3\n8ODBD2XdtGkT/vjjD7z55pto2bIljh8/jr/++gsJCQkPPf9R57WwsAAAlJaW1ue3TStYJpPRmjlz\nJlxcXBAUFCQ6ChERaVhCQgK6dOkCHx8f0VG0gjfgI2o6lskP69evH3r37o1FixaJjkJkFPR9Mlmp\nVGLJkiXw9PSs9fEPPvgAHTt2hFQqhbm5Odq1a4fp06ejsLCwzvMGBgbCwsICEokEJSUlmoguXHh4\nOOzs7BAXFyc6ihAqlQqZmZkICQl56DF7e3sMHToUlZWVyMjIqP54Tk4Ozp07B+DBFO/ChQvh7u6O\nc+fO1flY586dYWNjg59//rnGdY4fP46ysrLqlaht27aFg4MD9u3b16DXcubMGZSXlyM4OBiurq7V\n/+/+nY2NzSPP27lzZ5iYmOC7775r0HU1iWUyGaXs7GxkZGRg0aJFMDc3Fx2HiIg0SC6XY+vWrZg+\nfbrR3GhVJpOhpKREpyYYiPQNy+TaxcTE4Msvv8Qvv/wiOgqRwdPnMvnixYvw8vJCWFgYioqKaj3m\nwIEDmDRpEuRyOW7duoX4+HikpqZi6NChdZ57/fr1iIiI0ERsnWFpaYnZs2djzZo1+O2330TH0bof\nfvgBUqkUvXv3rvXxqqHAv6+6yMnJwcSJE3H+/HmUlZXh5MmT+P333+Hh4VHnYxYWFggPD8eOHTuw\nadMm3Lt3D2fOnEFQUBBcXFwwYcIEAIC5uTliY2Nx+PBhTJkyBTdu3IBSqURBQUF1UV2bNm3aAHhw\n/5aSkhJcvHixeg9zlTfffPOR53VycsJbb72Fbdu2Yd26dbh37x5Onz5dfWNAEVgmk1GKjIxEjx49\n/vUfKSIi0n9LliyBi4sLhg0bJjqK1kilUgDgdDJRE7BMrt1rr72G559/vta36BKReunrDfiys7MR\nHR2NoKAgdOvW7ZHH2djYYMKECXBwcICtrS2GDRuGN998E3v27MG1a9eqjysuLn7kdLMhGz16NJ55\n5hnMnj1bdBStGjt2LAYMGIBz586hW7duOHnyZI3H4+Liqm/Kl52djdatW2PlypVwcnJCZWUlPD09\nYWVlhcGDB2PixImYNGlSnY8BwJw5cxAfH48PPvgAjo6O6Nu3L9q2bYtDhw7B2tq6+trh4eFIS0vD\noUOH0K5dO1hbW6Nv37513py2S5cuiIqKwooVK+Di4oIZM2bgpZdeAgD06dMHwIMVGXWdNz09HYGB\ngYiKisLjjz+OkJAQvPjiiwAAX19fnD59GkuXLkVSUlL1NY8ePYqEhARMnDgRADBw4EBkZGRgy5Yt\nGDhwIABgypQpyMzMbPCfkUSlUqka/Kz/qSrisrKyGnsKIq3bs2cPBg0ahG+//RYvv/yy6Dj0N1u3\nbkVAQACa8GmJiKiG/Px8PPnkk4iLi2vwXZb12cmTJ+Hu7o6LFy+iXbt2ouMQ6Z3i4mJYWVlh165d\nGDx4sOg4Omfbtm0ICAhAdnY2OnfuLDoOkcGKjY3Fnj17cOLECdFRGs3DwwMlJSU4depUvY4PCQnB\nypUrcf78eTzzzDMAgLS0NKSmpuLSpUvVx82cORMLFixASUmJQb/beNeuXfDz88PRo0cfOaVL+k0i\nkWDLli36NPiSxclkMipKpRKxsbHw8/NjkUxEZATS0tLQvHlzjBkzRnQUrZLJZAA4mUzUWFU39OFk\ncu3eeustdOzYEYmJiaKjEBk0fV5z0Vg3btyApaUlnnrqKQBAaGgowsPDcfnyZUgkkho/JDcxMcHu\n3bsxaNAgyGQyuLi4ID09XVR0jfD19YWXlxdiYmJERyGqxjKZjMrGjRtx+vRpxMfHi45CREQaVlJS\ngpUrVyIkJAQ2Njai42hV1ZqLv/76S3ASIv2kUCgAsEx+FIlEgqioKGRkZODixYui4xAZLGMrk4uK\ninDgwAGMGzcOzZs3BwCkpqbC19cXbm5uUKlUNaaTlUol7OzskJmZCblcDnd3dwQHBz9yR7O+SkhI\nwJEjR/D111+LjkIEgGUyGZGSkhLMnj0bY8aMQadOnUTHISIiDVu3bh3++usvBAcHi46idZxMJmqa\nqjLZ2dlZcBLdNWLECLi6umLx4sWioxAZLGMrk+Pj4+Hi4oK4uLh6P8fT0xMymQz29vYYPnw4SktL\ncfXqVQ2m1D4PDw/4+vpi5syZXAlJOoFlMhmNpUuX4ubNm5g1a5boKEREpGGVlZVITU1FYGAgWrZs\nKTqO1pmZmcHS0pKTyUSNpFAoIJPJYGFhITqKzjI1NUVkZCTWr1+P33//XXQcIoMklUpRVFSEiooK\n0VE0bseOHdi6dSv27t0LW1vbRp3DzMwMAFBeXq7OaDphwYIFOHXqFHbs2CE6ChHLZDIOd+7cQWJi\nIiIiItC6dWvRcYiISMO2bduGq1evYtq0aaKjCCOTyVgmEzWSQqHgiot6eP/999GqVSukpKSIjkJk\nkKpK1cLCQsFJNCszMxOLFi3CoUOH0LZtW9FxdFKXLl0wbNgwzJgxwyh+uEC6jWUyGYX58+fD1NQU\nERERoqMQEZEWLFmyBP7+/nj66adFRxFGKpVyzQVRI7FMrh8zMzOEhYXhww8/xJ9//ik6DpHBqboH\ngiH/e758+XJs2rQJBw4cQKtWrUTH0WkffPABLl++jIyMDNFRyMixTCaDJ5fLsXLlSsyZM6f6H2Mi\nIjJc3377LY4fP46wsDDRUYTiZDJR47FMrr/x48fD3t4eS5cuFR2FyODY2dkBePBOW0OjUqkQFRWF\nM2fO4PPPPze6myU3xtNPP4333nsPc+bMQVlZmeg4OiEjIwMSiQSenp6ioxgVlslk8GJjY/H4449j\n3LhxoqMQEZEWJCUlwdvbGx4eHqKjCMXJZKLGY5lcfxYWFggNDUVaWhpu3bolOg6RQbG3twdgmGXy\nuXPnkJiYiI8++ghmZmaQSCQ1fv395p4ODg7IycmBXC5HQUGBQe5Erq/Zs2fjzz//xLp160RH0QkZ\nGRlwc3PDsWPHcOnSJdFxjAbLZDJop06dwpYtW5CYmIjmzZuLjkNERBp2+vRp7Nu3D9OnTxcdRThO\nJhM1HsvkhgkJCYGlpSXS0tJERyEyKPb29pBIJHpXJv/444/o06cPWrVqhePHjyM7OxsuLi7o3bs3\nDh8+DODBZHJ9BQUFwdnZGR07dsSrr76K6Ojo6l3tXbt2xZUrV7B582YEBwcDAHx9fXH27Fn1vzAd\n0KZNG4wfPx4LFy40+unk27dv49y5c5g3bx4A4JNPPhGcyHg0Ex2ASJMiIiLwwgsvwN/fX3QUIiLS\ngsTERHTu3BkDBgwQHUU4mUyG3Nxc0TGI9BLL5IaxtrbGpEmTsGTJEkybNg0ymUx0JCKD0KxZM9jY\n2Ohdmezh4YGjR4/WeUznzp3rXSh3794dcrm8xseSk5Nr/LerqytGjhzZoJz6avr06fjwww/x8ccf\nG/U7sLdu3YrXXnsNfn5+sLCwwMaNGzFv3jxIJBLR0QweJ5PJYB04cADffvstFi1axE8mRERG4Nq1\na9i6dSumT5/Oz/vgmguixiotLcW9e/dYJjdQaGgoJBIJVq1aJToKkUGxt7dHfn6+6BikQ1q3bo33\n338fixYtQkVFheg4wmRkZMDf3x+2trbw8fGBXC7HkSNHaj32u+++wwsvvAArKytIpVJ06dKl+uvk\nuh5TqVRISUlBhw4dYG5uDnt7e7zxxhs4f/78Q9fYuHEjevToAQsLC1hbW6Nt27aYP3/+I/N36NCh\n+nuW6OhoFBUVAXjwwwKZTAYLCwts2LBB4zkag2UyGay5c+fCx8cHffv2FR2FiIi0IDk5GY899hgC\nAgJER9EJXHNB1DgKhQIqlYplcgPJZDJMnDgRycnJKCwsFB2HyGDY29vr3WQyaV5sbCyuX7+OzZs3\ni44ixB9//IELFy7Ay8sLADB06FAAta+6uH//Pvz8/DBkyBDk5+fj4sWLaN++PcrKyup8DHjQK8XE\nxGDGjBnIy8vD4cOHce3aNbz44otQKBTV10hNTcW7776LIUOGICcnB9evX0dsbCwuXLjwyNfw66+/\nom3btgCA+Ph4WFlZAXjwTssxY8Zg0aJFeP/99zWeozFYJpNB2r17N44cOVK9O4eIiAxbfn4+1q1b\nh/DwcJiZmYmOoxM4mUzUOFXflLFMbriwsDAUFRVh7dq1oqMQGQyWyVSbNm3a4O2330ZcXJxRTidn\nZGRg8ODBMDU1BQD4+fnB3NwcWVlZKC4urnGsXC7HvXv30KlTJ1hYWOCxxx7D9u3b4ejoWOdjxcXF\nSElJgb+/P0aNGgWZTIYuXbpg9erVuHXrFj788EMAQHl5OebNmwdvb29ER0fDwcEB9vb2GDNmDHr2\n7PnI12BqaoqpU6cCAP773/9Wf7yoqAjbt2/H6NGjAUDjORqDZTIZHJVKhXnz5uH111+Hh4eH6DhE\nRKQFK1asgJmZGcaOHSs6is7gZDJR41SVyc7OzoKT6B8nJyeMHz8eycnJRn9jKCJ1YZlMjzJz5kzI\n5XJkZWWJjqJ1VSsuqkilUvj4+ODevXvYuXNnjWNdXV3h7OyMUaNGYe7cuTX2b9f12NmzZ1FYWIge\nPXrUOF/Pnj3RvHlzHD9+HMCDG4DfvXv3oXu2/L0sfpSq7112795d/bFNmzbhjTfegFQq1VqOhmKZ\nTAZn+/bt+PnnnzF37lzRUYiISAtKSkqwcuVKhISEwMbGRnQcnSGTyVBQUAClUik6CpFeUSgUsLGx\ngbW1tegoeik8PBx5eXm1vtWYiBrOwcGBZTLVys3NDcOGDUNcXJxRfb3366+/4syZM/D19YVEIqn+\ntWvXLgAPr7qwtLTEgQMH0KdPHyxYsACurq4YPnw4iouL63zs7t27AFDr9xd2dnYoKCgAgOp3AtrZ\n2dWa98svv6yRUyKRYNSoUTXO/X//93/V08mrVq3ClClTqp+vrhzqxDKZDIpSqcT8+fMREBCAbt26\niY5DRERakJ6ejjt37iAkJER0FJ0ilUqhVCq5u5SogRQKBVdcNEHr1q3xzjvvGP2NoYjUhZPJVJdg\n+PpxAAAgAElEQVSZM2fi/Pnz+Pzzz0VH0ZrNmzdjxIgRUKlUNX7l5+fD0tIS+/btQ25ubo3ndOrU\nCbt27UJOTg6ioqKwZcsWLF68uM7HqkrZqrL27+7evYvWrVsDAFq1agUAuHXrVq15Bw8e/FDWTZs2\n1TjG1NQUS5YsweHDh/HEE0/Azc2t+jF15VAnlslkUDIyMnD27FnMnj1bdBQiItKCyspKLFmyBIGB\ngWjZsqXoODpFJpMBAPcmEzUQy+Smi4mJgVwux7Zt20RHIdJ7ulomK5VKLFmyBJ6enlq9bklJCZ59\n9lnMnDmzzuMCAwNhYWEBiUSCkpISLaXTvg4dOsDf3x/z58+HSqUSHUfjVCoVMjMzax0isbe3x9Ch\nQ1FZWYmMjIzqj+fk5ODcuXMAHqxjWrhwIdzd3XHu3Lk6H+vcuTNsbGzw888/17jO8ePHUVZWhuef\nfx4A0LZtWzg4OGDfvn2Nfl29evXCtm3bMGvWLISGhtZ4TJs56otlMhmMyspKxMXF4Z133kGHDh1E\nxyEiIi3Yvn07rly5grCwMNFRdE7VnjXuTSZqGJbJTefm5oYhQ4Zg4cKFRlFuEGmSLpbJFy9ehJeX\nV/VNN7VpxowZuHDhwr8et379ekRERGghkXgzZ85EdnZ2jb27huqHH36AVCpF7969a308KCgIQM1V\nFzk5OZg4cSLOnz+PsrIynDx5Er///js8PDzqfMzCwgLh4eHYsWMHNm3ahHv37uHMmTMICgqCi4sL\nJkyYAAAwNzdHbGwsDh8+jClTpuDGjRtQKpUoKCioLqr/ja+vLyoqKnDnzh28/PLLNR7TZo76YplM\nBiM9PR1XrlzBrFmzREchIiItqbqz8dNPPy06is6pmkxmmUzUMCyT1SMmJgZnzpzBV199JToKkV6r\nKpN15Qcz2dnZiI6ORlBQUKNXSxYXFzdqovmHH37Ar7/+qtZzGoLnnnsOgwcPxty5c3Xm/xNNGDt2\nLAYMGIBz586hW7duOHnyZI3H4+Liqm/Kl52djdatW2PlypVwcnJCZWUlPD09YWVlhcGDB2PixImY\nNGlSnY8BwJw5cxAfH48PPvgAjo6O6Nu3L9q2bYtDhw7VuLdCeHg40tLScOjQIbRr1w7W1tbo27cv\nDh06VK/X1rZtW3h7ez/yRnnaylFfElUT/k8bOnQoABjlnSNJt5SVleHZZ5/FwIEDsXLlStFxqJG2\nbt2KgIAAg/4HkIjU58CBA+jXrx+OHTsGDw8P0XF0TmFhIWxtbfH1119j4MCBouMQ6Y2OHTti2LBh\nvJmzGgwePBi3b9/GsWPHREch0lt79+7FwIED8ddff1W/60hXeHh4oKSkBKdOnWrQ89LS0pCamopL\nly7V+znFxcV45ZVXsHbtWnTs2BEzZsxAXFxcneecOXMmFixYgJKSEpibmzcoo745ceIEevTogT17\n9sDHx0d0HGoAiUSCLVu2YNiwYaKj1FcWJ5PJIKxZswZ//vknYmNjRUchIiItSUpKwksvvcQi+RGs\nra1hamrKyWSiBuJksvrMnDkTP/74I7777jvRUYj0lr29PQDo3KqLf/Pdd9/hhRdegJWVFaRSKbp0\n6YJ79+4hNDQU4eHhuHz5MiQSCdq1a1ev882YMQMhISFwcnJ66LG6zmliYoLdu3dj0KBBkMlkcHFx\nQXp6utpep65wd3eHj48PfxBKWsEymfRecXExEhISEBwcXH0XSyIiMmxnzpzB3r17MX36dNFRdJZE\nIoGtrS1vwEfUAOXl5bhz5w7LZDXx8PBA3759sXDhQtFRiPSWg4MDAOD27duCk9Tf/fv34efnhyFD\nhiA/Px8XL15E+/btUVZWhtTUVPj6+sLNzQ0qlape08nff/89Ll++jLfffrvWx+s6p1KphJ2dHTIz\nMyGXy+Hu7o7g4GCt73rWhjlz5uDYsWNqX2lA9E8sk0nvLV++HHfv3mWhQERkRBITE9G5c2eub/gX\nMpmMk8lEDZCXlweVSsUyWY1iYmKwd+/eh+5CT0T14+zsDODB5yd9IZfLce/ePXTq1AkWFhZ47LHH\nsH37djg6Ojb4XMXFxQgNDW3SOktPT0/IZDLY29tj+PDhKC0txdWrVxt9Pl3Vq1cveHt7Y8GCBaKj\nkIFjmUx6rbCwEMnJyQgNDeUX/URERuL69evYsmULIiMjIZFIRMfRaTKZjJPJRA2gUCgAgF9XqtGA\nAQPQo0cPLFq0SHQUIr0klUphYWGBmzdvio5Sb66urnB2dsaoUaMwd+5cyOXyRp8rNjYW48ePx+OP\nP66WbGZmZgAevBPFEEVHR2P//v0P3ZyOSJ1YJpNeS01NRVlZGcLDw0VHISIiLUlOTsZjjz2GgIAA\n0VF0nlQq5WQyUQOwTNaM6Oho7NixA/+PvfuMa/Lq+wD+CzPMJCgoSwQUB6KVWksRV5fVitUClQpV\nUauCVlEQsNhdBxv3aIW6qKhYW6VVn9ZWxH1X2yoWRDQqRoNCkT0k53nhTe4iG5JcBP7fz4cXXrly\nzi+RJJx/znVORkYG11EIUUumpqZqNTNZT08PJ06cgJubG1auXAk7Ozt4e3ujvLy8wfOPHDkCHo9X\n58fX1xfp6em4cuUK5syZo+JHoL5ef/11DBkyBHFxcVxHIZ0YFZOJ2iopKcHatWuxePFi+aYEhBBC\nOrd//vkHX3/9NZYuXQodHR2u43R4tMwFIa0jlUqhp6cHIyMjrqN0KlOmTMHAgQMRHR3NdRRC1JKp\nqalazUwGAEdHRxw+fBgSiQShoaFITk5u9D1g4sSJYIzV+dm9eze2b9+OX375BRoaGvIic+0GfCtX\nrgSPx6MldBqwePFi7N27F3fv3uU6CumkqJhM1NaGDRtQVVWFRYsWcR1F6ebPn1/vW9qUlBTY2dnJ\nj7333nv17vf666/DyMgImpqacHR0xKVLlxpsPykpCTweD66uro1mOHToUJ0MbVnvSlmefS5qf3R0\ndGBmZoYxY8YgKipK7XZAJoTUt3HjRmhra9MMlRYyNjZudJkL+mwhpD6pVNrorGR6zbSdhoYGgoOD\nsWfPnnZd7k5IV2VmZqZWM5MlEgmuXbsG4GkhfPXq1XB2dpYfa6nExMR6Rebaonp4eDgYYxg2bJjC\n86u7adOmoXv37ti4cSPXUZSmsRqAlpYWunfvjldffRUHDx5UaJ9+fn7g8/ng8XioqKhoczvR0dEw\nMzMDj8fDli1bFJhQdaiYTNRSaWkp4uLisHDhQvnutp2diYkJfvrpJ2RlZWH79u3w8PDAzZs3YW9v\nj27dumH37t1ITU2tc5/jx49j//79cHd3R0ZGBpydnRtsOykpCfb29jh79myju+m+9dZbyM3NRVpa\nGiZMmKDwx9ce/34uBAIBGGOQyWTIy8vDvn37YGtri9DQUDg6OtI314SoscrKSmzcuBEBAQE0a7CF\nmpuZTJ8thNTVVDEZoNdMe/j4+MDS0hIxMTFcRyFE7ahjMXn+/PnIzMxEVVUVLl++jNu3b8PFxQXA\n0/dSiUQCsViM4uJihaxfrIw21ZWuri4WLFiArVu3oqSkhOs4StFQDaD2y4bk5GTcu3cPHh4eSE5O\nVlifiYmJCA4Obnc7wcHBOHPmjAIScYeKyUQtbdmyBaWlpQgMDOQ6isro6enhjTfegIODA3R1devc\ntm7dOmhoaGDevHmtvpw5Pz8f165dw2effQYA2LlzZ4Pn8Xg8WFpaYuTIkejbt2/bHoQK8Xg8CIVC\njBkzBomJidi3bx+kUinefPNNpVzyXV5e3uRMImXgok9CuJSYmIh//vkHCxYs4DqK2mhuAz76bCGk\nruaKyfSaaTttbW0EBQXh66+/xv3797mOQ4ha6UjLXJw7dw5ubm6wsLDA+fPn8eeff8Lc3BwjRoxA\nWloagKd5a2pq4OrqCn19fUycOBHz58/HwoULAQD+/v4wMzPDwIEDMWHCBBQUFLQ717NthoWFITY2\nFgAwePBg3Lx5E3v27EFAQAAAyL/g66z8/f1RXV2NhIQErqOolEgkwiuvvIK1a9cCAPbt28dxos6J\nislE7VRUVCA2NhYBAQHy9ZK6OldXVwQGBuLevXut/qZs3759ePPNNzFp0iTw+Xzs2rULjDElJeWO\np6cnZs6ciby8PKVcSrJ9+3aVzxbgok9CuFJTU4PY2FjMnDkT5ubmXMdRG+3ZgI8+W0hX1FwxuSn0\nmmnenDlzIBKJsG7dOq6jEKJWOtIGfC4uLkhPT4dEIpHPBr1//z5Onz6NUaNGAQBsbGxw+vRpFBQU\n4MmTJ7h37x6+/PJLaGpqAgCGDh0KsViMsrIynDp1qlXvu927dwdjDF9++WWd48+2GRMTg7KyMjDG\ncP36ddjZ2cHHxwcFBQVgjOHu3btwdHRU3BPTwZiYmGDGjBmIi4vDkydPuI6jcr179wYAFBYWKqV9\nHo+nlHbVBRWTidrZunUrCgoKsGTJEq6jdChffvklHBwc8PXXX+Pnn39u8f2SkpLw9ttvw8jICK+/\n/jrEYjFOnTqlxKTcmTlzJgDgp59+kh9jjCE2NhYDBgyArq4uRCIRJk+ejMzMzHr337VrF4YNGwY+\nnw8DAwP07t0bX3zxBQIDAxEUFIScnBzweDz06dOnxW1HRkZCX18fRkZGyMvLQ1BQECwtLZGVlYVT\np05h4MCBEAgE4PP5cHJywrFjxwCg0T5ramrw8ccfo1evXtDT08PgwYMVemkPIVw5ePAgcnJyEBQU\nxHUUtdLeDfjos4V0Ne0pJgP0mmkOn8/HokWLsGnTJtrLgpBWULdlLkjHEBQUhLt37+LQoUNcR1G5\nv/76CwAwevToOsebGmPXamzcX0tDQwOpqakYP348BAIBzM3N680Ab8u4vL31g5MnT2L48OHQ19eH\nsbExnJycmrxCsT2omEzUSmVlJaKiojB//nyamfYMPT09fPPNN9DQ0MD777+P0tLSZu9z584dZGVl\nyb9B9vLyAtD4pZXq7rnnngMA3Lx5U37s008/xfLlyxEeHo68vDykpaXh7t27GDlyJKRSqfy8+Ph4\nTJ8+HZ6enpBIJMjNzcWHH36IrKwsxMfHw93dHfb29mCMydc5bEnbISEhWLp0KUpKSrBq1SrY2trC\nxcUFjDFIpVJMnToVYrEYEokEhoaG8PHxkedpqM+wsDBERkYiLi4O9+/fh7u7O6ZNm0ZrRRO1Fxsb\niylTpqjdZd1ca2oDvpagzxbS1eTl5bWrmEyvmeYFBARAU1OzU28MRYiimZmZoaysrEXvKYTUsrOz\ng7u7OyIjI7mOojLl5eU4evQogoOD8frrr9ebiNLUGBtoetxfSyaTQSgUYu/evRCLxXB2dkZAQADK\nysrk57RlXN6e+kFJSQkmTZoET09PFBQUIDs7Gw4ODqiqqlLUU1sHFZOJWtm+fTsePXqkkEXPO6OX\nXnoJS5YsgVgsRlhYWLPnJyUlYeLEifLLjSZNmgRdXV3s378f5eXlyo6rckZGRuDxeCguLgbw9IMm\nNjYWb7/9Nnx9fSEQCODk5IQtW7bg0aNH2LZtGwCguroan332GcaOHYuwsDCYmJhAJBJh9uzZeOGF\nFxrsq6Vt/9uaNWuwcOFCpKSkoH///vD09MQnn3wCkUgEExMTTJo0Cfn5+Y2ul1ZRUYFNmzZhypQp\n8PDwgFAoxIoVK6CtrY3ExEQFPYuEqN6vv/6Kc+fO0azkNhAIBKioqEBlZWWb26DPFtJV1NTUID8/\nv13FZIBeM80xNjbGggULEB8f32k3hiJE0WqXd6TZyaS1goKCcPHiRZw9e5brKErz+PFj8Hg88Hg8\n6OvrY/z48TA2NoaPjw+0tbXrnNvUGLs1435XV1cIBAKIRCJ4e3ujsrISt27dAtC2cXlVVVW76gd8\nPh9FRUVwdHQEn89Hjx49kJKSgu7duyvoWa6LislEbVRXVyMqKgpz5syBpaUl13E6rC+//BL9+vXD\nxo0bkZ6e3uS5tZdU1jI2Nsbrr7+OoqIifP/998qOqnKlpaVgjMHY2BgAkJGRgZKSEgwbNqzOeS+8\n8AJ0dHRw/vx5AE8vkSksLMS4cePqnKepqYnFixc32FdL226N2g/CmpqaBm/PyspCWVkZBg0aJD+m\np6eHnj17NrhsByHqIioqCqNHj8ZLL73EdRS1U/t+195L3OizhXQFjx49Qk1NTbuLyQC9ZpoTGBiI\nyspKbN++nesohKgFMzMzAOgwm/AR9eHm5gYXFxfExMRwHUVpBAKBfP3u6upq5ObmYsmSJVi0aBEG\nDx6MR48eNXrff4+x2zLu/3cb1dXVANo2Ls/NzW1X/cDOzg5mZmbw9fXFp59+CrFY3OT57UXFZKI2\nvvnmG9y7dw/Lli3jOkqHxufzkZiYCB6Ph1mzZjU6o+Xq1au4cuUK3N3d5d/i8Xg8HD58GEDnvLTy\n+vXrAID+/fsD+N9i/IaGhvXOFQqF8hnMtUUYoVDY4r5a2nZTUlNTMWbMGJiamkJXVxchISFNnl97\n2duKFSvq/J/evn27ziU3hKiTK1eu4OjRo/Te30YCgQAA2rVuMkCfLaRrqL2EVBHFZHrNNK1bt26Y\nPXs2oqOjlXYJLiGdSW0xmWYmk7ZYsmQJDh06hJycHK6jKJ2WlhYsLS3h5+eH6OhoZGVlYfXq1fLb\nmxpjt2Xc35C2jMtr79PW+oGenh5OnDgBNzc3rFy5EnZ2dvD29lbaFU5UTCZqoaamBlFRUfDz84ON\njQ3XcTq8l156CUuXLkV2dnadheL/bc+ePXj33Xfl3+DV/hQUFEBPTw/Hjx/HgwcPVJxcuY4ePQoA\nGD9+PID/fUg09MZcWFgIKysrAICFhQUANPmN5rNa2nZj7ty5gylTpqBnz544f/48Hj9+jIiIiCbv\nU3v5W1xcXL3/1858WRPp3KKiotC/f3/565a0Tm0xWRGbb9BnC+nsFFlMBug105zg4GDk5eVh165d\nXEchpMPT19eHgYEBFZNJm3h4eMDGxgZr167lOopKOTk5AQCuXbsGoPkxdlvG/Q1py7jcwMAAQNvr\nBwDg6OiIw4cPQyKRIDQ0FMnJyYiOjm7HI2kcFZOJWti1a1eL150jT33xxRfo378/Ll++XO82xhj2\n7t2LBQsW1LtNJBLBy8sLNTU1SEpKUkVUlXjw4AHi4uJgZWWFWbNmAQAGDRoEQ0PDeovgnz9/HlVV\nVXj++ecBAL1794aJiQmOHz/e4v5a2nZjrly5gurqagQEBMDOzg58Ph88Hq/J+1hbW4PP5+OPP/5o\ncU5COrLc3FwkJycjLCwMGhr0J0tb1C5z0d6ZybXos4V0ZlKpFLq6uvIvYRSBXjONs7KywnvvvYfV\nq1c3uoQXIeR/zMzMOF/m4ssvv6wz07L259+X8zfEz89PPp6pqKhoc//V1dVYtWoV+vTpAx0dHQiF\nQgwaNKjJS/oV1bc609TUxAcffIDt27cjPz+f6zgq8/vvvwMA+vXrB6D5MXZbxv0Nacu43Nraul31\nA4lEIi+am5qaYvXq1XB2dpYfUzQamZEOr6amBmvWrIGvry9sbW25jqM2ai+vrN3M5d/OnDkDY2Nj\njBgxosH7+vv7A1DPSysZYygpKYFMJgNjDA8fPkRycjJGjBgBTU1NHDp0SF5c4fP5CAoKwsGDB7F7\n924UFRXhypUr8Pf3h7m5OebNmwcA0NXVxYcffoi0tDQsWrQI9+7dg0wmQ3FxsfzN2cTEBBKJBGKx\nGMXFxdDU1GxR243p1asXAODnn39GRUUFsrOz662T1FCffn5++Pbbb7Fp0yYUFRWhpqYGubm5uH//\nvqKfakKULjY2FmZmZvD29uY6itpS5MxkoOt+tpCuQSqVwszMrNkvb1uDXjNNW758OcRiMQ4cOMB1\nFEI6vB49esivoFA3iYmJCA4Obnc7U6dOxc6dO7Fnzx6UlZXh77//hr29fZObeSqqb3U3e/ZsaGtr\nIyEhgesoSlFeXi6vAUgkEiQmJmLFihXo3r07lixZAqD5MXZLxv0twefzWz0u19bWblf9QCKRYP78\n+cjMzERVVRUuX76M27dvw8XFpcW5W4W1g6enJ/P09GxPE4Q0a/fu3UxTU5NlZWVxHYUz8+bNY5aW\nlnWOHTx4kNnb2zMArHv37mzhwoUN3nfZsmXsrbfekv979uzZzMDAgGlpabEhQ4awS5cu1Tn/iy++\nYObm5gwAA8AsLS3Zxo0b65yzePFi1q1bNwU9uv9JTk5mbXlb+uGHH9jgwYOZvr4+09HRYRoaGgwA\n4/F4TCgUsuHDh7PPP/+c5efn17uvTCZjUVFRrG/fvkxbW5uJRCI2ZcqUBn/fNmzYwJycnBifz2d8\nPp8NHTpU/txcunSJ2djYMD09Pebm5sYePHjQorYjIiKYnp4eA8Csra3Zrl275LeFhoYyExMTJhQK\nmZeXF9uwYQMDwOzt7dmdO3ca7LOyspKFhoayXr16MS0tLWZqaso8PDxYRkZGq59XQrhUUFDAjIyM\nWExMDNdRWqWqqoolJCSw6dOncx1FTk9Pj+3YsaPe8a7y2UJISy1btowNGzas0dvpNaMcU6dOZUOG\nDGEymYzrKC1SUVHBXn75ZfbLL79wHYV0MW+//TZ75513OM3wxRdf1BmvNKasrIy99NJLdY6Fh4cz\nAKy8vLxNfX/77beMx+Oxv/76S+V9dxaLFi1idnZ2rKamhuso7fbvz99nf3R1dVnfvn1ZQEAAu3Pn\nTp37NTfGZqzxcf+/x+59+/ZlOTk5bPfu3UwkEjEAzMrKil29epUxxpocl8fExLAePXowAMzAwIAB\nYMnJye2qH4jFYubq6spEIhHT1NRkFhYWLDw8nD158kQZT/8+KiaTDk0mkzFHR0fm4+PDdRRONTR4\n4VJHKyYTQjqfL7/8kgkEAlZYWMh1lBapqKhgmzZtYoMGDWJBQUFMIpFwHUmuZ8+ebN26dfWOd5XP\nFkJaavr06ezNN99s9HZ6zSjHH3/8wXg8HktNTeU6SotdunSJeXp6shEjRrAjR45wHYd0EQsXLmRu\nbm6cZmhpMXn9+vXM3t6+zrHagm5FRUWb+h41ahR7/vnnOem7s/j7778Zj8djP/74I9dRyL/UFpPV\nyD5a5oJ0aIcPH8a1a9dorWQ8vWzj2LFjyM7O5mTXa/bfy0XS09Nx48YNlfdPCOk6KisrsXHjRixY\nsECha5cqQ2lpKWJjYzFs2DDk5eXh5MmTiI6Ohrm5OdfR5IyNjRtd5oI+Wwj5H6lU2uzme/SaUbwh\nQ4ZgwoQJjW5S2BENHToU+/fvR0JCAlJSUvDiiy/iwIEDkMlkdc4rKSlpcH3Zf/8o7RJk0ulYWlri\n3r17XMdoVmBgIIKCgpCTkwMej4c+ffrIb9PQ0EBqairGjx8PgUAAc3PzFi27UFVVhXPnzuG5555T\ned+dSf/+/TFmzBhs3ryZ6yhEzVExmXRokZGRmDhxYrML+ncFBQUFeOONN+Dg4CDfQE6Vvv/+e1ha\nWmLkyJFITU1Vef+EkK7jm2++QUFBARYuXMh1lEY9fvwYK1euhKurKxhjOHfuHD755BOYmJhwHa0e\ngUDQ6AZ89NlCyP+0pJhMrxnlWLFiBc6dO4e0tDSuo7SKg4MDEhIScODAAZw6dQrDhw/Hrl278OTJ\nEwCAoaEhGGNN/pw7d47jR0HUhYWFBSQSCRhjnOZYvnw5RCIRdHR0YGtri8mTJ+PixYvy2+Pj4+Hu\n7g57e3swxup88SWTySAUCrF3716IxWI4OzsjICAAZWVlTfYpkUhQVVWF33//HWPHjoW5uTn4fD4G\nDBiAjRs3yp8TZfTd2fj7+yM1NRW3bt3iOgpRY1RMJh3WqVOncPr0aYSGhnIdhXNbtmyp80fn7t27\nVZ5h8uTJdTI8evRI5RkIIZ2fTCZDTEwMZsyY0aFm9/5bdXU1hgwZguPHjyM9PR1BQUEwMDDgOlaj\nGpuZTJ8thNTVXDGZXjPK4+LigtGjR2PVqlVcR2kTa2trrF27Frt27UJYWBimTZvGdSTSCVlaWqK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uEcYwwxMTF499130atXL67jcC49PR1XrlzBnDlzFNJe7WBlxYoVddauvH37tnwZBUNDQ8yd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\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L6f5kX3/9tfRYPT09bN68GXfu3EFDQwNu3ryJb7/9FgCkMwU7cl5M2nsUFRUV9O7dG76+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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2J82sQgNtqiw",
        "colab_type": "text"
      },
      "source": [
        "From this surrogate model we see - \n",
        "- Some of the most important variables\n",
        "- Important interactions\n",
        "- The decision path for the most expensive houses\n",
        "- The decision path for the least expensive houses\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "yBzTzdZ5N_L6"
      },
      "source": [
        "## `interpret`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Z3YaKohDj0ar"
      },
      "source": [
        "**Motivation behind `interpret`**:\n",
        "\n",
        "> Historically, the most intelligible models were not very accurate, and the most accurate models were not intelligible. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM)* which has both high accuracy and intelligibility."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DOQysFB6NCGP",
        "colab": {}
      },
      "source": [
        "!pip install pyscaffold\n",
        "!pip install -U interpret"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TCeivjulj2IS",
        "colab": {}
      },
      "source": [
        "from interpret import show\n",
        "from interpret.data import ClassHistogram\n",
        "\n",
        "hist = ClassHistogram().explain_data(train.iloc[:, 0:-1], train['Income'], name = 'Train Data')\n",
        "show(hist)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pxfqkjIRYmiu",
        "colab_type": "text"
      },
      "source": [
        "Refer to this video if you are unable to see the output of this cell: https://www.loom.com/share/1512be40265f4205a6451a97dea663e3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Z6FH87FYmix",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Turning response into 0 and 1\n",
        "y_train = train['Income'].apply(lambda x: 0 if x == \" <=50K\" else 1) \n",
        "y_test = test['Income'].apply(lambda x: 0 if x == \" <=50K\" else 1) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CmppNyPzkUZU",
        "outputId": "a581f984-5ab8-4cc8-adca-4c59cb486de6",
        "colab": {}
      },
      "source": [
        "from interpret.glassbox import ExplainableBoostingClassifier\n",
        "import time\n",
        "\n",
        "ebm = ExplainableBoostingClassifier(random_state=7)\n",
        "\n",
        "start = time.time()\n",
        "ebm.fit(train.iloc[:, 0:-1], y_train) \n",
        "print('It took {} seconds'.format(time.time()-start))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "It took 306.07323694229126 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8lnK4sMYmjC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ebm_global = ebm.explain_global(name='EBM')\n",
        "show(ebm_global)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cf5sl7-zYmjF",
        "colab_type": "text"
      },
      "source": [
        "If the output is not there in the above cell, here is the demo: https://www.loom.com/share/ab37697a1b70482baaf2455079f257fc"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvfbT1thYmjG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ebm_local = ebm.explain_local(test.iloc[:5, 0:-1], y_test[:5], name='EBM')\n",
        "show(ebm_local)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f32AoYoiYmjJ",
        "colab_type": "text"
      },
      "source": [
        "Demo: https://www.loom.com/share/1655ed05299547e68bacacbbf8801ee4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HerCz4hhYmjR",
        "colab_type": "code",
        "outputId": "3301f345-0542-45a2-fda8-1f09c3e589c5",
        "colab": {}
      },
      "source": [
        "preds = ebm.predict(test.iloc[:, 0:-1])\n",
        "skplt.metrics.plot_confusion_matrix(y_test, preds)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MrIrV9IRHRKy7rG1mll8imfHt7Ip6h4ekIZJOTz+vI+lr5W2WmVWzOhW3VLM2w0/S5cD/AIenRXOBK8vZKDOrYkW+vKjaJ0WKme3dPiK2kvQsLH5ZSJcyt8vMqliV51pRigm/zyXVkb4EWNLqwKKytsrMqpbIz0XOVwC3A1+QdA5wMHBOWVtlZlWtpmd7m0TEKEnPALunRQdFxOTyNsvMqpVy9mCDeuBzkqFvUTPEZla7amHYW8xs70+Bm4AvAusAf5Z0WrkbZmbVS0Uube5H+oGkKZImS7pJ0oqS+kp6UtJUSbc0TbBK6pquT0u39+nIbyimF/cdYOuIOCMifgpsAxzRkYOaWedWiktdJPUG/hfoHxGbkYwwhwAXAZdGRD/gA2BY+pVhwAcRsSFwaVqv3YoJvzdZcnjcALzekYOaWeeVzPaW7CLnBmAlSQ1AN2A6sCtwW7r9emD/9PN+6Trp9t3UgYsJW3uwwaUk5/jmAlMkjUnXBwLj23tAM+vklOlhpr0kTSxYHxERIwAi4h1JlwBvAfOAB4BngH9HxIK0fiPQO/3cG3g7/e4CSXOA1YGZ7fkZrU14NM3oTgHuLSif0J4DmVntyNDhmhkR/Zexj54kvbm+wL+BvwCDW6gaTV9pZVtmrT3Y4Jr27tTMalfTsLcEdgfeiIj3ASTdAWwP9JDUkPb+1gHeTes3AusCjekweVVgdnsPXsxs7waSbpb0gqRXm5b2HtDMOr8S3dv7FjBAUrf03N1uwD+Ah4AD0zpDgbvSz3en66Tb/xYR7e75FTPhMRK4jiTwBwO3Aje394Bm1vmV4lKXiHiSZOJiEvAiSR6NAE4BhkuaRnJOr2kUeg2welo+HDi1I7+hmIucu0XEGEmXRMRrwBmSHu3IQc2s85KgvkTj3og4CzirWfHrJJfUNa87HzioJAemuPD7NO2SvibpOOAdYI1SNcDMOp9qf1xVMYoJvx8A3UkuRjyf5CTj0eVslJlVtxrIvqIebPBk+vEj/vNAUzPLKaGauLe3tYuc76SVa2gi4ptlaZGZVbccPNXl8uXWitTmm6zLA3+/dHkf1jpg6r8+rnQTLIP5n5fmOcQ1fc4vIsYtz4aYWecgoL6Ww8/MbFlq4EHODj8zyy5X4Sepa0R8Ws7GmFn1Sx5j3/nTr5h7e7eR9CIwNV3fQtLvyt4yM6tauXhpOXAZsA8wCyAinid5ibmZ5VTTS4zaWqpZMcPeuoh4s1k3d2GZ2mNmVU5AQ7UnWxGKCb+3JW0DhKR64CTAj7Qyy7EayL6iwu97JEPf9YD3gAfTMjPLIanGb29rEhEzSN6oZGYG5KTnJ+kqWrjHNyKOKUuLzKzqVftMbjGKGfY+WPB5ReAA0jcomVn+iNI9zLSSihn23lK4LukGYGzZWmRm1a0TXMNXjPbc3tYXWL/UDTGzzkNtvqGj+hVzzu8D/nPOr47kVXEdenGImXVeJXx1ZUW1Gn7puzu2IHlvB8CijrwqzsxqQy2EX6u3t6VBd2dELEwXB5+Zleq9vRVVzL29T0naquwtMbNOIXl1ZXFLNWvtHR4NEbEA+DrwXUmvAZ+QDPkjIhyIZjlV63d4PAVsBey/nNpiZp1AHiY8BBARry2ntphZJ1Gqjp+kHsDVwGYkV5UcDbwC3AL0Af4JHBwRH6QTsL8F9gLmAkdGxKT2Hru18PuCpOHL2hgRv27vQc2sMxN1pbvO77fA/RFxoKQuQDfgdGBcRFwo6VSSS+tOAQYD/dJlW+AP6Z/t0lr41QPdoQauZjSzkhGl6flJWgXYCTgSICI+Az6TtB+wS1rteuBhkvDbDxiVXnUyQVIPSWtHxPT2HL+18JseEee2Z6dmVsMEDcWf9OslaWLB+oiIGJF+/hLwPnCdpC2AZ4CTgTWbAi0ipktaI63fmyWfK9CYlpU8/NzjM7OlZOz5zYyI/svY1kAyqXpSRDwp6be0fvdYS0dt97XHrV2Js1t7d2pmta0ufaBpW0sbGoHGiHgyXb+NJAzfk7Q2QPrnjIL66xZ8fx3g3Xb/hmVtiIjZ7d2pmdW2UrzAKCL+RfKajI3Tot2AfwB3A0PTsqHAXennu4EjlBgAzGnv+T7wS8vNLCNR3K1hRToJ+FM60/s6cFS6+1slDQPeAg5K644mucxlGsmlLkd15MAOPzPLRqW7wyMingNaOie41Gm3dJb3hJIcGIefmWWU3OHR+edDHX5mllnnjz6Hn5m1Qw10/Bx+ZpZV9T+rrxgOPzPLpMSzvRXj8DOzzDzhYWb5IzzsNbP88bDXzHLLPT8zy6XOH30OPzPLSEC9e35mlkc1kH0OPzPLSqgGBr4OPzPLzD0/M8ud5FKXzp9+Dj8zy6aIpzR3Bg4/M8vMt7eZWe4kDzOtdCs6zuFnZpl5ttfMcqkGRr01cX9yxZ18/HfZ9Eu92WnbLReXTX7hOQbv+nV23aE/A3cewKSJTwNw2y1/ZpfttmKX7bZi7913YsqLz1eq2bn16fz5fHufXThw4HYcsNvWXPGr8wE460fHc+DA7fjWHgMYfux3mPvJxwDcesM1fHP3bTloz+0Z+s09eO3VlyvZ/KqgIv9XzcoWfpKulTRD0uRyHaNaDDnsCG6+454lys4983R+dOoZ/O2xifzk9LM472enAbB+n7783+hxPPzEJIb/5HR++L/HV6LJudala1euvuUebnvgCW69/3Eee/hBnp/0FD8+60Jue+AJbh87gbW/uA43jfwjAHvtfxB3PPgkfxnzOEce930uPve0Cv+Cymo651fMUs3K2fMbCQwq4/6rxnY77EiPnj2XKJPERx99CMCHH85hzbXWBmDrbbdbXPdrW2/L9HffWb6NNSTR7b+6A7BgwecsWPA5kui+8ioARATz589f/OSSpnKAeXPn1sQTTTpEoq7IpZqV7ZxfRDwiqU+59l/tzrvoEoYcsA/nnHEqixYt4p6xf1+qzp9vuI5d99izAq2zhQsXMmSvHXnrn68zZOh32fyrWwNw5vDjePShB9ig3yb86Ge/WFz/5pEjGHXV5Xz++Wdcfcs9y9ptblR3rBWn4uf8JB0jaaKkibNmzqx0c0pm5NUjOPeCi3n2pdc594KL+cGJxy6xffwjD/PnUddx5jm/WMYerJzq6+v5y5jHGfvUy0x+7hmmvvwPAM779ZWMmziVvhtuzJi7b19cf8iRxzD6sRf4/mnnMuKyX1aq2VWh6b29per5SaqX9Kyke9L1vpKelDRV0i2SuqTlXdP1aen2Ph35HRUPv4gYERH9I6L/6r16Vbo5JXPrTTew974HALDvAQfy7DNPL942ZfILDD/xOK6/6XZWW331SjXRgFVW7UH/7XbksYfHLi6rr69n0De+xYP33bVU/cH7HchDY+5dnk2sSipyKdLJwEsF6xcBl0ZEP+ADYFhaPgz4ICI2BC5N67VbxcOvVq211to8Pv4RAB79+0N8aYMNAWh8+y2OPuwQrrjqOjbot1Elm5hbs2e9z4dz/g3A/HnzmPDoQ/TZoB9vvfEakJzze/jB0fTZIPn7efONaYu/+8i4+1mvzwbLv9HVpkTpJ2kdYG/g6nRdwK7AbWmV64H908/7peuk23dTB07A+jq/Ejj2qO/w+PhHmD1rJltu0pcfn/4zfvW7KznjlOEsWLCArl1X5JLf/gGAX110Ph98MItThp8EQENDAw/8fUIlm587M2e8xxk/OJaFCxeyaNEi9vzGN9lpt0Ec+a2BfPzRR0QEG2/635zxi0sBuGnkCJ4c/xANDSuwyqo9+Pmlf6zwL6i8DJMZvSRNLFgfEREjCtZ/A/wEWDldXx34d0QsSNcbgd7p597A2wARsUDSnLR+u86XlS38JN0E7ELy4xuBsyLimnIdr5L+eN2NLZaPfeTJpcouvfyPXHq5/+OppI2+vBm33v/YUuWj7nywxfqnnpPvc3wtydDdmhkR/Vvch7QPMCMinpG0Syu7jiK2ZVbO2d5Dy7VvM6uw0kz37gDsK2kvYEVgFZKeYA9JDWnvbx3g3bR+I7Au0CipAVgVmN3eg/ucn5llkpzO6/gdHhFxWkSsExF9gCHA3yLiMOAh4MC02lCgaebp7nSddPvfIqLdPT+Hn5llkz7Pr5ilnU4BhkuaRnJOr+l02TXA6mn5cODUjvwMT3iYWWalvsg5Ih4GHk4/vw5s00Kd+cBBpTqmw8/MMlJN3OLn8DOzzGog+xx+ZpZNxrs3qpbDz8yyq4H0c/iZWWbV/qDSYjj8zCwzn/Mzs/zxe3vNLK887DWz3BHu+ZlZTtVA9jn8zKwdaiD9HH5mllm1v5mtGA4/M8us80efw8/M2qMG0s/hZ2aZND3MtLNz+JlZNr7I2czyqgayz+FnZln5YaZmllM1kH0OPzPLxg8zNbP8qoH0c/iZWWa+1MXMcsnn/MwsfwR1Dj8zy6fOn351lW6AmXUuTQ8zLWZpdT/SupIekvSSpCmSTk7LV5M0VtLU9M+eabkkXSZpmqQXJG3Vkd/h8DOzzFTk0oYFwA8j4svAAOAESZsCpwLjIqIfMC5dBxgM9EuXY4A/dOQ3OPzMLLNS9PwiYnpETEo/fwS8BPQG9gOuT6tdD+yfft4PGBWJCUAPSWu39zf4nJ+ZZZbh9rZekiYWrI+IiBEt7K8P8FXgSWDNiJgOSUBKWiOt1ht4u+BrjWnZ9EyNTzn8zCyzDNMdMyOif6v7kroDtwPfj4gPWwnWljZE8U1Zkoe9ZpZJsUPeYjqHklYgCb4/RcQdafF7TcPZ9M8ZaXkjsG7B19cB3m3v73D4mVlmKvJ/re4j6eJdA7wUEb8u2HQ3MDT9PBS4q6D8iHTWdwAwp2l43B4e9ppZdqW5zG8H4HDgRUnPpWWnAxcCt0oaBrwFHJRuGw3sBUwD5gJHdeTgDj8zy6wU2RcR41vZ1W4t1A/ghBIcGnD4mVlm8qsrzSx/mu7w6Ow84WFmueSen5llVgs9P4efmWXmh5maWf74vb1mlke1MuHh8DOzzDzsNbNccs/PzHKpBrLP4Wdm7VAD6efwM7NMBDVxe5uSe4Wrg6T3gTcr3Y4y6AXMrHQjLJNa/TtbPyK+0JEdSLqf5J9PMWZGxKCOHK9cqir8apWkiW09zdaqi//Oap/v7TWzXHL4mVkuOfyWj6XeVmVVz39nNc7n/Mwsl9zzM7NccviZWS45/MpI0iBJr0iaJunUSrfH2ibpWkkzJE2udFusvBx+ZSKpHrgCGAxsChwqadPKtsqKMBKoyotyrbQcfuWzDTAtIl6PiM+Am4H9Ktwma0NEPALMrnQ7rPwcfuXTG3i7YL0xLTOzKuDwK5+W7vz2dUVmVcLhVz6NwLoF6+sA71aoLWbWjMOvfJ4G+knqK6kLMAS4u8JtMrOUw69MImIBcCIwBngJuDUiplS2VdYWSTcBTwAbS2qUNKzSbbLy8O1tZpZL7vmZWS45/Mwslxx+ZpZLDj8zyyWHn5nlksOvE5G0UNJzkiZL+oukbh3Y1y6S7kk/79vaU2ck9ZB0fDuOcbakHxVb3qzOSEkHZjhWHz+JxbJw+HUu8yJiy4jYDPgMOK5woxKZ/04j4u6IuLCVKj2AzOFnVs0cfp3Xo8CGaY/nJUm/ByYB60oaKOkJSZPSHmJ3WPx8wZcljQe+2bQjSUdKujz9vKakOyU9ny7bAxcCG6S9zovTej+W9LSkFySdU7Cvn6bPMHwQ2LitHyHpu+l+npd0e7Pe7O6SHpX0qqR90vr1ki4uOPaxHf0Hafnk8OuEJDWQPCfwxbRoY2BURHwV+AQ4A9g9IrYCJgLDJa0IXAV8A9gRWGsZu78M+HtEbAFsBUwBTgVeS3udP5Y0EOhH8tiuLYGvSdpJ0tdIbuP7Kkm4bl3Ez7kjIrZOj/cSUHhHRR9gZ2Bv4Mr0NwwD5kTE1un+vyupbxHHMVtCQ6UbYJmsJOm59POjwDXAF4E3I2JCWj6A5OGpj0kC6EJyu9YmwBsRMRVA0o3AMS0cY1fgCICIWAjMkdSzWZ2B6fJsut6dJAxXBu6MiLnpMYq5l3kzST8nGVp3J7kdsMmtEbEImCrp9fQ3DAQ2LzgfuGp67FeLOJbZYg6/zmVeRGxZWJAG3CeFRcDYiDi0Wb0tKd0jtQRcEBF/bHaM77fjGCNkDNsRAAABIElEQVSB/SPieUlHArsUbGu+r0iPfVJEFIYkkvpkPK7lnIe9tWcCsIOkDQEkdZO0EfAy0FfSBmm9Q5fx/XHA99Lv1ktaBfiIpFfXZAxwdMG5xN6S1gAeAQ6QtJKklUmG2G1ZGZguaQXgsGbbDpJUl7b5S8Ar6bG/l9ZH0kaS/quI45gtwT2/GhMR76c9qJskdU2Lz4iIVyUdA9wraSYwHtishV2cDIxIn2ayEPheRDwh6bH0UpL70vN+XwaeSHueHwPfiYhJkm4BngPeJBmat+VM4Mm0/ossGbKvAH8H1gSOi4j5kq4mORc4ScnB3wf2L+6fjtl/+KkuZpZLHvaaWS45/Mwslxx+ZpZLDj8zyyWHn5nlksPPzHLJ4WdmufT/AS2T1kMVjTvMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yjgQmJ_hYmjW",
        "colab_type": "code",
        "outputId": "da622ab5-2a4b-4b97-d740-c0d64c2785db",
        "colab": {}
      },
      "source": [
        "print(accuracy_score(y_test, preds))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.863\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pHendzn-Ymja",
        "colab_type": "text"
      },
      "source": [
        "- 0: <=50K\n",
        "- 1: >50K"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFLxuFg4Ymjb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "train_preprocessed = train.apply(LabelEncoder().fit_transform)\n",
        "test_preprocessed = test.apply(LabelEncoder().fit_transform)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGcmjXhMYmje",
        "colab_type": "code",
        "outputId": "b12e9ad0-6d4a-4adf-cf90-62de47264440",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf = RandomForestClassifier(n_estimators=100, n_jobs=-1)\n",
        "start = time.time()\n",
        "rf.fit(train_preprocessed.iloc[:, 0:-1], train_preprocessed['Income'])\n",
        "print('It took {} seconds'.format(time.time()-start))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "It took 2.0011560916900635 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UBo61eYXYmjj",
        "colab_type": "code",
        "outputId": "3d53b2cb-ac56-49a7-cadd-e53c2448ff81",
        "colab": {}
      },
      "source": [
        "preds = rf.predict(test_preprocessed.iloc[:, 0:-1])\n",
        "accuracy_score(test_preprocessed['Income'], preds)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7945"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0d6eh1Z0Ymjm",
        "colab_type": "code",
        "outputId": "07a79b46-f739-42ec-f85c-79bdefe1cc41",
        "colab": {}
      },
      "source": [
        "skplt.metrics.plot_confusion_matrix(test_preprocessed['Income'], preds)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPUFvTo3Ymjp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from interpret.blackbox import LimeTabular\n",
        "from interpret import show\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "lime = LimeTabular(predict_fn=rf.predict_proba, data=train_preprocessed.iloc[:,0:-1], random_state=7)\n",
        "\n",
        "lime_local = lime.explain_local(test_preprocessed.iloc[:5, 0:-1],\n",
        "                                test_preprocessed['Income'][:5], name='LIME')\n",
        "\n",
        "show(lime_local)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_NcXakZDYmju",
        "colab_type": "text"
      },
      "source": [
        "Demo: https://www.loom.com/share/c8e194eacb934ebabd8735b6d2300ce2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "duYI-PRhYmjx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from interpret.blackbox import MorrisSensitivity\n",
        "\n",
        "sensitivity = MorrisSensitivity(predict_fn=rf.predict_proba, data=train_preprocessed.iloc[:,0:-1])\n",
        "sensitivity_global = sensitivity.explain_global(name=\"Global Sensitivity\")\n",
        "\n",
        "show(sensitivity_global)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPemLF6WYmj2",
        "colab_type": "text"
      },
      "source": [
        "Demo: https://www.loom.com/share/d19a9b56d97f48da85e9d02681e96b0c"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "hCdKcXfMYmj3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from interpret.blackbox import PartialDependence\n",
        "\n",
        "pdp = PartialDependence(predict_fn=rf.predict_proba, data=train_preprocessed.iloc[:,0:-1])\n",
        "pdp_global = pdp.explain_global(name='Partial Dependence')\n",
        "\n",
        "show(pdp_global)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHLxsv0GYmj6",
        "colab_type": "text"
      },
      "source": [
        "Demo: https://www.loom.com/share/8f31af4ec336476e9d18bcac106e10b3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ONj98LS6Ymj6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "show([lime_local, sensitivity_global, pdp_global])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JX1_pk2mYmkA",
        "colab_type": "text"
      },
      "source": [
        "Demo: https://www.loom.com/share/13e1434c52d142dab06d0f7b0595983d"
      ]
    }
  ]
}