Chapter_08_EnbPI.ipynb

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          ]
        }
      ],
      "source": [
        "!pip install aws-fortuna"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.ensemble import HistGradientBoostingRegressor\n",
        "\n",
        "from fortuna.conformal import EnbPI\n",
        "from fortuna.metric.regression import prediction_interval_coverage_probability"
      ],
      "metadata": {
        "id": "CoQ60dzpqGL-"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import fetch_openml\n",
        "\n",
        "bike_sharing = fetch_openml(\n",
        "    \"Bike_Sharing_Demand\", version=2, as_frame=True, parser=\"pandas\"\n",
        ")\n",
        "df = bike_sharing.frame"
      ],
      "metadata": {
        "id": "e7TdwJB8qD5C"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "b2fepHelqLCf",
        "outputId": "74db7f86-00f7-4ac9-ac4b-6473b9484931"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   season  year  month  hour holiday  weekday workingday weather  temp  \\\n",
              "0  spring     0      1     0   False        6      False   clear  9.84   \n",
              "1  spring     0      1     1   False        6      False   clear  9.02   \n",
              "2  spring     0      1     2   False        6      False   clear  9.02   \n",
              "3  spring     0      1     3   False        6      False   clear  9.84   \n",
              "4  spring     0      1     4   False        6      False   clear  9.84   \n",
              "\n",
              "   feel_temp  humidity  windspeed  count  \n",
              "0     14.395      0.81        0.0     16  \n",
              "1     13.635      0.80        0.0     40  \n",
              "2     13.635      0.80        0.0     32  \n",
              "3     14.395      0.75        0.0     13  \n",
              "4     14.395      0.75        0.0      1  "
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>season</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>hour</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>feel_temp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>spring</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>6</td>\n",
              "      <td>False</td>\n",
              "      <td>clear</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>spring</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>6</td>\n",
              "      <td>False</td>\n",
              "      <td>clear</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spring</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>6</td>\n",
              "      <td>False</td>\n",
              "      <td>clear</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>spring</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
              "      <td>6</td>\n",
              "      <td>False</td>\n",
              "      <td>clear</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>spring</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>False</td>\n",
              "      <td>6</td>\n",
              "      <td>False</td>\n",
              "      <td>clear</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-32c4d22f-8a27-49e0-946c-bd72e64ce9b3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-32c4d22f-8a27-49e0-946c-bd72e64ce9b3 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-32c4d22f-8a27-49e0-946c-bd72e64ce9b3');\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",
              "\n",
              "\n",
              "<div id=\"df-f3674fec-27f4-45a8-81ed-95c410318071\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f3674fec-27f4-45a8-81ed-95c410318071')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f3674fec-27f4-45a8-81ed-95c410318071 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vvIWLfeOtuPT",
        "outputId": "83b741f5-366a-4b9e-b54e-f6e562541bee"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17379, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby([\"weekday\", \"hour\"])[\"count\"].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ClP-NCWnqxtp",
        "outputId": "3886b654-0e2f-4c9a-973c-cd410310f264"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "weekday  hour\n",
              "0        0        93.990385\n",
              "         1        77.432692\n",
              "         2        61.627451\n",
              "         3        31.144231\n",
              "         4         9.362745\n",
              "                    ...    \n",
              "6        19      239.932692\n",
              "         20      180.865385\n",
              "         21      156.000000\n",
              "         22      139.663462\n",
              "         23      115.855769\n",
              "Name: count, Length: 168, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(12, 2))\n",
        "average_week_demand = df.groupby([\"weekday\", \"hour\"])[\"count\"].mean()\n",
        "average_week_demand.plot(ax=ax)\n",
        "_ = ax.set(\n",
        "    title=\"Average hourly bike demand during the week\",\n",
        "    xticks=[i * 24 for i in range(7)],\n",
        "    xticklabels=[\"Sun\", \"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\"],\n",
        "    xlabel=\"Time of the week\",\n",
        "    ylabel=\"Number of bike rentals\",\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "4cSbP4M0qS_h",
        "outputId": "33bac597-4ed2-488f-adfa-e2ad1642a803"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.sample(500)\n",
        "y = df[\"count\"] / df[\"count\"].max()\n",
        "X = df.drop(\"count\", axis=\"columns\")\n",
        "X_train, X_test = train_test_split(X, test_size=0.2, shuffle=False)\n",
        "y_train, y_test = train_test_split(y, test_size=0.2, shuffle=False)"
      ],
      "metadata": {
        "id": "o6-RyuDVqwhb"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKd1AN0Sr5zU",
        "outputId": "f4d743d5-11e4-490c-f064-b3353569454c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500,)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data bootstrapping"
      ],
      "metadata": {
        "id": "SQ_jeT1prAlU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "EnbPI requires bootstrapping the data, i.e. sampling with replacement random subsets of the time series and training a model for each of these samples."
      ],
      "metadata": {
        "id": "DCISOnTNrHD7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class DataFrameBootstrapper:\n",
        "    def __init__(self, n_samples: int):\n",
        "        self.n_samples = n_samples\n",
        "\n",
        "    def __call__(\n",
        "        self, X: np.ndarray, y: np.ndarray\n",
        "    ) -> tuple[np.ndarray, list[tuple[np.ndarray, np.ndarray]]]:\n",
        "        indices = np.random.choice(y.shape[0], size=(self.n_samples, y.shape[0]))\n",
        "        return indices, [(X.iloc[idx], y.iloc[idx]) for idx in indices]"
      ],
      "metadata": {
        "id": "fHB-rH-kq8kI"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_bs_samples = 10\n",
        "bs_indices, bs_train_data = DataFrameBootstrapper(n_samples=n_bs_samples)(\n",
        "    X_train, y_train\n",
        ")"
      ],
      "metadata": {
        "id": "98mH9VwBrOX_"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# returns 10 boostrap samples from the training set\n",
        "bs_indices.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nS8LpddpsYn4",
        "outputId": "e6665e01-5a36-4858-eba7-66d116503d59"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10, 400)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bs_indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kqsvx6ce8tnN",
        "outputId": "9e844456-6834-467e-90a2-895bda4e259d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[376,  53, 126, ..., 201, 396, 321],\n",
              "       [321,  47, 146, ..., 252,  45, 102],\n",
              "       [244, 258,  84, ..., 326,  75,  47],\n",
              "       ...,\n",
              "       [ 40, 273, 201, ..., 334, 333,  61],\n",
              "       [196, 239, 262, ..., 167, 168,  47],\n",
              "       [ 24, 235, 317, ..., 249, 303, 214]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tu6jax1ZsZn7",
        "outputId": "d20440e0-852c-4bea-f212-150bad8fde61"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(bs_train_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2vorlBrsn6U",
        "outputId": "8eadf470-0993-46f2-ac3c-3b4d14fe824d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# first bootstrap sample - X\n",
        "bs_train_data[0][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "raAlWZQS9A8H",
        "outputId": "65f929a0-72c8-4e6c-b27c-f3d17133aead"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       season  year  month  hour holiday  weekday workingday weather   temp  \\\n",
              "16864  winter     1     12    11   False        1       True    rain  18.86   \n",
              "14012    fall     1      8     1   False        0      False   clear  25.42   \n",
              "11443  summer     1      4     0   False        5       True   clear  22.14   \n",
              "6325   winter     0      9     3   False        1       True   misty  25.42   \n",
              "8490   spring     0     12    11   False        0      False   clear  12.30   \n",
              "...       ...   ...    ...   ...     ...      ...        ...     ...    ...   \n",
              "11931  summer     1      5     8   False        4       True   clear  21.32   \n",
              "4606     fall     0      7    20   False        5       True   clear  28.70   \n",
              "10823  summer     1      4     2   False        0      False   misty  14.76   \n",
              "15921  winter     1     11     2   False        4       True   clear  13.12   \n",
              "15264  winter     1     10     5   False        3       True   misty  25.42   \n",
              "\n",
              "       feel_temp  humidity  windspeed  \n",
              "16864     22.725      0.94    15.0013  \n",
              "14012     29.545      0.78     8.9981  \n",
              "11443     25.760      0.68    36.9974  \n",
              "6325      27.275      0.94     0.0000  \n",
              "8490      15.150      0.70    11.0014  \n",
              "...          ...       ...        ...  \n",
              "11931     25.000      0.55    30.0026  \n",
              "4606      32.575      0.51    15.0013  \n",
              "10823     17.425      0.76    11.0014  \n",
              "15921     16.665      0.66     8.9981  \n",
              "15264     27.275      0.94     7.0015  \n",
              "\n",
              "[400 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d81279f7-903c-44a9-bf3d-6bfa419a1d5e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>season</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>hour</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>feel_temp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16864</th>\n",
              "      <td>winter</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>11</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>rain</td>\n",
              "      <td>18.86</td>\n",
              "      <td>22.725</td>\n",
              "      <td>0.94</td>\n",
              "      <td>15.0013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14012</th>\n",
              "      <td>fall</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>clear</td>\n",
              "      <td>25.42</td>\n",
              "      <td>29.545</td>\n",
              "      <td>0.78</td>\n",
              "      <td>8.9981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11443</th>\n",
              "      <td>summer</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>5</td>\n",
              "      <td>True</td>\n",
              "      <td>clear</td>\n",
              "      <td>22.14</td>\n",
              "      <td>25.760</td>\n",
              "      <td>0.68</td>\n",
              "      <td>36.9974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6325</th>\n",
              "      <td>winter</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>misty</td>\n",
              "      <td>25.42</td>\n",
              "      <td>27.275</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8490</th>\n",
              "      <td>spring</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>11</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>clear</td>\n",
              "      <td>12.30</td>\n",
              "      <td>15.150</td>\n",
              "      <td>0.70</td>\n",
              "      <td>11.0014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11931</th>\n",
              "      <td>summer</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>False</td>\n",
              "      <td>4</td>\n",
              "      <td>True</td>\n",
              "      <td>clear</td>\n",
              "      <td>21.32</td>\n",
              "      <td>25.000</td>\n",
              "      <td>0.55</td>\n",
              "      <td>30.0026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4606</th>\n",
              "      <td>fall</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>20</td>\n",
              "      <td>False</td>\n",
              "      <td>5</td>\n",
              "      <td>True</td>\n",
              "      <td>clear</td>\n",
              "      <td>28.70</td>\n",
              "      <td>32.575</td>\n",
              "      <td>0.51</td>\n",
              "      <td>15.0013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10823</th>\n",
              "      <td>summer</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>misty</td>\n",
              "      <td>14.76</td>\n",
              "      <td>17.425</td>\n",
              "      <td>0.76</td>\n",
              "      <td>11.0014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15921</th>\n",
              "      <td>winter</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>4</td>\n",
              "      <td>True</td>\n",
              "      <td>clear</td>\n",
              "      <td>13.12</td>\n",
              "      <td>16.665</td>\n",
              "      <td>0.66</td>\n",
              "      <td>8.9981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15264</th>\n",
              "      <td>winter</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>False</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>misty</td>\n",
              "      <td>25.42</td>\n",
              "      <td>27.275</td>\n",
              "      <td>0.94</td>\n",
              "      <td>7.0015</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 12 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d81279f7-903c-44a9-bf3d-6bfa419a1d5e 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-d81279f7-903c-44a9-bf3d-6bfa419a1d5e');\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",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-693c11d0-6f5d-4a51-8996-f2f1b139ede5')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-693c11d0-6f5d-4a51-8996-f2f1b139ede5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# first bootstrap sample - y\n",
        "bs_train_data[0][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1U1IT7AX84R_",
        "outputId": "d3a03e24-d238-4941-9d13-0f1a2a289a83"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16864    0.150901\n",
              "14012    0.127252\n",
              "11443    0.082207\n",
              "6325     0.005631\n",
              "8490     0.095721\n",
              "           ...   \n",
              "11931    0.713964\n",
              "4606     0.386261\n",
              "10823    0.091216\n",
              "15921    0.022523\n",
              "15264    0.038288\n",
              "Name: count, Length: 400, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get a boolean series where True indicates the index is duplicated\n",
        "duplicate_indices = bs_train_data[0][0].index.duplicated(keep=False)\n",
        "\n",
        "# Print the duplicated indices\n",
        "print(bs_train_data[0][0].index[duplicate_indices])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lWfqu-2JtUah",
        "outputId": "91d6773b-8792-44a4-ccb9-ac319cd9cb76"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Int64Index([ 8490, 15992,  6257, 13288, 13755, 11931, 10703,  3047, 17219,\n",
            "            10120,\n",
            "            ...\n",
            "             8592, 15881, 15642, 11398, 14076, 15668,  3345, 11931,  4606,\n",
            "            15264],\n",
            "           dtype='int64', length=237)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bs_train_data[0][0].loc[bs_train_data[0][0].index[duplicate_indices][0]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "eZDCXMjiuMgp",
        "outputId": "a419515d-c766-446a-cb6e-22483b97b7db"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      season  year  month  hour holiday  weekday workingday weather  temp  \\\n",
              "8490  spring     0     12    11   False        0      False   clear  12.3   \n",
              "8490  spring     0     12    11   False        0      False   clear  12.3   \n",
              "\n",
              "      feel_temp  humidity  windspeed  \n",
              "8490      15.15       0.7    11.0014  \n",
              "8490      15.15       0.7    11.0014  "
            ],
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>season</th>\n",
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              "      <th>holiday</th>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-2769546f-7116-4cb5-b3ec-282b8bd6e0ba 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-2769546f-7116-4cb5-b3ec-282b8bd6e0ba');\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",
              "\n",
              "\n",
              "<div id=\"df-4639188f-4435-4dc0-99b7-f03ab64082ef\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4639188f-4435-4dc0-99b7-f03ab64082ef')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4639188f-4435-4dc0-99b7-f03ab64082ef button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Definition"
      ],
      "metadata": {
        "id": "0zKIYtRg_XGY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_columns = [\"weather\", \"season\", \"holiday\", \"workingday\"]\n",
        "categories = [\n",
        "    [\"clear\", \"misty\", \"rain\", \"heavy_rain\"],\n",
        "    [\"spring\", \"summer\", \"fall\", \"winter\"],\n",
        "    [\"False\", \"True\"],\n",
        "    [\"False\", \"True\"],\n",
        "]\n",
        "ordinal_encoder = OrdinalEncoder(categories=categories)\n",
        "\n",
        "gbrt_pipeline = make_pipeline(\n",
        "    ColumnTransformer(\n",
        "        transformers=[\n",
        "            (\"categorical\", ordinal_encoder, categorical_columns),\n",
        "        ],\n",
        "        remainder=\"passthrough\",\n",
        "        verbose_feature_names_out=False,\n",
        "    ),\n",
        "    HistGradientBoostingRegressor(\n",
        "        categorical_features=categorical_columns,\n",
        "    ),\n",
        ").set_output(transform=\"pandas\")"
      ],
      "metadata": {
        "id": "AGv-WVKq_bNS"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model training for each bootstrap sample"
      ],
      "metadata": {
        "id": "9XxsJhwD_n5T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bs_train_preds = np.zeros((n_bs_samples, X_train.shape[0]))\n",
        "bs_test_preds = np.zeros((n_bs_samples, X_test.shape[0]))\n",
        "for i, batch in enumerate(bs_train_data):\n",
        "    gbrt_pipeline.fit(*batch)\n",
        "    bs_train_preds[i] = gbrt_pipeline.predict(X_train)\n",
        "    bs_test_preds[i] = gbrt_pipeline.predict(X_test)"
      ],
      "metadata": {
        "id": "2GA4uSk2_gEt"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conformal_intervals = EnbPI().conformal_interval(\n",
        "    bootstrap_indices=bs_indices,\n",
        "    bootstrap_train_preds=bs_train_preds,\n",
        "    bootstrap_test_preds=bs_test_preds,\n",
        "    train_targets=y_train.values,\n",
        "    error=0.05,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lk-gAR5S_s-5",
        "outputId": "9c8ca552-e655-47af-d089-b93a5b8f8653"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:jax._src.xla_bridge:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# In order to evaluate conditional coverage, we measure the Prediction Interval Coverage Probability (PICP), i.e. the percentage of\n",
        "# test target variables that actually falls within the conformal intervals. We further measure the percentage of intervals that contain the point\n",
        "# predictions given by the model. Finally, we measure the size of the conformal intervals, which EnbPI takes to be the same for every intervals\n",
        "# if no online feedback is provided, like in this case."
      ],
      "metadata": {
        "id": "iUi9MCBV_8RY"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\n",
        "    \"Percentage of intervals containing average bootstrap predictions: \"\n",
        "    f\"{prediction_interval_coverage_probability(*conformal_intervals.T, bs_test_preds.mean(0))}.\"\n",
        ")\n",
        "print(\n",
        "    \"Percentage of intervals containing true targets: \"\n",
        "    f\"{prediction_interval_coverage_probability(*conformal_intervals.T, y_test.values)}.\"\n",
        ")\n",
        "print(f\"Size of the conformal intervals: {np.diff(conformal_intervals)[0][0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CmNVlBBMAHJi",
        "outputId": "84ba5809-2b92-423d-8cde-7fff784845c1"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of intervals containing average bootstrap predictions: 1.0.\n",
            "Percentage of intervals containing true targets: 0.949999988079071.\n",
            "Size of the conformal intervals: 0.444624125957489\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the example above, EnbPI assumes that all the predictions in the test dataset are done at once, without any online feedback from incoming observations of the test targets. As a consequence, the size of the conformal intervals is the same for all test data points."
      ],
      "metadata": {
        "id": "ulBFhJzzDV-l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def weakly_avg(x):\n",
        "    s = x.shape[0] // 7\n",
        "    x = x[: s * 7]\n",
        "    return x.reshape(7, s, *x.shape[1:]).mean(0)\n",
        "\n",
        "\n",
        "weekly_avg_test = weakly_avg(y_test.values)\n",
        "n_weeks = weekly_avg_test.shape[0]\n",
        "\n",
        "plt.figure(figsize=(12, 2))\n",
        "plt.plot(weakly_avg(y_test.values), label=\"weekly averaged true test target\")\n",
        "plt.fill_between(\n",
        "    np.arange(n_weeks),\n",
        "    *weakly_avg(conformal_intervals).T,\n",
        "    alpha=0.5,\n",
        "    color=\"C0\",\n",
        "    label=\"weekly averaged conformal interval\",\n",
        ")\n",
        "plt.xlabel(\"test weeks\", fontsize=14)\n",
        "plt.legend(fontsize=11, loc=\"upper right\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "EIWxqJxzAJSv",
        "outputId": "50908b8b-fb81-4daa-91d2-3fe2ace915c6"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7a713060ad10>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EnbPI with online feedback"
      ],
      "metadata": {
        "id": "I8VJ9u0_DjCQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 1\n",
        "conformal_intervals2 = np.zeros((len(y_test), 2))\n",
        "for i in range(0, len(y_test), batch_size):\n",
        "    if i == 0:\n",
        "        conformal_intervals2[:batch_size], train_residuals = EnbPI().conformal_interval(\n",
        "            bootstrap_indices=bs_indices,\n",
        "            bootstrap_train_preds=bs_train_preds,\n",
        "            bootstrap_test_preds=bs_test_preds[:, :batch_size],\n",
        "            train_targets=y_train.values,\n",
        "            error=0.05,\n",
        "            return_residuals=True,\n",
        "        )\n",
        "    else:\n",
        "        (\n",
        "            conformal_intervals2[i : i + batch_size],\n",
        "            train_residuals,\n",
        "        ) = EnbPI().conformal_interval_from_residuals(\n",
        "            train_residuals=train_residuals,\n",
        "            bootstrap_new_train_preds=bs_test_preds[:, i - batch_size : i],\n",
        "            bootstrap_new_test_preds=bs_test_preds[:, i : i + batch_size],\n",
        "            new_train_targets=y_test.values[i - batch_size : i],\n",
        "            error=0.05,\n",
        "        )"
      ],
      "metadata": {
        "id": "CAqXY5dlDLRb"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Similarly as done above, we compute the percentage of predictions and true test targets falling within the conformal intervals. Again, it is good to see that all conformal intervals include the point predictions. Also, the percentage of intervals containing the true targets increased to around 83%, getting closer to the desired coverage of 95%."
      ],
      "metadata": {
        "id": "XXIm5MXuDuNW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\n",
        "    \"Percentage of intervals containing average bootstrap predictions: \"\n",
        "    f\"{prediction_interval_coverage_probability(*conformal_intervals2.T, bs_test_preds.mean(0))}.\"\n",
        ")\n",
        "print(\n",
        "    \"Percentage of intervals containing true targets: \"\n",
        "    f\"{prediction_interval_coverage_probability(*conformal_intervals2.T, y_test.values)}.\"\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLTd8j_GDtSW",
        "outputId": "ebadd0a5-53ba-4b57-ff82-28d487fb6086"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of intervals containing average bootstrap predictions: 1.0.\n",
            "Percentage of intervals containing true targets: 0.9300000071525574.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 2))\n",
        "plt.plot(weakly_avg(y_test.values), label=\"weekly averaged true test target\")\n",
        "plt.fill_between(\n",
        "    np.arange(n_weeks),\n",
        "    *weakly_avg(conformal_intervals2).T,\n",
        "    alpha=0.5,\n",
        "    color=\"C0\",\n",
        "    label=\"weekly averaged conformal interval\",\n",
        ")\n",
        "plt.xlabel(\"test weeks\", fontsize=14)\n",
        "plt.legend(fontsize=11, loc=\"upper right\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "DB81XarvDyxY",
        "outputId": "bf879979-49f7-4066-ef43-ae4eeca5b98b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7a713479a350>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The following plot compares the size of the intervals without and with online feedback.\n",
        "plt.figure(figsize=(12, 2))\n",
        "plt.title(\"Conformal interval size comparison\")\n",
        "plt.plot(np.diff(conformal_intervals), label=\"without online feedback\")\n",
        "plt.plot(np.diff(conformal_intervals2), label=\"with online feedback\")\n",
        "plt.xlabel(\"test set\", fontsize=14)\n",
        "plt.ylabel(\"size\", fontsize=14)\n",
        "plt.legend(fontsize=12)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "XjwczhwdD8_3",
        "outputId": "dfd8d7cb-7b58-4601-d048-7ef1ad1d2c59"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7a71303325f0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x200 with 1 Axes>"
            ],
            "image/png": 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33pvtOPPz94KIyO1Eyb2IiGSqdevWzJkzh23btvHggw9mWrdcuXKYzWYOHTpk7aEEiIyMJDo62rry/a3y9/enSJEiHDx4MM22AwcO4ODgkKbHMy8sXbqUHj168N5771nLrl27lmYF8PyQ0RcqISEhAAQEBNCsWbNbOkanTp348MMPiY2NZfHixQQHB/PAAw9Yt2/cuJELFy7wzTff0LBhQ2v50aNHb+m4DzzwAA888ADjx4/n888/p2vXrnz55Zc888wz6dY3m8107dqV6Oho1q1bl2ZhQ8s58fb2ztE5CQkJYfv27SQlJWV4v/dy5cqxbt064uLibHrvLVMTcutnweL06dNpblv4119/AViH+y9dupQKFSrwzTff2FwvY8aMueXjh4SE8MILL/DCCy9w6NAhatasyXvvvcenn35q/awHDx60mUaTmJjI0aNHb/m6FBGRVBqWLyIimXr55Zfx8PDgmWeeITIyMs32I0eO8OGHHwLw6KOPAjBlyhSbOu+//z4ArVq1ypWYHB0deeSRR/j2229thhxHRkby+eef06BBA+uUgrzk6OiYpjdx6tSpOb7t262wJHU3frHQvHlzvL29efvtt9Odt37u3LksH+PJJ58kISGB8PBwvv/+ezp16mSz3dLjev05SUxMZMaMGVk+xvUuXbqU5vxaetozG5o/btw41qxZwxdffJHu0PJatWoREhLCu+++a7OGgMXNzknHjh05f/4806ZNS7PNEu+jjz5KSkpKmjoffPABJpMp11d9T05OZvbs2db3iYmJzJ49G39/f2rVqgWk/++zfft2tm3bluPjxsfHc+3aNZuykJAQvLy8rP9GzZo1w8XFhY8++sjm2J988gkxMTG59ntBROROp557ERHJVEhICJ9//jlPPvkkVatWpXv37lSvXp3ExER++uknlixZQs+ePQGoUaMGPXr0YM6cOdYh2r/88gvh4eG0b9/euqBZbnjrrbdYu3YtDRo0YMCAATg5OTF79mwSEhLy7Z7YrVu3ZtGiRfj4+BAaGsq2bdtYt24dxYsXz5fjX69mzZo4OjoyceJEYmJicHV15aGHHiIgIICZM2fSrVs37rvvPjp37oy/vz8nTpxg1apV1K9fP90kNT333XcfFStW5LXXXiMhIcFmSD5AvXr1KFq0KD169GDIkCGYTCYWLVqU4+HU4eHhzJgxg8cee4yQkBDi4uKYO3cu3t7e1i+SbrR3717efPNNGjZsSFRUFJ9++qnN9qeffhoHBwc+/vhjWrZsSbVq1ejVqxelSpXi1KlTRERE4O3tzcqVKzOMq3v37ixcuJDhw4fzyy+/EBYWxpUrV1i3bh0DBgygXbt2tGnThiZNmvDaa69x7NgxatSowQ8//MC3337L0KFDraMHckvJkiWZOHEix44do1KlSixevJjdu3czZ84c6+iC1q1b88033/DYY4/RqlUrjh49yqxZswgNDU33S46s+Ouvv2jatCmdOnUiNDQUJycnli1bRmRkpHWhSn9/f0aMGMG4ceNo0aIFbdu25eDBg8yYMYP777/fZvE8ERHJOSX3IiJyU23btmXPnj1MnjyZb7/9lpkzZ+Lq6so999zDe++9R9++fa11P/74YypUqMCCBQtYtmwZQUFBjBgxIleG/l6vWrVqbNmyhREjRjBhwgTMZjN169bl008/TXOP+7zy4Ycf4ujoyGeffca1a9eoX78+69ato3nz5vly/OsFBQUxa9YsJkyYQJ8+fUhJSSEiIoKAgACeeuopSpYsyTvvvMPkyZNJSEigVKlShIWF0atXr2wd58knn2T8+PFUrFiR++67z2Zb8eLF+d///scLL7zA66+/TtGiRXn66adp2rRpjs6J5cuhL7/8ksjISHx8fKhTpw6fffZZhou9XbhwAcMw2LRpE5s2bUqz3ZJINm7cmG3btvHmm28ybdo0Ll++TFBQEHXr1rVZdT49jo6OrF692jpN4Ouvv6Z48eI0aNCAu+++GwAHBwdWrFjB6NGjWbx4MfPnzyc4OJjJkydb7x6Rm4oWLUp4eDiDBw9m7ty5BAYGMm3aNJufzZ49e3L27Flmz57NmjVrCA0N5dNPP2XJkiVs3LgxR8ctU6YMXbp0Yf369SxatAgnJyeqVKnCV199RceOHa31xo4di7+/P9OmTWPYsGEUK1aMfv368fbbb2c4tUFERLLHZGh1EhEREZFCq3Hjxpw/fz7bC1+KiMjtRXPuRURERERERAo5JfciIiIiIiIihZySexEREREREZFCTnPuRURERERERAo59dyLiIiIiIiIFHJK7kVEREREREQKuQJ7n/vp06czefJkzp49S40aNZg6dSp16tS56X5ffvklXbp0oV27dixfvtxabjKZ0q0/adIkXnrppZu2azabOX36NF5eXhm2JSIiIiIiIpJbDMMgLi6OkiVL4uCQed98gZxzv3jxYrp3786sWbOoW7cuU6ZMYcmSJRw8eJCAgIAM9zt27BgNGjSgQoUKFCtWzCa5P3v2rE3d7777jj59+nD48GEqVKhw05j++ecfypQpk+PPJCIiIiIiIpITJ0+epHTp0pnWKZDJfd26dbn//vuZNm0akNprXqZMGQYPHsyrr76a7j4pKSk0bNiQ3r17s2XLFqKjo22S+xu1b9+euLg41q9fn6WYYmJi8PX15eTJk3h7e2f7M4mIiIiIiIhkR2xsLGXKlCE6OhofH59M6xa4YfmJiYns3LmTESNGWMscHBxo1qwZ27Zty3C/N954g4CAAPr06cOWLVsyPUZkZCSrVq0iPDw8wzoJCQkkJCRY38fFxQHg7e2t5F5ERERERETyTVamhhe4BfXOnz9PSkoKgYGBNuWBgYFphtZbbN26lU8++YS5c+dm6Rjh4eF4eXnRoUOHDOtMmDABHx8f60ND8kVERERERKSgKnDJfXbFxcXRrVs35s6di5+fX5b2mTdvHl27dsXNzS3DOiNGjCAmJsb6OHnyZG6FLCIiIiIiIpKrCtywfD8/PxwdHYmMjLQpj4yMJCgoKE39I0eOcOzYMdq0aWMtM5vNADg5OXHw4EFCQkKs27Zs2cLBgwdZvHhxpnG4urri6up6Kx9FRERERG5nZjOc+AniL9o7kttPYDUoHnLzeiJiVeCSexcXF2rVqsX69etp3749kJqsr1+/nkGDBqWpX6VKFfbu3WtT9vrrrxMXF8eHH36YZjj9J598Qq1atahRo0aefQYRERERuQMcXA2Lu9o7ituTixe8+Be4FLF3JCKFRoFL7gGGDx9Ojx49qF27NnXq1GHKlClcuXKFXr16AdC9e3dKlSrFhAkTcHNzo3r16jb7+/r6AqQpj42NZcmSJbz33nt5Gr9hGKSkpJCcnJynxxGRgsfZ2RlHR0d7hyEiIvkhan/qs2cQFA22ayi3lVO/QmIcxJ4Cv7vsHY1IoVEgk/snn3ySc+fOMXr0aM6ePUvNmjX5/vvvrYvsnThxAgeH7C8X8OWXX2IYBl26dMntkIHUpD46Oppz586RkpKSJ8cQkYLP19eXoKCgLK1qKiIihVjcmdTnWj2gyUj7xnI7+eg+uHgELkcpuRfJhgJ5n/uCKDY2Fh8fH2JiYjK8Fd6ZM2eIjo623i7PyclJf9yL3EEMwyA+Pp6oqCh8fX0pUaKEvUMSEZG89MVTcHAVtHof7u9j72huH/NawIlt8MQCqPaYvaMRsaus5KEWBbLnvjBKSUkhJiYGf3//LK/aLyK3H3d3dwCioqIICAjQEH0RkduZpefeS1/m5ioP/9Tny+fsG4dIIVPob4VXUCQlJWEYBh4eHvYORUTsrEiR1MV/kpKS7ByJiIjkqcv/3t3JK9C+cdxuPANSn69E2TcOkUJGyX0u0zB8EdHvARGRO4DZfF1yr577XOXxb3J/Wcm9SHYouRcRERERya74C2BOBkz/JaOSOzz/HZZ/RcPyRbJDyb2IiIiISHZdPpv67OEPjlrGKldZe+4j7RuHSCGj5F7yVOPGjWncuHGW61avXj1vAyqgjh07hslkYsGCBdaysWPH2m14d3JyMi+//DJlypTBwcGB9u3b5+vxe/bsSXBwsPW95fy8++67+RrHggULMJlM/Prrr/l6XBERKQTi/k3uNd8+91nm3GtBPZFsUXIv+er06dOMHTuW3bt32zsUG6tXr2bs2LH2DqPAmDdvHpMnT+bxxx8nPDycYcOG2TskERGRgsWa3Gu+fa67fkE93bVbJMuU3Eue+uGHH/jhhx+s70+fPs24ceMKZHI/btw4e4dh4/XXX+fq1at2OfaGDRsoVaoUH3zwAd26daNRo0Z2iUNERKTAsiT3nuq5z3WWYfnJ1yAhzr6xiBQiSu4lT7m4uODi4mLvMAolJycn3Nzc7HLsqKgofH197XJsERGRQuGyeu7zjEsRcPFMfa1F9USyTMm93NSePXswmUysWLHCWrZz505MJhP33XefTd2WLVtSt25d6/vr59xv3LiR+++/H4BevXphMpnSzDMH2L9/P02aNKFIkSKUKlWKSZMmpYkpKiqKPn36EBgYiJubGzVq1CA8PNymzsaNGzGZTGzcuNGm/Mb57T179mT69OkA1piyMtd9xowZVKtWDVdXV0qWLMnAgQOJjo62qWNZRyArn+lG6c25N5lMDBo0iOXLl1O9enVcXV2pVq0a33//fZr9T506Re/evQkMDLTWmzdvXqbHtJybiIgI9u3bZz0XlnNoNpuZMmUK1apVw83NjcDAQJ599lkuXbqUpq3vvvuOsLAwPDw88PLyolWrVuzbty9NPctncXNzo3r16ixbtizTGD/44APKlSuHu7s7jRo14o8//rDZvmfPHnr27EmFChVwc3MjKCiI3r17c+HChXTPUZ8+fShZsiSurq6UL1+e/v37k5iYmOHxL126RJ06dShdujQHDx7MNFYREbmNac593vL4d8V83Q5PJMu0tKfcVPXq1fH19WXz5s20bdsWgC1btuDg4MDvv/9ObGws3t7emM1mfvrpJ/r165duO1WrVuWNN95g9OjR9OvXj7CwMADq1atnrXPp0iVatGhBhw4d6NSpE0uXLuWVV17h7rvvpmXLlgBcvXqVxo0bc/jwYQYNGkT58uVZsmQJPXv2JDo6mueffz5bn+/ZZ5/l9OnTrF27lkWLFmVpn7FjxzJu3DiaNWtG//79OXjwIDNnzmTHjh38+OOPODs7Z+szZcfWrVv55ptvGDBgAF5eXnz00Ud07NiREydOULx4cQAiIyN54IEHrF8G+Pv7891339GnTx9iY2MZOnRoum37+/uzaNEixo8fz+XLl5kwYQKQ+m9nOVcLFiygV69eDBkyhKNHjzJt2jR+++03m8+9aNEievToQfPmzZk4cSLx8fHMnDmTBg0a8Ntvv1kXy/vhhx/o2LEjoaGhTJgwgQsXLtCrVy9Kly6dbnwLFy4kLi6OgQMHcu3aNT788EMeeugh9u7dS2Bg6h9Xa9eu5e+//6ZXr14EBQWxb98+5syZw759+/j555+tX5icPn2aOnXqEB0dTb9+/ahSpQqnTp1i6dKlxMfHpzvi5Pz58zz88MNcvHiRTZs2ERISku1/PxERuU1Yh+UH2TeO25VnAFw6mjrvXkSyxpAsiYmJMQAjJiYm3e1Xr1419u/fb1y9etWm3Gw2G1cSkgrMw2w25+jzt2rVyqhTp471fYcOHYwOHToYjo6OxnfffWcYhmHs2rXLAIxvv/3WWq9Ro0ZGo0aNrO937NhhAMb8+fPTHKNRo0YGYCxcuNBalpCQYAQFBRkdO3a0lk2ZMsUAjE8//dRalpiYaDz44IOGp6enERsbaxiGYURERBiAERERYXOco0ePpolh4MCBRlZ/HKKiogwXFxfjkUceMVJSUqzl06ZNMwBj3rx52f5M6cU0ZsyYNDEBhouLi3H48GFr2e+//24AxtSpU61lffr0MUqUKGGcP3/eZv/OnTsbPj4+Rnx8fKafsVGjRka1atVsyrZs2WIAxmeffWZT/v3339uUx8XFGb6+vkbfvn1t6p09e9bw8fGxKa9Zs6ZRokQJIzo62lr2ww8/GIBRrlw5a5nl/Li7uxv//POPtXz79u0GYAwbNsxalt5n++KLLwzA2Lx5s7Wse/fuhoODg7Fjx4409S0/J/PnzzcAY8eOHcaZM2eMatWqGRUqVDCOHTuW9qRdJ6PfByIicht5L9Qwxngbxslf7R3J7emLp1LP7/Y59o5ExK5ulodeTz33eexqUgqho9fYOwyr/W80p4hL9v/Zw8LCeP3117ly5QoeHh5s3bqVt99+m+PHj7NlyxZatGjBli1bMJlMNGjQIMfxeXp68vTTT1vfu7i4UKdOHf7++29r2erVqwkKCqJLly7WMmdnZ4YMGUKXLl3YtGkTrVu3znEMN7Nu3ToSExMZOnQoDg7/zWzp27cvI0eOZNWqVfTq1Stbnyk7mjVrZtNjfM899+Dt7W1tzzAMvv76azp16oRhGJw/f95at3nz5nz55Zfs2rWL+vXrZ+u4S5YswcfHh4cfftimzVq1auHp6UlERARPPfUUa9euJTo6mi5dutjUc3R0pG7dukRERABw5swZdu/ezauvvoqPj4+13sMPP0xoaChXrlxJE0P79u0pVaqU9X2dOnWoW7cuq1ev5v333wfA3d3duv3atWtcvnyZBx54AIBdu3YRFhaG2Wxm+fLltGnThtq1a6c5zo3TIf755x+6du0KwObNm21iEBGRO5DZ/N892DUsP29YV8zXnHuRrFJyL1kSFhZGcnIy27Zto0yZMkRFRREWFsa+ffvYsmULkDpUPzQ0lGLFiuX4OKVLl06TWBUtWpQ9e/ZY3x8/fpy77rrLJrGG/4aOHz9+PMfHzwpL+5UrV7Ypd3FxoUKFCmmOn5XPlB1ly5ZNU1a0aFHrvPdz584RHR3NnDlzmDNnTrptREVlf4jboUOHiImJISAgINM2Dx06BMBDDz2Ubj1vb2/gv/N41113palTuXJldu3alaY8vbqVKlXiq6++sr6/ePEi48aN48svv0zzOWNiYoDUcxQbG0v16tXTjfFG3bp1w8nJiT///JOgIA2/FBG54129COak1NdaLT9vWFbM15x7kSxTcp/H3J0d2f9Gc3uHYeXu7Jij/WrXro2bmxubN2+mbNmyBAQEUKlSJcLCwpgxYwYJCQls2bKFxx577Jbic3RMPz4jB/c4zWhRvJSUlGy3dSty8zNlpT2z2QzA008/TY8ePdKte88992T7uGazmYCAAD777LN0t/v7+9scf9GiRekmwk5Oeftrp1OnTvz000+89NJL1KxZE09PT8xmMy1atLDGll0dOnRg4cKFfPjhh9Z1CERE5A5mmW9fxA8cnTOvKznj+e+Ceuq5F8kyJfd5zGQy5WgYfEFjGUq+ZcsWypYta10MLywsjISEBD777DMiIyNp2LBhpu1kZRX6mylXrhx79uzBbDbb9N4fOHDAuh1Se7OBNCvYp9ezn524LO0fPHiQChUqWMsTExM5evQozZo1y3JbecHf3x8vLy9SUlJyNZaQkBDWrVtH/fr1bYa+p1cPICAgINPjW86jpaf/ehmtQp9e3b/++su6QN+lS5dYv34948aNY/To0Rnu5+/vj7e3d5qV9jMyePBgKlasyOjRo/Hx8eHVV1/N0n4iInKbitNt8PKceu5Fsk23wpMsCwsLY/v27URERFiTez8/P6pWrcrEiROtdTLj4eEBpE24s+PRRx/l7NmzLF682FqWnJzM1KlT8fT0pFGjRkBq8ujo6MjmzZtt9p8xY8YtxdWsWTNcXFz46KOPbHrfP/nkE2JiYmjVqlVOPlaucXR0pGPHjnz99dfpJq/nzuXsG/BOnTqRkpLCm2++mWZbcnKy9dw1b94cb29v3n77bZKSkjI8fokSJahZsybh4eHW4fKQutr9/v37041h+fLlnDp1yvr+l19+Yfv27da7DlhGNdw4KmLKlCk27x0cHGjfvj0rV67k119/TXOc9EZVjBo1ihdffJERI0Ywc+bMdOMTEZE7xGXdBi/PWefcK7kXyarC36Us+SYsLIzx48dz8uRJmyS+YcOGzJ49m+Dg4AxvYWYREhKCr68vs2bNwsvLCw8PD+rWrUv58uWzHEe/fv2YPXs2PXv2ZOfOnQQHB7N06VJ+/PFHpkyZgpeXFwA+Pj488cQTTJ06FZPJREhICP/73//SnW9eq1YtAIYMGULz5s1xdHSkc+fO6R7f39+fESNGMG7cOFq0aEHbtm05ePAgM2bM4P7777dZPM9e3nnnHSIiIqhbty59+/YlNDSUixcvsmvXLtatW8fFixez3WajRo149tlnmTBhArt37+aRRx7B2dmZQ4cOsWTJEj788EMef/xxvL29mTlzJt26deO+++6jc+fO+Pv7c+LECVatWkX9+vWZNm0aABMmTKBVq1Y0aNCA3r17c/HiRaZOnUq1atW4fPlymhgqVqxIgwYN6N+/PwkJCUyZMoXixYvz8ssvA6nz+Rs2bMikSZNISkqiVKlS/PDDDxw9ejRNW2+//TY//PADjRo1ol+/flStWpUzZ86wZMkStm7diq+vb5p9Jk+eTExMDAMHDsTLy6tA/FuLiIgdxJ1JffbSOix5xnqfew3LF8kqJfeSZfXq1cPR0ZEiRYpQo0YNa3lYWBizZ8++aa89pK5qHx4ezogRI3juuedITk5m/vz52Uru3d3d2bhxI6+++irh4eHExsZSuXJl5s+fT8+ePW3qTp06laSkJGbNmoWrqyudOnVi8uTJaRZS69ChA4MHD+bLL7/k008/xTCMDJN7SL3Pvb+/P9OmTWPYsGEUK1aMfv368fbbb9vc495eAgMD+eWXX3jjjTf45ptvmDFjBsWLF6datWrWURY5MWvWLGrVqsXs2bMZOXIkTk5OBAcH8/TTT9usvv/UU09RsmRJ3nnnHSZPnkxCQgKlSpUiLCzM5k4CLVq0YMmSJbz++uuMGDGCkJAQ5s+fz7fffsvGjRvTHL979+44ODgwZcoUoqKiqFOnDtOmTaNEif+GRX7++ecMHjyY6dOnYxgGjzzyCN999x0lS5a0aatUqVJs376dUaNG8dlnnxEbG0upUqVo2bIlRYoUyfQcXL58mV69euHl5UW7du1yfD5FRKSQivt3pXzd4z7vWHruk65AwmVw9bRvPCKFgMnI6aped5jY2Fh8fHyIiYmxrvZ9vWvXrnH06FHKly+Pm5ubHSIUkYJCvw9ERG5zX3aFA/+DR9+FOn3tHc3tyTBgfAlIvgpDfoNiFW6+j8ht6GZ56PU0515EREREJDus97hXz32eMZn+WzFfQ/NFskTJvYiIiIhIdliG5Wu1/Lzl+e+ChVpUTyRLlNyLiIiIiGSVYfy3Wr6nVsvPU7odnki2KLkXEREREcmqq5cgJTH1tZL7vGUZln9Fw/JFskLJvYiIiIhIVllug1ekODi52DeW25167kWyRcm9iIiIiEhWxVmG5GsxvTxnuR2e5tyLZImSexERERGRrLIk91opP+95aLV8kexQci8iIiIiklWXldznG/Xci2SLknsRERERkaxSz33+sc65V8+9SFYouRcRERERySrNuc8/ltXyE+Mg6ap9YxEpBJTci4iIiIhklXru84+rNzi6pr7WivkiN6XkXkREREQkqzTnPv+YTNfNu9fQfJGbUXIveapx48Y0btw4y3WrV6+etwFlw8aNGzGZTGzcuNFa1rNnT4KDg+0Sz+XLl3nmmWcICgrCZDIxdOjQfD3+jf+WlvOzdOnSfI1j7NixmEwmzp8/n6/HFRERwTDUc5/frCvmq+de5Gac7B2A3FlOnz7NnDlzaN++PTVr1rR3OIXK22+/zYIFCxg1ahQhISFUrVrV3iGJiIjcWa5egpTE1NeegfaN5U6hFfNFskzJveSpH374web96dOnGTduHMHBwYUyuZ87dy5ms9kux96wYQMPPPAAY8aMscvxRURE7niWXnv3YuDkat9Y7hTquRfJMg3Llzzl4uKCi4uLvcPINc7Ozri62uc/86ioKHx9fe1ybBEREUHz7e3B0nOv5F7kppTcy03t2bMHk8nEihUrrGU7d+7EZDJx33332dRt2bIldevWtb6/fp72xo0buf/++wHo1asXJpMJk8nEggULbNrYv38/TZo0oUiRIpQqVYpJkyZlKc7k5GTefPNNQkJCcHV1JTg4mJEjR5KQkGBTLzg4mNatW7N161bq1KmDm5sbFSpUYOHChTc9xo1z7o8dO4bJZOLdd99lzpw51mPff//97NixI83+Bw4c4PHHH6dYsWK4ublRu3Ztm/OaHsvc9qNHj7Jq1SrreTt27BgACQkJjBkzhooVK+Lq6kqZMmV4+eWX03xugE8//ZRatWrh7u5OsWLF6Ny5MydPnkxTz/JZ3N3dqVOnDlu2bMkwvpSUFEaOHElQUBAeHh60bds2TZtbtmzhiSeeoGzZstYYhw0bxtWraW9rc+DAATp16oS/vz/u7u5UrlyZ1157LdNzdPz4cSpWrEj16tWJjIzMtK6IiEiOWW+DpyH5+cZyrjUsX+SmNCw/rxkGJMXbO4r/OBdJXXk0G6pXr46vry+bN2+mbdu2QGqy5uDgwO+//05sbCze3t6YzWZ++ukn+vXrl247VatW5Y033mD06NH069ePsLAwAOrVq2etc+nSJVq0aEGHDh3o1KkTS5cu5ZVXXuHuu++mZcuWmcb5zDPPEB4ezuOPP84LL7zA9u3bmTBhAn/++SfLli2zqXv48GEef/xx+vTpQ48ePZg3bx49e/akVq1aVKtWLVvnB+Dzzz8nLi6OZ599FpPJxKRJk+jQoQN///03zs7OAOzbt4/69etTqlQpXn31VTw8PPjqq69o3749X3/9NY899liG523RokUMGzaM0qVL88ILLwDg7++P2Wymbdu2bN26lX79+lG1alX27t3LBx98wF9//cXy5cut7YwfP55Ro0bRqVMnnnnmGc6dO8fUqVNp2LAhv/32m3VUwCeffMKzzz5LvXr1GDp0KH///Tdt27alWLFilClTJk1848ePx2Qy8corrxAVFcWUKVNo1qwZu3fvxt3dHYAlS5YQHx9P//79KV68OL/88gtTp07ln3/+YcmSJda29uzZQ1hYGM7OzvTr14/g4GCOHDnCypUrGT9+fLrn58iRIzz00EMUK1aMtWvX4ufnl+1/PxERkSyxLqZXwr5x3Emsw/K1Wr7IzSi5z2tJ8fB2SXtH8Z+Rp8HFI1u7ODg4UL9+fZve2y1bttC+fXu+/fZbfvrpJ1q0aGFN9C1J+40CAwNp2bIlo0eP5sEHH+Tpp59OU+f06dMsXLiQbt26AdCnTx/KlSvHJ598kmly//vvvxMeHs4zzzzD3LlzARgwYAABAQG8++67RERE0KRJE2v9gwcPsnnzZmusnTp1okyZMsyfP5933303W+cH4MSJExw6dIiiRYsCULlyZdq1a8eaNWto3bo1AM8//zxly5Zlx44d1qH9AwYMoEGDBrzyyisZJveBgYE8/fTTvP7665QqVcrmvH366aesW7eOTZs20aBBA2t59erVee655/jpp5+oV68ex48fZ8yYMbz11luMHDnSWq9Dhw7ce++9zJgxg5EjR5KUlMTIkSOpWbMmERER1ikVoaGh9OvXL93k/uLFi/z55594eXkBcN9999GpUyfmzp3LkCFDAJg4caI10Qfo168fFStWZOTIkZw4cYKyZcsCMHjwYAzDYNeuXdYygHfeeSfdc3PgwAGaNm1KqVKlWLNmjfX8i4iI5Alrcq+e+3yjBfVEskzD8iVLwsLC2LVrF1euXAFg69atPProo9SsWdOa9G/ZsgWTyWSTZGaXp6enTfLq4uJCnTp1+PvvvzPdb/Xq1QAMHz7cptzSy71q1Sqb8tDQUJsvIfz9/alcufJNj5ORJ5980iaxtLRtae/ixYts2LCBTp06ERcXx/nz5zl//jwXLlygefPmHDp0iFOnTmX7uEuWLKFq1apUqVLF2ub58+d56KGHAIiIiADgm2++wWw206lTJ5t6QUFB3HXXXdZ6v/76K1FRUTz33HM2ayX07NkTHx+fdGPo3r27NbEHePzxxylRooT13wSwSeyvXLnC+fPnqVevHoZh8NtvvwFw7tw5Nm/eTO/evW0SewBTOqNN/vjjDxo1akRwcDDr1q1TYi8iInnvsnru852HZc69eu5FbkY993nNuUhqb3lB4VwkR7uFhYWRnJzMtm3bKFOmDFFRUYSFhbFv3z6b5D40NJRixYrlOLzSpUunSeSKFi3Knj17Mt3v+PHjODg4ULFiRZvyoKAgfH19OX78uE35jcmj5TiXLl3KUdw3tmdJNC3tHT58GMMwGDVqFKNGjUq3jaioKEqVKpWt4x46dIg///wTf3//DNu01DMMg7vuuivdepapA5bzdGM9Z2dnKlSokO6+N9Y1mUxUrFjRuiYApI5sGD16NCtWrEhzjmNiYoD/vgipXr16use5UZs2bQgMDGTNmjV4enpmaR8REZFbEvfvui6ac59/PP/9GychBpKugbObfeMRKcCU3Oc1kynbw+ALotq1a+Pm5sbmzZspW7YsAQEBVKpUibCwMGbMmEFCQgJbtmzJcGh5Vjk6OqZbbhhGlvZPr4c3L46T3fYst8978cUXad68ebp1b/xiIivMZjN3330377//frrbLcPozWYzJpOJ7777Lt1Y8zI5TklJ4eGHH+bixYu88sorVKlSBQ8PD06dOkXPnj1zfGvBjh07Eh4ezmeffcazzz6by1GLiIikI+5M6rN67vOPmy84ukBKIlw5B75ppwiKSCol95IlluHxW7ZsoWzZstZh52FhYSQkJPDZZ58RGRlJw4YNM20nq8l3dpUrVw6z2cyhQ4eoWrWqtTwyMpLo6GjKlSuXJ8fNKkuvt7OzM82aNcu1dkNCQvj9999p2rRppuc2JCQEwzAoX748lSpVyrCe5TwdOnTIOrQfICkpiaNHj1KjRo00+xw6dMjmvWEYHD58mHvuuQeAvXv38tdffxEeHk737t2t9dauXWuzn+Uc/fHHHxnGd73Jkyfj5OTEgAED8PLy4qmnnsrSfiIiIjliGHD53557zbnPPyZT6qJ6sadS590ruRfJkObcS5aFhYWxfft2IiIirMm9n58fVatWZeLEidY6mfHwSB3FEB0dnauxPfroowBMmTLFptzSo92qVatcPV52BQQE0LhxY2bPns2ZM2fSbD93LmfzyDp16sSpU6esiwhe7+rVq9Y1Ejp06ICjoyPjxo1LMzrBMAwuXLgApI7Q8Pf3Z9asWSQmJlrrLFiwIMN/s4ULFxIXF2d9v3TpUs6cOWNdANEyUuD64xqGwYcffmjTjr+/Pw0bNmTevHmcOHEiTYw3MplMzJkzh8cff5wePXrc9JaCIiIit+RaNCRfS33tqfvc5yutmC+SJeq5lywLCwtj/PjxnDx50iaJb9iwIbNnzyY4OJjSpUtn2kZISAi+vr7MmjULLy8vPDw8qFu3LuXLl7+l2GrUqEGPHj2YM2cO0dHRNGrUiF9++YXw8HDat29vs1K+vUyfPp0GDRpw991307dvXypUqEBkZCTbtm3jn3/+4ffff892m926deOrr77iueeeIyIigvr165OSksKBAwf46quvWLNmDbVr1yYkJIS33nqLESNGcOzYMdq3b4+XlxdHjx5l2bJl9OvXjxdffBFnZ2feeustnn32WR566CGefPJJjh49yvz58zOcc1+sWDEaNGhAr169iIyMZMqUKVSsWJG+ffsCUKVKFUJCQnjxxRc5deoU3t7efP311+mub/DRRx/RoEED7rvvPvr160f58uU5duwYq1atYvfu3WnqOzg48Omnn9K+fXs6derE6tWrbUYciIiI5BrLfHs3X837zm9aMV8kS5TcS5bVq1cPR0dHihQpYjM8OywsjNmzZ9+01x5Sh6WHh4czYsQInnvuOZKTk5k/f/4tJ/cAH3/8MRUqVGDBggUsW7aMoKAgRowYwZgxY2657dwQGhrKr7/+yrhx41iwYAEXLlwgICCAe++9l9GjR+eoTQcHB5YvX84HH3zAwoULWbZsGUWKFKFChQo8//zzNkPwX331VSpVqsQHH3zAuHHjgNQ5+Y888ght27a11uvXrx8pKSlMnjyZl156ibvvvpsVK1ZkuBDgyJEj2bNnDxMmTCAuLo6mTZsyY8YMihRJXbzR2dmZlStXMmTIECZMmICbmxuPPfYYgwYNSjPMv0aNGvz888+MGjWKmTNncu3aNcqVK0enTp0yPAfOzs4sXbqUli1b0q5dO9atW0fdunVzdD5FREQypPn29mNdMV/JvUhmTEZOVxDLQ9OnT2fy5MmcPXuWGjVqMHXqVOrUqXPT/b788ku6dOlCu3btWL58uc22P//8k1deeYVNmzaRnJxMaGgoX3/9dbqrpqcnNjYWHx8fYmJi8Pb2TrP92rVrHD16lPLly+Pmpm9zRe5k+n0gInIb+v1LWPYsVGgM3b+1dzR3lnVjYesHUPc5aDnR3tGI5Kub5aHXK3Bz7hcvXszw4cMZM2YMu3btokaNGjRv3tx6S6+MHDt2jBdffDHd3uMjR47QoEEDqlSpwsaNG9mzZw+jRo3SH90iIiIikjXqubcf9dyLZEmBS+7ff/99+vbtS69evQgNDWXWrFkUKVKEefPmZbhPSkoKXbt2Zdy4cenOC37ttdd49NFHmTRpEvfeey8hISG0bduWgICAvPwoIiIiInK70D3u7cc6514L6olkpkAl94mJiezcudPmVmEODg40a9aMbdu2ZbjfG2+8QUBAAH369EmzzWw2s2rVKipVqkTz5s0JCAigbt26aYbt3yghIYHY2Fibh4iIiIjcoaw991opP99ZV8uPtG8cIgVcgUruz58/T0pKCoGBtt+IBgYGcvbs2XT32bp1K5988km6twIDiIqK4vLly7zzzju0aNGCH374gccee4wOHTqwadOmDGOZMGECPj4+1keZMrqnpoiIiMgdy3qPeyX3+c5Tw/JFsqJAJffZFRcXR7du3Zg7dy5+fn7p1jGbzQC0a9eOYcOGUbNmTV599VVat27NrFmzMmx7xIgRxMTEWB8nT57Mk88gIiIiIoWApede97jPf5apENeiITnRrqGIFGS3lNwnJyfzwQcfUKdOHby9vXFy+u/Oert372bAgAH89ddfWW7Pz88PR0dHIiNth9xERkYSFJT2F+mRI0c4duwYbdq0wcnJCScnJxYuXMiKFStwcnLiyJEj+Pn54eTkRGhoqM2+VatW5cSJExnG4urqire3t80jKwrgzQdEJJ/p94CIyG3GMP6bc6+e+/zn5gsO/+YZmncvkqEcJ/dXr16lSZMmvPjiixw/fhxvb2+bP2jLly/P/PnzWbhwYZbbdHFxoVatWqxfv95aZjabWb9+PQ8++GCa+lWqVGHv3r3s3r3b+mjbti1NmjRh9+7dlClTBhcXF+6//34OHjxos+9ff/1FuXLlcvDJ0+fo6AhAUlJSrrUpIoVTcnIygM0XniIiUohdi4Hkq6mvldznPweH/+bdX9HQfJGM5Di5f/vtt/nxxx+ZMGECZ8+e5ZlnnrHZ7uPjQ6NGjVizZk222h0+fDhz584lPDycP//8k/79+3PlyhV69eoFQPfu3RkxYgQAbm5uVK9e3ebh6+uLl5cX1atXx8XFBYCXXnqJxYsXM3fuXA4fPsy0adNYuXIlAwYMyOnHT8PZ2RlXV1diYmLUaydyh4uNjcXR0dH6pZ+IiBRylvn2bj7g7G7fWO5U1kX11HMvkpEcdystXryYJk2a8PLLLwNgMpnS1KlQoQK//fZbttp98sknOXfuHKNHj+bs2bPUrFmT77//3rrI3okTJ3BwyN53Eo899hizZs1iwoQJDBkyhMqVK/P111/ToEGDbLVzM35+fpw6dYp//vkHHx8fnJ2d0z0vInJ7MgyDK1euEBsbS4kSJfTzLyJyu9B8e/uz3g5PPfciGclxcn/ixAkee+yxTOt4eXkRExOT7bYHDRrEoEGD0t22cePGTPddsGBBuuW9e/emd+/e2Y4lOyzz8s+fP8+pU6fy9FgiUjCZTCZ8fX3x8fGxdygiIpJbNN/e/jy0Yr7IzeQ4uffy8iIqKvMfriNHjuDv75/TQxRKlsX3kpKSSElJsXc4IpLPnJ2dNRxfROR2o3vc25+nZc69huWLZCTHyf0DDzzAypUriY6OxtfXN832kydPsnr16pv27t+unJ2dcXZ2tncYIiIiInKrdI97+1PPvchN5XhBvZdeeolLly7RtGlTfvzxR+vq0PHx8axfv57mzZuTnJzM8OHDcy1YEREREZF8F3c29Vlz7u1Hc+5FbirHPfcNGzZk2rRpPP/88zRs2NBa7uXlBaTeGm7GjBnUqlXr1qMUEREREbEXS3Kvnnv70Wr5Ijd1Szdh7t+/P40bN2bWrFls376dixcv4u3tTd26dRkwYADVqlXLrThFREREROzjspJ7u1PPvchN3VJyD1C1alU+/PDD3IhFRERERKRgMYzrhuUH2jeWO5llzn38RUhJBsdbTmNEbjs5nnN/4sSJm66WHxsby4kTJ3J6CBERERER+0qIg6T41NfqubefIsXA5AAYEH/e3tGIFEg5Tu6Dg4MpXbp0pr32H3zwAeXLl8/pIURERERE7MvSa+/qDS4e9o3lTubgCEX8Ul9b7l4gIjZynNwDmM1mhg8fzsCBAzGbzbkVk4iIiIhIwaD59gWHZd69FtUTSdctJffDhw/n0UcfZebMmbRq1Yq4uLjciktERERExP40377g0KJ6Ipm6peTe09OTFStWMHjwYNasWUO9evU0x15EREREbh/W2+CVsG8c8t+iepeV3Iuk55aSewCTycSHH37ItGnTOHDgAHXr1mX79u25EZuIiIiIiH1Zk3v13Nud57/3ur+iYfki6bnl5N5iwIABrFy5kvj4eJo0acLixYtzq2kREREREfu4rJ77AkM99yKZyrXkHqBFixb8+OOPBAQE8NRTT/H555/nZvMiIiIiIvlLc+4LDs25F8lUrib3ANWrV+eXX36hdu3aHDp0KLebFxERERHJP5pzX3B4/DssX6vli6TLKac7RkREEBwcnO62gIAANm3axEcffcTVq1dzeggREREREfuK063wCgz13ItkKsfJfaNGjTLd7ubmxssvv5zT5kVERERE7CshDpKupL7WsHz7s8y5j78A5hRwcLRvPCIFTK4PyxcRERERuS1Yeu1dvMDV076xCBQpDpjAMKcm+CJiI8s99w899BAmk4nw8HBKly7NQw89lKX9TCYT69evz3GAIiIiIiJ2odvgFSyOTqkJfvz51BXzLcP0RQTIRnK/ceNGTCYT8fHx1vdZYTKZchSYiIiIiIhdaTG9gsczIDW517x7kTSynNybzeZM34uIiIiI3FYu6zZ4BY5WzBfJUK7PuTcMg0OHDnHy5MncblpEREREJP9opfyCRyvmi2Qox8n9N998Q/fu3bl06ZK17NixY9xzzz1UqVKF4OBgOnfuTEpKSq4EKiIiIiKSr5TcFzyWFfMvK7kXuVGOk/uZM2eye/duihYtai0bNmwY+/bto0mTJtxzzz0sWbKEefPm5UqgIiIiIiL56nJk6rPm3BccnpZh+UruRW6U4/vc79+/n5YtW1rfx8XFsWrVKp588km++OILkpKSuPfee5k3bx59+/bNlWBFRCQT12Lg9y8h8Urm9UreCyFN8icmEZEbXY6CvUsgOcHekdzcuYOpz5pzX3BY/i00LF8kjRwn9xcvXiQo6L8hSlu3biU5OZkuXboA4OzszMMPP8xnn31261GKiMjN/fghbHnv5vUcnOCFv8CjeN7HJCJyo3XjYPen9o4ie3xK2zsCsbAOy9eCeiI3ynFy7+3tzYULF6zvIyIicHBwICwszFrm7OzMlSs36UESEZHc8c+vqc/BYeBbLv06B1fD1Ytwdo9670XEPs78nvpcsRl4FoK57EF3Q7Hy9o5CLCzD8tVzL5JGjpP7KlWqsHLlSt566y0cHR35/PPPqVWrls0c/OPHjxMYqGFMIiJ5zjAg8o/U1w+Pg1K10q+3uBv8uSK1rpJ7Eclv5hQ4/1fq60cnQ7EK9o1HCh9Lz/2V82A2g0Ou3/xLpNDK8U/DkCFDOH36NKVLl6Zs2bKcOXOG/v3729T5+eefqVGjxi0HKSIiNxF3FuIvgMkBAkIzrhd0T+rz2T/yJy4RketdOgYpCeDklvEII5HMePilPhspqSPRRMQqx8l9x44dmT59OtWqVaNSpUpMnDiRnj17Wrdv2rSJ2NhYWrRokRtxiohIZiy99sXvAmf3jOsFVbetLyKSnywL1PndBQ6O9o1FCidHZ3AvlvpaK+aL2MjxsHyA/v37p+mtt2jUqBGXLl26leZFRCSrzu5NfbYk7xkJ/Hf7uYOQnAhOLnkbl4jI9c4dSH32r2LfOKRw8wxI7bW/EgVkMlpN5A6jSSoiIrcDS0984E2Se5/S4OYD5iQ4fzDv4xIRuZ6l596/sn3jkMLNw3Kve62YL3I9JfciIrcDyxz6oLszr2cyQeDdtvuIiOQX9dxLbvC0LKqnYfki17ulYfkiIlIAJF2FC4dSX9+s5x5Sh+4f36p59yKSv8zm/1bKV3Ivt8KyYv4f30DMKfvGIoWbd0moN8jeUeQaJfciIoVd1J9gmKFIcfDKwj2jLV8AWObpi4jkh5iTkBQPDs5QVPeNl1tQ9N87LZz6NfUhklMlaiq5FxGRAuT6+fYm083rX79ivmFkbR8RkVt1/Ur5jvoTVG5Bza6QfA2uxdg7EinsvEraO4Jcpd+sIiKFXVbn21v4VwWTI8RfgLiz4F0i72ITEbGwzrfXYnpyi9y8ocEwe0chUuBoQT0RkcIuqyvlWzi7pfacXb+viEhes9yhQ/PtRUTyhJJ7EZHCzDCu67nPYnIPmncvIvnPOiy/kn3jEBG5TSm5FxEpzGJOQkJM6gJVftkY6nr9vHsRkbxmGNfd41499yIieUHJvYhIYWbptfevDE4uWd9P97oXkfwUdwYSYlPX+ygeYu9oRERuS0ruRUQKs+zOt7ew9NxfOARJV3M3JhGRG1kW0ytWAZxc7RuLiMhtSsm9iEhhZpkzn5359gCegVDEDwwzRP2Z+3GJiFzPOiRfK+WLiOQVJfciIoVZTnvuTSbNuxeR/GO9DZ7m24uI5BUl9yIihVXCZbh4NPV1Vu9xfz3rivlK7kUkj2kxPRGRPFdgk/vp06cTHByMm5sbdevW5ZdffsnSfl9++SUmk4n27dvblPfs2ROTyWTzaNGiRR5ELiKST6L2AwZ4BoGHX/b3t3whoJ57EclLhvHf9B8NyxcRyTMFMrlfvHgxw4cPZ8yYMezatYsaNWrQvHlzoqKiMt3v2LFjvPjii4SFhaW7vUWLFpw5c8b6+OKLL/IifBGR/JHT+fYW1/fcG0buxCQicqMr5+BaNGACv7vsHY2IyG2rQCb377//Pn379qVXr16EhoYya9YsihQpwrx58zLcJyUlha5duzJu3DgqVKiQbh1XV1eCgoKsj6JFi+bVRxARyXs5nW9v4VcJHJwhIQZiTuZeXCIi17PMty8aDM7udg1FROR2VuCS+8TERHbu3EmzZs2sZQ4ODjRr1oxt27ZluN8bb7xBQEAAffr0ybDOxo0bCQgIoHLlyvTv358LFy5kWDchIYHY2Fibh4hIgWKZK5+T+fYATi7/zX/VvHsRySuaby8iki8KXHJ//vx5UlJSCAwMtCkPDAzk7Nmz6e6zdetWPvnkE+bOnZthuy1atGDhwoWsX7+eiRMnsmnTJlq2bElKSkq69SdMmICPj4/1UaZMmZx/KBGR3GY2Q+S+1Nc57bkHrZgvInlPt8ETEckXTvYO4FbFxcXRrVs35s6di59fxgtKde7c2fr67rvv5p577iEkJISNGzfStGnTNPVHjBjB8OHDre9jY2OV4ItIwXHpKCRdAUdXKF4x5+1Y593vzZ24RERuZL0NnpJ7EZG8VOCSez8/PxwdHYmMjLQpj4yMJCgoKE39I0eOcOzYMdq0aWMtM5vNADg5OXHw4EFCQkLS7FehQgX8/Pw4fPhwusm9q6srrq6ut/pxRETyhqWnPaAqON7Cr3L13ItIXlPPvYhIvihww/JdXFyoVasW69evt5aZzWbWr1/Pgw8+mKZ+lSpV2Lt3L7t377Y+2rZtS5MmTdi9e3eGve3//PMPFy5coESJEnn2WURE8ox1vv0tDMkHCPx3vv7Fo5Bw+dbaEhG5UfxFuPLv3Y78Ktk3FhGR21yB67kHGD58OD169KB27drUqVOHKVOmcOXKFXr16gVA9+7dKVWqFBMmTMDNzY3q1W3/uPX19QWwll++fJlx48bRsWNHgoKCOHLkCC+//DIVK1akefPm+frZRERyhXWl/BwupmfhURy8SkDcGYjaD2Xq3HpsIiIWll57nzLg6mXfWEREbnMFMrl/8sknOXfuHKNHj+bs2bPUrFmT77//3rrI3okTJ3BwyPqgA0dHR/bs2UN4eDjR0dGULFmSRx55hDfffFND70WkcMqtnntInXcfdyZ13r2SexHJTZpvLyKSbwpkcg8waNAgBg0alO62jRs3ZrrvggULbN67u7uzZs2aXIpMRMTOrkZDzInU14HVbr29oOpweK3m3YtI7tNt8ERE8k2Bm3MvIiI3YbkFnk8ZcC966+1ZV8xXci8iuUw99yIi+UbJvYhIYWOdb58LQ/IBgv6dtx+5D/6924iISK5Qz72ISL5Rci8iUthY7kmfG/PtAYqFgJMbJF2BS0dzp00RkWsxEHc69bVWyhcRyXNK7kVECpvc7rl3dIKAqrZti4jcqvOHUp+9SoC7r11DERG5Eyi5FxEpTFKSIerP1NdBt3gbvOtp3r2I5DbNtxcRyVdK7kVECpOLRyD5Gjh7QNHyudeudd69knsRySWW5N5Pyb2ISH5Qci8iUphY5tsHhoJDLv4KV8+9iOQ262J6Su5FRPKDknsRkcIkt+fbWwRWS32OOQFXo3O3bRG5M1mH5WulfBGR/KDkXkSkMLH0rOfWSvkW7r7gUzb1deS+3G1bRO48iVcg+kTqayX3IiL5Qsm9iEhhYu25z8XF9CwsXxho3r2I3Krzf6U+F/EDj+L2jUVE5A6h5F5EpLC4cgHizqS+DgzN/fat8+735n7bInJnsc63V6+9iEh+UXIvIlJYRP6bdBctD65eud++eu5FJLfoNngiIvlOyb2ISGGRV/PtLSw991F/Qkpy3hxDRO4M6rkXEcl3Su5FRAqLvJxvD6kjAlw8IfkaXDySN8cQkTuDeu5FRPKdknsRkcIir3vuHRwg4N+5/Jp3LyI5lXQNLh1Lfa2eexGRfONk7wBEpJC5dAyWD4TTu+wdyZ0nKT71ObfvcX+9oOrwzy+w7DlYMTjvjnMnCgiFh8dBcAN7RyKSty4cBsMMbr7gGWDvaERE7hhK7kUk6w6tha+fgWvR9o7kzhVQDXzL5l37dzWHX+eDOSn1Ibnn1K+woBWEtoeH34Ci5ewdkUjesA7JrwImk31jERG5gyi5F5GbM5th8yTY+A5gQKla0OZDcPW2d2R3Hu+SefvHcuUW8NIRSLycd8e4E6Ukws8zYed82L8cDn4H9YdA/aHg6mnv6ERyl3UxvUr2jUNE5A6j5F5EMhd/EZY9C4d+SH1fuw+0mABOrvaNS/KOR/HUh+Su1u9D7d6wZgQc3QybJ8Nvn0KzcXD3E6lrHojcDq7vuRcRkXyj5F5EMnbmd1j8NESfACc3aP0B1HzK3lGJFF5B1aH7CjiwCn54LXUNi2X94Jc5qV+aBeXRnRBym4MTODrbOwopqKw991opX0QkPym5F5H0/fYprHoh9bZovuXgyU+hxD32jkqk8DOZoGprqNgMfp4BW95LnY//ycP2jix73HygiB94+IOHHxQpft1rP43uuWMZ/91KUz33IiL5Ssm9SF5LuAyxp1IfyYn2jiZrDq6GXeGpr+9qDh1mg3tR+8YkcrtxdoOw4amjYda/Cb9/AUaKvaPKumsxqQ9LIidyPRcv8C5l7yhERO4oSu5vM/t//p6EmEh7h3FHcki+hvvVM7jF//u4eha3+LO4JMXYO7QcMTBxOHQwR6o+B0euAWfsHZLI7avi65jKv4yDUTjuUOCQkohLwiVcEi7gknDxusd/7x3MyfYOU+zEwMSZcm04ue+svUMREcmUt5sz9Sr62TuMXKPk/jZjbHibexN/t3cYcoNYw52zRjHiKRzDVOMNN2altGHzrhqwa7e9wxGRAssR8P/3IXKdU8BPu+wdhYhIpu4u5cPKwQ3sHUauUXJ/m4nzqcSf0YWlt8T49/n2uAdussmJCw5+nHcM4IKjH+cd/bng4M8FR3/iHTyy1IZhFKxbAt+fybaCFmtGDOPmdeDWP0tBOR9Z/bw3UxA+ixRc+XG9F5SfqdtNbv2OyA23y+9dubPl598ZeX2M201WfkdU8Lu9bker5P4288DAj+0dgoiIiIiIiOQz3VRXREREREREpJBTci8iIiIiIiJSyCm5FxERERERESnklNyLiIiIiIiIFHJaUC+LjH+XqIyNjbVzJCIiIiIiInInsOSfRhZumaDkPovi4uIAKFOmjJ0jERERERERkTtJXFwcPj4+mdYxGVn5CkAwm82cPn0aLy8vTAX4JpKxsbGUKVOGkydP4u3tbe9wRDKka1UKC12rUljoWpXCQNepFBYF5Vo1DIO4uDhKliyJg0Pms+rVc59FDg4OlC5d2t5hZJm3t7d+YUqhoGtVCgtdq1JY6FqVwkDXqRQWBeFavVmPvYUW1BMREREREREp5JTci4iIiIiIiBRySu5vM66urowZMwZXV1d7hyKSKV2rUljoWpXCQteqFAa6TqWwKIzXqhbUExERERERESnk1HMvIiIiIiIiUsgpuRcREREREREp5JTci4iIiIiIiBRySu5FRERERERECjkl97eZ6dOnExwcjJubG3Xr1uWXX36xd0hyB5swYQL3338/Xl5eBAQE0L59ew4ePGhT59q1awwcOJDixYvj6elJx44diYyMtFPEIqneeecdTCYTQ4cOtZbpWpWC4tSpUzz99NMUL14cd3d37r77bn799VfrdsMwGD16NCVKlMDd3Z1mzZpx6NAhO0Ysd6KUlBRGjRpF+fLlcXd3JyQkhDfffJPr1/LWtSr2sHnzZtq0aUPJkiUxmUwsX77cZntWrsuLFy/StWtXvL298fX1pU+fPly+fDkfP0X6lNzfRhYvXszw4cMZM2YMu3btokaNGjRv3pyoqCh7hyZ3qE2bNjFw4EB+/vln1q5dS1JSEo888ghXrlyx1hk2bBgrV65kyZIlbNq0idOnT9OhQwc7Ri13uh07djB79mzuuecem3Jdq1IQXLp0ifr16+Ps7Mx3333H/v37ee+99yhatKi1zqRJk/joo4+YNWsW27dvx8PDg+bNm3Pt2jU7Ri53mokTJzJz5kymTZvGn3/+ycSJE5k0aRJTp0611tG1KvZw5coVatSowfTp09PdnpXrsmvXruzbt4+1a9fyv//9j82bN9OvX7/8+ggZM+S2UadOHWPgwIHW9ykpKUbJkiWNCRMm2DEqkf9ERUUZgLFp0ybDMAwjOjracHZ2NpYsWWKt8+effxqAsW3bNnuFKXewuLg446677jLWrl1rNGrUyHj++ecNw9C1KgXHK6+8YjRo0CDD7Waz2QgKCjImT55sLYuOjjZcXV2NL774Ij9CFDEMwzBatWpl9O7d26asQ4cORteuXQ3D0LUqBQNgLFu2zPo+K9fl/v37DcDYsWOHtc53331nmEwm49SpU/kWe3rUc3+bSExMZOfOnTRr1sxa5uDgQLNmzdi2bZsdIxP5T0xMDADFihUDYOfOnSQlJdlct1WqVKFs2bK6bsUuBg4cSKtWrWyuSdC1KgXHihUrqF27Nk888QQBAQHce++9zJ0717r96NGjnD171uZa9fHxoW7durpWJV/Vq1eP9evX89dffwHw+++/s3XrVlq2bAnoWpWCKSvX5bZt2/D19aV27drWOs2aNcPBwYHt27fne8zXc7Lr0SXXnD9/npSUFAIDA23KAwMDOXDggJ2iEvmP2Wxm6NCh1K9fn+rVqwNw9uxZXFxc8PX1takbGBjI2bNn7RCl3Mm+/PJLdu3axY4dO9Js07UqBcXff//NzJkzGT58OCNHjmTHjh0MGTIEFxcXevToYb0e0/t7QNeq5KdXX32V2NhYqlSpgqOjIykpKYwfP56uXbsC6FqVAikr1+XZs2cJCAiw2e7k5ESxYsXsfu0quReRfDFw4ED++OMPtm7dau9QRNI4efIkzz//PGvXrsXNzc3e4YhkyGw2U7t2bd5++20A7r33Xv744w9mzZpFjx497BydyH+++uorPvvsMz7//HOqVavG7t27GTp0KCVLltS1KpJHNCz/NuHn54ejo2OalZsjIyMJCgqyU1QiqQYNGsT//vc/IiIiKF26tLU8KCiIxMREoqOjberrupX8tnPnTqKiorjvvvtwcnLCycmJTZs28dFHH+Hk5ERgYKCuVSkQSpQoQWhoqE1Z1apVOXHiBID1etTfA2JvL730Eq+++iqdO3fm7rvvplu3bgwbNowJEyYAulalYMrKdRkUFJRmwfLk5GQuXrxo92tXyf1twsXFhVq1arF+/XprmdlsZv369Tz44IN2jEzuZIZhMGjQIJYtW8aGDRsoX768zfZatWrh7Oxsc90ePHiQEydO6LqVfNW0aVP27t3L7t27rY/atWvTtWtX62tdq1IQ1K9fP80tRf/66y/KlSsHQPny5QkKCrK5VmNjY9m+fbuuVclX8fHxODjYphqOjo6YzWZA16oUTFm5Lh988EGio6PZuXOntc6GDRswm83UrVs332O+nobl30aGDx9Ojx49qF27NnXq1GHKlClcuXKFXr162Ts0uUMNHDiQzz//nG+//RYvLy/rPCQfHx/c3d3x8fGhT58+DB8+nGLFiuHt7c3gwYN58MEHeeCBB+wcvdxJvLy8rGtBWHh4eFC8eHFrua5VKQiGDRtGvXr1ePvtt+nUqRO//PILc+bMYc6cOQCYTCaGDh3KW2+9xV133UX58uUZNWoUJUuWpH379vYNXu4obdq0Yfz48ZQtW5Zq1arx22+/8f7779O7d29A16rYz+XLlzl8+LD1/dGjR9m9ezfFihWjbNmyN70uq1atSosWLejbty+zZs0iKSmJQYMG0blzZ0qWLGmnT/Uvu67VL7lu6tSpRtmyZQ0XFxejTp06xs8//2zvkOQOBqT7mD9/vrXO1atXjQEDBhhFixY1ihQpYjz22GPGmTNn7Be0yL+uvxWeYehalYJj5cqVRvXq1Q1XV1ejSpUqxpw5c2y2m81mY9SoUUZgYKDh6upqNG3a1Dh48KCdopU7VWxsrPH8888bZcuWNdzc3IwKFSoYr732mpGQkGCto2tV7CEiIiLdv0979OhhGEbWrssLFy4YXbp0MTw9PQ1vb2+jV69eRlxcnB0+jS2TYRiGnb5XEBEREREREZFcoDn3IiIiIiIiIoWcknsRERERERGRQk7JvYiIiIiIiEghp+ReREREREREpJBTci8iIiIiIiJSyCm5FxERERERESnklNyLiIiIiIiIFHJK7kVEREREREQKOSX3IiIiIiIiIoWcknsREZHb0MaNGzGZTIwdOzZfjzt27FhMJhMbN27M1+NmpKDFIyIikleU3IuIiIiIiIgUckruRURERERERAo5JfciIiK3mbFjx9KkSRMAxo0bh8lksj6OHTtmrZeYmMj777/Pfffdh4eHB15eXoSFhbFixYo0bcbExDB69GhCQ0Px9PTE29ubihUr0qNHD44fPw5A48aNGTduHABNmjSxHjM4OPimMWelfQvDMJg3bx7169fH29ubIkWKULt2bebNm2dT71biERERKWyc7B2AiIiI5K7GjRtz7NgxwsPDadSoEY0bN7Zu8/X1BSAhIYEWLVqwceNGatasSZ8+fUhKSmLVqlW0a9eOqVOnMmjQICA1mW7evDnbt2+nfv36tGjRAgcHB44fP86KFSvo1q0b5cqVo2fPngBs2rSJHj16WJNoyzEzktX2LXW7du3KF198wV133cVTTz2Fi4sLa9eupU+fPuzfv593330XIMfxiIiIFEqGiIiI3HYiIiIMwBgzZky620eOHGkAxqhRowyz2Wwtj42NNWrXrm24uLgYp06dMgzDMPbs2WMARvv27dO0c+3aNSMuLs76fsyYMQZgREREZDnW7LQ/Z84cAzB69eplJCYmWssTEhKMNm3aGIDx66+/3lI8IiIihZGG5YuIiNxhzGYzM2fOJCQkxDps38LLy4vRo0eTmJjIN998Y7Ofu7t7mrZcXV3x9PTMlbiy0v60adPw8PBg+vTpODs7W8tdXFwYP348AF988UWuxCMiIlKYaFi+iIjIHebgwYNcunSJkiVLWuekX+/cuXMAHDhwAICqVatyzz338MUXX/DPP//Qvn17GjduTM2aNXFwuPV+gqy2Hx8fz969eylZsiQTJ05M005SUpJN3CIiIncSJfciIiJ3mIsXLwKwb98+9u3bl2G9K1euAODk5MSGDRsYO3YsX3/9NS+88AIA/v7+DBo0iNdeew1HR8ccx5PV9i9duoRhGJw6dSrdLyVujFtEROROomH5IiIidxhvb28AOnbsiGEYGT7mz59v3ad48eJMnTqVU6dOsX//fqZNm0axYsUYM2YMkyZNuuWYstK+Je5atWplGndERMQtxyMiIlLYKLkXERG5DVl60lNSUtJsq1q1Kt7e3vz666/WoexZZTKZqFq1KgMHDmTt2rUANrfOy+y4t9q+l5cXVatW5c8//yQ6OjpL7d1qPCIiIoWFknsREZHbULFixQA4efJkmm1OTk7079+f48eP8+KLL6ab4P/xxx9ERUUBcOzYMY4dO5amTmRkJABubm5ZOm5GstP+kCFDiI+Pp2/fvukOvz969KhNWzmJR0REpDAyGYZh2DsIERERyV0pKSmULVuWCxcu0KNHD0qXLo3JZGLw4MH4+PiQkJBAmzZtWLt2LSEhITRs2JCAgABOnTrF3r17+f3339m2bRsPPPAAy5cvp0OHDtSpU4fQ0FCCgoI4deoUy5cv5/Llyyxbtoy2bdsCsH//fqpXr05QUBBdu3bFx8cHX19fBg0alGGs2WnfMAx69epFeHg4JUqUoFmzZpQsWZLIyEgOHDjA9u3b+fzzz+ncuXOO4xERESmMlNyLiIjcprZv384rr7zCrl27iIuLA1J7toODg4HULwA++eQTFi5cyN69e0lISCAwMJDQ0FDatWtHt27d8PDw4J9//mH69Ols3LiRv//+m+joaIKCgqhduzYvvfQSDzzwgM1xw8PDee+99/jrr79ISEigXLly6fbMW2S3fYCvvvqKuXPnsnPnTi5fvkxAQAB33XUXbdq0oXv37vj5+eU4HhERkcJIyb2IiIiIiIhIIac59yIiIiIiIiKFnJJ7ERERERERkUJOyb2IiIiIiIhIIafkXkRERERERKSQU3IvIiIiIiIiUsgpuRcREREREREp5JTci4iIiIiIiBRySu5FRERERERECjkl9yIiIiIiIiKFnJJ7ERERERERkUJOyb2IiIiIiIhIIafkXkRERERERKSQ+z9c22a8P8AyMgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We should remark again that the feedback on the conformal intervals was provided without having to retrain the model, which is useful in practice. However, if the distribution of the data starts drifting over time, the conformal intervals may progressively become large and unusable. In such case, one may track the conformal interval size and trigger retraining after it reaches a certain threshold."
      ],
      "metadata": {
        "id": "Qe-qPNVEEOXB"
      }
    }
  ]
}