4_PDP_other_example.ipynb

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        "<a href=\"https://colab.research.google.com/github/silviazandoli/explanaible_ai/blob/master/4_PDP_other_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "53IJZEWvnhok"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Guide from here https://www.kaggle.com/code/dansbecker/partial-plots"
      ],
      "metadata": {
        "id": "ZVnkarmdoI3L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#download dataset here\n",
        "#https://www.kaggle.com/datasets/mathan/fifa-2018-match-statistics\n",
        "data = pd.read_csv('FIFA 2018 Statistics.csv')\n",
        "data"
      ],
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          "height": 887
        },
        "id": "JD5Vro7ynvWY",
        "outputId": "c97586b6-825b-49c0-9b6a-1978abec0006"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "           Date          Team      Opponent  Goal Scored  Ball Possession %  \\\n",
              "0    14-06-2018        Russia  Saudi Arabia            5                 40   \n",
              "1    14-06-2018  Saudi Arabia        Russia            0                 60   \n",
              "2    15-06-2018         Egypt       Uruguay            0                 43   \n",
              "3    15-06-2018       Uruguay         Egypt            1                 57   \n",
              "4    15-06-2018       Morocco          Iran            0                 64   \n",
              "..          ...           ...           ...          ...                ...   \n",
              "123  11-07-2018       England       Croatia            1                 46   \n",
              "124  14-07-2018       Belgium       England            2                 43   \n",
              "125  14-07-2018       England       Belgium            0                 57   \n",
              "126  15-07-2018        France       Croatia            4                 39   \n",
              "127  15-07-2018       Croatia        France            2                 61   \n",
              "\n",
              "     Attempts  On-Target  Off-Target  Blocked  Corners  ...  Yellow Card  \\\n",
              "0          13          7           3        3        6  ...            0   \n",
              "1           6          0           3        3        2  ...            0   \n",
              "2           8          3           3        2        0  ...            2   \n",
              "3          14          4           6        4        5  ...            0   \n",
              "4          13          3           6        4        5  ...            1   \n",
              "..        ...        ...         ...      ...      ...  ...          ...   \n",
              "123        11          1           6        4        4  ...            1   \n",
              "124        12          4           3        5        4  ...            1   \n",
              "125        15          5           7        3        5  ...            2   \n",
              "126         8          6           1        1        2  ...            2   \n",
              "127        15          3           8        4        6  ...            1   \n",
              "\n",
              "     Yellow & Red  Red  Man of the Match  1st Goal         Round  PSO  \\\n",
              "0               0    0               Yes      12.0   Group Stage   No   \n",
              "1               0    0                No       NaN   Group Stage   No   \n",
              "2               0    0                No       NaN   Group Stage   No   \n",
              "3               0    0               Yes      89.0   Group Stage   No   \n",
              "4               0    0                No       NaN   Group Stage   No   \n",
              "..            ...  ...               ...       ...           ...  ...   \n",
              "123             0    0                No       5.0  Semi- Finals   No   \n",
              "124             0    0               Yes       4.0     3rd Place   No   \n",
              "125             0    0                No       NaN     3rd Place   No   \n",
              "126             0    0               Yes      18.0         Final   No   \n",
              "127             0    0                No      28.0         Final   No   \n",
              "\n",
              "     Goals in PSO  Own goals  Own goal Time  \n",
              "0               0        NaN            NaN  \n",
              "1               0        NaN            NaN  \n",
              "2               0        NaN            NaN  \n",
              "3               0        NaN            NaN  \n",
              "4               0        1.0           90.0  \n",
              "..            ...        ...            ...  \n",
              "123             0        NaN            NaN  \n",
              "124             0        NaN            NaN  \n",
              "125             0        NaN            NaN  \n",
              "126             0        1.0           18.0  \n",
              "127             0        NaN            NaN  \n",
              "\n",
              "[128 rows x 27 columns]"
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              "      <td>Uruguay</td>\n",
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              "      <td>14-07-2018</td>\n",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fc7ac38a-3a22-4b83-853d-8f8daf73913b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fc7ac38a-3a22-4b83-853d-8f8daf73913b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = (data['Man of the Match'] == \"Yes\")  # Convert from string \"Yes\"/\"No\" to binary\n",
        "feature_names = [i for i in data.columns if data[i].dtype in [np.int64]]\n",
        "X = data[feature_names]\n",
        "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)\n",
        "tree_model = DecisionTreeClassifier(random_state=0, max_depth=5, min_samples_split=5).fit(train_X, train_y)"
      ],
      "metadata": {
        "id": "TGTeuo1UnwjI"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "import graphviz\n",
        "\n",
        "tree_graph = tree.export_graphviz(tree_model, out_file=None, feature_names=feature_names)\n",
        "graphviz.Source(tree_graph)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 816
        },
        "id": "shvHEBJ6n5fH",
        "outputId": "6bc98540-3da9-43e6-c3ea-898c602256df"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "<graphviz.sources.Source at 0x7b65d45d6b90>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "from sklearn.inspection import PartialDependenceDisplay\n",
        "\n",
        "# Create and plot the data\n",
        "disp1 = PartialDependenceDisplay.from_estimator(tree_model, val_X, ['Goal Scored'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "H-OOix4Fpkjt",
        "outputId": "c3df34b5-619c-4341-cb9c-afc76bad631c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_to_plot = 'Distance Covered (Kms)'\n",
        "disp2 = PartialDependenceDisplay.from_estimator(tree_model, val_X, [feature_to_plot])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "QIBoQWFrpr8G",
        "outputId": "fb7373c5-81f6-49b2-cbc2-0b8e752bd532"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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TY3vLli2Vn5/vlqCA+mK2FPkzpggAYMvloiguLk5ffPFFje1ffPGFYmNj3RIUUF+qnlPk70dLEQDAlssDrTMyMvT444+rvLxct9xyiyQpOztbY8eO1ZNPPun2AAF3qrCyICwAwD6Xi6IxY8bo6NGjeuSRR1RWViZJCg4O1lNPPaVx48a5PUDAnaq6zwLpPgMAnMPloshisWjq1KkaP368du7cqZCQEF111VUKCgqqj/gAt6pe5oOWIgCArfN6TpEkhYWF6brrrnNnLEC9Y0o+AMARl4uikpISvfDCC8rOztahQ4dktVpt9v/vf/9zW3CAu5VXMqYIAGCfy0XRiBEjtGbNGg0dOlQxMTGyWBibgUtHhdlSxOcWAGDL5aLo008/1ccff6xf/OIX9REPUK/KaCkCADjgcmZo2rSpmjVrVh+xAPWOMUUAAEdczgzPPvusJkyYoFOnTtVHPEC9MqfkUxQBAM7hcvfZ9OnT9cMPPygqKkoJCQkKCAiw2Z+Tk+O24AB3Y5kPAIAjLhdFd911Vz2EAdQ/wzCYfQYAcMjlomjixIn1EQdQ76qW+JCkANY+AwCc47wyw4kTJ/TGG29o3LhxOnbsmKQz3Wb79+93a3CAO1V1nUl0nwEAanK5pWj79u1KSkpSZGSkfvzxR2VkZKhZs2ZatGiR8vLyNH/+/PqIE7hg5RVntRTRfQYAOIfLmSEzM1PDhg3Trl27FBwcbG4fMGCA1q5d69bgAHcqO6ulyN+PliIAgC2Xi6J///vf+u1vf1tje+vWrVVQUOCWoID6cPZ0fJ7EDgA4l8tFUVBQkIqKimps/+6779SyZUu3BAXUhwpz5hkFEQCgJpeLooEDB2rSpEkqLy+XJFksFuXl5empp57SPffc4/YAAXep6j7zZzwRAMAOl7PD9OnTVVxcrFatWumnn35Sv3791LZtW4WHh2vy5Mn1ESPgFizxAQCojcuzzyIjI7VixQqtX79e27dvV3Fxsbp3766kpKT6iA9wm+oxRXSfAQBqOu+vzNdff70eeeQRjR079oILotmzZyshIUHBwcHq3bu3Nm/e7PDYefPmyWKx2LzOngV3rocfflgWi0UzZ868oBhx6ate4oOWIgBATU61FL3yyitOX3D06NEuBbBw4UJlZmZqzpw56t27t2bOnKnk5GTl5uaqVatWds+JiIhQbm6u+bOjmUSLFy/Wl19+qdjYWJdigncqq2CJDwCAY04VRS+99JLNz4cPH9apU6fUpEkTSWeecN24cWO1atXK5aJoxowZysjI0PDhwyVJc+bM0ccff6ysrCz94Q9/sHuOxWJRdHR0rdfdv3+/Hn30US1fvly33367SzHBO1VYfx5ozTOKAAB2OPWVeffu3eZr8uTJ6tq1q3bu3Kljx47p2LFj2rlzp7p3765nn33WpV9eVlamLVu22HS/+fn5KSkpSRs3bnR4XnFxseLj4xUXF6fU1FR9/fXXNvutVquGDh2qMWPG6JprrqkzjtLSUhUVFdm84H3MMUV0nwEA7HA5O4wfP16zZs1Su3btzG3t2rXTSy+9pD/96U8uXevIkSOqrKxUVFSUzfaoqCiHD4Js166dsrKytGTJEi1YsEBWq1V9+/bVvn37zGOmTp0qf39/p1utpkyZosjISPMVFxfn0vvApYHuMwBAbVzODvn5+aqoqKixvbKyUgcPHnRLULVJTExUWlqaunbtqn79+mnRokVq2bKl5s6dK0nasmWLXn75ZXNAtjPGjRunwsJC87V37976fAvwkOop+XSfAQBqcrkouvXWW/Xb3/5WOTk55rYtW7bod7/7ncuz0Fq0aKFGjRrVKKYOHjxY55ihKgEBAerWrZu+//57SdK6det06NAhXXbZZfL395e/v7/27NmjJ598UgkJCXavERQUpIiICJsXvA/PKQIA1Mbl7JCVlaXo6Gj17NlTQUFBCgoKUq9evRQVFaU33njDpWsFBgaqR48eys7ONrdZrVZlZ2crMTHRqWtUVlZqx44diomJkSQNHTpU27dv19atW81XbGysxowZo+XLl7sUH7zL2WufAQBwLpcf3tiyZUt98skn+u677/Ttt99Kktq3b6+rr776vALIzMxUenq6evbsqV69emnmzJkqKSkxZ6OlpaWpdevWmjJliiRp0qRJ6tOnj9q2basTJ05o2rRp2rNnj0aMGCFJat68uZo3b27zOwICAhQdHW0zDgq+p/zntc/86T4DANjhclFU5eqrrz7vQuhsgwcP1uHDhzVhwgQVFBSoa9euWrZsmTn4Oi8vT35+1d/sjx8/royMDBUUFKhp06bq0aOHNmzYoI4dO15wLPBudJ8BAGpjMQzDcOWEyspKzZs3T9nZ2Tp06JCsPz/7pcqqVavcGqAnFBUVKTIyUoWFhYwv8iKvrf1Bz3/yre7u1lozBnf1dDgAADe70PztckvRY489pnnz5un222/Xtdde6/QML8DTqrrPaCkCANjjclH0zjvv6N1339WAAQPqIx6g3pRVVK19RiEPAKjJ5a/MgYGBatu2bX3EAtSrqmU+aCkCANjjcnZ48skn9fLLL8vFoUiAx9F9BgCojcvdZ+vXr9fq1av16aef6pprrlFAQIDN/kWLFrktOMCdzO4zpuQDAOxwuShq0qSJBg0aVB+xAPWKKfkAgNq4XBS9+eab9REHUO8oigAAtTmv7FBRUaGVK1dq7ty5OnnypCTpwIEDKi4udmtwgDtVjSlimQ8AgD0utxTt2bNHKSkpysvLU2lpqW677TaFh4dr6tSpKi0t1Zw5c+ojTuCCVbUUscwHAMAel78yP/bYY+rZs6eOHz+ukJAQc/ugQYNsFnYFGhq6zwAAtXG5pWjdunXasGGDAgMDbbYnJCRo//79bgsMcDe6zwAAtXE5O1itVlVWVtbYvm/fPoWHh7slKKA+mC1FPNEaAGCHy0VR//79NXPmTPNni8Wi4uJiTZw4kaU/0KBVP6eIliIAQE0ud59Nnz5dycnJ6tixo06fPq3f/OY32rVrl1q0aKG33367PmIE3KLCyhOtAQCOuVwUtWnTRtu2bdM777yj7du3q7i4WA8++KCGDBliM/AaaGiqB1rTfQYAqMnlokiS/P399cADD7g7FqBe0X0GAKjNeRVFubm5mjVrlnbu3ClJ6tChg0aNGqX27du7NTjAnZiSDwCojcvZ4f3339e1116rLVu2qEuXLurSpYtycnLUqVMnvf/++/URI+AWVVPyKYoAAPa43FI0duxYjRs3TpMmTbLZPnHiRI0dO1b33HOP24ID3KmqpYjnFAEA7HE5O+Tn5ystLa3G9gceeED5+fluCQqoD2ZLEc8pAgDY4XJRdNNNN2ndunU1tq9fv1433HCDW4IC6oO59pkfLUUAgJpc7j4bOHCgnnrqKW3ZskV9+vSRJH355Zf617/+pWeeeUZLly61ORZoKOg+AwDUxmIYhuHKCX5Ofsu2WCx2lwO5FBQVFSkyMlKFhYWKiIjwdDhwk6v++InKKw1tHHeLYiJ5phYAeJsLzd8utxRZrVaXfwngaYZhMPsMAFCrC8oOp0+fdlccQL2qKogkiiIAgH0uZ4fKyko9++yzat26tcLCwvS///1PkjR+/Hj9/e9/d3uAgDtUnNXCyTIfAAB7XC6KJk+erHnz5unFF19UYGCguf3aa6/VG2+84dbgAHcpr6ClCABQO5ezw/z58/Xaa69pyJAhatSokbm9S5cu+vbbb90aHOAuZZXVLUX+frQUAQBqcrko2r9/v9q2bVtju9VqVXl5uVuCAtzt7On4FgtFEQCgJpeLoo4dO9p9eON7772nbt26uSUowN2qF4OlIAIA2OfylPwJEyYoPT1d+/fvl9Vq1aJFi5Sbm6v58+fro48+qo8YgQtWvcQH44kAAPa5nCFSU1P14YcfauXKlQoNDdWECRO0c+dOffjhh7rtttvqI0bggrHEBwCgLi63FEnSDTfcoBUrVrg7FqDeVI8povsMAGAfX5vhE8wxRXSfAQAccKqlqGnTpk7P2Dl27NgFBQTUh7IKlvgAANTOqaJo5syZ5r+PHj2q5557TsnJyUpMTJQkbdy4UcuXL9f48ePrJUjgQlXPPqMoAgDY51RRlJ6ebv77nnvu0aRJkzRq1Chz2+jRo/XXv/5VK1eu1BNPPOH+KIELVLXMB2OKAACOuPy1efny5UpJSamxPSUlRStXrnRLUIC7VXWf+dNSBABwwOUM0bx5cy1ZsqTG9iVLlqh58+ZuCQpwNx7eCACoi8tT8p955hmNGDFCn3/+uXr37i1J2rRpk5YtW6bXX3/d7QEC7sCYIgBAXVwuioYNG6YOHTrolVde0aJFiyRJHTp00Pr1680iCWhozl77DAAAe87r4Y29e/fWW2+95e5YgHpjLvNBUQQAcIAMAZ9gLvPBmCIAgAMURfAJdJ8BAOpChoBPoPsMAFAXMgR8QllF1dpndJ8BAOyjKIJPYEo+AKAuTs0+u/vuu52+YNU0faAhqbCe6T5jTBEAwBGniqLIyMj6jgOoV1XdZ8w+AwA44lRR9Oabb9Z3HEC9ovsMAFAXMgR8AkURAKAu5/VE6/fee0/vvvuu8vLyVFZWZrMvJyfHLYEB7lQ1JZ8xRQAAR1zOEK+88oqGDx+uqKgoffXVV+rVq5eaN2+u//3vf/rlL395XkHMnj1bCQkJCg4OVu/evbV582aHx86bN08Wi8XmFRwcbO4vLy/XU089pU6dOik0NFSxsbFKS0vTgQMHzis2eIcys6WIMUUAAPtcLor+9re/6bXXXtOsWbMUGBiosWPHasWKFRo9erQKCwtdDmDhwoXKzMzUxIkTlZOToy5duig5OVmHDh1yeE5ERITy8/PN1549e8x9p06dUk5OjsaPH6+cnBwtWrRIubm5GjhwoMuxwXtUVBVF/rQUAQDsc7n7LC8vT3379pUkhYSE6OTJk5KkoUOHqk+fPvrrX//q0vVmzJihjIwMDR8+XJI0Z84cffzxx8rKytIf/vAHu+dYLBZFR0fb3RcZGakVK1bYbPvrX/+qXr16KS8vT5dddplL8cE7mE+09qMoAgDY53KGiI6O1rFjxyRJl112mb788ktJ0u7du2UYhkvXKisr05YtW5SUlFQdkJ+fkpKStHHjRofnFRcXKz4+XnFxcUpNTdXXX39d6+8pLCyUxWJRkyZN7O4vLS1VUVGRzQvexRxozROtAQAOuFwU3XLLLVq6dKkkafjw4XriiSd02223afDgwRo0aJBL1zpy5IgqKysVFRVlsz0qKkoFBQV2z2nXrp2ysrK0ZMkSLViwQFarVX379tW+ffvsHn/69Gk99dRTuv/++xUREWH3mClTpigyMtJ8xcXFufQ+0PCZy3ww0BoA4IDL3WevvfaarNYzCWbkyJFq3ry5NmzYoIEDB+q3v/2t2wM8V2JiohITE82f+/btqw4dOmju3Ll69tlnbY4tLy/XvffeK8Mw9Oqrrzq85rhx45SZmWn+XFRURGHkZZiSDwCoi8tFkZ+fn/zOGpdx33336b777juvX96iRQs1atRIBw8etNl+8OBBh2OGzhUQEKBu3brp+++/t9leVRDt2bNHq1atcthKJElBQUEKCgpy/Q3gksEyHwCAujhVFG3fvl3XXnut/Pz8tH379lqP7dy5s9O/PDAwUD169FB2drbuuusuSZLValV2drZGjRrl1DUqKyu1Y8cODRgwwNxWVRDt2rVLq1evVvPmzZ2OCd6JZT4AAHVxqijq2rWrCgoK1KpVK3Xt2lUWi8XuoGqLxaLKykqXAsjMzFR6erp69uypXr16aebMmSopKTFno6Wlpal169aaMmWKJGnSpEnq06eP2rZtqxMnTmjatGnas2ePRowYIelMQfSrX/1KOTk5+uijj1RZWWmOT2rWrJkCAwNdig/ege4zAEBdnCqKdu/erZYtW5r/dqfBgwfr8OHDmjBhggoKCtS1a1ctW7bMHHydl5dn0113/PhxZWRkqKCgQE2bNlWPHj20YcMGdezYUZK0f/9+cyB4165dbX7X6tWrddNNN7k1flwazCn5FEUAAAcshovz6NeuXau+ffvK39+2nqqoqNCGDRt04403ujVATygqKlJkZKQKCwtrHYuES0filGzlF57Wh6OuV6c2kZ4OBwBQDy40f7v8tfnmm282n1N0tsLCQt18880uBwBcDDynCABQF5eLIsMwZLHUTCxHjx5VaGioW4IC3I3uMwBAXZyekn/33XdLOjOYetiwYTZT2CsrK7V9+3Zz+Q+goTFbiljmAwDggNNFUWTkmXEYhmEoPDxcISEh5r7AwED16dNHGRkZ7o8QcAO6zwAAdXG6KHrzzTfNafizZs1SWFhYvQUFuJNhGHSfAQDq5FKGMAxDb731lvLz8+srHsDtqgoiiaIIAOCYSxnCz89PV111lY4ePVpf8QBuV/HzWn0Sy3wAABxzOUO88MILGjNmjP773//WRzyA25VXnN1SxJgiAIB9Li8Im5aWplOnTqlLly4KDAy0GXAtye4zjABPKqusbilq5EdRBACwz+WiaObMmfUQBlB/qmaeBTbys/uMLQAApPMoitLT0+sjDqDeVC8GS0EEAHDM5aLobKdPn1ZZWZnNNtYKQ0NT/YwiBlkDABxzOUuUlJRo1KhRatWqlUJDQ9W0aVObF9DQ8IwiAIAzXM4SY8eO1apVq/Tqq68qKChIb7zxhp555hnFxsZq/vz59REjcEHOHlMEAIAjLnefffjhh5o/f75uuukmDR8+XDfccIPatm2r+Ph4vfXWWxoyZEh9xAmct6qiyJ8xRQCAWrj81fnYsWO64oorJJ0ZP1Q1Bf/666/X2rVr3Rsd4AZlFXSfAQDq5nKWuOKKK7R7925JUvv27fXuu+9KOtOC1KRJE7cGB7hD9ewziiIAgGMuZ4nhw4dr27ZtkqQ//OEPmj17toKDg/XEE09ozJgxbg8QuFDVY4roPgMAOObymKInnnjC/HdSUpK+/fZbbdmyRW3btlXnzp3dGhzgDsw+AwA4w+miyGq1atq0aVq6dKnKysp06623auLEiYqPj1d8fHx9xghcEAZaAwCc4fRX58mTJ+vpp59WWFiYWrdurZdfflkjR46sz9gAt2BMEQDAGU5nifnz5+tvf/ubli9frg8++EAffvih3nrrLVmt1rpPBjyI5xQBAJzhdJbIy8vTgAEDzJ+TkpJksVh04MCBegkMcJcyxhQBAJzgdJaoqKhQcHCwzbaAgACVl5e7PSjAnSpY+wwA4ASnB1obhqFhw4YpKCjI3Hb69Gk9/PDDCg0NNbctWrTIvRECF6h6TBEDrQEAjjldFKWnp9fY9sADD7g1GKA+mFPy/WgpAgA45nRR9Oabb9ZnHEC9Kauo6j6jpQgA4BhfneH1mJIPAHAGWQJejyn5AABnkCXg9VjmAwDgDLIEvB7dZwAAZ5Al4PVY+wwA4AyKIni9qu4zxhQBAGpDloDXK+PhjQAAJ1AUweuxzAcAwBlkCXg9Zp8BAJxBloDXY+0zAIAzKIrg9cxlPmgpAgDUgiwBr8dzigAAziBLwOsxJR8A4AyyBLweLUUAAGeQJeD1GGgNAHAGRRG8XlX3mT8tRQCAWpAl4PWqWooYUwQAqA1ZAl7P7D7zp/sMAOAYRRG8Hs8pAgA4gywBr1dhZUo+AKBuZAl4PabkAwCcQZaAVzMM46zZZ4wpAgA4RlEEr1ZVEEm0FAEAakeWgFer6jqTGFMEAKgdWQJercKmpYjuMwCAYw2iKJo9e7YSEhIUHBys3r17a/PmzQ6PnTdvniwWi80rODjY5hjDMDRhwgTFxMQoJCRESUlJ2rVrV32/DTRAZT+3FFksUiM/iiIAgGMeL4oWLlyozMxMTZw4UTk5OerSpYuSk5N16NAhh+dEREQoPz/ffO3Zs8dm/4svvqhXXnlFc+bM0aZNmxQaGqrk5GSdPn26vt8OGhhz5pmfnywWiiIAgGMeL4pmzJihjIwMDR8+XB07dtScOXPUuHFjZWVlOTzHYrEoOjrafEVFRZn7DMPQzJkz9ac//Umpqanq3Lmz5s+frwMHDuiDDz64CO8IDQmLwQIAnOXRoqisrExbtmxRUlKSuc3Pz09JSUnauHGjw/OKi4sVHx+vuLg4paam6uuvvzb37d69WwUFBTbXjIyMVO/evR1es7S0VEVFRTYveIfqJT48Xv8DABo4j2aKI0eOqLKy0qalR5KioqJUUFBg95x27dopKytLS5Ys0YIFC2S1WtW3b1/t27dPkszzXLnmlClTFBkZab7i4uIu9K2hgSirODPQmun4AIC6XHKZIjExUWlpaeratav69eunRYsWqWXLlpo7d+55X3PcuHEqLCw0X3v37nVjxPCkCuuZliKm4wMA6uLRTNGiRQs1atRIBw8etNl+8OBBRUdHO3WNgIAAdevWTd9//70kmee5cs2goCBFRETYvOAdGFMEAHCWR4uiwMBA9ejRQ9nZ2eY2q9Wq7OxsJSYmOnWNyspK7dixQzExMZKkyy+/XNHR0TbXLCoq0qZNm5y+JrxHVfeZPy1FAIA6+Hs6gMzMTKWnp6tnz57q1auXZs6cqZKSEg0fPlySlJaWptatW2vKlCmSpEmTJqlPnz5q27atTpw4oWnTpmnPnj0aMWKEpDMz0x5//HE999xzuuqqq3T55Zdr/Pjxio2N1V133eWptwkPYTFYAICzPF4UDR48WIcPH9aECRNUUFCgrl27atmyZeZA6by8PPn5VSe048ePKyMjQwUFBWratKl69OihDRs2qGPHjuYxY8eOVUlJiR566CGdOHFC119/vZYtW1bjIY/wflVFUSDdZwCAOlgMwzDqPsy3FBUVKTIyUoWFhYwvusQt+2+BHl6wRT3jm+q93/X1dDgAgHp0ofmbPgV4NbrPAADOIlPAq/HwRgCAs8gU8GrVa58xpggAUDuKIni1skqeaA0AcA6ZAl6tvILuMwCAc8gU8GpVy3zwRGsAQF0oiuDVyn/uPmPtMwBAXcgU8GplP3ef+dNSBACoA0URvBrPKQIAOItMAa9WvcwHH3UAQO3IFPBq5UzJBwA4iUwBr0b3GQDAWWQKeLXqZT4YaA0AqB1FEbya2X3mx0cdAFA7MgW8WlklD28EADiHoghejWU+AADOIlPAq1VYmX0GAHAOmQJejecUAQCcRaaAV6ta5oOWIgBAXcgU8GpVLUWsfQYAqAtFEbxa1ZR8us8AAHUhU8Cr8URrAICzyBTwauU8pwgA4CSKIng184nWPKcIAFAHMgW8mtlSxDIfAIA6kCng1VgQFgDgLIoieDWeUwQAcBaZAl6tapkPpuQDAOpCpoBXY0o+AMBZZAp4LcMwqmefMSUfAFAHiiJ4raqCSJL8aSkCANSBTAGvVdV1JjGmCABQNzIFvNbZRRHdZwCAulAUwWtVdZ9ZLFIjP4oiAEDtKIrgtc6eeWaxUBQBAGpHUQSvVVUUMZ4IAOAMsgW8VlVR5M94IgCAEyiK4LXKKqqeUcTHHABQN7IFvFaFle4zAIDzyBbwWtUDrek+AwDUjaIIXovuMwCAK8gW8FosBgsAcAXZAl6L7jMAgCsoiuC1aCkCALiCbAGvVbXMB0URAMAZZAt4LbOlyJ+POQCgbmQLeK3qZT4YUwQAqBtFEbxW2c/dZ/5+fMwBAHUjW8BrlVfQfQYAcB7ZAl6LKfkAAFdQFMFrVVjPdJ+x9hkAwBlkC3itsgqeUwQAcJ7Hs8Xs2bOVkJCg4OBg9e7dW5s3b3bqvHfeeUcWi0V33XWXzfbi4mKNGjVKbdq0UUhIiDp27Kg5c+bUQ+Ro6Hh4IwDAFR7NFgsXLlRmZqYmTpyonJwcdenSRcnJyTp06FCt5/3444/6/e9/rxtuuKHGvszMTC1btkwLFizQzp079fjjj2vUqFFaunRpfb0NNFCMKQIAuMKjRdGMGTOUkZGh4cOHmy06jRs3VlZWlsNzKisrNWTIED3zzDO64oorauzfsGGD0tPTddNNNykhIUEPPfSQunTpUmsLVGlpqYqKimxeuPTxRGsAgCs8li3Kysq0ZcsWJSUlVQfj56ekpCRt3LjR4XmTJk1Sq1at9OCDD9rd37dvXy1dulT79++XYRhavXq1vvvuO/Xv39/hNadMmaLIyEjzFRcXd/5vDA0G3WcAAFd4LFscOXJElZWVioqKstkeFRWlgoICu+esX79ef//73/X66687vO6sWbPUsWNHtWnTRoGBgUpJSdHs2bN14403Ojxn3LhxKiwsNF979+49vzeFBqV6mQ+6zwAAdfP3dADOOnnypIYOHarXX39dLVq0cHjcrFmz9OWXX2rp0qWKj4/X2rVrNXLkSMXGxtq0Sp0tKChIQUFB9RU6PKSq+4wp+QAAZ3isKGrRooUaNWqkgwcP2mw/ePCgoqOjaxz/ww8/6Mcff9Sdd95pbrNaz7QE+Pv7Kzc3V7GxsXr66ae1ePFi3X777ZKkzp07a+vWrfrLX/7isCiCdyqj+wwA4AKPZYvAwED16NFD2dnZ5jar1ars7GwlJibWOL59+/basWOHtm7dar4GDhyom2++WVu3blVcXJzKy8tVXl4uv3PWumrUqJFZQMF3VC3z4c/sMwCAEzzafZaZman09HT17NlTvXr10syZM1VSUqLhw4dLktLS0tS6dWtNmTJFwcHBuvbaa23Ob9KkiSSZ2wMDA9WvXz+NGTNGISEhio+P15o1azR//nzNmDHjor43eB4DrQEArvBoUTR48GAdPnxYEyZMUEFBgbp27aply5aZg6/z8vJqtPrU5Z133tG4ceM0ZMgQHTt2TPHx8Zo8ebIefvjh+ngLLik6Xa6in8o9HUaDc6qkRO3iWkmScvceUuPQULddL+6J9+yOKSopKVFYWJikMw/8DL3A3+muazs61x3xNtT3DFxMfFZRG48PtB41apRGjRpld9/nn39e67nz5s2rsS06OlpvvvmmGyJzvwVf7tGLy3I9HUaDYy07bf47acZa+QUGu+16Et1nAADneLwo8iX+fhYF+dOVcy6rtfqeBPn7ye8C79HZ14trFqKe8c0u6HoAAN9AUXQRPXTjlXroxis9HUaDU1JSorBpZ/69dWL/C27OPvt6nz3RT6GhF9byBADwDTRbAAAAiKIIAABAEkURAACAJIoiAAAASRRFAAAAkiiKAAAAJFEUAQAASKIoAgAAkERRBAAAIImiCAAAQBJFEQAAgCSKIgAAAEkURQAAAJIoigAAACRJ/p4OoCEyDEOSVFRU5OFIfENJSYn576KiIlVWVtb79dz9O911bUfnuiPehvqegYuJz6p3q8rbVXncVRbjfM/0Yvv27VNcXJynwwAAAOdh7969atOmjcvnURTZYbVadeDAAYWHh8tisVy031tUVKS4uDjt3btXERERF+33Xsq4Z67hfrmG++U67plruF+uq+2eGYahkydPKjY2Vn5+ro8QovvMDj8/v/OqMN0lIiKC/zhcxD1zDffLNdwv13HPXMP9cp2jexYZGXne12SgNQAAgCiKAAAAJFEUNShBQUGaOHGigoKCPB3KJYN75hrul2u4X67jnrmG++W6+rxnDLQGAAAQLUUAAACSKIoAAAAkURQBAABIoigCAACQRFHkESdPntTjjz+u+Ph4hYSEqG/fvvr3v/9t7jcMQxMmTFBMTIxCQkKUlJSkXbt2eTDii2vt2rW68847FRsbK4vFog8++MBmvzP359ixYxoyZIgiIiLUpEkTPfjggyouLr6I7+Liqet+LVq0SP3791fz5s1lsVi0devWGtc4ffq0Ro4cqebNmyssLEz33HOPDh48eHHegAfUds/Ky8v11FNPqVOnTgoNDVVsbKzS0tJ04MABm2vwGav25z//We3bt1doaKiaNm2qpKQkbdq0yeYYX7pfUt337GwPP/ywLBaLZs6cabPdl+5ZXfdr2LBhslgsNq+UlBSbY9xxvyiKPGDEiBFasWKF/vGPf2jHjh3q37+/kpKStH//fknSiy++qFdeeUVz5szRpk2bFBoaquTkZJ0+fdrDkV8cJSUl6tKli2bPnm13vzP3Z8iQIfr666+1YsUKffTRR1q7dq0eeuihi/UWLqq67ldJSYmuv/56TZ061eE1nnjiCX344Yf617/+pTVr1ujAgQO6++676ytkj6vtnp06dUo5OTkaP368cnJytGjRIuXm5mrgwIE2x/EZq3b11Vfrr3/9q3bs2KH169crISFB/fv31+HDh81jfOl+SXXfsyqLFy/Wl19+qdjY2Br7fOmeOXO/UlJSlJ+fb77efvttm/1uuV8GLqpTp04ZjRo1Mj766COb7d27dzf++Mc/Glar1YiOjjamTZtm7jtx4oQRFBRkvP322xc7XI+TZCxevNj82Zn788033xiSjH//+9/mMZ9++qlhsViM/fv3X7TYPeHc+3W23bt3G5KMr776ymb7iRMnjICAAONf//qXuW3nzp2GJGPjxo31GG3DUNs9q7J582ZDkrFnzx7DMPiM1XW/CgsLDUnGypUrDcPw7ftlGI7v2b59+4zWrVsb//3vf434+HjjpZdeMvf58j2zd7/S09ON1NRUh+e4637RUnSRVVRUqLKyUsHBwTbbQ0JCtH79eu3evVsFBQVKSkoy90VGRqp3797auHHjxQ63wXHm/mzcuFFNmjRRz549zWOSkpLk5+dXo0kf0pYtW1ReXm5zT9u3b6/LLruMz9zPCgsLZbFY1KRJE0l8xmpTVlam1157TZGRkerSpYsk7pc9VqtVQ4cO1ZgxY3TNNdfU2M89q+nzzz9Xq1at1K5dO/3ud7/T0aNHzX3uul8URRdZeHi4EhMT9eyzz+rAgQOqrKzUggULtHHjRuXn56ugoECSFBUVZXNeVFSUuc+XOXN/CgoK1KpVK5v9/v7+atasGffQjoKCAgUGBpoJvwqfuTNOnz6tp556Svfff7+5+CSfsZo++ugjhYWFKTg4WC+99JJWrFihFi1aSOJ+2TN16lT5+/tr9OjRdvdzz2ylpKRo/vz5ys7O1tSpU7VmzRr98pe/VGVlpST33S9/t0YNp/zjH//Q//3f/6l169Zq1KiRunfvrvvvv19btmzxdGgAzlJeXq57771XhmHo1Vdf9XQ4DdrNN9+srVu36siRI3r99dd17733atOmTTUSFc60zr788svKycmRxWLxdDiXhPvuu8/8d6dOndS5c2ddeeWV+vzzz3Xrrbe67ffQUuQBV155pdasWaPi4mLt3btXmzdvVnl5ua644gpFR0dLUo2ZPwcPHjT3+TJn7k90dLQOHTpks7+iokLHjh3jHtoRHR2tsrIynThxwma7r3/mqgqiPXv2aMWKFWYrkcRnzJ7Q0FC1bdtWffr00d///nf5+/vr73//uyTu17nWrVunQ4cO6bLLLpO/v7/8/f21Z88ePfnkk0pISJDEPavLFVdcoRYtWuj777+X5L77RVHkQaGhoYqJidHx48e1fPlypaam6vLLL1d0dLSys7PN44qKirRp0yYlJiZ6MNqGwZn7k5iYqBMnTti0vK1atUpWq1W9e/e+6DE3dD169FBAQIDNPc3NzVVeXp7PfuaqCqJdu3Zp5cqVat68uc1+PmN1s1qtKi0tlcT9OtfQoUO1fft2bd261XzFxsZqzJgxWr58uSTuWV327duno0ePKiYmRpL77hfdZx6wfPlyGYahdu3a6fvvv9eYMWPUvn17DR8+XBaLRY8//riee+45XXXVVbr88ss1fvx4xcbG6q677vJ06BdFcXGxWf1LZwZXb926Vc2aNdNll11W5/3p0KGDUlJSlJGRoTlz5qi8vFyjRo3SfffdZ3fa66Wurvt17Ngx5eXlmc/Zyc3NlXTmm1V0dLQiIyP14IMPKjMzU82aNVNERIQeffRRJSYmqk+fPh55T/WttnsWExOjX/3qV8rJydFHH32kyspKc0xCs2bNFBgYyGfsrPvVvHlzTZ48WQMHDlRMTIyOHDmi2bNna//+/fr1r38tyff+m5Tq/u/y3EI7ICBA0dHRateunSTfu2e13a9mzZrpmWee0T333KPo6Gj98MMPGjt2rNq2bavk5GRJbrxfrk2UgzssXLjQuOKKK4zAwEAjOjraGDlypHHixAlzv9VqNcaPH29ERUUZQUFBxq233mrk5uZ6MOKLa/Xq1YakGq/09HTDMJy7P0ePHjXuv/9+IywszIiIiDCGDx9unDx50gPvpv7Vdb/efPNNu/snTpxoXuOnn34yHnnkEaNp06ZG48aNjUGDBhn5+fmeeUMXQW33rOrRBfZeq1evNq/BZ+zM/frpp5+MQYMGGbGxsUZgYKARExNjDBw40Ni8ebPNNXzpfhlG3f9dnuvcKfmG4Vv3rLb7derUKaN///5Gy5YtjYCAACM+Pt7IyMgwCgoKbK7hjvtlMQzDcL6EAgAA8E6MKQIAABBFEQAAgCSKIgAAAEkURQAAAJIoigAAACRRFAEAAEiiKAIAAJBEUQQAACCJogjwShaLRR988IGnw4CThg0b5tQyPkOHDtXzzz9f/wHV4ptvvlGbNm1UUlLi0TiA+kBRBFwihg0bJovFIovFooCAAEVFRem2225TVlaWrFarzbH5+fn65S9/6dR1L7UCqqioSH/84x/Vvn17BQcHKzo6WklJSVq0aJG8+QH927Zt0yeffKLRo0eb22666SY9/vjjNse9/PLLCgoK0jvvvFMvcXTs2FF9+vTRjBkz6uX6gCdRFAGXkJSUFOXn5+vHH3/Up59+qptvvlmPPfaY7rjjDlVUVJjHRUdHKygoyIOR1o8TJ06ob9++mj9/vsaNG6ecnBytXbtWgwcP1tixY1VYWOix2MrKyur1+rNmzdKvf/1rhYWFOTxm4sSJevrpp7VkyRLdd9999RbL8OHD9eqrr9p85gBvQFEEXEKCgoIUHR2t1q1bq3v37mYC/PTTTzVv3jzzuLNbf8rKyjRq1CjFxMQoODhY8fHxmjJliiQpISFBkjRo0CBZLBbz5x9++EGpqamKiopSWFiYrrvuOq1cudImloSEBD3//PP6v//7P4WHh+uyyy7Ta6+9ZnPMvn37dP/996tZs2YKDQ1Vz549tWnTJnP/kiVL1L17dwUHB+uKK67QM888U2uiffrpp/Xjjz9q06ZNSk9PV8eOHXX11VcrIyNDW7duNQuG48ePKy0tTU2bNlXjxo31y1/+Urt27ZJ0pqUpJCREn376qc21Fy9erPDwcJ06dUqStHfvXt17771q0qSJmjVrptTUVP3444/m8VVdXpMnT1ZsbKy5unld51VWViozM1NNmjRR8+bNNXbs2DpbuCorK/Xee+/pzjvvtLvfMAw9+uijeuWVV7RixQqlpKSY+xISEvTcc88pLS1NYWFhio+P19KlS3X48GGlpqYqLCxMnTt31n/+8x/znD179ujOO+9U06ZNFRoaqmuuuUaffPKJuf+2227TsWPHtGbNmlrjBi41FEXAJe6WW25Rly5dtGjRIrv7X3nlFS1dulTvvvuucnNz9dZbb5nFz7///W9J0ptvvqn8/Hzz5+LiYg0YMEDZ2dn66quvlJKSojvvvFN5eXk2154+fbp69uypr776So888oh+97vfKTc317xGv379tH//fi1dulTbtm3T2LFjza6+devWKS0tTY899pi++eYbzZ07V/PmzdPkyZPtvg+r1ap33nlHQ4YMUWxsbI39YWFh8vf3l3SmYPnPf/6jpUuXauPGjTIMQwMGDFB5ebkiIiJ0xx136J///KfN+W+99ZbuuusuNW7cWOXl5UpOTlZ4eLjWrVunL774QmFhYUpJSbFpEcrOzlZubq5WrFihjz76yKnzpk+frnnz5ikrK0vr16/XsWPHtHjxYsd/YEnbt29XYWGhevbsWWNfRUWFHnjgAb333ntas2aN+vbtW+OYl156Sb/4xS/01Vdf6fbbb9fQoUOVlpamBx54QDk5ObryyiuVlpZmFmcjR45UaWmp1q5dqx07dmjq1Kk2LVSBgYHq2rWr1q1bV2vcwCXHAHBJSE9PN1JTU+3uGzx4sNGhQwfzZ0nG4sWLDcMwjEcffdS45ZZbDKvVavfcs4+tzTXXXGPMmjXL/Dk+Pt544IEHzJ+tVqvRqlUr49VXXzUMwzDmzp1rhIeHG0ePHrV7vVtvvdV4/vnnbbb94x//MGJiYuwef/DgQUOSMWPGjFrj/O677wxJxhdffGFuO3LkiBESEmK8++67hmEYxuLFi42wsDCjpKTEMAzDKCwsNIKDg41PP/3UjKNdu3Y296y0tNQICQkxli9fbhjGmb9HVFSUUVpaahN/XefFxMQYL774orm/vLzcaNOmjcO/bVW8jRo1qvE37NevnxEYGGgEBgYaO3futHvuuX+n/Px8Q5Ixfvx4c9vGjRsNSUZ+fr5hGIbRqVMn489//rPDeAzDMAYNGmQMGzas1mOASw0tRYAXMAxDFovF7r5hw4Zp69atateunUaPHq3PPvuszusVFxfr97//vTp06KAmTZooLCxMO3furNFS1LlzZ/PfFotF0dHROnTokCRp69at6tatm5o1a2b3d2zbtk2TJk1SWFiY+crIyFB+fr7ZhXXue3TGzp075e/vr969e5vbmjdvrnbt2mnnzp2SpAEDBiggIEBLly6VJL3//vuKiIhQUlKSGdv333+v8PBwM7ZmzZrp9OnT+uGHH8zrdurUSYGBgTbvqbbzCgsLlZ+fbxObv7+/3Rags/30008KCgqy+ze+/vrrFRYWpvHjxzvsejz77xQVFWXGfu62qr/d6NGj9dxzz+kXv/iFJk6cqO3bt9e4ZkhIiN2/E3Ap8/d0AAAu3M6dO3X55Zfb3de9e3ft3r1bn376qVauXKl7771XSUlJeu+99xxe7/e//71WrFihv/zlL2rbtq1CQkL0q1/9qsZg4oCAAJufLRaL2T0WEhJSa8zFxcV65plndPfdd9fYFxwcXGNby5Yt1aRJE3377be1XtcZgYGB+tWvfqV//vOfuu+++/TPf/5TgwcPNrvfiouL1aNHD7311lt246gSGhpqs8/Z81zVokULnTp1SmVlZTZFmHSmuJk+fbqSkpI0ePBgLVy40HwfVc7+O1UVVva2Vf3tRowYoeTkZH388cf67LPPNGXKFE2fPl2PPvqoec6xY8d05ZVXnvd7AhoiWoqAS9yqVau0Y8cO3XPPPQ6PiYiI0ODBg/X6669r4cKFev/993Xs2DFJZ5JjZWWlzfFffPGFhg0bpkGDBqlTp06Kjo62GSzsjM6dO2vr1q3m7zlX9+7dlZubq7Zt29Z4+fnV/F+Tn5+f7rvvPr311ls6cOBAjf3FxcWqqKhQhw4dVFFRYTOg++jRo8rNzVXHjh3NbUOGDNGyZcv09ddfa9WqVRoyZIhNbLt27VKrVq1qxBYZGenwPdd1XmRkpGJiYmxiq6io0JYtW2q9l127dpV05hlBjvZnZ2dr7dq1uvfee1VeXl7r9ZwRFxenhx9+WIsWLdKTTz6p119/3Wb/f//7X3Xr1u2Cfw/QkFAUAZeQ0tJSFRQUaP/+/crJydHzzz+v1NRU3XHHHUpLS7N7zowZM/T222/r22+/1Xfffad//etfio6OVpMmTSSdmZ2UnZ2tgoICHT9+XJJ01VVXadGiRdq6dau2bdum3/zmNzWehVSX+++/X9HR0brrrrv0xRdf6H//+5/ef/99bdy4UZI0YcIEzZ8/X88884y+/vpr7dy5U++8847+9Kc/Obzm5MmTFRcXp969e2v+/Pn65ptvtGvXLmVlZalbt24qLi7WVVddpdTUVGVkZGj9+vXatm2bHnjgAbVu3VqpqanmtW688UZFR0dryJAhuvzyy226tIYMGaIWLVooNTVV69at0+7du/X5559r9OjR2rdvn8P4nDnvscce0wsvvKAPPvhA3377rR555BGdOHGi1nvZsmVLde/eXevXr3d4TJcuXbRq1SqtX7/+ggujxx9/XMuXL9fu3buVk5Oj1atXq0OHDub+H3/8Ufv37ze7GwFvQVEEXEKWLVummJgYJSQkKCUlRatXr9Yrr7yiJUuWqFGjRnbPCQ8P14svvqiePXvquuuu048//qhPPvnEbI2ZPn26VqxYobi4OPOb/4wZM9S0aVP17dtXd955p5KTk9W9e3eXYg0MDNRnn32mVq1aacCAAerUqZNeeOEFM87k5GR99NFH+uyzz3TdddepT58+eumllxQfH+/wms2aNdOXX36pBx54QM8995y6deumG264QW+//bamTZtmtuK8+eab6tGjh+644w4lJibKMAx98sknNbqM7r//fm3bts2mlUiSGjdurLVr1+qyyy7T3XffrQ4dOujBBx/U6dOnFRER4TA+Z8578sknNXToUKWnpysxMVHh4eEaNGhQnfdzxIgRdrvlztapUyetWrVKGzZs0K9//evzfnZSZWWlRo4cqQ4dOiglJUVXX321/va3v5n73377bfXv37/WvxVwKbIYzo5eBAB4zE8//aR27dpp4cKFSkxM9FgcZWVluuqqq/TPf/5Tv/jFLzwWB1AfaCkCgEtASEiI5s+fryNHjng0jry8PD399NMURPBKtBQBAACIliIAAABJFEUAAACSKIoAAAAkURQBAABIoigCAACQRFEEAAAgiaIIAABAEkURAACAJIoiAAAASdL/Axn5oow9zkURAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build Random Forest model\n",
        "rf_model = RandomForestClassifier(random_state=0).fit(train_X, train_y)\n",
        "\n",
        "disp3 = PartialDependenceDisplay.from_estimator(rf_model, val_X, [feature_to_plot])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "BOvsRwoQptom",
        "outputId": "35e45c33-287a-4d96-e141-72ed0b15aa02"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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miIjINMzefRYXF4dZs2YhLCwMI0aMwJo1a1BeXo7Y2FgAQExMDPz9/bFixQoAwLJlyzBy5EgEBwejuLgY77zzDtLS0vDYY48BqJ+Z9txzz+GNN95A79690aNHDyxatAh+fn6YNGmSuT4mmVhSehEAYFh3NzNX0n4+zppQxJYiIiJTMHsomjp1KvLy8rB48WJkZ2djyJAh2Ldvn3agdHp6OsTiGw1aRUVFmDt3LrKzs+Hm5obQ0FAcO3YMAwYM0L5mwYIFKC8vx+OPP47i4mKMHj0a+/bta7LII3VdpxtC0dDuruYtpAM0+58Vlteguk4Fqa2NmSsiIuraRIIgcA+Bm5SUlEAul0OpVMLFpXMO0rVmxRU1GLJsPwAgadG9cHeUmLmi9hEEAX1f24calRpHFoxFgHvnW4SSiMiUOvr9bfbFG4kMLflaMQAgyMOh0wYioL4r2LthsHVuKbvQiIiMjaGIupykhkHWnXk8kYaPZq0iJQdbExEZG0MRdTldYTyRxo1VrdlSRERkbAxF1KWo1YK2+2xoF2gp8ub+Z0REJsNQRF3KlbwylFbVQWYnRj9F51y0sTG2FBERmQ5DEXUpmvWJBndzha1N5//x9uH+Z0REJtP5vzWIGjndhQZZAzdCUS5XtSYiMjqGIupSNKGoKwyyBnT3P+OSYkRExsVQRF1GSVUtLuaWAuhKoai+paiiRoWy6jozV0NE1LUxFFGXcfaaEoIAdHOzh7dz19jSxVFqC2dp/W48HGxNRGRcDEXUZXSFTWCb4yPXzEDjuCIiImNiKKIuoyst2tiYdlyRki1FRETGxFBEXYIgCDjdsGhjl2sp0qxVxP3PiIiMiqGIuoSr+eUorqiFxFaM/r7674xsybShiC1FRERGxVBEXYJmE9hB/nJIbLvWj/WNVa05poiIyJi61rcHWa3T2kHWruYtxAgar1VERETGw1BEXcKNRRu71ngioPGq1gxFRETGxFBEnV55dR0uZJcA6HqDrIFGoai0Gmo1V7UmIjIWhiLq9M5mKKEWAF+5DAp511i0sTEvZylEIqBOLaCgvMbc5RARdVkMRdTpddVFGzXsbMTwcKwfV8RVrYmIjIehiDq9rrYJbHMUcoYiIiJjYyiiTk0QhEYrWXfNliIA8GnYy40z0IiIjIehiDq1a4WVKCivgZ2NCLf5da1FGxvj/mdERMbHUESdmmY80QA/OWR2Nmauxng0LUVc1ZqIyHgYiqhT68qLNjamHVPE/c+IiIyGoYg6taQuvGhjY94NaxVls6WIiMhoGIqo06qsUeF8lmbRRlfzFmNkikYLOBIRkXEwFFGn9ft1JerUArycpfB3tTd3OUalWdW6sLwG1XUqM1dDRNQ1MRRRp/V7phIAENLNFSKRyMzVGJebgx0kNvV/XXM5A42IyCjaFYrq6upw4MABfPTRRygtLQUAXL9+HWVlZQYtjqg1aQUVAIBeXo5mrsT4RCIRvF24gCMRkTHZ6ntCWloaoqOjkZ6ejurqatx7771wdnbGypUrUV1djfXr1xujTqIm0grKAQDdPRzMXIlpKFxkyCiq5FpFRERGondL0bPPPouwsDAUFRXB3v7GOI7JkycjPj7eoMURtSatsL6lKNC967cUATfGFXFVayIi49C7pejIkSM4duwYJBKJzvGgoCBkZmYarDCi1qjUAjIKKwEAgVbSUqQJRbkMRURERqF3S5FarYZK1XT2S0ZGBpydnQ1SFNGtZJdUoUalhq1YBN+GLTC6Op+GMUVsKSIiMg69Q9G4ceOwZs0a7e9FIhHKysqwZMkS3H///YasjahFmvFE3dzsYWtjHZMoFdr9zxiKiIiMQe/us1WrViEqKgoDBgxAVVUV/vGPf+DSpUvw9PTEtm3bjFEjURPpDTPPAj2sYzwRAHg7c1NYIiJj0jsUdevWDWfOnMGOHTtw5swZlJWVYc6cOZgxY4bOwGsiY9IOsraS8USAbkuRIAhdfm0mIiJTa1e/g62tLWbMmIG3334bH3zwAR577LF2B6J169YhKCgIMpkM4eHhOHHiRJvO2759O0QiESZNmqRzPCcnB7Nnz4afnx8cHBwQHR2NS5cutas2slyalqLu7tYTijRjiipqVCitrjNzNUREXY/eoWjFihXYtGlTk+ObNm3CypUr9brWjh07EBcXhyVLliApKQkhISGIiopCbm5uq+elpqbixRdfxJgxY3SOC4KASZMm4a+//sLu3btx+vRpBAYGIjIyEuXl5XrVRpYtrbD+z9Oaus8cJLZwltU37nIGGhGR4ekdij766CP069evyfHbbrtN74UbV69ejblz5yI2NhYDBgzA+vXr4eDg0Gzo0lCpVJgxYwaWLl2Knj176jx36dIl/Prrr/jwww8xfPhw9O3bFx9++CEqKytbHe9UXV2NkpISnQdZLkEQkJZvfd1nQKO1ipQcV0REZGh6h6Ls7Gz4+vo2Oe7l5YWsrKw2X6empganTp1CZGTkjWLEYkRGRiIxMbHF85YtWwZvb2/MmTOnyXPV1fVfFDLZjSnaYrEYUqkUCQkJLV5zxYoVkMvl2kdAQECbPweZXlFFrbb7yJq6z4D6Va0BzkAjIjIGvUNRQEAAjh492uT40aNH4efn1+br5OfnQ6VSwcfHR+e4j48PsrOzmz0nISEBGzduxIYNG5p9vl+/fujevTsWLlyIoqIi1NTUYOXKlcjIyGg1sC1cuBBKpVL7uHbtWps/B5meZjq+j4sUMjsbM1djWt5cq4iIyGj0nn02d+5cPPfcc6itrcXdd98NAIiPj8eCBQvwwgsvGLxAjdLSUsycORMbNmyAp6dns6+xs7PDzp07MWfOHLi7u8PGxgaRkZG47777IAhCi9eWSqWQSqXGKp0MLN3KtvdoTMFVrYmIjEbvUPTSSy+hoKAATz31FGpqagDUd1e9/PLLWLhwYZuv4+npCRsbG+Tk5Ogcz8nJgUKhaPL6K1euIDU1FePHj9ceU6vV9R/C1hYpKSno1asXQkNDkZycDKVSiZqaGnh5eSE8PBxhYWH6flSyUGmamWdWNp4I4P5nRETGpHf3mUgkwsqVK5GXl4dff/0VZ86cQWFhIRYvXqzXdSQSCUJDQ3U2kVWr1YiPj0dEREST1/fr1w/nzp1DcnKy9jFhwgSMHTsWycnJTcYByeVyeHl54dKlSzh58iQmTpyo70clC6UJRUFWHYo40JqIyND0binScHJywvDhwzv05nFxcZg1axbCwsIwYsQIrFmzBuXl5YiNjQUAxMTEwN/fHytWrIBMJsPAgQN1znd1dQUAneNffvklvLy80L17d5w7dw7PPvssJk2ahHHjxnWoVrIc6Q3T8btb0XR8DS/n+o2YC8sZioiIDE3vUFReXo633noL8fHxyM3N1XZhafz1119tvtbUqVORl5eHxYsXIzs7G0OGDMG+ffu0g6/T09MhFuvXmJWVlYW4uDjk5OTA19cXMTExWLRokV7XIMumaSkKtLKZZwAgt68PRcUVtWauhIio6xEJrY1Absb06dNx6NAhzJw5E76+vk22Gnj22WcNWqA5lJSUQC6XQ6lUwsXFxdzlUCOVNSr0X7wPAJC8+F64OkjMXJFpFZRVI/SNAwCAy8vvs5rNcImI2qKj3996txTt3bsX33//PW6//Xa934yoozQzz1xktlYXiABAbm+n/bWyshYeTpw1SURkKHr/M9PNzQ3u7u7GqIXoljRrFFnT9h6N2dqItVt9FLELjYjIoPQORa+//joWL16MiooKY9RD1Cprno6v4epQ31qkrKwxcyVERF2L3t1nq1atwpUrV+Dj44OgoCDY2dnpPJ+UlGSw4ohupt0I1goHWWu4OUhwrbASReVsKSIiMiS9Q9GkSZOMUAZR22hnnll1S1HDDLRKhiIiIkPSOxQtWbLEGHUQtYlmoHV3K9ziQ8O1YbB1cQW7z4iIDKld83mLi4vx3//+FwsXLkRhYSGA+m6zzMxMgxZH1FidSo3MokoAQJCn9bYUuTWMKSpiKCIiMii9W4rOnj2LyMhIyOVypKamYu7cuXB3d8fOnTuRnp6OLVu2GKNOIlwvrkKdWoDEVgwfZ5m5yzEbuQMXcCQiMga9W4ri4uIwe/ZsXLp0CTLZjS+m+++/H4cPHzZocUSNaQZZd3d3gFgsusWruy5NSxFDERGRYekdin777Tf885//bHLc398f2dnZBimKqDnWvL1HY5op+cWckk9EZFB6hyKpVIqSkpImxy9evAgvLy+DFEXUHO0gayueeQbcmH3GKflERIaldyiaMGECli1bhtra+v8hi0QipKen4+WXX8aUKVMMXiCRhnY1aytvKXJrCEVKTsknIjIovUPRqlWrUFZWBm9vb1RWVuLOO+9EcHAwnJ2dsXz5cmPUSASg8RpF1jsdH7gxJZ+zz4iIDEvv2WdyuRz79+9HQkICzp49i7KyMgwbNgyRkZHGqI8IACAIArvPGmhaiipqVKiuU0Fqa2PmioiIuga9Q5HG6NGjMXr0aEPWQtSivLJqVNSoIBIB3dzszV2OWTnLbCEWAWoBUFbUwtuFoYiIyBDaFIrWrl3b5gvOnz+/3cUQtSS9oevMT25v9S0jYrEIcns7FFXUoriyFt4u1rtmExGRIbUpFL377rs6v8/Ly0NFRQVcXV0B1K9w7eDgAG9vb4YiMgrueabL1UGCoopaFJVzXBERkaG0aaD11atXtY/ly5djyJAhOH/+PAoLC1FYWIjz589j2LBheP31141dL1mptEKGosZctVt9cAYaEZGh6D37bNGiRXj//ffRt29f7bG+ffvi3XffxWuvvWbQ4og00gs0q1lb98wzDc0MNCUXcCQiMhi9Q1FWVhbq6uqaHFepVMjJyTFIUUQ3Y0uRLs0MNLYUEREZjt6h6J577sE///lPJCUlaY+dOnUKTz75JKflk9FoBlp3t/KFGzVcuSksEZHB6R2KNm3aBIVCgbCwMEilUkilUowYMQI+Pj7473//a4waycqVVdehoGFAMVuK6mn3P+MCjkREBqP3OkVeXl744YcfcPHiRVy4cAEA0K9fP/Tp08fgxREBN7b3cHeUwFlmZ+ZqLIObNhSxpYiIyFDavXhjnz59GITIJNh11pRcO6aILUVERIaidyhSqVTYvHkz4uPjkZubC7VarfP8zz//bLDiiAAOsm6OpqWIm8ISERmO3qHo2WefxebNm/G3v/0NAwcOhEgkMkZdRFrcCLYpV3u2FBERGZreoWj79u344osvcP/99xujHqImNGOKAtl9ptV48UZBEPiPEyIiA9B79plEIkFwcLAxaiFqFrf4aEoTimrq1KiqVd/i1URE1BZ6h6IXXngB7733HgRBMEY9RDpq6tTIUlYCALozFGk5SW1hK65vHWIXGhGRYejdfZaQkIBffvkFe/fuxW233QY7O90p0jt37jRYcUQZRRVQC4CDxAZeTlJzl2MxRCIRXB0kyC+rRnFFLfxc7c1dEhFRp6d3KHJ1dcXkyZONUQtRE5qZZ93dHThu5iauDnYNoYgtRUREhqB3KPrkk0+MUQdRs7hGUcu0CzhyWj4RkUHoPaYIAOrq6nDgwAF89NFHKC0tBQBcv34dZWVlBi2OiIOsWybntHwiIoPSu6UoLS0N0dHRSE9PR3V1Ne699144Oztj5cqVqK6uxvr1641RJ1mp9ML66fjduUZRE9zqg4jIsPRuKXr22WcRFhaGoqIi2NvfGNw5efJkxMfHG7Q4Im1LEbvPmuCmsEREhqV3S9GRI0dw7NgxSCQSneNBQUHIzMw0WGFEarWA9IaB1kFsKWrCtWH/M7YUEREZht4tRWq1GiqVqsnxjIwMODs7G6QoIgAoKK9BdZ0aIhHg6yozdzkWp/Gq1kRE1HF6h6Jx48ZhzZo12t+LRCKUlZVhyZIl7dr6Y926dQgKCoJMJkN4eDhOnDjRpvO2b98OkUiESZMm6RwvKyvD008/jW7dusHe3h4DBgzgOKdOKqekCgDg6SSFnU275gR0aW7aliJ2nxERGYLe3zSrVq3C0aNHMWDAAFRVVeEf//iHtuts5cqVel1rx44diIuLw5IlS5CUlISQkBBERUUhNze31fNSU1Px4osvYsyYMU2ei4uLw759+/C///0P58+fx3PPPYenn34ae/bs0as2Mr/c0vpQ5OPCRRub48op+UREBqV3KOrWrRvOnDmDV199Fc8//zyGDh2Kt956C6dPn4a3t7de11q9ejXmzp2L2NhYbYuOg4MDNm3a1OI5KpUKM2bMwNKlS9GzZ88mzx87dgyzZs3CXXfdhaCgIDz++OMICQlpcwsUWY6ckmoAgMKFXWfNcbVnSxERkSHpPdAaAGxtbfHII4906I1rampw6tQpLFy4UHtMLBYjMjISiYmJLZ63bNkyeHt7Y86cOThy5EiT50eNGoU9e/bg0UcfhZ+fHw4ePIiLFy/i3XffbfGa1dXVqK6u1v6+pKSknZ+KDClbWd9S5M1Q1Cw3xxtT8gVB4IrfREQd1K5QlJKSgvfffx/nz58HAPTv3x9PP/00+vXr1+Zr5OfnQ6VSwcfHR+e4j48PLly40Ow5CQkJ2LhxI5KTk1u87vvvv4/HH38c3bp1g62tLcRiMTZs2IA77rijxXNWrFiBpUuXtrl2Mg1t95kzQ1FzNC1FdWoBZdV1cJbZ3eIMIiJqjd7dZ19//TUGDhyIU6dOISQkBCEhIUhKSsKgQYPw9ddfG6NGAEBpaSlmzpyJDRs2wNPTs8XXvf/++/j111+xZ88enDp1CqtWrcK8efNw4MCBFs9ZuHAhlEql9nHt2jVjfATSk6b7jGOKmmcvsYHUtv6vMKflExF1nN4tRQsWLMDChQuxbNkyneNLlizBggULMGXKlDZdx9PTEzY2NsjJydE5npOTA4VC0eT1V65cQWpqKsaPH689plar6z+ErS1SUlLg5+eHV199Fbt27cLf/vY3AMDgwYORnJyM//u//0NkZGSztUilUkil/OK1NJrZZz7sPmuRq4MdckqqUVxRiwB3c1dDRNS56d1SlJWVhZiYmCbHH3nkEWRlZbX5OhKJBKGhoTqrYKvVasTHxyMiIqLJ6/v164dz584hOTlZ+5gwYQLGjh2L5ORkBAQEoLa2FrW1tRCLdT+WjY2NNkBR58FQdGvaafmVHGxNRNRRercU3XXXXThy5AiCg4N1jickJDQ7Rb41cXFxmDVrFsLCwjBixAisWbMG5eXliI2NBQDExMTA398fK1asgEwmw8CBA3XOd3V1BQDtcYlEgjvvvBMvvfQS7O3tERgYiEOHDmHLli1YvXq1vh+VzKhWpUZ+Wf0XPbvPWia35wKORESGoncomjBhAl5++WWcOnUKI0eOBAD8+uuv+PLLL7F06VKd9YAmTJjQ6rWmTp2KvLw8LF68GNnZ2RgyZAj27dunHXydnp7epNXnVrZv346FCxdixowZKCwsRGBgIJYvX44nnnhCz09K5pRXWj+eyM5GpG0Noaa4gCMRkeGIBEEQ9DmhrSFFJBI1ux1IZ1BSUgK5XA6lUgkXFxdzl2OVTqcXYfIHx+Dvao+jr9xt7nIs1sKdZ7HtxDXE3dsH8+/pbe5yiIjMqqPf33q3FHFsDpkCZ561jbxhWn4RW4qIiDqsQxtKVVVVGaoOIh0cZN02bg1bfSg5poiIqMP0DkUqlQqvv/46/P394eTkhL/++gsAsGjRImzcuNHgBZJ1YihqG83+Z2wpIiLqOL1D0fLly7F582a8/fbbkEhuDIAdOHAg/vvf/xq0OLJemu4zb3aftcpVOyWfLUVERB2ldyjasmULPv74Y8yYMQM2Njba4yEhIS1uz0GkL80WH9wMtnWu9jf2PyMioo7ROxRlZmY2WaMIqB+AXVvL/zGTYWg2g2X3WevcHDkln4jIUPQORQMGDGh2d/qvvvoKQ4cONUhRRDfGFLH7rDWaliJlZS3Uar1W1yAiopvoPSV/8eLFmDVrFjIzM6FWq7Fz506kpKRgy5Yt+O6774xRI1mZyhoVSqrqAADebClqlWZMkVoASqvqIG8YeE1ERPrTu6Vo4sSJ+Pbbb3HgwAE4Ojpi8eLFOH/+PL799lvce++9xqiRrIxmPJG9nQ2cpXrndqsisRXDUVI/to8z0IiIOqZd3zhjxozB/v37DV0LEYAb44kUchlEIpGZq7F8rg4SlNdUoqiiBkFwNHc5RESdVocWbyQyhpyGfc+8nTmeqC00axVxWj4RUce0qaXIzc2tzf9iLyws7FBBRLlcuFEv2lDE7jMiog5pUyhas2aN9tcFBQV44403EBUVhYiICABAYmIifvzxRyxatMgoRZJ14cwz/WgXcORaRUREHdKmUDRr1iztr6dMmYJly5bh6aef1h6bP38+/vOf/+DAgQN4/vnnDV8lWZVs7WawbClqC820/CKGIiKiDtF7TNGPP/6I6OjoJsejo6Nx4MABgxRF1o37nunHraGlSGnk7rOES/mYu+Uktp1IR2WNyqjvRURkDnqHIg8PD+zevbvJ8d27d8PDw8MgRZF145gi/dzYFNa4LUX/91MK9v+Zg4U7z2Hkinis+OE8rhVWGPU9iYhMSe8p+UuXLsVjjz2GgwcPIjw8HABw/Phx7Nu3Dxs2bDB4gWRdBEHQbgbLMUVtY4pNYcur6/B7phIA4O9qj8ziSnx0+C9sOPIX7unvg9hRQYjo5cElFIioU9M7FM2ePRv9+/fH2rVrsXPnTgBA//79kZCQoA1JRO1VWl2Hytr6rhm2FLWNmwlmn51OL0adWoC/qz0OLxiLXy7k4tPEVBy5lI/9f+Zg/5856OPjhFmjgjB5qD8cJFx0k4g6n3b9nys8PBxbt241dC1EyGlYuFFubweZnY2Zq+kcbnSfGS8UnbhaAAAY0cMdNmIRIgf4IHKADy7nluLTY2n4OikDF3PK8K9dv2Pl3gt4OCwAMRFB6O7hYLSaqGspr65DakE5Csq4tIQhebtI0U/hYu4yOg3+c44sCrvO9GeKKfnHr9avPzaih7vO8WBvZ7w+aSBeiu6LL09mYEtiKtIKKvDfhKvYePQq7unnjVmjgjA62JNda4SSqlqk5VcgtaAcaQXlSC2o0P43r2HRVjK8x+/oiVei+0Es5t/BW2EoIovCmWf600zJL62qQ51KDVsbwy5UX12nQvK1YgBNQ5GGi8wOc0b3QOyoIBy8mIvNx9Jw+GIeDpzPxYHzuQj2dsKsiEA8OKwbHLmfXZclCAKKK2obQk9Fk/8WlrfeCuTuKIG3s5QB2kAEQcCF7FJ8fPgvZBRVYPXDQ9gCfwv8vxNZlJyGzWC9nRmK2kreEIoAQFlZCw8nw7aynctQorpODU8nCXp6tr63mlgswt39fHB3Px9cySvDlmOp+OpUBi7nlmHR7j/w9r4UPBQWgJiIQATd4lpkmQRBQEF5DVLzdVt60grKkZpfjpKqulbP93SSIsjDAYEejgjycECQpyOCPBzR3cNB52eZDGPX6Qws+OosfjiXjWzlr9gQE2bw/0d0JQxFZFFytJvB8i9tW9naiOEss0VpVR2KKgwfijRdZ8OD3PX6F3wvLycsnTgQL0b1xdenMvBpYhqu5pdj09Gr+OTYVdzVxwuzb++BMcGebNa3MIIgILe0Gqn5zbf4lFW3HnwULjIEejggyMMRgZ4N/20IQk5sKTSpyUO7wVduj8e3nERSejEmf3AMm2OHo6eXk7lLs0j86SSLksPVrNvFzUGC0qo6KCsNP0j1RAvjidrKWWaH2bf3QExEEA5fysOnx1LxS0qe9tHT0xGzRgVhSmg3fmGakFotIKukCmk6LT71oSetoEI7C7Q5IhHgJ7dHkOeNFp/6/zqiu7sD7CXsorEkI3t6YOdTozD7k9+QXliBBz88ho9nhrX773RX1qb/Az344INtvqBmmj5Re7D7rH3cHOyQXggUlRt2sHWdSo1TaUUA2h+KNMRiEe7q6427+nrjan45tiSm4suTGfgrvxxL9vyBd35Mwd9DuyEmIpD/ijWQOpUaWcoqpGq6uPLLtb9OL6xATZ26xXPFIqCbm4O2xae+m6s+/AS420Nqy+DTmQR7O2PXU7fjsS0nceZaMR7573H838MhmBDiZ+7SLEqbQpFcLjd2HUQAgFzOPmsXuZEWcDyfVYqy6jo4y2wNOq23h6cjloy/DS+M64udSRn49FgqruSVY/OxVGw+loo7+3hh9qgg3NnHi11rt1CrUiOjqLK+ladRq09aQQWuFVWgViW0eK6tWITu7g7arq0gDwcENozx8Xe1h8TWsIP2yby8nKXYPncknttxGj/+kYP5204jo6gCT97Zi4PbG7QpFH3yySfGroMIarWgnX2mkLOlSB/GWsDxROqN8UQ2RggnTlJbxEQEYebIQCRczsfmo6n4OSUXhy7m4dDFPAR5OCAmIgh/D+sGF5n1DsKtqlUho6gCqflNx/dkFldCpW45+EhsxQh0vzn01Lf++MplBp+tSJbNXmKDD2aE4s0fzmNjwlW8vS8F1worsGziQNjxZ4FjishyFFbUoE4tQCSqn6FCbaeZlm/otYoaL9poTCKRCGN6e2FMby+kFZRjS2Iavjh5DakFFVj23Z9Y9VMKpoR2Q0xEEIK9u2bXWmWNCumFumv4aAY6X1dWQmg598DezqbZgc1BHo5QuMjY2kY6bMQiLHpgAALc7LHsuz+x7cQ1ZBZXYd0/hsLZiv/xAbQzFH311Vf44osvkJ6ejpoa3X+ZJiUlGaQwsj6aViIPRyn/xaInzQKOhlzVWhAE7SDr4UGmG5AZ6OGIRQ8MQNy9fbDzdCY+PZaKy7ll2JKYhi2JaRjT2xOzRwVhbF/vTvdlX1Zdp+3aqu/uutHik93w898SJ6ntjeDT+L+ejlzbh9pl9u094O/mgPnbTuPwxTw8tD4Rn8QOh6/c3tylmY3eoWjt2rX417/+hdmzZ2P37t2IjY3FlStX8Ntvv2HevHnGqJGsBMcTtZ+rg+Fbii7nlqGoohYyOzEG+Zt+XKGj1BYzRwbikfDuOHalAJ8cTUX8hRwcuZSPI5fy0d3dATERgXgoLMCi1rdRVtYivaACV28a45NaUIH8stZXbXaR2aKHp6PujK6GGV4ejhIGHzK4ewf4YMc/R+LRzSdxIbsUk9YdxabZw3Gbn3WOJdY7FH3wwQf4+OOPMX36dGzevBkLFixAz549sXjxYhQWFhqjRrIS2VzNut3ctAOtDddSpFmfaFh3N7MOuBWJRLg92BO3B3viWmEFPvs1DdtPpCO9sAJvfH8eq366iAeH+WP2qCD09nE2ej2GWLU5SNvS46gzrV3T4kdkSoO7uWLXU6MQu/k3XM4tw8PrE/GfGcMwtq+3uUszOb1DUXp6OkaNGgUAsLe3R2lpKQBg5syZGDlyJP7zn/8YtkKyGtzio/3kmk1hDTglv6PrExlDgLsDXr2/P56L7I1vTl/Hp8dSkZJTiq3H07H1eDpuD/bArIgg3NPfp0MDwwVBQH5ZTZP9udq6arOXs+6qzdo1fLhqM1moAHcHfP3kKDzx2Skk/lWAxz49idcnDsQ/wrubuzST0jsUKRQKFBYWIjAwEN27d8evv/6KkJAQXL16FUJrIwGJboGbwbafpqVIaaAp+Y3HE1lSKNJwkNjiH+HdMX1EABL/KsCnx1Kx/88cHL1cgKOXC9DNzR4xEYGYGtZdGxhv1njV5lSd0FP/3/KalhcvBABfeaNVmxuFn0APB+7vRp2S3N4Onz46Aq/sPIudSZl4ddc5pBdWYEFU3043fq+99P6be/fdd2PPnj0YOnQoYmNj8fzzz+Orr77CyZMn9VrkkehmuWwpajfNlHxDDbTOKKpEdkkV7GxEGBrgZpBrGoNIJMKoXp4Y1csTGUX1XWs7fruGjKJKvPnDBazefxGTh/rj3gE+yFZW66zanFpQjqralhcvFIkAf1f7Zgc2d3d34Maa1CVJbMVY9VAIurs7YM2BS1h/6AquFVVg1UMhVvEzr3co+vjjj6FW1/+PZN68efDw8MCxY8cwYcIE/POf/zR4gWQ9NKtZs6VIf6729S1FFTUqVNepOrzasGY80SB/eafZsqGbmwMW3tcfz93TB3vOZOKTo6m4kF2KbSeuYduJa82eYyMWoZub/U3dXFy1maybSCTCc5F9EODmgFd2nsX3Z7OQo6zCxzFhcHfs2uPe9A5FYrEYYvGNQZfTpk3DtGnTDFoUWadsJfc9ay9nmS3EIkAtAMqKWni7dOzL/Mb6RB6GKM+k7CU2mDq8Ox4OC8CJq4XYkpiGizmlCHB3aDKl3d/Nnss/ELVgSmg3+Mpl+Of/TuFkWhGmfHgMn8wejiBPR3OXZjRtCkVnz57FwIEDIRaLcfbs2VZfO3jwYIMURtalVqVGQTlDUXuJxSLI7e1QVFGL4spaeHfwHmrGE4Vb4HiithKJRAjv6YHwnp0v2BFZilHBntj5ZP1mslfzyzH5g6P476wwhAZ23v83tKZN/0QaMmQI8vPztb8eOnQohgwZ0uQxdOjQdhWxbt06BAUFQSaTITw8HCdOnGjTedu3b4dIJMKkSZN0jotEomYf77zzTrvqI+PLL6uGINTvxeTOacntohlsXXSLKeG3kltShdSCCohEQGiQ5Y4nIiLT6O3jjF3zRmFwNzmKKmoxfcNxfHf2urnLMoo2tRRdvXoVXl5e2l8b0o4dOxAXF4f169cjPDwca9asQVRUFFJSUuDt3fIaCampqXjxxRcxZsyYJs9lZWXp/H7v3r2YM2cOpkyZYtDayXA0M8+8naVWM8vB0LTT8ju4gKNmv7MBvi5Wvd8YEd3g7SzD9sdHYv62ZBw4n4OnPz+NjKJK/POOnl1qUdE2tRQFBgZqP3RaWhr8/f0RGBio8/D390daWpreBaxevRpz585FbGwsBgwYgPXr18PBwQGbNm1q8RyVSoUZM2Zg6dKl6NmzZ5PnFQqFzmP37t0YO3Zss68ly5CtbBhkzY1g2+3GtPyOtRSZY2sPIrJ8DhJbfDQzFLNHBQEA3tp7Aa998zvqVC3P4uxs9B5hOHbs2GZXrlYqlRg7dqxe16qpqcGpU6cQGRl5oyCxGJGRkUhMTGzxvGXLlsHb2xtz5sy55Xvk5OTg+++/b/W11dXVKCkp0XmQaeVqZp45MxS1l2ZT2A63FHWB8UREZBw2YhH+PeE2LH5gAEQiYOvxdDy25STKqltf0LSz0DsUCYLQbFNZQUEBHB31G5Gen58PlUoFHx8fneM+Pj7Izs5u9pyEhARs3LgRGzZsaNN7fPrpp3B2dm51DaUVK1ZALpdrHwEBAW3/EGQQN1az5nT89tJsEdGR/c+KK2pwIbt+lfrhDEVE1IJHR/fA+kdCIbMT42BKHh5en6ht8e/M2jwlXxMqRCIRZs+eDan0xpeXSqXC2bNntdt/GEtpaSlmzpyJDRs2wNPTs03nbNq0CTNmzIBM1nILxMKFCxEXF6f9fUlJCYORiWnHFHHmWbu5aTeFbX/32W+pRQCAXl6O8HRiQCWilkXdpsD2xyPw2Ke/4c+sEkz+oH4z2f6+LuYurd3aHIrk8vodcwVBgLOzM+zt7bXPSSQSjBw5EnPnztXrzT09PWFjY4OcnByd4zk5OVAoFE1ef+XKFaSmpmL8+PHaY5qFJG1tbZGSkoJevXppnzty5AhSUlKwY8eOVuuQSqU6IY9Mj/uedZyrNhS1v6WoM69PRESmNyTAFbueuh2zPzmBK3nleGh9Ij6YMQx39PEyd2nt0uZQ9Mknn2j3Nnv//ffh5OTU4TeXSCQIDQ1FfHy8dlq9Wq1GfHw8nn766Sav79evH86dO6dz7LXXXkNpaSnee++9Jq07GzduRGhoKEJCQjpcKxmXJhQpGIraTdN91pGtPk40tBSN6MGp+ETUNgHuDtj55O14/LOTOH61ELGbf8PySQMxbUTn20xWrzFFgiBg69atTaa8d0RcXBw2bNiATz/9FOfPn8eTTz6J8vJyxMbGAgBiYmKwcOFCAIBMJsPAgQN1Hq6urnB2dsbAgQMhkdxY36akpARffvklHnvsMYPVSsbDzWA7TtNS1N5NYcur6/B7phIAW4qISD9yBztsmTMCk4f6Q6UW8MrOc3jnxwtQqzvXRvF6bfMhFovRu3dvFBQUoHfv3gYpYOrUqcjLy8PixYuRnZ2NIUOGYN++fdrB1+np6TrbirTV9u3bIQgCpk+fbpA6yXiqalXaL3KOKWo/tw60FKnVAj44eBkqtQB/V3v4u9rf+iQiokaktjZY/XAIAtzssfbny1j3yxVcK6zEOw8N7jT7CIoETZ9YG3377bd4++238eGHH2LgwIHGqsusSkpKIJfLoVQq4eLSeQeMdRZpBeW4852DkNmJcX5ZdJdaCMyUrhVWYMzbv0BqK0bKG/e1+bySqlrE7TiDA+frx/a9cl8/PHFnr1ucRUTUsi9OXsOrO8+hTi1gRJA7PpoZCjcTbCbb0e9vvTeEjYmJQUVFBUJCQiCRSHQGXANodg0jotZous4ULjIGog7Q/A+nuk6NyhpVm3a3v5hTin9+dgpX88shsRXjjYkD8fBwzrwkoo55OCwA/q72eOKzUziRWli/mWzscAR6WPZmsnqHojVr1hihDLJmmkHW7DrrGEeJDWzFItSpBRRV1MBe0noX2Hdnr2PBV2dRUaOCv6s9PnxkGAZ3czVNsUTU5d0e7ImvnhyFRzf/hr/yyzH5g2P476wwDOtuuRM59A5Fs2bNMkYdZMU4Hd8wRCIRXB0kyC+rRnFFLfxaGBdUp1Lj7R9T8PHhvwAAtwd74P3pw+BugqZtIrIufRXO2PXUKDz66W/4PbME0z/+FWumDsF9g3zNXVqz9B/B3EhVVRW3x6AOyy1tmHnmzJlnHXWrBRzzSqsxc+MJbSB64s5e+DR2BAMRERmNt4sMOx6PwN39vFFdp8ZTnydhw+G/oOeQZpPQu6WovLwcL7/8Mr744gsUFBQ0eV6lUhmkMLIe2s1g2VLUYdoFHCtrUVpViz+ul+D3TGX943oJruSVQRDqu9reeSgE91vov9aIqGtxlNri45mhWPrtn/js1zQs/+E8spRVWDx+gLlL06F3KFqwYAF++eUXfPjhh5g5cybWrVuHzMxMfPTRR3jrrbeMUSN1cdruMzlDUUdpFnBcuPNci+sV9fd1wdppQ9Dbx9mUpRGRlbO1EWPZxNsQ6OGAN384j0HdLG92t96h6Ntvv8WWLVtw1113ITY2FmPGjEFwcDACAwOxdetWzJgxwxh1UhfG7jPDCXBzAHBjAUd/V3sM9HfBQD85BvrLcZu/C7ydGT6JyDxEIhEeG9MTY/t5o5dXx3fGMDS9Q1FhYSF69uwJAHBxcdFOwR89ejSefPJJw1ZHXZ4gCBxobUBP3x2Mvgon+MrtMdBfzrFCRGSRLDEQAe0YaN2zZ09cvXoVQP1eZF988QWA+hYkV1dXgxZHXV9pdR0qaurHoTEUdZy7owRTh3fHHX28GIiIiPSkdyiKjY3FmTNnAACvvPIK1q1bB5lMhueffx4vvfSSwQukri23oZXIRWbbpsUGiYiIjEXv7rPnn39e++vIyEhcuHABp06dQnBwMAYPHmzQ4qjru7ERLFuJiIjIvNocitRqNd555x3s2bMHNTU1uOeee7BkyRIEBgYiMDDQmDVSF8bxREREZCna3H22fPlyvPrqq3BycoK/vz/ee+89zJs3z5i1kRXI1m7xwZlnRERkXm0ORVu2bMEHH3yAH3/8Ed988w2+/fZbbN26FWq12pj1UReX22gzWCIiInNqcyhKT0/H/fffr/19ZGQkRCIRrl+/bpTCyDqw+4yIiCxFm0NRXV0dZDLdLy47OzvU1ja/ai5RW9wIRew+IyIi82rzQGtBEDB79mxIpTe+vKqqqvDEE0/A0dFRe2znzp2GrZC6NM3sM2+2FBERkZm1ORTNmjWrybFHHnnEoMWQdVGrBeSWsvuMiIgsQ5tD0SeffGLMOsgKFVXUoFYlAAC8ue8ZERGZmd4rWhMZiqbrzNNJAjsb/igSEZF58ZuIzCanoeuMu7YTEZElYCgis8lRcuYZERFZDoYiMhtN95lCzpYiIiIyP4YiMht2nxERkSVhKCKzyeVq1kREZEEYishssrmaNRERWRCGIjIbzZgithQREZElYCgis6hTqZFfxlBERESWg6GIzCK/rAaCANiIRfBwlJi7HCIiIoYiMg/NeCJvZynEYpGZqyEiImIoIjPJ0YQidp0REZGFYCgis9BMx1dw5hkREVkIhiIyC848IyIiS8NQRGaRzYUbiYjIwjAUkVnkNBpoTUREZAkYisgscrkZLBERWRiGIjILzWaw7D4jIiJLYfZQtG7dOgQFBUEmkyE8PBwnTpxo03nbt2+HSCTCpEmTmjx3/vx5TJgwAXK5HI6Ojhg+fDjS09MNXDm1V1WtCsUVtQAAH2eGIiIisgxmDUU7duxAXFwclixZgqSkJISEhCAqKgq5ubmtnpeamooXX3wRY8aMafLclStXMHr0aPTr1w8HDx7E2bNnsWjRIshk/PK1FJquM6mtGC72tmauhoiIqJ5IEATBXG8eHh6O4cOH4z//+Q8AQK1WIyAgAM888wxeeeWVZs9RqVS444478Oijj+LIkSMoLi7GN998o31+2rRpsLOzw2effdbuukpKSiCXy6FUKuHi4tLu61DzfkstxEPrExHo4YBDL401dzlERNRFdPT722wtRTU1NTh16hQiIyNvFCMWIzIyEomJiS2et2zZMnh7e2POnDlNnlOr1fj+++/Rp08fREVFwdvbG+Hh4TqhqTnV1dUoKSnReZDxaGaeseuMiIgsidlCUX5+PlQqFXx8fHSO+/j4IDs7u9lzEhISsHHjRmzYsKHZ53Nzc1FWVoa33noL0dHR+OmnnzB58mQ8+OCDOHToUIu1rFixAnK5XPsICAho/wejW9Is3OjN1ayJiMiCmH2gdVuVlpZi5syZ2LBhAzw9PZt9jVqtBgBMnDgRzz//PIYMGYJXXnkFDzzwANavX9/itRcuXAilUql9XLt2zSifgerlcOFGIiKyQGYb5erp6QkbGxvk5OToHM/JyYFCoWjy+itXriA1NRXjx4/XHtOEIFtbW6SkpCAgIAC2trYYMGCAzrn9+/dHQkJCi7VIpVJIpWy1MJUboYj3nIiILIfZWookEglCQ0MRHx+vPaZWqxEfH4+IiIgmr+/Xrx/OnTuH5ORk7WPChAkYO3YskpOTERAQAIlEguHDhyMlJUXn3IsXLyIwMNDon4nahi1FRERkicw6HzouLg6zZs1CWFgYRowYgTVr1qC8vByxsbEAgJiYGPj7+2PFihWQyWQYOHCgzvmurq4AoHP8pZdewtSpU3HHHXdg7Nix2LdvH7799lscPHjQVB+LbiGXm8ESEZEFMmsomjp1KvLy8rB48WJkZ2djyJAh2Ldvn3bwdXp6OsRi/RqzJk+ejPXr12PFihWYP38++vbti6+//hqjR482xkcgPQmCwM1giYjIIpl1nSJLxXWKjKe0qhaD/v0TAODPZVFwkHDxRiIiMoxOu04RWSfNdHxnmS0DERERWRSGIjKpXHadERGRhWIoIpPK5nR8IiKyUAxFZFKa7jNu8UFERJaGoYhMSrNGkTe7z4iIyMIwFJFJ5ZbWhyIFu8+IiMjCMBSRSeVw4UYiIrJQDEVkUtlKdp8REZFlYigikxEEQdt9xtlnRERkaRiKyGSKKmpRq6pfQN2bs8+IiMjCMBSRyWhmnnk4SiCx5Y8eERFZFn4zkclkczo+ERFZMIYiMplcrmZNREQWjKGITEYzHV/BliIiIrJADEVkMlzNmoiILBlDEZlMDrvPiIjIgjEUkclwM1giIrJkDEVkMjdaihiKiIjI8jAUkUnUqdTIL2toKZKz+4yIiCwPQxGZRH5ZDdQCYCMWwcORoYiIiCwPQxGZhKbrzMtJChuxyMzVEBERNcVQRCbBmWdERGTpGIrIJHJKG8YTcZA1ERFZKIYiMolczjwjIiILx1BEJpGtZPcZERFZNoYiMglN9xm3+CAiIkvFUEQmoek+42awRERkqRiKyCS4mjUREVk6hiIyuqpaFYoqagFwTBEREVkuhiIyuryG8UQSWzHk9nZmroaIiKh5DEVkdI0XbhSJuJo1ERFZJoYiMrqckvqWIg6yJiIiS8ZQREaX3dBSxOn4RERkyRiKyOi0q1k7MxQREZHlYigio+NmsERE1BkwFJHRaccUydlSRERElouhiIxO01Lkze4zIiKyYAxFZHTsPiMios7AIkLRunXrEBQUBJlMhvDwcJw4caJN523fvh0ikQiTJk3SOT579myIRCKdR3R0tBEqp1spq65DeY0KAGefERGRZTN7KNqxYwfi4uKwZMkSJCUlISQkBFFRUcjNzW31vNTUVLz44osYM2ZMs89HR0cjKytL+9i2bZsxyqdb0LQSOUlt4SS1NXM1RERELTN7KFq9ejXmzp2L2NhYDBgwAOvXr4eDgwM2bdrU4jkqlQozZszA0qVL0bNnz2ZfI5VKoVAotA83N7cWr1ddXY2SkhKdBxnG1bxyAOw6IyIiy2fWUFRTU4NTp04hMjJSe0wsFiMyMhKJiYktnrds2TJ4e3tjzpw5Lb7m4MGD8Pb2Rt++ffHkk0+ioKCgxdeuWLECcrlc+wgICGjfById35zOxPztpwEA/XxdzFwNERFR68waivLz86FSqeDj46Nz3MfHB9nZ2c2ek5CQgI0bN2LDhg0tXjc6OhpbtmxBfHw8Vq5ciUOHDuG+++6DSqVq9vULFy6EUqnUPq5du9b+D0WoqlXhla/P4rkdyaioUWFkT3csGT/A3GURERG1qlMN8igtLcXMmTOxYcMGeHp6tvi6adOmaX89aNAgDB48GL169cLBgwdxzz33NHm9VCqFVMruHUO4nFuGpz9PwoXsUohEwDN398az9/SGjZgbwRIRkWUzayjy9PSEjY0NcnJydI7n5ORAoVA0ef2VK1eQmpqK8ePHa4+p1WoAgK2tLVJSUtCrV68m5/Xs2ROenp64fPlys6GIDOOb05l4ddc5VNSo4OkkwZqpQzG6d8vhlYiIyJKYNRRJJBKEhoYiPj5eO61erVYjPj4eTz/9dJPX9+vXD+fOndM59tprr6G0tBTvvfdei2OBMjIyUFBQAF9fX4N/BgIqa1T4954/sONkfbdjRE8PvDdtCKfgExFRp2L27rO4uDjMmjULYWFhGDFiBNasWYPy8nLExsYCAGJiYuDv748VK1ZAJpNh4MCBOue7uroCgPZ4WVkZli5diilTpkChUODKlStYsGABgoODERUVZdLPZg0u55Zh3tYkpOTUd5fNv7s35rO7jIiIOiGzh6KpU6ciLy8PixcvRnZ2NoYMGYJ9+/ZpB1+np6dDLG77eHAbGxucPXsWn376KYqLi+Hn54dx48bh9ddf57ghA9t1OgP/2vV7Q3eZFO9NG4Lbg9ldRkREnZNIEATB3EVYmpKSEsjlciiVSri4cCr5zZrtLps+hHubERGRWXX0+9vsLUXUuVzOLcW8rafZXUZERF0OQxG12denMvDaN7+jspbdZURE1PUwFNEtVdaosGTP7/jiZAYAYFQvD6yZxu4yIiLqWhiKqFWXckox7/MkXMwpg0gEPHdPHzx9dzC7y4iIqMthKKIW3dxdtnbaEIxidxkREXVRDEXUREVNHRbv/gNfnarvLrs92APvTmV3GRERdW0MRaTjUk4pntqahEu57C4jIiLrwlBEWl+dysCihu4yL+f62WWjerG7jIiIrANDETXpLhsd7Il3pw6BlzNXACciIuvBUGTlLuaUYl5Dd5lYBDwX2QfzxrK7jIiIrA9DkRX78uQ1LNr9O6pq1fBylmLttKGI6OVh7rKIiIjMgqHIClXU1GHRN3/g6yR2lxEREWkwFFmZiw2zyy43dJc9H9kHT7G7jIiIiKHImnxx8hoWN3SXeTtLsXb6UIzsye4yIiIigKHIKlTU1OG1b37HzqRMAMCY3vXdZZ5O7C4jIiLSYCgyoYRL+fjfr2kY6O+Cgf5yDPSXGz2YpGTX712m6S6Lu7cPnrorGGJ2lxEREelgKDKhE1cLsO+PbOz7I1t7zFcuw21+cgzyl2OgvwsG+cvh7dLx7TQEQcCXJzOweA+7y4iIiNqCociEogf6wllmh9+vK3EuU4mr+eXIUlYhS1mFA+dztK/zcpbWhyQ/F9zmXx+YfOUyiERta90pr67Dom9+x87T7C4jIiJqK5EgCIK5i7A0JSUlkMvlUCqVcHFxMdr7lFXX4c/rJfg9U1n/uK7E5dwyqJv5E3F3lNR3ufm5NLQqydHNzb5JUErJLsVTW0/hSl45xCLghXF98eSdvdhdRkREXV5Hv78ZipphqlDUnIqaOpzPKtUGpXOZSlzKLYOqmaQkt7erH5/kVx+SiitqsPyH86iqVcPHpX4xxvBO0F1WXl4OJycnAEBZWRkcHR2Nfj1Dv6ehrt3SuYao11I/M5Ep8We1a+vo9ze7zyyMg8QWoYFuCA100x6rqlXhQnapTotSSnYplJW1OHq5AEcvF+hc444+Xnj34RB4sLuMiIiozRiKOgGZnQ2GBLhiSICr9lh1nQqXcsq0rUm/Xy9BXkkVZowMZHcZERFROzAUdVJSWxvttP5p5i6GiIioCxCbuwAiIiIiS8BQRERERASGIiIiIiIADEVEREREABiKiIiIiAAwFBEREREBYCgiIiIiAsBQRERERASAoYiIiIgIAEMREREREQCGIiIiIiIADEVEREREABiKiIiIiAAwFBEREREBAGzNXYAlEgQBAFBSUmLmSqxDeXm59tclJSVQqVRGv56h39NQ127pXEPUa6mfmciU+LPatWm+tzXf4/oSCe09swvLyMhAQECAucsgIiKidrh27Rq6deum93kMRc1Qq9W4fv06nJ2dIRKJTPa+JSUlCAgIwLVr1+Di4mKy9+3MeM/0w/ulH94v/fGe6Yf3S3+t3TNBEFBaWgo/Pz+IxfqPEGL3WTPEYnG7EqahuLi48C+HnnjP9MP7pR/eL/3xnumH90t/Ld0zuVze7mtyoDURERERGIqIiIiIADAUWRSpVIolS5ZAKpWau5ROg/dMP7xf+uH90h/vmX54v/RnzHvGgdZEREREYEsREREREQCGIiIiIiIADEVEREREABiKiIiIiAAwFJlFaWkpnnvuOQQGBsLe3h6jRo3Cb7/9pn1eEAQsXrwYvr6+sLe3R2RkJC5dumTGik3r8OHDGD9+PPz8/CASifDNN9/oPN+W+1NYWIgZM2bAxcUFrq6umDNnDsrKykz4KUznVvdr586dGDduHDw8PCASiZCcnNzkGlVVVZg3bx48PDzg5OSEKVOmICcnxzQfwAxau2e1tbV4+eWXMWjQIDg6OsLPzw8xMTG4fv26zjX4M3bDv//9b/Tr1w+Ojo5wc3NDZGQkjh8/rvMaa7pfwK3vWWNPPPEERCIR1qxZo3Pcmu7Zre7X7NmzIRKJdB7R0dE6rzHE/WIoMoPHHnsM+/fvx2effYZz585h3LhxiIyMRGZmJgDg7bffxtq1a7F+/XocP34cjo6OiIqKQlVVlZkrN43y8nKEhIRg3bp1zT7flvszY8YM/PHHH9i/fz++++47HD58GI8//ripPoJJ3ep+lZeXY/To0Vi5cmWL13j++efx7bff4ssvv8ShQ4dw/fp1PPjgg8Yq2exau2cVFRVISkrCokWLkJSUhJ07dyIlJQUTJkzQeR1/xm7o06cP/vOf/+DcuXNISEhAUFAQxo0bh7y8PO1rrOl+Abe+Zxq7du3Cr7/+Cj8/vybPWdM9a8v9io6ORlZWlvaxbds2necNcr8EMqmKigrBxsZG+O6773SODxs2TPjXv/4lqNVqQaFQCO+88472ueLiYkEqlQrbtm0zdblmB0DYtWuX9vdtuT9//vmnAED47bfftK/Zu3evIBKJhMzMTJPVbg4336/Grl69KgAQTp8+rXO8uLhYsLOzE7788kvtsfPnzwsAhMTERCNWaxlau2caJ06cEAAIaWlpgiDwZ+xW90upVAoAhAMHDgiCYN33SxBavmcZGRmCv7+/8PvvvwuBgYHCu+++q33Omu9Zc/dr1qxZwsSJE1s8x1D3iy1FJlZXVweVSgWZTKZz3N7eHgkJCbh69Sqys7MRGRmpfU4ulyM8PByJiYmmLtfitOX+JCYmwtXVFWFhYdrXREZGQiwWN2nSJ+DUqVOora3Vuaf9+vVD9+7d+TPXQKlUQiQSwdXVFQB/xlpTU1ODjz/+GHK5HCEhIQB4v5qjVqsxc+ZMvPTSS7jtttuaPM971tTBgwfh7e2Nvn374sknn0RBQYH2OUPdL4YiE3N2dkZERARef/11XL9+HSqVCv/73/+QmJiIrKwsZGdnAwB8fHx0zvPx8dE+Z83acn+ys7Ph7e2t87ytrS3c3d15D5uRnZ0NiUSi/cLX4M9cvaqqKrz88suYPn26dvNJ/ow19d1338HJyQkymQzvvvsu9u/fD09PTwC8X81ZuXIlbG1tMX/+/Gaf5z3TFR0djS1btiA+Ph4rV67EoUOHcN9990GlUgEw3P2yNWjV1CafffYZHn30Ufj7+8PGxgbDhg3D9OnTcerUKXOXRkSN1NbW4uGHH4YgCPjwww/NXY5FGzt2LJKTk5Gfn48NGzbg4YcfxvHjx5t8UVF96+x7772HpKQkiEQic5fTKUybNk3760GDBmHw4MHo1asXDh48iHvuucdg78OWIjPo1asXDh06hLKyMly7dg0nTpxAbW0tevbsCYVCAQBNZv7k5ORon7Nmbbk/CoUCubm5Os/X1dWhsLCQ97AZCoUCNTU1KC4u1jlu7T9zmkCUlpaG/fv3a1uJAP6MNcfR0RHBwcEYOXIkNm7cCFtbW2zcuBEA79fNjhw5gtzcXHTv3h22trawtbVFWloaXnjhBQQFBQHgPbuVnj17wtPTE5cvXwZguPvFUGRGjo6O8PX1RVFREX788UdMnDgRPXr0gEKhQHx8vPZ1JSUlOH78OCIiIsxYrWVoy/2JiIhAcXGxTsvbzz//DLVajfDwcJPXbOlCQ0NhZ2enc09TUlKQnp5utT9zmkB06dIlHDhwAB4eHjrP82fs1tRqNaqrqwHwft1s5syZOHv2LJKTk7UPPz8/vPTSS/jxxx8B8J7dSkZGBgoKCuDr6wvAcPeL3Wdm8OOPP0IQBPTt2xeXL1/GSy+9hH79+iE2NhYikQjPPfcc3njjDfTu3Rs9evTAokWL4Ofnh0mTJpm7dJMoKyvTpn+gfnB1cnIy3N3d0b1791ven/79+yM6Ohpz587F+vXrUVtbi6effhrTpk1rdtprZ3er+1VYWIj09HTtOjspKSkA6v9lpVAoIJfLMWfOHMTFxcHd3R0uLi545plnEBERgZEjR5rlMxlba/fM19cXf//735GUlITvvvsOKpVKOybB3d0dEomEP2ON7peHhweWL1+OCRMmwNfXF/n5+Vi3bh0yMzPx0EMPAbC+v5PArf9e3hy07ezsoFAo0LdvXwDWd89au1/u7u5YunQppkyZAoVCgStXrmDBggUIDg5GVFQUAAPeL/0mypEh7NixQ+jZs6cgkUgEhUIhzJs3TyguLtY+r1arhUWLFgk+Pj6CVCoV7rnnHiElJcWMFZvWL7/8IgBo8pg1a5YgCG27PwUFBcL06dMFJycnwcXFRYiNjRVKS0vN8GmM71b365NPPmn2+SVLlmivUVlZKTz11FOCm5ub4ODgIEyePFnIysoyzwcygdbumWbpguYev/zyi/Ya/Bmrv1+VlZXC5MmTBT8/P0EikQi+vr7ChAkThBMnTuhcw5rulyDc+u/lzW6eki8I1nXPWrtfFRUVwrhx4wQvLy/Bzs5OCAwMFObOnStkZ2frXMMQ90skCILQ9ghFRERE1DVxTBERERERGIqIiIiIADAUEREREQFgKCIiIiICwFBEREREBIChiIiIiAgAQxERERERAIYiIiIiIgAMRURdkkgkwjfffGPuMqiNZs+e3aZtfGbOnIk333zT+AW14s8//0S3bt1QXl5u1jqIjIGhiKiTmD17NkQiEUQiEezs7ODj44N7770XmzZtglqt1nltVlYW7rvvvjZdt7MFqJKSEvzrX/9Cv379IJPJoFAoEBkZiZ07d6IrL9B/5swZ/PDDD5g/f7722F133YXnnntO53XvvfcepFIptm/fbpQ6BgwYgJEjR2L16tVGuT6ROTEUEXUi0dHRyMrKQmpqKvbu3YuxY8fi2WefxQMPPIC6ujrt6xQKBaRSqRkrNY7i4mKMGjUKW7ZswcKFC5GUlITDhw9j6tSpWLBgAZRKpdlqq6mpMer133//fTz00ENwcnJq8TVLlizBq6++it27d2PatGlGqyU2NhYffvihzs8cUVfAUETUiUilUigUCvj7+2PYsGHaL8C9e/di8+bN2tc1bv2pqanB008/DV9fX8hkMgQGBmLFihUAgKCgIADA5MmTIRKJtL+/cuUKJk6cCB8fHzg5OWH48OE4cOCATi1BQUF488038eijj8LZ2Rndu3fHxx9/rPOajIwMTJ8+He7u7nB0dERYWBiOHz+ufX737t0YNmwYZDIZevbsiaVLl7b6Rfvqq68iNTUVx48fx6xZszBgwAD06dMHc+fORXJysjYwFBUVISYmBm5ubnBwcMB9992HS5cuAahvabK3t8fevXt1rr1r1y44OzujoqICAHDt2jU8/PDDcHV1hbu7OyZOnIjU1FTt6zVdXsuXL4efn592d/NbnadSqRAXFwdXV1d4eHhgwYIFt2zhUqlU+OqrrzB+/PhmnxcEAc888wzWrl2L/fv3Izo6WvtcUFAQ3njjDcTExMDJyQmBgYHYs2cP8vLyMHHiRDg5OWHw4ME4efKk9py0tDSMHz8ebm5ucHR0xG233YYffvhB+/y9996LwsJCHDp0qNW6iTobhiKiTu7uu+9GSEgIdu7c2ezza9euxZ49e/DFF18gJSUFW7du1Yaf3377DQDwySefICsrS/v7srIy3H///YiPj8fp06cRHR2N8ePHIz09Xefaq1atQlhYGE6fPo2nnnoKTz75JFJSUrTXuPPOO5GZmYk9e/bgzJkzWLBggbar78iRI4iJicGzzz6LP//8Ex999BE2b96M5cuXN/s51Go1tm/fjhkzZsDPz6/J805OTrC1tQVQH1hOnjyJPXv2IDExEYIg4P7770dtbS1cXFzwwAMP4PPPP9c5f+vWrZg0aRIcHBxQW1uLqKgoODs748iRIzh69CicnJwQHR2t0yIUHx+PlJQU7N+/H999912bzlu1ahU2b96MTZs2ISEhAYWFhdi1a1fLf8AAzp49C6VSibCwsCbP1dXV4ZFHHsFXX32FQ4cOYdSoUU1e8+677+L222/H6dOn8be//Q0zZ85ETEwMHnnkESQlJaFXr16IiYnRhrN58+ahuroahw8fxrlz57By5UqdFiqJRIIhQ4bgyJEjrdZN1OkIRNQpzJo1S5g4cWKzz02dOlXo37+/9vcAhF27dgmCIAjPPPOMcPfddwtqtbrZcxu/tjW33Xab8P7772t/HxgYKDzyyCPa36vVasHb21v48MMPBUEQhI8++khwdnYWCgoKmr3ePffcI7z55ps6xz777DPB19e32dfn5OQIAITVq1e3WufFixcFAMLRo0e1x/Lz8wV7e3vhiy++EARBEHbt2iU4OTkJ5eXlgiAIglKpFGQymbB3715tHX379tW5Z9XV1YK9vb3w448/CoJQ/+fh4+MjVFdX69R/q/N8fX2Ft99+W/t8bW2t0K1btxb/bDX12tjYNPkzvPPOOwWJRCJIJBLh/PnzzZ57859TVlaWAEBYtGiR9lhiYqIAQMjKyhIEQRAGDRok/Pvf/26xHkEQhMmTJwuzZ89u9TVEnQ1bioi6AEEQIBKJmn1u9uzZSE5ORt++fTF//nz89NNPt7xeWVkZXnzxRfTv3x+urq5wcnLC+fPnm7QUDR48WPtrkUgEhUKB3NxcAEBycjKGDh0Kd3f3Zt/jzJkzWLZsGZycnLSPuXPnIisrS9uFdfNnbIvz58/D1tYW4eHh2mMeHh7o27cvzp8/DwC4//77YWdnhz179gAAvv76a7i4uCAyMlJb2+XLl+Hs7Kytzd3dHVVVVbhy5Yr2uoMGDYJEItH5TK2dp1QqkZWVpVObra1tsy1AjVVWVkIqlTb7Zzx69Gg4OTlh0aJFLXY9Nv5z8vHx0dZ+8zHNn938+fPxxhtv4Pbbb8eSJUtw9uzZJte0t7dv9s+JqDOzNXcBRNRx58+fR48ePZp9btiwYbh69Sr27t2LAwcO4OGHH0ZkZCS++uqrFq/34osvYv/+/fi///s/BAcHw97eHn//+9+bDCa2s7PT+b1IJNJ2j9nb27dac1lZGZYuXYoHH3ywyXMymazJMS8vL7i6uuLChQutXrctJBIJ/v73v+Pzzz/HtGnT8Pnnn2Pq1Kna7reysjKEhoZi69atzdah4ejoqPNcW8/Tl6enJyoqKlBTU6MTwoD6cLNq1SpERkZi6tSp2LFjh/ZzaDT+c9IEq+aOaf7sHnvsMURFReH777/HTz/9hBUrVmDVqlV45plntOcUFhaiV69e7f5MRJaILUVEndzPP/+Mc+fOYcqUKS2+xsXFBVOnTsWGDRuwY8cOfP311ygsLARQ/+WoUql0Xn/06FHMnj0bkydPxqBBg6BQKHQGC7fF4MGDkZycrH2fmw0bNgwpKSkIDg5u8hCLm/6vSSwWY9q0adi6dSuuX7/e5PmysjLU1dWhf//+qKur0xnQXVBQgJSUFAwYMEB7bMaMGdi3bx/++OMP/Pzzz5gxY4ZObZcuXYK3t3eT2uRyeYuf+VbnyeVy+Pr66tRWV1eHU6dOtXovhwwZAqB+jaCWno+Pj8fhw4fx8MMPo7a2ttXrtUVAQACeeOIJ7Ny5Ey+88AI2bNig8/zvv/+OoUOHdvh9iCwJQxFRJ1JdXY3s7GxkZmYiKSkJb775JiZOnIgHHngAMTExzZ6zevVqbNu2DRcuXMDFixfx5ZdfQqFQwNXVFUD97KT4+HhkZ2ejqKgIANC7d2/s3LkTycnJOHPmDP7xj380WQvpVqZPnw6FQoFJkybh6NGj+Ouvv/D1118jMTERALB48WJs2bIFS5cuxR9//IHz589j+/bteO2111q85vLlyxEQEIDw8HBs2bIFf/75Jy5duoRNmzZh6NChKCsrQ+/evTFx4kTMnTsXCQkJOHPmDB555BH4+/tj4sSJ2mvdcccdUCgUmDFjBnr06KHTpTVjxgx4enpi4sSJOHLkCK5evYqDBw9i/vz5yMjIaLG+tpz37LPP4q233sI333yDCxcu4KmnnkJxcXGr99LLywvDhg1DQkJCi68JCQnBzz//jISEhA4Ho+eeew4//vgjrl69iqSkJPzyyy/o37+/9vnU1FRkZmZquxuJugqGIqJOZN++ffD19UVQUBCio6Pxyy+/YO3atdi9ezdsbGyaPcfZ2Rlvv/02wsLCMHz4cKSmpuKHH37QtsasWrUK+/fvR0BAgPZf/qtXr4abmxtGjRqF8ePHIyoqCsOGDdOrVolEgp9++gne3t64//77MWjQILz11lvaOqOiovDdd9/hp59+wvDhwzFy5Ei8++67CAwMbPGa7u7u+PXXX/HII4/gjTfewNChQzFmzBhs27YN77zzjrYV55NPPkFoaCgeeOABREREQBAE/PDDD026jKZPn44zZ87otBIBgIODAw4fPozu3bvjwQcfRP/+/TFnzhxUVVXBxcWlxfract4LL7yAmTNnYtasWYiIiICzszMmT558y/v52GOPNdst19igQYPw888/49ixY3jooYfavXaSSqXCvHnz0L9/f0RHR6NPnz744IMPtM9v27YN48aNa/XPiqgzEgltHb1IRERmU1lZib59+2LHjh2IiIgwWx01NTXo3bs3Pv/8c9x+++1mq4PIGNhSRETUCdjb22PLli3Iz883ax3p6el49dVXGYioS2JLERERERHYUkREREQEgKGIiIiICABDEREREREAhiIiIiIiAAxFRERERAAYioiIiIgAMBQRERERAWAoIiIiIgLAUEREREQEAPh/e7P42B9gVjkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "f_names = [('Goal Scored', 'Distance Covered (Kms)')]\n",
        "# Similar to previous PDP plot except we use tuple of features instead of single feature\n",
        "disp4 = PartialDependenceDisplay.from_estimator(tree_model, val_X, f_names, ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "o-yd6q7vpz_H",
        "outputId": "e3de3aef-e5d3-4f93-ddf5-7d0e6faf1a9a"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For example, we see the highest predictions when a team scores at least 1 goal and they run a total distance close to 100km. If they score 0 goals, distance covered doesn't matter. Can you see this by tracing through the decision tree with 0 goals?\n",
        "\n",
        "But distance can impact predictions if they score goals. Make sure you can see this from the 2D partial dependence plot. Can you see this pattern in the decision tree too?"
      ],
      "metadata": {
        "id": "9UQ82dXyqOuE"
      }
    }
  ]
}