Day 74/Model_2.ipynb

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      "name": "Model-2.ipynb",
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      "name": "python3",
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    {
      "cell_type": "markdown",
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      "source": [
        "# Data Analysis on Precision Agriculture\n",
        "\n",
        "## Business Understanding\n",
        "Machine Learning in precision agriculture comes with the idea of optimizing the usage of pesticides using smart devices. However, the use of excessive pesticides leads to high amount of crop damage which can result in a negative effect towards the agriculture. This can prove to be harmful in terms of generating a higher yield for the crop. A good harvest is ensured by several factors such as availability of water, soil fertility, protecting crops from rodents, timely use of pesticides & other useful chemicals and nature. While a lot of these factors are difficult to control for, the amount and frequency of pesticides is something the farmer can control.\n",
        "\n",
        "Pesticides are also special, because while they protect the crop with the right dosage. But, if you add more than required, they may spoil the entire harvest. A high level of pesticide can deem the crop dead / unsuitable for consumption among many outcomes. This data is based on crops harvested by various farmers at the end of harvest season. To simplify the problem, you can assume that all other factors like variations in farming techniques have been controlled for.\n",
        "\n",
        "## Data Understanding\n",
        "\n",
        "The target variable is the Crop Damage which essentially refers to the damage done to the crop due to excessive pesticide usage (referring to be either 0 or 1, which indicates not damaged and damaged respectively). Hence, it is a binary classification problem.\n",
        "\n",
        "The input variables in the problem are:\n",
        "\n",
        "1. ID: Unique ID (object)\n",
        "2. Estimated_Insects_Count: The number of insects on the crop (int64)\n",
        "3. Crop_Type: The category of the crop (0, 1) (int64)\n",
        "4. Soil_Type: The category of the soil (0, 1) (int64)\n",
        "5. Pesticide_Use_Category: Type of pesticides uses (1- Never, 2-Previously Used, 3-Currently Using) (int64)\n",
        "6. Number_Doses_Week: Number of Doses per week (int64)\n",
        "7. Number_Weeks_Used: Number of weeks used (float64)\n",
        "8. Number_Weeks_Quit: Number of weeks quitted (int64)\n",
        "9. Season: Season Category (1, 2, 3)\n",
        "\n",
        "Hence we have a single target variable, and 9 input features to deal with.\n",
        "The expected output file should contain Unique ID along with the indicated crop damage.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nkby7z4DYjIc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "6fc39b08-6957-4672-9bed-d348914baa4c"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zCUlVzxAYrAG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "from sklearn.preprocessing import *\n",
        "from sklearn.model_selection import *\n",
        "from sklearn.metrics import *\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from lightgbm import LGBMClassifier\n",
        "from sklearn.ensemble import VotingClassifier"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwnNYkOwEXRJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = pd.read_csv(\"/content/drive/My Drive/train.csv\", encoding=\"utf-8\")\n",
        "test = pd.read_csv(\"/content/drive/My Drive/test.csv\", encoding=\"utf-8\")"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p_J1lxD5DPV3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b2a4a3e4-1cf2-452d-b7e9-47760660e244"
      },
      "source": [
        "train['train_flag'] = 1\n",
        "test['train_flag'] = 0\n",
        "test['Crop_Damage'] = 0\n",
        "\n",
        "print(train.shape, test.shape)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(88858, 11) (59310, 11)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2pCPapR4_3k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d362d232-84f1-4228-e051-61567b0536c7"
      },
      "source": [
        "data = pd.concat([train, test])\n",
        "data.shape"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(148168, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Opxspw55Oro",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fa0e29db-09fb-46b3-f56f-bbdde06d2f4e"
      },
      "source": [
        "features = data.columns.tolist()\n",
        "features.remove('ID')\n",
        "features.remove('Crop_Damage')\n",
        "features.remove('train_flag')\n",
        "label_col = 'Crop_Damage'\n",
        "print(features)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Estimated_Insects_Count', 'Crop_Type', 'Soil_Type', 'Pesticide_Use_Category', 'Number_Doses_Week', 'Number_Weeks_Used', 'Number_Weeks_Quit', 'Season']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bNXP4PHm5hMy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "774c3901-5881-4f2b-ffb4-f27077851a33"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Estimated_Insects_Count</th>\n",
              "      <th>Crop_Type</th>\n",
              "      <th>Soil_Type</th>\n",
              "      <th>Pesticide_Use_Category</th>\n",
              "      <th>Number_Doses_Week</th>\n",
              "      <th>Number_Weeks_Used</th>\n",
              "      <th>Number_Weeks_Quit</th>\n",
              "      <th>Season</th>\n",
              "      <th>Crop_Damage</th>\n",
              "      <th>train_flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F00000005</td>\n",
              "      <td>257</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>F00000006</td>\n",
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              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "          ID  Estimated_Insects_Count  ...  Crop_Damage  train_flag\n",
              "0  F00000001                      188  ...            0           1\n",
              "1  F00000003                      209  ...            1           1\n",
              "2  F00000004                      257  ...            1           1\n",
              "3  F00000005                      257  ...            1           1\n",
              "4  F00000006                      342  ...            1           1\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NShL40Md6LJd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "190e1389-40c6-4581-ec18-b45a9858e7ab"
      },
      "source": [
        "data['ID_Value'] = data['ID'].apply(lambda x: x.strip('F')).astype(int)\n",
        "data.head()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Estimated_Insects_Count</th>\n",
              "      <th>Crop_Type</th>\n",
              "      <th>Soil_Type</th>\n",
              "      <th>Pesticide_Use_Category</th>\n",
              "      <th>Number_Doses_Week</th>\n",
              "      <th>Number_Weeks_Used</th>\n",
              "      <th>Number_Weeks_Quit</th>\n",
              "      <th>Season</th>\n",
              "      <th>Crop_Damage</th>\n",
              "      <th>train_flag</th>\n",
              "      <th>ID_Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>F00000001</td>\n",
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              "      <td>1</td>\n",
              "      <td>3</td>\n",
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              "      <th>2</th>\n",
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              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F00000005</td>\n",
              "      <td>257</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>F00000006</td>\n",
              "      <td>342</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          ID  Estimated_Insects_Count  ...  train_flag  ID_Value\n",
              "0  F00000001                      188  ...           1         1\n",
              "1  F00000003                      209  ...           1         3\n",
              "2  F00000004                      257  ...           1         4\n",
              "3  F00000005                      257  ...           1         5\n",
              "4  F00000006                      342  ...           1         6\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9llCqNz6xjF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = data.sort_values(['ID_Value'])"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vkvqy-pB652-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "626b9156-92b1-46c5-e9e2-bb31a19ca12e"
      },
      "source": [
        "data = data.reset_index(drop=True)\n",
        "data.head()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Estimated_Insects_Count</th>\n",
              "      <th>Crop_Type</th>\n",
              "      <th>Soil_Type</th>\n",
              "      <th>Pesticide_Use_Category</th>\n",
              "      <th>Number_Doses_Week</th>\n",
              "      <th>Number_Weeks_Used</th>\n",
              "      <th>Number_Weeks_Quit</th>\n",
              "      <th>Season</th>\n",
              "      <th>Crop_Damage</th>\n",
              "      <th>train_flag</th>\n",
              "      <th>ID_Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>F00000001</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>F00000002</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>F00000003</td>\n",
              "      <td>209</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F00000004</td>\n",
              "      <td>257</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>F00000005</td>\n",
              "      <td>257</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          ID  Estimated_Insects_Count  ...  train_flag  ID_Value\n",
              "0  F00000001                      188  ...           1         1\n",
              "1  F00000002                      188  ...           0         2\n",
              "2  F00000003                      209  ...           1         3\n",
              "3  F00000004                      257  ...           1         4\n",
              "4  F00000005                      257  ...           1         5\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMMWbvCGC8q4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "outputId": "5e878363-f2cf-4064-b887-569dfe5c350d"
      },
      "source": [
        "def data_info(data, data_types, dataframe_name):\n",
        "    print(\" Information of \",dataframe_name,\": Rows = \",data.shape[0],\"| Columns = \",data.shape[1],\"\\n\")\n",
        "    data.info()\n",
        "    print(\"\\n\")\n",
        "    for VARIABLE in data_types:\n",
        "        data_type = data.select_dtypes(include=[VARIABLE]).dtypes\n",
        "        if len(data_type) > 0:\n",
        "            print(str(len(data_type))+\" \"+VARIABLE+\" Features\\n\"+str(data_type)+\"\\n\"  )        \n",
        "\n",
        "\n",
        "data_types  = [\"float32\",\"float64\",\"int32\",\"int64\",\"object\",\"category\",\"datetime64[ns]\"]\n",
        "data_info(data, data_types, \"train\")"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Information of  train : Rows =  148168 | Columns =  12 \n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 148168 entries, 0 to 148167\n",
            "Data columns (total 12 columns):\n",
            " #   Column                   Non-Null Count   Dtype  \n",
            "---  ------                   --------------   -----  \n",
            " 0   ID                       148168 non-null  object \n",
            " 1   Estimated_Insects_Count  148168 non-null  int64  \n",
            " 2   Crop_Type                148168 non-null  int64  \n",
            " 3   Soil_Type                148168 non-null  int64  \n",
            " 4   Pesticide_Use_Category   148168 non-null  int64  \n",
            " 5   Number_Doses_Week        148168 non-null  int64  \n",
            " 6   Number_Weeks_Used        133275 non-null  float64\n",
            " 7   Number_Weeks_Quit        148168 non-null  int64  \n",
            " 8   Season                   148168 non-null  int64  \n",
            " 9   Crop_Damage              148168 non-null  int64  \n",
            " 10  train_flag               148168 non-null  int64  \n",
            " 11  ID_Value                 148168 non-null  int64  \n",
            "dtypes: float64(1), int64(10), object(1)\n",
            "memory usage: 13.6+ MB\n",
            "\n",
            "\n",
            "1 float64 Features\n",
            "Number_Weeks_Used    float64\n",
            "dtype: object\n",
            "\n",
            "10 int64 Features\n",
            "Estimated_Insects_Count    int64\n",
            "Crop_Type                  int64\n",
            "Soil_Type                  int64\n",
            "Pesticide_Use_Category     int64\n",
            "Number_Doses_Week          int64\n",
            "Number_Weeks_Quit          int64\n",
            "Season                     int64\n",
            "Crop_Damage                int64\n",
            "train_flag                 int64\n",
            "ID_Value                   int64\n",
            "dtype: object\n",
            "\n",
            "1 object Features\n",
            "ID    object\n",
            "dtype: object\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eeoBr8G5GZDv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "outputId": "949dcb0c-deca-46fb-b940-e05ff5cc3767"
      },
      "source": [
        "def display_head_tail(data, head_size=5, tail_size=5):\n",
        "  print(\"The head and tail of the data is given as follows:\")\n",
        "  display(data.head(head_size).append(data.tail(tail_size)))\n",
        "\n",
        "display_head_tail(data, 3, 3)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The head and tail of the data is given as follows:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Estimated_Insects_Count</th>\n",
              "      <th>Crop_Type</th>\n",
              "      <th>Soil_Type</th>\n",
              "      <th>Pesticide_Use_Category</th>\n",
              "      <th>Number_Doses_Week</th>\n",
              "      <th>Number_Weeks_Used</th>\n",
              "      <th>Number_Weeks_Quit</th>\n",
              "      <th>Season</th>\n",
              "      <th>Crop_Damage</th>\n",
              "      <th>train_flag</th>\n",
              "      <th>ID_Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>F00000001</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>F00000002</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>F00000003</td>\n",
              "      <td>209</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148165</th>\n",
              "      <td>F00155943</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>28.0</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>155943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148166</th>\n",
              "      <td>F00155944</td>\n",
              "      <td>3895</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>52.0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>155944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148167</th>\n",
              "      <td>F00155945</td>\n",
              "      <td>3895</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>37.0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>155945</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               ID  Estimated_Insects_Count  ...  train_flag  ID_Value\n",
              "0       F00000001                      188  ...           1         1\n",
              "1       F00000002                      188  ...           0         2\n",
              "2       F00000003                      209  ...           1         3\n",
              "148165  F00155943                     3702  ...           0    155943\n",
              "148166  F00155944                     3895  ...           0    155944\n",
              "148167  F00155945                     3895  ...           1    155945\n",
              "\n",
              "[6 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6y5OR58IJXUs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "90dce6d3-21f9-4c33-e1e0-4629c98b497a"
      },
      "source": [
        "def remove_duplicates(data):\n",
        "  print(\"Before Removing Duplicate Data: \", data.shape[0])\n",
        "  data.drop_duplicates(keep='first', inplace=True)\n",
        "  print(\"After Removing Duplicate Data: \", data.shape[0])\n",
        "remove_duplicates(data)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before Removing Duplicate Data:  148168\n",
            "After Removing Duplicate Data:  148168\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KOZs-oPCV-O4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "f2b897bf-9961-426c-9a04-935f0f149628"
      },
      "source": [
        "def handle_missing_values(data, fill_value, fill_types, columns, dataframe_name):\n",
        "  print(\"Missing Values BEFORE REMOVAL in \", dataframe_name)\n",
        "  display(data.isna().sum())\n",
        "  for column in columns :\n",
        "        \n",
        "      # Fill Missing Values with Specific Value :\n",
        "      if \"Value_Fill\" in fill_types :\n",
        "          data[ column ] = data[ column ].fillna(fill_value)\n",
        "#            print(\"Value_Fill\")\n",
        "\n",
        "        # Fill Missing Values with Forward Fill  (Previous Row Value as Current Row in Table) :\n",
        "      if \"Forward_Fill\" in fill_types :\n",
        "          data[ column ] = data[ column ].ffill(axis = 0)\n",
        "#            print(\"Forward_Fill\")\n",
        "\n",
        "        # Fill Missing Values with Backward Fill (Next Row Value as Current Row in Table) :\n",
        "      if \"Backward_Fill\" in fill_types :\n",
        "          data[ column ] = data[ column ].bfill(axis = 0)\n",
        "#             print(\"Backward_Fill\")\n",
        "    \n",
        "  print(\"Missing Values AFTER REMOVAL in \",dataframe_name,\" data\")\n",
        "  display(data.isnull().sum())\n",
        "    \n",
        "  return data\n",
        "\n",
        "fill_types = [ \"Forward_Fill\"]\n",
        "# Median Replacement of Null values\n",
        "fill_value = train[\"Number_Weeks_Used\"].median()\n",
        "train = handle_missing_values(data, fill_value, fill_types, [\"Number_Weeks_Used\"],\"train\")"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Missing Values BEFORE REMOVAL in  train\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "ID                             0\n",
              "Estimated_Insects_Count        0\n",
              "Crop_Type                      0\n",
              "Soil_Type                      0\n",
              "Pesticide_Use_Category         0\n",
              "Number_Doses_Week              0\n",
              "Number_Weeks_Used          14893\n",
              "Number_Weeks_Quit              0\n",
              "Season                         0\n",
              "Crop_Damage                    0\n",
              "train_flag                     0\n",
              "ID_Value                       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Missing Values AFTER REMOVAL in  train  data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "ID                         0\n",
              "Estimated_Insects_Count    0\n",
              "Crop_Type                  0\n",
              "Soil_Type                  0\n",
              "Pesticide_Use_Category     0\n",
              "Number_Doses_Week          0\n",
              "Number_Weeks_Used          0\n",
              "Number_Weeks_Quit          0\n",
              "Season                     0\n",
              "Crop_Damage                0\n",
              "train_flag                 0\n",
              "ID_Value                   0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wkULsmPVCxbs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7482a1f6-fa4b-49b0-bdcb-66082bb19d4b"
      },
      "source": [
        "def unique_values(data):\n",
        "  for column in data.columns:\n",
        "    print(\"Number of unique values are: \", str(data[column].nunique()))\n",
        "    print(\"Actual Unique Values in \"+column+\" Column are : \"+\n",
        "          str(data[column].sort_values(ascending=True,na_position='last').unique() ))\n",
        "    print(\"Value Counts :\")\n",
        "    print(data[column].value_counts())\n",
        "    print(\"\")\n",
        "unique_values(data)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of unique values are:  148168\n",
            "Actual Unique Values in ID Column are : ['F00000001' 'F00000002' 'F00000003' ... 'F00155943' 'F00155944'\n",
            " 'F00155945']\n",
            "Value Counts :\n",
            "F00089306    1\n",
            "F00087917    1\n",
            "F00025043    1\n",
            "F00040119    1\n",
            "F00137415    1\n",
            "            ..\n",
            "F00133707    1\n",
            "F00110913    1\n",
            "F00137934    1\n",
            "F00112367    1\n",
            "F00078213    1\n",
            "Name: ID, Length: 148168, dtype: int64\n",
            "\n",
            "Number of unique values are:  71\n",
            "Actual Unique Values in Estimated_Insects_Count Column are : [ 150  151  168  169  188  209  231  232  256  257  283  311  312  342\n",
            "  375  410  411  448  488  489  531  577  625  626  677  731  732  789\n",
            "  790  850  851  915  916  984 1056 1132 1212 1296 1297 1385 1478 1575\n",
            " 1576 1678 1785 1786 1898 2015 2016 2138 2139 2267 2401 2402 2541 2542\n",
            " 2687 2688 2840 2999 3164 3165 3336 3337 3515 3516 3702 3895 3896 4096\n",
            " 4097]\n",
            "Value Counts :\n",
            "1132    5664\n",
            "1212    5639\n",
            "1478    5590\n",
            "1385    5580\n",
            "1056    5368\n",
            "        ... \n",
            "3896     237\n",
            "3515     220\n",
            "4097     194\n",
            "231      150\n",
            "169      103\n",
            "Name: Estimated_Insects_Count, Length: 71, dtype: int64\n",
            "\n",
            "Number of unique values are:  2\n",
            "Actual Unique Values in Crop_Type Column are : [0 1]\n",
            "Value Counts :\n",
            "0    105873\n",
            "1     42295\n",
            "Name: Crop_Type, dtype: int64\n",
            "\n",
            "Number of unique values are:  2\n",
            "Actual Unique Values in Soil_Type Column are : [0 1]\n",
            "Value Counts :\n",
            "0    80441\n",
            "1    67727\n",
            "Name: Soil_Type, dtype: int64\n",
            "\n",
            "Number of unique values are:  3\n",
            "Actual Unique Values in Pesticide_Use_Category Column are : [1 2 3]\n",
            "Value Counts :\n",
            "2    106265\n",
            "3     40495\n",
            "1      1408\n",
            "Name: Pesticide_Use_Category, dtype: int64\n",
            "\n",
            "Number of unique values are:  20\n",
            "Actual Unique Values in Number_Doses_Week Column are : [ 0  5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95]\n",
            "Value Counts :\n",
            "20    37627\n",
            "40    20747\n",
            "10    20170\n",
            "30    17322\n",
            "15    13710\n",
            "5      9173\n",
            "25     8090\n",
            "60     7295\n",
            "50     5565\n",
            "35     3039\n",
            "45     1544\n",
            "0      1408\n",
            "80     1047\n",
            "70      554\n",
            "55      456\n",
            "90      135\n",
            "65      130\n",
            "75       98\n",
            "95       41\n",
            "85       17\n",
            "Name: Number_Doses_Week, dtype: int64\n",
            "\n",
            "Number of unique values are:  64\n",
            "Actual Unique Values in Number_Weeks_Used Column are : [ 0.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21.\n",
            " 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39.\n",
            " 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57.\n",
            " 58. 59. 60. 61. 62. 63. 64. 65. 66. 67.]\n",
            "Value Counts :\n",
            "20.0    7256\n",
            "30.0    7046\n",
            "25.0    5792\n",
            "40.0    5138\n",
            "15.0    4568\n",
            "        ... \n",
            "63.0      88\n",
            "64.0      70\n",
            "65.0      63\n",
            "66.0      31\n",
            "67.0      13\n",
            "Name: Number_Weeks_Used, Length: 64, dtype: int64\n",
            "\n",
            "Number of unique values are:  51\n",
            "Actual Unique Values in Number_Weeks_Quit Column are : [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
            " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n",
            " 48 49 50]\n",
            "Value Counts :\n",
            "0     45472\n",
            "10     4806\n",
            "5      4677\n",
            "2      4633\n",
            "12     4555\n",
            "15     4470\n",
            "1      4451\n",
            "14     4397\n",
            "6      4344\n",
            "3      4281\n",
            "13     4170\n",
            "4      4158\n",
            "7      4040\n",
            "8      3801\n",
            "20     3621\n",
            "11     3580\n",
            "16     3532\n",
            "18     3396\n",
            "17     3356\n",
            "9      3169\n",
            "19     3027\n",
            "21     2366\n",
            "22     2362\n",
            "23     1929\n",
            "25     1796\n",
            "24     1729\n",
            "26     1481\n",
            "27     1358\n",
            "28     1310\n",
            "30     1221\n",
            "29     1196\n",
            "31      900\n",
            "32      846\n",
            "33      645\n",
            "34      578\n",
            "35      510\n",
            "36      419\n",
            "37      380\n",
            "38      243\n",
            "40      217\n",
            "39      189\n",
            "41      143\n",
            "42      128\n",
            "43       80\n",
            "44       53\n",
            "45       53\n",
            "46       38\n",
            "47       30\n",
            "48       17\n",
            "50       10\n",
            "49        5\n",
            "Name: Number_Weeks_Quit, dtype: int64\n",
            "\n",
            "Number of unique values are:  3\n",
            "Actual Unique Values in Season Column are : [1 2 3]\n",
            "Value Counts :\n",
            "2    73715\n",
            "1    44744\n",
            "3    29709\n",
            "Name: Season, dtype: int64\n",
            "\n",
            "Number of unique values are:  3\n",
            "Actual Unique Values in Crop_Damage Column are : [0 1 2]\n",
            "Value Counts :\n",
            "0    133548\n",
            "1     12307\n",
            "2      2313\n",
            "Name: Crop_Damage, dtype: int64\n",
            "\n",
            "Number of unique values are:  2\n",
            "Actual Unique Values in train_flag Column are : [0 1]\n",
            "Value Counts :\n",
            "1    88858\n",
            "0    59310\n",
            "Name: train_flag, dtype: int64\n",
            "\n",
            "Number of unique values are:  148168\n",
            "Actual Unique Values in ID_Value Column are : [     1      2      3 ... 155943 155944 155945]\n",
            "Value Counts :\n",
            "2047      1\n",
            "3243      1\n",
            "124182    1\n",
            "130325    1\n",
            "128276    1\n",
            "         ..\n",
            "146194    1\n",
            "140049    1\n",
            "142096    1\n",
            "86783     1\n",
            "2049      1\n",
            "Name: ID_Value, Length: 148168, dtype: int64\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KpdwEgOpaSKt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "54f51299-d0d7-4f5f-e496-201929263a4c"
      },
      "source": [
        "def heatmap(df):\n",
        "  plt.figure(figsize=(10,10))\n",
        "  corr = df.corr()\n",
        "  return sns.heatmap(corr, annot=True, cmap='viridis')\n",
        "\n",
        "def cntplt(df, feature):\n",
        "  plt.figure(figsize=(10, 10))\n",
        "  return sns.countplot(data=df, x=df[feature])\n",
        "\n",
        "heatmap(data)\n",
        "cntplt(data, 'Crop_Damage')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f820ce9f7f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xkgsCAFGUSAY",
        "colab_type": "text"
      },
      "source": [
        "The data is highly imbalanced and can be in need of oversampling before running the model. However, in a real time use perspective we can also say that the amount of crops damaged due to excessive pesticides can actually be low, and hence the data is justified as it is. However, for a generalized model, we will try handling the imbalance in the class.\n",
        "\n",
        "Let us try to answer a few more questions:\n",
        "\n",
        "## Is there any relation between the Season and Crops Damaged?\n",
        "- Season 2 has the highest number of damaged crops, although the count of the insects does not remain high during the Season 2 compared to other seasons, which can mean that the insects are more actively eating crops during Season 2 which justifies the damage done.\n",
        "## Is there any relation between type of crop and damages?\n",
        "\n",
        "- Season \n",
        "\n",
        "## Is a particular pesticide causing more damage than others?\n",
        "- Pesticide 2 seems to have an overall neutralizing effect in either cases since it has caused maximum crop damage, and at same time has caused "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RnnWNolXBdec",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "3a03743e-885f-427e-cd08-08a8e3f12a61"
      },
      "source": [
        "# Season 2 is the most damaging season for a crop\n",
        "sns.catplot(x=\"Crop_Damage\", hue=\"Season\", kind=\"count\", data=data);"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 404.125x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sKLhOEDWPO_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "cf1a74c0-a2e7-4ea3-889a-3b98fcfa4fd5"
      },
      "source": [
        "# All seasons have almost same estimated insects, hence we can say that Season 2 \n",
        "# is the active period for the insects\n",
        "sns.barplot(x='Season', y='Estimated_Insects_Count', data=data)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f82051054e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fj3RiDi8XjyQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "outputId": "474651df-109b-4f58-dca3-da514d583669"
      },
      "source": [
        "sns.catplot(y='Crop_Type', hue='Crop_Damage', kind='count', data=data)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f82094b8cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 438.125x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7soQFXJYf_s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "5f37e9c9-545c-41c3-9b56-e8727370c778"
      },
      "source": [
        "# Clearly, there is imbalance within type of crop as well\n",
        "sns.countplot(x='Crop_Type', data=data)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8204ff70f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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15fcBNwInA59rH0nSmIw9XKrqvwMH+t7JufP0L+DyA+xrK7B1nvoUcNZRTFOSdBT8hr4kqTvDRZLUneEiSerOcJEkdWe4SJK6M1wkSd0ZLpKk7gwXSVJ3hoskqTvDRZLUneEiSerOcJEkdWe4SJK6M1wkSd0ZLpKk7gwXSVJ3hoskqTvDRZLUneEiSerOcJEkdWe4SJK6M1wkSd0ZLpKk7gwXSVJ3hoskqTvDRZLUneEiSerOcJEkdWe4SJK6M1wkSd2dsOGSZEOSR5NMJ7li0vORpMXkhAyXJEuAjwEXAGuBdyVZO9lZSdLicUKGC7AemK6qx6vq28BNwMYJz0mSFo2lk57AAlkBPDW0vgd489xOSTYDm9vqnyd5dAxzWyxOB/5s0pOYtHx406SnoJfy3+asLemxl788X/FEDZeRVNV1wHWTnseJKMlUVa2b9Dykufy3OR4n6mWxvcCZQ+srW02SNAYnarjcC6xJsjrJScDFwI4Jz0mSFo0T8rJYVe1P8n5gJ7AE2FpVuyc8rcXGy406VvlvcwxSVZOegyTpBHOiXhaTJE2Q4SJJ6s5wUVe+dkfHqiRbkzyb5KFJz2UxMFzUja/d0THuRmDDpCexWBgu6snX7uiYVVVfAPZNeh6LheGinuZ77c6KCc1F0gQZLpKk7gwX9eRrdyQBhov68rU7kgDDRR1V1X5g9rU7jwA3+9odHSuSfAr4I+CHk+xJctmk53Qi8/UvkqTuPHORJHVnuEiSujNcJEndGS6SpO4MF0lSd4aLJKm7E/JnjqVxSvJa4LeBnwC+CnwF+GBV/a+OY7wGuL2tvhb4LjDT1te3F4VKxwy/5yIdhSQB/gewrap+t9XeAJxSVV9s60vbF0x7jflrwJ9X1Yd77VPqzcti0tH5aeA7s8ECUFX/E1iS5ItJdgAPJ3llkhuSPJjk/iQ/DZDk3UluTXJnkseSbBl14CTfn+SJJC9v66fMrrf9fTTJl5I8lGR96/N97Uez7mnz8CcRtCC8LCYdnbOA+w7QdjZwVlU9keRXgKqqH0vyI8B/SfJDrd/6tp9vAPcm+YOqmjrUwFX1tSR3Aj8L/D6Dd7l9tqq+Mzih4lVV9VeT/CSwtY3xz4E7quo9SU4F7knyX6vq60d4/NK8PHORFs49VfVEW34r8G8BqupPgCeB2XDZVVXPVdX/AT7b+o7q94BL2/KlwA1DbZ9q430BOKWFyXnAFUm+BNwJvBL4gcM8LumQPHORjs5u4KIDtI16NjD3xufIN0Kr6g+TrEryU8CSqhr+ffj59hvg71bVo6OOIR0Jz1yko3MH8Iokm2cLSX4c+Btz+n0R+IXW/kMMzhZm/4P/m0lOS3IycCHwh4c5h+3Av+P/P2sB+HttvLcCL1TVCwzeWP3L7UEEkrzxMMeSRmK4SEehBo9b/h3gZ5L87yS7gX8JPDOn68eBlyV5EPg08O6q+lZruwf4DPAA8JlR7rfM8UlgGe0y2JBvJrkf+F1g9vXyVwEvBx5oc73qMMeSRuKjyNIEJXk3sK6q3n8U+7gI2FhVvzRUuxP41SMIKqkL77lIx7EkvwNcALx90nORhnnmIh1j5nwbf9i5VfXcuOcjHQnDRZLUnTf0JUndGS6SpO4MF0lSd4aLJKm7/wfGW1ISIzEBuQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bcHT8kFpZ2wN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "cb236cd0-44ae-4666-ccf4-569558b7df01"
      },
      "source": [
        "sns.catplot(x='Pesticide_Use_Category', col='Crop_Damage', kind='count', data=data)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f8205105358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ED0YrIPkDK5q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "124be76e-fe8e-4958-dc97-f101b101a24e"
      },
      "source": [
        "def timeseries(data, new_feature, old_feature):\n",
        "  data[new_feature] = data.sort_values(['ID_Value']).groupby([old_feature])['Crop_Damage'].apply(lambda x: x.shift().rolling(10, min_periods=1).mean()).fillna(-999).values\n",
        "  return data\n",
        "timeseries(data, 'Pesticide_Use_Category_Damage', 'Pesticide_Use_Category')\n",
        "timeseries(data, 'Estimated_Insects_Count_Damage', 'Estimated_Insects_Count')\n",
        "timeseries(data, 'Crop_Type_Damage', 'Crop_Type')\n",
        "timeseries(data, 'Soil_Type_Damage', 'Soil_Type')\n",
        "timeseries(data, 'Number_Doses_Week_Damage', 'Number_Doses_Week')\n",
        "timeseries(data, 'Number_Weeks_Used_Damage', 'Number_Weeks_Used')\n",
        "timeseries(data, 'Number_Weeks_Quit_Damage', 'Number_Weeks_Quit')\n",
        "timeseries(data, 'Season_Damage', 'Season')"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Estimated_Insects_Count</th>\n",
              "      <th>Crop_Type</th>\n",
              "      <th>Soil_Type</th>\n",
              "      <th>Pesticide_Use_Category</th>\n",
              "      <th>Number_Doses_Week</th>\n",
              "      <th>Number_Weeks_Used</th>\n",
              "      <th>Number_Weeks_Quit</th>\n",
              "      <th>Season</th>\n",
              "      <th>Crop_Damage</th>\n",
              "      <th>train_flag</th>\n",
              "      <th>ID_Value</th>\n",
              "      <th>Pesticide_Use_Category_Damage</th>\n",
              "      <th>Estimated_Insects_Count_Damage</th>\n",
              "      <th>Crop_Type_Damage</th>\n",
              "      <th>Soil_Type_Damage</th>\n",
              "      <th>Number_Doses_Week_Damage</th>\n",
              "      <th>Number_Weeks_Used_Damage</th>\n",
              "      <th>Number_Weeks_Quit_Damage</th>\n",
              "      <th>Season_Damage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>F00000001</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>F00000002</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>F00000003</td>\n",
              "      <td>209</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F00000004</td>\n",
              "      <td>257</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>F00000005</td>\n",
              "      <td>257</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148163</th>\n",
              "      <td>F00155941</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>32.0</td>\n",
              "      <td>48</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>155941</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148164</th>\n",
              "      <td>F00155942</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>25.0</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>155942</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148165</th>\n",
              "      <td>F00155943</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>28.0</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>155943</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.100000</td>\n",
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              "    <tr>\n",
              "      <th>148166</th>\n",
              "      <td>F00155944</td>\n",
              "      <td>3895</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>52.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>155944</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148167</th>\n",
              "      <td>F00155945</td>\n",
              "      <td>3895</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>37.0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>155945</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>148168 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               ID  ...  Season_Damage\n",
              "0       F00000001  ...    -999.000000\n",
              "1       F00000002  ...    -999.000000\n",
              "2       F00000003  ...       0.000000\n",
              "3       F00000004  ...       0.500000\n",
              "4       F00000005  ...       0.666667\n",
              "...           ...  ...            ...\n",
              "148163  F00155941  ...       0.000000\n",
              "148164  F00155942  ...       0.000000\n",
              "148165  F00155943  ...       0.100000\n",
              "148166  F00155944  ...       0.000000\n",
              "148167  F00155945  ...       0.000000\n",
              "\n",
              "[148168 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mZNkZdnyHhFp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "3aa7fa98-fdaa-4527-94cf-f16bd710772b"
      },
      "source": [
        "data.loc[data['train_flag'] == 0, 'Crop_Damage'] = -999\n",
        "data.head()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Estimated_Insects_Count</th>\n",
              "      <th>Crop_Type</th>\n",
              "      <th>Soil_Type</th>\n",
              "      <th>Pesticide_Use_Category</th>\n",
              "      <th>Number_Doses_Week</th>\n",
              "      <th>Number_Weeks_Used</th>\n",
              "      <th>Number_Weeks_Quit</th>\n",
              "      <th>Season</th>\n",
              "      <th>Crop_Damage</th>\n",
              "      <th>train_flag</th>\n",
              "      <th>ID_Value</th>\n",
              "      <th>Pesticide_Use_Category_Damage</th>\n",
              "      <th>Estimated_Insects_Count_Damage</th>\n",
              "      <th>Crop_Type_Damage</th>\n",
              "      <th>Soil_Type_Damage</th>\n",
              "      <th>Number_Doses_Week_Damage</th>\n",
              "      <th>Number_Weeks_Used_Damage</th>\n",
              "      <th>Number_Weeks_Quit_Damage</th>\n",
              "      <th>Season_Damage</th>\n",
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              "      <td>F00000002</td>\n",
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              "    <tr>\n",
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              "      <th>4</th>\n",
              "      <td>F00000005</td>\n",
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              "      <td>0.500000</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          ID  Estimated_Insects_Count  ...  Number_Weeks_Quit_Damage  Season_Damage\n",
              "0  F00000001                      188  ...               -999.000000    -999.000000\n",
              "1  F00000002                      188  ...                  0.000000    -999.000000\n",
              "2  F00000003                      209  ...                  0.000000       0.000000\n",
              "3  F00000004                      257  ...                  0.333333       0.500000\n",
              "4  F00000005                      257  ...                  0.500000       0.666667\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "joUT6lmBH2Pd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "81e158d5-e128-4204-f4cb-ee9d18a99ed8"
      },
      "source": [
        "corr = data.corr()\n",
        "plt.figure(figsize=(20,20))\n",
        "sns.heatmap(corr, annot=True, cmap='coolwarm')"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f820515bf98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zui12JJWHuad",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "d87db947-abb9-4274-8689-647c522a294c"
      },
      "source": [
        "def first_lag(data, new_feature, old_feature):\n",
        "  data[new_feature] = data[old_feature].shift(fill_value=-99)\n",
        "  return data\n",
        "\n",
        "first_lag(data, 'Estimated_Insects_Count_Lag_1', 'Estimated_Insects_Count')\n",
        "first_lag(data, 'Crop_Type_Lag_1', 'Crop_Type')\n",
        "first_lag(data, 'Soil_Type_Lag_1', 'Soil_Type')\n",
        "first_lag(data, 'Pesticide_Use_Category_Lag_1', 'Pesticide_Use_Category')\n",
        "first_lag(data, 'Number_Doses_Week_Lag_1', 'Number_Doses_Week')\n",
        "first_lag(data, 'Number_Weeks_Used_Lag_1', 'Number_Weeks_Used')\n",
        "first_lag(data, 'Season_Lag_1', 'Season')"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Estimated_Insects_Count</th>\n",
              "      <th>Crop_Type</th>\n",
              "      <th>Soil_Type</th>\n",
              "      <th>Pesticide_Use_Category</th>\n",
              "      <th>Number_Doses_Week</th>\n",
              "      <th>Number_Weeks_Used</th>\n",
              "      <th>Number_Weeks_Quit</th>\n",
              "      <th>Season</th>\n",
              "      <th>Crop_Damage</th>\n",
              "      <th>train_flag</th>\n",
              "      <th>ID_Value</th>\n",
              "      <th>Pesticide_Use_Category_Damage</th>\n",
              "      <th>Estimated_Insects_Count_Damage</th>\n",
              "      <th>Crop_Type_Damage</th>\n",
              "      <th>Soil_Type_Damage</th>\n",
              "      <th>Number_Doses_Week_Damage</th>\n",
              "      <th>Number_Weeks_Used_Damage</th>\n",
              "      <th>Number_Weeks_Quit_Damage</th>\n",
              "      <th>Season_Damage</th>\n",
              "      <th>Estimated_Insects_Count_Lag_1</th>\n",
              "      <th>Crop_Type_Lag_1</th>\n",
              "      <th>Soil_Type_Lag_1</th>\n",
              "      <th>Pesticide_Use_Category_Lag_1</th>\n",
              "      <th>Number_Doses_Week_Lag_1</th>\n",
              "      <th>Number_Weeks_Used_Lag_1</th>\n",
              "      <th>Seaso_Lag_1</th>\n",
              "      <th>Season_Lag_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>F00000001</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-99</td>\n",
              "      <td>-99</td>\n",
              "      <td>-99</td>\n",
              "      <td>-99</td>\n",
              "      <td>-99</td>\n",
              "      <td>-99.0</td>\n",
              "      <td>-99</td>\n",
              "      <td>-99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>F00000002</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>-999</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>F00000003</td>\n",
              "      <td>209</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F00000004</td>\n",
              "      <td>257</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>209</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>F00000005</td>\n",
              "      <td>257</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>257</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148163</th>\n",
              "      <td>F00155941</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>32.0</td>\n",
              "      <td>48</td>\n",
              "      <td>1</td>\n",
              "      <td>-999</td>\n",
              "      <td>0</td>\n",
              "      <td>155941</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3516</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148164</th>\n",
              "      <td>F00155942</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>25.0</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>155942</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148165</th>\n",
              "      <td>F00155943</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>28.0</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>-999</td>\n",
              "      <td>0</td>\n",
              "      <td>155943</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>25.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148166</th>\n",
              "      <td>F00155944</td>\n",
              "      <td>3895</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>52.0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>-999</td>\n",
              "      <td>0</td>\n",
              "      <td>155944</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>28.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148167</th>\n",
              "      <td>F00155945</td>\n",
              "      <td>3895</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>37.0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>155945</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3895</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>148168 rows × 28 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               ID  Estimated_Insects_Count  ...  Seaso_Lag_1  Season_Lag_1\n",
              "0       F00000001                      188  ...          -99           -99\n",
              "1       F00000002                      188  ...            1             1\n",
              "2       F00000003                      209  ...            2             2\n",
              "3       F00000004                      257  ...            2             2\n",
              "4       F00000005                      257  ...            2             2\n",
              "...           ...                      ...  ...          ...           ...\n",
              "148163  F00155941                     3702  ...            2             2\n",
              "148164  F00155942                     3702  ...            1             1\n",
              "148165  F00155943                     3702  ...            3             3\n",
              "148166  F00155944                     3895  ...            2             2\n",
              "148167  F00155945                     3895  ...            1             1\n",
              "\n",
              "[148168 rows x 28 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLZH_tSB4Bmo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "7996f5c0-a6d3-4b23-c600-c90814f5afa3"
      },
      "source": [
        "def second_lag(data, new_feature, old_feature):\n",
        "  data[new_feature] = data[old_feature].shift(periods=2, fill_value=-999)\n",
        "  return data\n",
        "second_lag(data, 'Estimated_Insects_Count_Lag_2', 'Estimated_Insects_Count')\n",
        "second_lag(data, 'Crop_Type_Lag_2', 'Crop_Type')\n",
        "second_lag(data, 'Soil_Type_Lag_2', 'Soil_Type')\n",
        "second_lag(data, 'Pesticide_Use_Category_Lag_2', 'Pesticide_Use_Category')\n",
        "second_lag(data, 'Number_Doses_Week_Lag_2', 'Number_Doses_Week')\n",
        "second_lag(data, 'Number_Weeks_Used_Lag_2', 'Number_Weeks_Used')\n",
        "second_lag(data, 'Season_Lag_2', 'Season')"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Estimated_Insects_Count</th>\n",
              "      <th>Crop_Type</th>\n",
              "      <th>Soil_Type</th>\n",
              "      <th>Pesticide_Use_Category</th>\n",
              "      <th>Number_Doses_Week</th>\n",
              "      <th>Number_Weeks_Used</th>\n",
              "      <th>Number_Weeks_Quit</th>\n",
              "      <th>Season</th>\n",
              "      <th>Crop_Damage</th>\n",
              "      <th>train_flag</th>\n",
              "      <th>ID_Value</th>\n",
              "      <th>Pesticide_Use_Category_Damage</th>\n",
              "      <th>Estimated_Insects_Count_Damage</th>\n",
              "      <th>Crop_Type_Damage</th>\n",
              "      <th>Soil_Type_Damage</th>\n",
              "      <th>Number_Doses_Week_Damage</th>\n",
              "      <th>Number_Weeks_Used_Damage</th>\n",
              "      <th>Number_Weeks_Quit_Damage</th>\n",
              "      <th>Season_Damage</th>\n",
              "      <th>Estimated_Insects_Count_Lag_1</th>\n",
              "      <th>Crop_Type_Lag_1</th>\n",
              "      <th>Soil_Type_Lag_1</th>\n",
              "      <th>Pesticide_Use_Category_Lag_1</th>\n",
              "      <th>Number_Doses_Week_Lag_1</th>\n",
              "      <th>Number_Weeks_Used_Lag_1</th>\n",
              "      <th>Seaso_Lag_1</th>\n",
              "      <th>Season_Lag_1</th>\n",
              "      <th>Estimated_Insects_Count_Lag_2</th>\n",
              "      <th>Crop_Type_Lag_2</th>\n",
              "      <th>Soil_Type_Lag_2</th>\n",
              "      <th>Pesticide_Use_Category_Lag_2</th>\n",
              "      <th>Number_Doses_Week_Lag_2</th>\n",
              "      <th>Number_Weeks_Used_Lag_2</th>\n",
              "      <th>Season_Lag_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>F00000001</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-99</td>\n",
              "      <td>-99</td>\n",
              "      <td>-99</td>\n",
              "      <td>-99</td>\n",
              "      <td>-99</td>\n",
              "      <td>-99.0</td>\n",
              "      <td>-99</td>\n",
              "      <td>-99</td>\n",
              "      <td>-999</td>\n",
              "      <td>-999</td>\n",
              "      <td>-999</td>\n",
              "      <td>-999</td>\n",
              "      <td>-999</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>F00000002</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>-999</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-999</td>\n",
              "      <td>-999</td>\n",
              "      <td>-999</td>\n",
              "      <td>-999</td>\n",
              "      <td>-999</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>F00000003</td>\n",
              "      <td>209</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F00000004</td>\n",
              "      <td>257</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>209</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>188</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>F00000005</td>\n",
              "      <td>257</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>257</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>209</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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>148163</th>\n",
              "      <td>F00155941</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>32.0</td>\n",
              "      <td>48</td>\n",
              "      <td>1</td>\n",
              "      <td>-999</td>\n",
              "      <td>0</td>\n",
              "      <td>155941</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3516</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3516</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>40.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148164</th>\n",
              "      <td>F00155942</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>25.0</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>155942</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3516</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148165</th>\n",
              "      <td>F00155943</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>28.0</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>-999</td>\n",
              "      <td>0</td>\n",
              "      <td>155943</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>25.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148166</th>\n",
              "      <td>F00155944</td>\n",
              "      <td>3895</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>52.0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>-999</td>\n",
              "      <td>0</td>\n",
              "      <td>155944</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>28.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>25.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148167</th>\n",
              "      <td>F00155945</td>\n",
              "      <td>3895</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>37.0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>155945</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3895</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3702</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>28.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>148168 rows × 35 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               ID  ...  Season_Lag_2\n",
              "0       F00000001  ...          -999\n",
              "1       F00000002  ...          -999\n",
              "2       F00000003  ...             1\n",
              "3       F00000004  ...             2\n",
              "4       F00000005  ...             2\n",
              "...           ...  ...           ...\n",
              "148163  F00155941  ...             2\n",
              "148164  F00155942  ...             2\n",
              "148165  F00155943  ...             1\n",
              "148166  F00155944  ...             3\n",
              "148167  F00155945  ...             2\n",
              "\n",
              "[148168 rows x 35 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dm-TzVNY5c_6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_train, df_test = data[data.train_flag == 1], data[data.train_flag == 0]"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JkFQhSV65h8s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "3fd391c9-191e-4bd4-86c2-677f7f6684c5"
      },
      "source": [
        "df_train.drop(['train_flag'], inplace=True, axis=1)\n",
        "df_test.drop(['train_flag'], inplace=True, axis=1)\n",
        "df_test.drop([label_col], inplace=True, axis=1)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:3997: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQIuv1sB5nR8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "814dbf9e-af98-4e8b-d649-f226b19e5bb3"
      },
      "source": [
        "print(df_train.shape, df_test.shape)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(88858, 34) (59310, 33)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D56sJC0y5qXv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "8bc46437-d11b-4b5d-8997-ad7c1b867150"
      },
      "source": [
        "df_test.isna().sum()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                                0\n",
              "Estimated_Insects_Count           0\n",
              "Crop_Type                         0\n",
              "Soil_Type                         0\n",
              "Pesticide_Use_Category            0\n",
              "Number_Doses_Week                 0\n",
              "Number_Weeks_Used                 0\n",
              "Number_Weeks_Quit                 0\n",
              "Season                            0\n",
              "ID_Value                          0\n",
              "Pesticide_Use_Category_Damage     0\n",
              "Estimated_Insects_Count_Damage    0\n",
              "Crop_Type_Damage                  0\n",
              "Soil_Type_Damage                  0\n",
              "Number_Doses_Week_Damage          0\n",
              "Number_Weeks_Used_Damage          0\n",
              "Number_Weeks_Quit_Damage          0\n",
              "Season_Damage                     0\n",
              "Estimated_Insects_Count_Lag_1     0\n",
              "Crop_Type_Lag_1                   0\n",
              "Soil_Type_Lag_1                   0\n",
              "Pesticide_Use_Category_Lag_1      0\n",
              "Number_Doses_Week_Lag_1           0\n",
              "Number_Weeks_Used_Lag_1           0\n",
              "Seaso_Lag_1                       0\n",
              "Season_Lag_1                      0\n",
              "Estimated_Insects_Count_Lag_2     0\n",
              "Crop_Type_Lag_2                   0\n",
              "Soil_Type_Lag_2                   0\n",
              "Pesticide_Use_Category_Lag_2      0\n",
              "Number_Doses_Week_Lag_2           0\n",
              "Number_Weeks_Used_Lag_2           0\n",
              "Season_Lag_2                      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tl5tASN06Jjl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "outputId": "3f35ebed-7e54-496e-ee94-ed89bb0596d7"
      },
      "source": [
        "df_train.isna().sum()"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                                0\n",
              "Estimated_Insects_Count           0\n",
              "Crop_Type                         0\n",
              "Soil_Type                         0\n",
              "Pesticide_Use_Category            0\n",
              "Number_Doses_Week                 0\n",
              "Number_Weeks_Used                 0\n",
              "Number_Weeks_Quit                 0\n",
              "Season                            0\n",
              "Crop_Damage                       0\n",
              "ID_Value                          0\n",
              "Pesticide_Use_Category_Damage     0\n",
              "Estimated_Insects_Count_Damage    0\n",
              "Crop_Type_Damage                  0\n",
              "Soil_Type_Damage                  0\n",
              "Number_Doses_Week_Damage          0\n",
              "Number_Weeks_Used_Damage          0\n",
              "Number_Weeks_Quit_Damage          0\n",
              "Season_Damage                     0\n",
              "Estimated_Insects_Count_Lag_1     0\n",
              "Crop_Type_Lag_1                   0\n",
              "Soil_Type_Lag_1                   0\n",
              "Pesticide_Use_Category_Lag_1      0\n",
              "Number_Doses_Week_Lag_1           0\n",
              "Number_Weeks_Used_Lag_1           0\n",
              "Seaso_Lag_1                       0\n",
              "Season_Lag_1                      0\n",
              "Estimated_Insects_Count_Lag_2     0\n",
              "Crop_Type_Lag_2                   0\n",
              "Soil_Type_Lag_2                   0\n",
              "Pesticide_Use_Category_Lag_2      0\n",
              "Number_Doses_Week_Lag_2           0\n",
              "Number_Weeks_Used_Lag_2           0\n",
              "Season_Lag_2                      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zzBWgi6N6MIz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_train, df_valid = train_test_split(df_train, test_size=0.30, random_state=42, shuffle=True, stratify=df_train[label_col])"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nk_rWGL26QZA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c72682c7-cf8b-4c63-f20b-235b9c6b7e24"
      },
      "source": [
        "df_train.shape"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(62200, 34)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZj06Odl6R6k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a3ce2d61-c9de-4df1-d537-3675afb920b8"
      },
      "source": [
        "df_valid.shape"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(26658, 34)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-twzjCx6Xnv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "5bf51a55-a9d5-4fed-cf10-3b36b4e5e571"
      },
      "source": [
        "df_train.dtypes"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                                 object\n",
              "Estimated_Insects_Count             int64\n",
              "Crop_Type                           int64\n",
              "Soil_Type                           int64\n",
              "Pesticide_Use_Category              int64\n",
              "Number_Doses_Week                   int64\n",
              "Number_Weeks_Used                 float64\n",
              "Number_Weeks_Quit                   int64\n",
              "Season                              int64\n",
              "Crop_Damage                         int64\n",
              "ID_Value                            int64\n",
              "Pesticide_Use_Category_Damage     float64\n",
              "Estimated_Insects_Count_Damage    float64\n",
              "Crop_Type_Damage                  float64\n",
              "Soil_Type_Damage                  float64\n",
              "Number_Doses_Week_Damage          float64\n",
              "Number_Weeks_Used_Damage          float64\n",
              "Number_Weeks_Quit_Damage          float64\n",
              "Season_Damage                     float64\n",
              "Estimated_Insects_Count_Lag_1       int64\n",
              "Crop_Type_Lag_1                     int64\n",
              "Soil_Type_Lag_1                     int64\n",
              "Pesticide_Use_Category_Lag_1        int64\n",
              "Number_Doses_Week_Lag_1             int64\n",
              "Number_Weeks_Used_Lag_1           float64\n",
              "Season_Lag_1                        int64\n",
              "Estimated_Insects_Count_Lag_2       int64\n",
              "Crop_Type_Lag_2                     int64\n",
              "Soil_Type_Lag_2                     int64\n",
              "Pesticide_Use_Category_Lag_2        int64\n",
              "Number_Doses_Week_Lag_2             int64\n",
              "Number_Weeks_Used_Lag_2           float64\n",
              "Season_Lag_2                        int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ngqt_-gY928w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "d372eff2-4e9f-40e4-aa2e-8c8d4b6394eb"
      },
      "source": [
        "feature_cols = df_train.columns.tolist()\n",
        "feature_cols.remove('ID')\n",
        "feature_cols.remove('Crop_Damage')\n",
        "feature_cols.remove('ID_Value')\n",
        "label_col = 'Crop_Damage'\n",
        "print(feature_cols)"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Estimated_Insects_Count', 'Crop_Type', 'Soil_Type', 'Pesticide_Use_Category', 'Number_Doses_Week', 'Number_Weeks_Used', 'Number_Weeks_Quit', 'Season', 'Pesticide_Use_Category_Damage', 'Estimated_Insects_Count_Damage', 'Crop_Type_Damage', 'Soil_Type_Damage', 'Number_Doses_Week_Damage', 'Number_Weeks_Used_Damage', 'Number_Weeks_Quit_Damage', 'Season_Damage', 'Estimated_Insects_Count_Lag_1', 'Crop_Type_Lag_1', 'Soil_Type_Lag_1', 'Pesticide_Use_Category_Lag_1', 'Number_Doses_Week_Lag_1', 'Number_Weeks_Used_Lag_1', 'Season_Lag_1', 'Estimated_Insects_Count_Lag_2', 'Crop_Type_Lag_2', 'Soil_Type_Lag_2', 'Pesticide_Use_Category_Lag_2', 'Number_Doses_Week_Lag_2', 'Number_Weeks_Used_Lag_2', 'Season_Lag_2']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mpIFyTV6-OzD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cat_cols = ['Crop_Type', 'Soil_Type', 'Pesticide_Use_Category', 'Season', 'Crop_Type_Lag_1', 'Soil_Type_Lag_1', 'Pesticide_Use_Category_Lag_1', 'Season_Lag_1','Crop_Type_Lag_2', 'Soil_Type_Lag_2', 'Pesticide_Use_Category_Lag_2', 'Season_Lag_2']"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGYUiBc5-pjb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "params = {}\n",
        "params['learning_rate'] = 0.04\n",
        "params['max_depth'] = 18\n",
        "params['n_estimators'] = 3000\n",
        "params['objective'] = 'multiclass'\n",
        "params['boosting_type'] = 'gbdt'\n",
        "params['subsample'] = 0.7\n",
        "params['random_state'] = 42\n",
        "params['colsample_bytree']=0.7\n",
        "params['min_data_in_leaf'] = 55\n",
        "params['reg_alpha'] = 1.7\n",
        "params['reg_lambda'] = 1.11"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lTR_qh3-wQ3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b49a2f80-0418-4d5a-97e9-e5f55a5a94fe"
      },
      "source": [
        "clf = LGBMClassifier(**params)\n",
        "    \n",
        "clf.fit(df_train[feature_cols], df_train[label_col], early_stopping_rounds=100, eval_set=[(df_train[feature_cols], df_train[label_col]), (df_valid[feature_cols], df_valid[label_col])], eval_metric='multi_error', verbose=True, categorical_feature=cat_cols)\n",
        "\n",
        "eval_score = accuracy_score(df_valid[label_col], clf.predict(df_valid[feature_cols]))\n",
        "\n",
        "print('Eval ACC: {}'.format(eval_score))"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/lightgbm/basic.py:1209: UserWarning: categorical_feature in Dataset is overridden.\n",
            "New categorical_feature is ['Crop_Type', 'Crop_Type_Lag_1', 'Crop_Type_Lag_2', 'Pesticide_Use_Category', 'Pesticide_Use_Category_Lag_1', 'Pesticide_Use_Category_Lag_2', 'Season', 'Season_Lag_1', 'Season_Lag_2', 'Soil_Type', 'Soil_Type_Lag_1', 'Soil_Type_Lag_2']\n",
            "  'New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1]\tvalid_0's multi_error: 0.164534\tvalid_0's multi_logloss: 0.501984\tvalid_1's multi_error: 0.164528\tvalid_1's multi_logloss: 0.502394\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[2]\tvalid_0's multi_error: 0.164534\tvalid_0's multi_logloss: 0.48791\tvalid_1's multi_error: 0.164528\tvalid_1's multi_logloss: 0.48873\n",
            "[3]\tvalid_0's multi_error: 0.164534\tvalid_0's multi_logloss: 0.475031\tvalid_1's multi_error: 0.164528\tvalid_1's multi_logloss: 0.476185\n",
            "[4]\tvalid_0's multi_error: 0.164534\tvalid_0's multi_logloss: 0.463182\tvalid_1's multi_error: 0.164528\tvalid_1's multi_logloss: 0.46451\n",
            "[5]\tvalid_0's multi_error: 0.164534\tvalid_0's multi_logloss: 0.453308\tvalid_1's multi_error: 0.164528\tvalid_1's multi_logloss: 0.454901\n",
            "[6]\tvalid_0's multi_error: 0.164534\tvalid_0's multi_logloss: 0.444041\tvalid_1's multi_error: 0.164528\tvalid_1's multi_logloss: 0.445837\n",
            "[7]\tvalid_0's multi_error: 0.164534\tvalid_0's multi_logloss: 0.434989\tvalid_1's multi_error: 0.164528\tvalid_1's multi_logloss: 0.436922\n",
            "[8]\tvalid_0's multi_error: 0.164534\tvalid_0's multi_logloss: 0.427404\tvalid_1's multi_error: 0.164528\tvalid_1's multi_logloss: 0.429592\n",
            "[9]\tvalid_0's multi_error: 0.164534\tvalid_0's multi_logloss: 0.420375\tvalid_1's multi_error: 0.164528\tvalid_1's multi_logloss: 0.422752\n",
            "[10]\tvalid_0's multi_error: 0.164534\tvalid_0's multi_logloss: 0.412992\tvalid_1's multi_error: 0.164528\tvalid_1's multi_logloss: 0.415554\n",
            "[11]\tvalid_0's multi_error: 0.164534\tvalid_0's multi_logloss: 0.406751\tvalid_1's multi_error: 0.164528\tvalid_1's multi_logloss: 0.409482\n",
            "[12]\tvalid_0's multi_error: 0.164534\tvalid_0's multi_logloss: 0.400703\tvalid_1's multi_error: 0.164528\tvalid_1's multi_logloss: 0.403704\n",
            "[13]\tvalid_0's multi_error: 0.164534\tvalid_0's multi_logloss: 0.394863\tvalid_1's multi_error: 0.164528\tvalid_1's multi_logloss: 0.398\n",
            "[14]\tvalid_0's multi_error: 0.164534\tvalid_0's multi_logloss: 0.389048\tvalid_1's multi_error: 0.164528\tvalid_1's multi_logloss: 0.392343\n",
            "[15]\tvalid_0's multi_error: 0.164534\tvalid_0's multi_logloss: 0.383805\tvalid_1's multi_error: 0.164528\tvalid_1's multi_logloss: 0.387238\n",
            "[16]\tvalid_0's multi_error: 0.164421\tvalid_0's multi_logloss: 0.378803\tvalid_1's multi_error: 0.164341\tvalid_1's multi_logloss: 0.382418\n",
            "[17]\tvalid_0's multi_error: 0.161431\tvalid_0's multi_logloss: 0.374316\tvalid_1's multi_error: 0.161302\tvalid_1's multi_logloss: 0.378162\n",
            "[18]\tvalid_0's multi_error: 0.15865\tvalid_0's multi_logloss: 0.369954\tvalid_1's multi_error: 0.159052\tvalid_1's multi_logloss: 0.373993\n",
            "[19]\tvalid_0's multi_error: 0.156286\tvalid_0's multi_logloss: 0.365117\tvalid_1's multi_error: 0.156426\tvalid_1's multi_logloss: 0.369294\n",
            "[20]\tvalid_0's multi_error: 0.153875\tvalid_0's multi_logloss: 0.360583\tvalid_1's multi_error: 0.154513\tvalid_1's multi_logloss: 0.364928\n",
            "[21]\tvalid_0's multi_error: 0.151833\tvalid_0's multi_logloss: 0.356669\tvalid_1's multi_error: 0.152487\tvalid_1's multi_logloss: 0.361171\n",
            "[22]\tvalid_0's multi_error: 0.145032\tvalid_0's multi_logloss: 0.352644\tvalid_1's multi_error: 0.145135\tvalid_1's multi_logloss: 0.357208\n",
            "[23]\tvalid_0's multi_error: 0.143006\tvalid_0's multi_logloss: 0.349247\tvalid_1's multi_error: 0.143447\tvalid_1's multi_logloss: 0.353881\n",
            "[24]\tvalid_0's multi_error: 0.140675\tvalid_0's multi_logloss: 0.345106\tvalid_1's multi_error: 0.140821\tvalid_1's multi_logloss: 0.34982\n",
            "[25]\tvalid_0's multi_error: 0.13836\tvalid_0's multi_logloss: 0.341975\tvalid_1's multi_error: 0.139095\tvalid_1's multi_logloss: 0.346822\n",
            "[26]\tvalid_0's multi_error: 0.135932\tvalid_0's multi_logloss: 0.338279\tvalid_1's multi_error: 0.136844\tvalid_1's multi_logloss: 0.343192\n",
            "[27]\tvalid_0's multi_error: 0.134068\tvalid_0's multi_logloss: 0.335226\tvalid_1's multi_error: 0.134631\tvalid_1's multi_logloss: 0.340221\n",
            "[28]\tvalid_0's multi_error: 0.132186\tvalid_0's multi_logloss: 0.332377\tvalid_1's multi_error: 0.132681\tvalid_1's multi_logloss: 0.337488\n",
            "[29]\tvalid_0's multi_error: 0.130273\tvalid_0's multi_logloss: 0.329141\tvalid_1's multi_error: 0.13103\tvalid_1's multi_logloss: 0.334279\n",
            "[30]\tvalid_0's multi_error: 0.128891\tvalid_0's multi_logloss: 0.326205\tvalid_1's multi_error: 0.129455\tvalid_1's multi_logloss: 0.331384\n",
            "[31]\tvalid_0's multi_error: 0.127685\tvalid_0's multi_logloss: 0.323872\tvalid_1's multi_error: 0.128329\tvalid_1's multi_logloss: 0.329131\n",
            "[32]\tvalid_0's multi_error: 0.12635\tvalid_0's multi_logloss: 0.321065\tvalid_1's multi_error: 0.126754\tvalid_1's multi_logloss: 0.326361\n",
            "[33]\tvalid_0's multi_error: 0.124695\tvalid_0's multi_logloss: 0.318239\tvalid_1's multi_error: 0.125141\tvalid_1's multi_logloss: 0.323605\n",
            "[34]\tvalid_0's multi_error: 0.12336\tvalid_0's multi_logloss: 0.316058\tvalid_1's multi_error: 0.123678\tvalid_1's multi_logloss: 0.321531\n",
            "[35]\tvalid_0's multi_error: 0.121994\tvalid_0's multi_logloss: 0.313808\tvalid_1's multi_error: 0.12304\tvalid_1's multi_logloss: 0.31934\n",
            "[36]\tvalid_0's multi_error: 0.120209\tvalid_0's multi_logloss: 0.311297\tvalid_1's multi_error: 0.121314\tvalid_1's multi_logloss: 0.316892\n",
            "[37]\tvalid_0's multi_error: 0.118907\tvalid_0's multi_logloss: 0.308923\tvalid_1's multi_error: 0.120039\tvalid_1's multi_logloss: 0.314556\n",
            "[38]\tvalid_0's multi_error: 0.117765\tvalid_0's multi_logloss: 0.306952\tvalid_1's multi_error: 0.118726\tvalid_1's multi_logloss: 0.312696\n",
            "[39]\tvalid_0's multi_error: 0.116672\tvalid_0's multi_logloss: 0.304717\tvalid_1's multi_error: 0.117376\tvalid_1's multi_logloss: 0.310583\n",
            "[40]\tvalid_0's multi_error: 0.115531\tvalid_0's multi_logloss: 0.302527\tvalid_1's multi_error: 0.116288\tvalid_1's multi_logloss: 0.308477\n",
            "[41]\tvalid_0's multi_error: 0.114293\tvalid_0's multi_logloss: 0.30069\tvalid_1's multi_error: 0.11535\tvalid_1's multi_logloss: 0.306708\n",
            "[42]\tvalid_0's multi_error: 0.113569\tvalid_0's multi_logloss: 0.298748\tvalid_1's multi_error: 0.114262\tvalid_1's multi_logloss: 0.304881\n",
            "[43]\tvalid_0's multi_error: 0.112685\tvalid_0's multi_logloss: 0.296962\tvalid_1's multi_error: 0.113099\tvalid_1's multi_logloss: 0.303183\n",
            "[44]\tvalid_0's multi_error: 0.111929\tvalid_0's multi_logloss: 0.295346\tvalid_1's multi_error: 0.112349\tvalid_1's multi_logloss: 0.301659\n",
            "[45]\tvalid_0's multi_error: 0.110949\tvalid_0's multi_logloss: 0.29377\tvalid_1's multi_error: 0.111524\tvalid_1's multi_logloss: 0.300123\n",
            "[46]\tvalid_0's multi_error: 0.110241\tvalid_0's multi_logloss: 0.29227\tvalid_1's multi_error: 0.111036\tvalid_1's multi_logloss: 0.298689\n",
            "[47]\tvalid_0's multi_error: 0.109695\tvalid_0's multi_logloss: 0.290756\tvalid_1's multi_error: 0.110173\tvalid_1's multi_logloss: 0.297311\n",
            "[48]\tvalid_0's multi_error: 0.108859\tvalid_0's multi_logloss: 0.289333\tvalid_1's multi_error: 0.109836\tvalid_1's multi_logloss: 0.29596\n",
            "[49]\tvalid_0's multi_error: 0.108183\tvalid_0's multi_logloss: 0.288138\tvalid_1's multi_error: 0.109498\tvalid_1's multi_logloss: 0.294856\n",
            "[50]\tvalid_0's multi_error: 0.107701\tvalid_0's multi_logloss: 0.286481\tvalid_1's multi_error: 0.108823\tvalid_1's multi_logloss: 0.293279\n",
            "[51]\tvalid_0's multi_error: 0.107186\tvalid_0's multi_logloss: 0.285323\tvalid_1's multi_error: 0.10841\tvalid_1's multi_logloss: 0.29219\n",
            "[52]\tvalid_0's multi_error: 0.106318\tvalid_0's multi_logloss: 0.283993\tvalid_1's multi_error: 0.10811\tvalid_1's multi_logloss: 0.290962\n",
            "[53]\tvalid_0's multi_error: 0.105756\tvalid_0's multi_logloss: 0.282594\tvalid_1's multi_error: 0.10751\tvalid_1's multi_logloss: 0.289624\n",
            "[54]\tvalid_0's multi_error: 0.105145\tvalid_0's multi_logloss: 0.28137\tvalid_1's multi_error: 0.107172\tvalid_1's multi_logloss: 0.288425\n",
            "[55]\tvalid_0's multi_error: 0.104566\tvalid_0's multi_logloss: 0.280146\tvalid_1's multi_error: 0.10676\tvalid_1's multi_logloss: 0.28729\n",
            "[56]\tvalid_0's multi_error: 0.104035\tvalid_0's multi_logloss: 0.279027\tvalid_1's multi_error: 0.105897\tvalid_1's multi_logloss: 0.286271\n",
            "[57]\tvalid_0's multi_error: 0.103617\tvalid_0's multi_logloss: 0.277858\tvalid_1's multi_error: 0.105447\tvalid_1's multi_logloss: 0.285162\n",
            "[58]\tvalid_0's multi_error: 0.103328\tvalid_0's multi_logloss: 0.276858\tvalid_1's multi_error: 0.104734\tvalid_1's multi_logloss: 0.284229\n",
            "[59]\tvalid_0's multi_error: 0.102621\tvalid_0's multi_logloss: 0.275568\tvalid_1's multi_error: 0.103684\tvalid_1's multi_logloss: 0.28302\n",
            "[60]\tvalid_0's multi_error: 0.102186\tvalid_0's multi_logloss: 0.274581\tvalid_1's multi_error: 0.103421\tvalid_1's multi_logloss: 0.282066\n",
            "[61]\tvalid_0's multi_error: 0.101768\tvalid_0's multi_logloss: 0.273521\tvalid_1's multi_error: 0.103234\tvalid_1's multi_logloss: 0.281059\n",
            "[62]\tvalid_0's multi_error: 0.101077\tvalid_0's multi_logloss: 0.272169\tvalid_1's multi_error: 0.102408\tvalid_1's multi_logloss: 0.279807\n",
            "[63]\tvalid_0's multi_error: 0.100756\tvalid_0's multi_logloss: 0.271103\tvalid_1's multi_error: 0.102296\tvalid_1's multi_logloss: 0.27881\n",
            "[64]\tvalid_0's multi_error: 0.100016\tvalid_0's multi_logloss: 0.270058\tvalid_1's multi_error: 0.101958\tvalid_1's multi_logloss: 0.277867\n",
            "[65]\tvalid_0's multi_error: 0.0998714\tvalid_0's multi_logloss: 0.26907\tvalid_1's multi_error: 0.101658\tvalid_1's multi_logloss: 0.276932\n",
            "[66]\tvalid_0's multi_error: 0.0995498\tvalid_0's multi_logloss: 0.268281\tvalid_1's multi_error: 0.101395\tvalid_1's multi_logloss: 0.276226\n",
            "[67]\tvalid_0's multi_error: 0.0991158\tvalid_0's multi_logloss: 0.267307\tvalid_1's multi_error: 0.101283\tvalid_1's multi_logloss: 0.275297\n",
            "[68]\tvalid_0's multi_error: 0.0988264\tvalid_0's multi_logloss: 0.266374\tvalid_1's multi_error: 0.101133\tvalid_1's multi_logloss: 0.274401\n",
            "[69]\tvalid_0's multi_error: 0.0983441\tvalid_0's multi_logloss: 0.265422\tvalid_1's multi_error: 0.10072\tvalid_1's multi_logloss: 0.273509\n",
            "[70]\tvalid_0's multi_error: 0.0980064\tvalid_0's multi_logloss: 0.264732\tvalid_1's multi_error: 0.100195\tvalid_1's multi_logloss: 0.272878\n",
            "[71]\tvalid_0's multi_error: 0.097492\tvalid_0's multi_logloss: 0.263694\tvalid_1's multi_error: 0.0997074\tvalid_1's multi_logloss: 0.271862\n",
            "[72]\tvalid_0's multi_error: 0.0973794\tvalid_0's multi_logloss: 0.263137\tvalid_1's multi_error: 0.0993698\tvalid_1's multi_logloss: 0.271379\n",
            "[73]\tvalid_0's multi_error: 0.0967524\tvalid_0's multi_logloss: 0.262052\tvalid_1's multi_error: 0.0986196\tvalid_1's multi_logloss: 0.270391\n",
            "[74]\tvalid_0's multi_error: 0.0962701\tvalid_0's multi_logloss: 0.261209\tvalid_1's multi_error: 0.0983195\tvalid_1's multi_logloss: 0.269625\n",
            "[75]\tvalid_0's multi_error: 0.0961415\tvalid_0's multi_logloss: 0.260491\tvalid_1's multi_error: 0.0980569\tvalid_1's multi_logloss: 0.268982\n",
            "[76]\tvalid_0's multi_error: 0.0957556\tvalid_0's multi_logloss: 0.259837\tvalid_1's multi_error: 0.0979068\tvalid_1's multi_logloss: 0.268427\n",
            "[77]\tvalid_0's multi_error: 0.0956431\tvalid_0's multi_logloss: 0.259064\tvalid_1's multi_error: 0.0974942\tvalid_1's multi_logloss: 0.267724\n",
            "[78]\tvalid_0's multi_error: 0.0953215\tvalid_0's multi_logloss: 0.258314\tvalid_1's multi_error: 0.096969\tvalid_1's multi_logloss: 0.267075\n",
            "[79]\tvalid_0's multi_error: 0.0949839\tvalid_0's multi_logloss: 0.257384\tvalid_1's multi_error: 0.0967815\tvalid_1's multi_logloss: 0.266225\n",
            "[80]\tvalid_0's multi_error: 0.0944212\tvalid_0's multi_logloss: 0.256512\tvalid_1's multi_error: 0.0962938\tvalid_1's multi_logloss: 0.265435\n",
            "[81]\tvalid_0's multi_error: 0.0940675\tvalid_0's multi_logloss: 0.255636\tvalid_1's multi_error: 0.0959937\tvalid_1's multi_logloss: 0.264653\n",
            "[82]\tvalid_0's multi_error: 0.0937942\tvalid_0's multi_logloss: 0.254878\tvalid_1's multi_error: 0.0958812\tvalid_1's multi_logloss: 0.263944\n",
            "[83]\tvalid_0's multi_error: 0.0935852\tvalid_0's multi_logloss: 0.254217\tvalid_1's multi_error: 0.0953935\tvalid_1's multi_logloss: 0.263318\n",
            "[84]\tvalid_0's multi_error: 0.0935852\tvalid_0's multi_logloss: 0.253666\tvalid_1's multi_error: 0.0947933\tvalid_1's multi_logloss: 0.262835\n",
            "[85]\tvalid_0's multi_error: 0.093119\tvalid_0's multi_logloss: 0.252972\tvalid_1's multi_error: 0.0948683\tvalid_1's multi_logloss: 0.262219\n",
            "[86]\tvalid_0's multi_error: 0.0929904\tvalid_0's multi_logloss: 0.252395\tvalid_1's multi_error: 0.0950184\tvalid_1's multi_logloss: 0.261708\n",
            "[87]\tvalid_0's multi_error: 0.0927974\tvalid_0's multi_logloss: 0.251876\tvalid_1's multi_error: 0.0946057\tvalid_1's multi_logloss: 0.261254\n",
            "[88]\tvalid_0's multi_error: 0.0925241\tvalid_0's multi_logloss: 0.251148\tvalid_1's multi_error: 0.0945307\tvalid_1's multi_logloss: 0.260578\n",
            "[89]\tvalid_0's multi_error: 0.0920096\tvalid_0's multi_logloss: 0.25035\tvalid_1's multi_error: 0.0938555\tvalid_1's multi_logloss: 0.259853\n",
            "[90]\tvalid_0's multi_error: 0.0915916\tvalid_0's multi_logloss: 0.249501\tvalid_1's multi_error: 0.0934054\tvalid_1's multi_logloss: 0.259023\n",
            "[91]\tvalid_0's multi_error: 0.0915113\tvalid_0's multi_logloss: 0.248766\tvalid_1's multi_error: 0.0933303\tvalid_1's multi_logloss: 0.258362\n",
            "[92]\tvalid_0's multi_error: 0.0912379\tvalid_0's multi_logloss: 0.248072\tvalid_1's multi_error: 0.0931428\tvalid_1's multi_logloss: 0.257704\n",
            "[93]\tvalid_0's multi_error: 0.0909807\tvalid_0's multi_logloss: 0.247353\tvalid_1's multi_error: 0.0928427\tvalid_1's multi_logloss: 0.257026\n",
            "[94]\tvalid_0's multi_error: 0.0906913\tvalid_0's multi_logloss: 0.24678\tvalid_1's multi_error: 0.0928052\tvalid_1's multi_logloss: 0.256535\n",
            "[95]\tvalid_0's multi_error: 0.0905949\tvalid_0's multi_logloss: 0.246229\tvalid_1's multi_error: 0.0926926\tvalid_1's multi_logloss: 0.256085\n",
            "[96]\tvalid_0's multi_error: 0.0901286\tvalid_0's multi_logloss: 0.245483\tvalid_1's multi_error: 0.0918298\tvalid_1's multi_logloss: 0.255399\n",
            "[97]\tvalid_0's multi_error: 0.0900482\tvalid_0's multi_logloss: 0.244926\tvalid_1's multi_error: 0.0918298\tvalid_1's multi_logloss: 0.254918\n",
            "[98]\tvalid_0's multi_error: 0.0899035\tvalid_0's multi_logloss: 0.244368\tvalid_1's multi_error: 0.0917548\tvalid_1's multi_logloss: 0.254456\n",
            "[99]\tvalid_0's multi_error: 0.0894534\tvalid_0's multi_logloss: 0.243727\tvalid_1's multi_error: 0.0914172\tvalid_1's multi_logloss: 0.253879\n",
            "[100]\tvalid_0's multi_error: 0.0892765\tvalid_0's multi_logloss: 0.243238\tvalid_1's multi_error: 0.0913047\tvalid_1's multi_logloss: 0.253457\n",
            "[101]\tvalid_0's multi_error: 0.0890354\tvalid_0's multi_logloss: 0.242396\tvalid_1's multi_error: 0.0909296\tvalid_1's multi_logloss: 0.252707\n",
            "[102]\tvalid_0's multi_error: 0.0885852\tvalid_0's multi_logloss: 0.241751\tvalid_1's multi_error: 0.0905919\tvalid_1's multi_logloss: 0.252172\n",
            "[103]\tvalid_0's multi_error: 0.088328\tvalid_0's multi_logloss: 0.241203\tvalid_1's multi_error: 0.0905544\tvalid_1's multi_logloss: 0.251721\n",
            "[104]\tvalid_0's multi_error: 0.0882958\tvalid_0's multi_logloss: 0.240797\tvalid_1's multi_error: 0.0904794\tvalid_1's multi_logloss: 0.251411\n",
            "[105]\tvalid_0's multi_error: 0.088135\tvalid_0's multi_logloss: 0.240237\tvalid_1's multi_error: 0.0903669\tvalid_1's multi_logloss: 0.250943\n",
            "[106]\tvalid_0's multi_error: 0.0878939\tvalid_0's multi_logloss: 0.239779\tvalid_1's multi_error: 0.0899167\tvalid_1's multi_logloss: 0.250561\n",
            "[107]\tvalid_0's multi_error: 0.0875402\tvalid_0's multi_logloss: 0.239251\tvalid_1's multi_error: 0.0895791\tvalid_1's multi_logloss: 0.250109\n",
            "[108]\tvalid_0's multi_error: 0.087299\tvalid_0's multi_logloss: 0.238646\tvalid_1's multi_error: 0.089354\tvalid_1's multi_logloss: 0.249556\n",
            "[109]\tvalid_0's multi_error: 0.0871865\tvalid_0's multi_logloss: 0.238157\tvalid_1's multi_error: 0.0893165\tvalid_1's multi_logloss: 0.249164\n",
            "[110]\tvalid_0's multi_error: 0.0869936\tvalid_0's multi_logloss: 0.237477\tvalid_1's multi_error: 0.089204\tvalid_1's multi_logloss: 0.248538\n",
            "[111]\tvalid_0's multi_error: 0.0865595\tvalid_0's multi_logloss: 0.236812\tvalid_1's multi_error: 0.0891665\tvalid_1's multi_logloss: 0.247971\n",
            "[112]\tvalid_0's multi_error: 0.0863505\tvalid_0's multi_logloss: 0.236415\tvalid_1's multi_error: 0.0887538\tvalid_1's multi_logloss: 0.247676\n",
            "[113]\tvalid_0's multi_error: 0.0862701\tvalid_0's multi_logloss: 0.235848\tvalid_1's multi_error: 0.0886413\tvalid_1's multi_logloss: 0.24716\n",
            "[114]\tvalid_0's multi_error: 0.086045\tvalid_0's multi_logloss: 0.235249\tvalid_1's multi_error: 0.0883412\tvalid_1's multi_logloss: 0.246628\n",
            "[115]\tvalid_0's multi_error: 0.0858842\tvalid_0's multi_logloss: 0.234692\tvalid_1's multi_error: 0.0879661\tvalid_1's multi_logloss: 0.246153\n",
            "[116]\tvalid_0's multi_error: 0.0857074\tvalid_0's multi_logloss: 0.234186\tvalid_1's multi_error: 0.0877785\tvalid_1's multi_logloss: 0.245724\n",
            "[117]\tvalid_0's multi_error: 0.0855145\tvalid_0's multi_logloss: 0.233818\tvalid_1's multi_error: 0.087666\tvalid_1's multi_logloss: 0.24545\n",
            "[118]\tvalid_0's multi_error: 0.0850482\tvalid_0's multi_logloss: 0.233266\tvalid_1's multi_error: 0.0874784\tvalid_1's multi_logloss: 0.244979\n",
            "[119]\tvalid_0's multi_error: 0.0849035\tvalid_0's multi_logloss: 0.232736\tvalid_1's multi_error: 0.0872158\tvalid_1's multi_logloss: 0.244524\n",
            "[120]\tvalid_0's multi_error: 0.0848071\tvalid_0's multi_logloss: 0.2324\tvalid_1's multi_error: 0.0871783\tvalid_1's multi_logloss: 0.244273\n",
            "[121]\tvalid_0's multi_error: 0.0846624\tvalid_0's multi_logloss: 0.231967\tvalid_1's multi_error: 0.0871408\tvalid_1's multi_logloss: 0.243972\n",
            "[122]\tvalid_0's multi_error: 0.0845338\tvalid_0's multi_logloss: 0.231422\tvalid_1's multi_error: 0.0869157\tvalid_1's multi_logloss: 0.243493\n",
            "[123]\tvalid_0's multi_error: 0.0844855\tvalid_0's multi_logloss: 0.23099\tvalid_1's multi_error: 0.0868782\tvalid_1's multi_logloss: 0.243113\n",
            "[124]\tvalid_0's multi_error: 0.0841961\tvalid_0's multi_logloss: 0.230637\tvalid_1's multi_error: 0.0867657\tvalid_1's multi_logloss: 0.242849\n",
            "[125]\tvalid_0's multi_error: 0.0840032\tvalid_0's multi_logloss: 0.23028\tvalid_1's multi_error: 0.0866907\tvalid_1's multi_logloss: 0.242594\n",
            "[126]\tvalid_0's multi_error: 0.0838264\tvalid_0's multi_logloss: 0.229817\tvalid_1's multi_error: 0.0866157\tvalid_1's multi_logloss: 0.242226\n",
            "[127]\tvalid_0's multi_error: 0.083746\tvalid_0's multi_logloss: 0.229293\tvalid_1's multi_error: 0.0863531\tvalid_1's multi_logloss: 0.241778\n",
            "[128]\tvalid_0's multi_error: 0.0836174\tvalid_0's multi_logloss: 0.228844\tvalid_1's multi_error: 0.0862405\tvalid_1's multi_logloss: 0.241421\n",
            "[129]\tvalid_0's multi_error: 0.0834566\tvalid_0's multi_logloss: 0.228478\tvalid_1's multi_error: 0.0859779\tvalid_1's multi_logloss: 0.241138\n",
            "[130]\tvalid_0's multi_error: 0.0834244\tvalid_0's multi_logloss: 0.228052\tvalid_1's multi_error: 0.0859404\tvalid_1's multi_logloss: 0.240768\n",
            "[131]\tvalid_0's multi_error: 0.0832154\tvalid_0's multi_logloss: 0.227591\tvalid_1's multi_error: 0.0858279\tvalid_1's multi_logloss: 0.240377\n",
            "[132]\tvalid_0's multi_error: 0.0829743\tvalid_0's multi_logloss: 0.227067\tvalid_1's multi_error: 0.0855653\tvalid_1's multi_logloss: 0.239936\n",
            "[133]\tvalid_0's multi_error: 0.0829582\tvalid_0's multi_logloss: 0.226635\tvalid_1's multi_error: 0.0853777\tvalid_1's multi_logloss: 0.239584\n",
            "[134]\tvalid_0's multi_error: 0.0827331\tvalid_0's multi_logloss: 0.226286\tvalid_1's multi_error: 0.0852277\tvalid_1's multi_logloss: 0.239315\n",
            "[135]\tvalid_0's multi_error: 0.0826367\tvalid_0's multi_logloss: 0.225952\tvalid_1's multi_error: 0.0850777\tvalid_1's multi_logloss: 0.239065\n",
            "[136]\tvalid_0's multi_error: 0.0825563\tvalid_0's multi_logloss: 0.225563\tvalid_1's multi_error: 0.0850401\tvalid_1's multi_logloss: 0.238759\n",
            "[137]\tvalid_0's multi_error: 0.0823312\tvalid_0's multi_logloss: 0.224996\tvalid_1's multi_error: 0.08474\tvalid_1's multi_logloss: 0.238257\n",
            "[138]\tvalid_0's multi_error: 0.0822508\tvalid_0's multi_logloss: 0.224642\tvalid_1's multi_error: 0.08459\tvalid_1's multi_logloss: 0.237987\n",
            "[139]\tvalid_0's multi_error: 0.0821704\tvalid_0's multi_logloss: 0.224291\tvalid_1's multi_error: 0.0847025\tvalid_1's multi_logloss: 0.237735\n",
            "[140]\tvalid_0's multi_error: 0.0821222\tvalid_0's multi_logloss: 0.223891\tvalid_1's multi_error: 0.0847025\tvalid_1's multi_logloss: 0.237443\n",
            "[141]\tvalid_0's multi_error: 0.0819132\tvalid_0's multi_logloss: 0.223488\tvalid_1's multi_error: 0.0843649\tvalid_1's multi_logloss: 0.237148\n",
            "[142]\tvalid_0's multi_error: 0.0818328\tvalid_0's multi_logloss: 0.223181\tvalid_1's multi_error: 0.0842899\tvalid_1's multi_logloss: 0.236916\n",
            "[143]\tvalid_0's multi_error: 0.0817203\tvalid_0's multi_logloss: 0.222799\tvalid_1's multi_error: 0.0841398\tvalid_1's multi_logloss: 0.23664\n",
            "[144]\tvalid_0's multi_error: 0.0815756\tvalid_0's multi_logloss: 0.22249\tvalid_1's multi_error: 0.0841398\tvalid_1's multi_logloss: 0.236418\n",
            "[145]\tvalid_0's multi_error: 0.0815113\tvalid_0's multi_logloss: 0.222241\tvalid_1's multi_error: 0.0839148\tvalid_1's multi_logloss: 0.236253\n",
            "[146]\tvalid_0's multi_error: 0.0814309\tvalid_0's multi_logloss: 0.221889\tvalid_1's multi_error: 0.0837272\tvalid_1's multi_logloss: 0.235979\n",
            "[147]\tvalid_0's multi_error: 0.0813666\tvalid_0's multi_logloss: 0.221456\tvalid_1's multi_error: 0.0836522\tvalid_1's multi_logloss: 0.235608\n",
            "[148]\tvalid_0's multi_error: 0.0812379\tvalid_0's multi_logloss: 0.221173\tvalid_1's multi_error: 0.0835021\tvalid_1's multi_logloss: 0.235439\n",
            "[149]\tvalid_0's multi_error: 0.0811093\tvalid_0's multi_logloss: 0.220905\tvalid_1's multi_error: 0.0833521\tvalid_1's multi_logloss: 0.235264\n",
            "[150]\tvalid_0's multi_error: 0.0809486\tvalid_0's multi_logloss: 0.220601\tvalid_1's multi_error: 0.0832771\tvalid_1's multi_logloss: 0.235045\n",
            "[151]\tvalid_0's multi_error: 0.0808199\tvalid_0's multi_logloss: 0.220317\tvalid_1's multi_error: 0.0831645\tvalid_1's multi_logloss: 0.234846\n",
            "[152]\tvalid_0's multi_error: 0.0806752\tvalid_0's multi_logloss: 0.220023\tvalid_1's multi_error: 0.0832396\tvalid_1's multi_logloss: 0.23461\n",
            "[153]\tvalid_0's multi_error: 0.0804662\tvalid_0's multi_logloss: 0.219521\tvalid_1's multi_error: 0.082977\tvalid_1's multi_logloss: 0.234184\n",
            "[154]\tvalid_0's multi_error: 0.0804019\tvalid_0's multi_logloss: 0.219247\tvalid_1's multi_error: 0.0828269\tvalid_1's multi_logloss: 0.233972\n",
            "[155]\tvalid_0's multi_error: 0.0802412\tvalid_0's multi_logloss: 0.218811\tvalid_1's multi_error: 0.0828269\tvalid_1's multi_logloss: 0.233631\n",
            "[156]\tvalid_0's multi_error: 0.0800161\tvalid_0's multi_logloss: 0.218432\tvalid_1's multi_error: 0.0828644\tvalid_1's multi_logloss: 0.233344\n",
            "[157]\tvalid_0's multi_error: 0.079791\tvalid_0's multi_logloss: 0.217907\tvalid_1's multi_error: 0.0825268\tvalid_1's multi_logloss: 0.232897\n",
            "[158]\tvalid_0's multi_error: 0.0796945\tvalid_0's multi_logloss: 0.217641\tvalid_1's multi_error: 0.0826018\tvalid_1's multi_logloss: 0.232724\n",
            "[159]\tvalid_0's multi_error: 0.0794373\tvalid_0's multi_logloss: 0.217365\tvalid_1's multi_error: 0.0824143\tvalid_1's multi_logloss: 0.232534\n",
            "[160]\tvalid_0's multi_error: 0.0793087\tvalid_0's multi_logloss: 0.216977\tvalid_1's multi_error: 0.0823017\tvalid_1's multi_logloss: 0.232205\n",
            "[161]\tvalid_0's multi_error: 0.0791318\tvalid_0's multi_logloss: 0.216757\tvalid_1's multi_error: 0.0822642\tvalid_1's multi_logloss: 0.23208\n",
            "[162]\tvalid_0's multi_error: 0.0790354\tvalid_0's multi_logloss: 0.216415\tvalid_1's multi_error: 0.0820767\tvalid_1's multi_logloss: 0.231828\n",
            "[163]\tvalid_0's multi_error: 0.0789228\tvalid_0's multi_logloss: 0.216085\tvalid_1's multi_error: 0.0819266\tvalid_1's multi_logloss: 0.231568\n",
            "[164]\tvalid_0's multi_error: 0.0788424\tvalid_0's multi_logloss: 0.215823\tvalid_1's multi_error: 0.0820017\tvalid_1's multi_logloss: 0.231395\n",
            "[165]\tvalid_0's multi_error: 0.0787781\tvalid_0's multi_logloss: 0.215573\tvalid_1's multi_error: 0.0819641\tvalid_1's multi_logloss: 0.231247\n",
            "[166]\tvalid_0's multi_error: 0.0786817\tvalid_0's multi_logloss: 0.215305\tvalid_1's multi_error: 0.0817391\tvalid_1's multi_logloss: 0.231075\n",
            "[167]\tvalid_0's multi_error: 0.0784084\tvalid_0's multi_logloss: 0.214693\tvalid_1's multi_error: 0.0812889\tvalid_1's multi_logloss: 0.230535\n",
            "[168]\tvalid_0's multi_error: 0.0782797\tvalid_0's multi_logloss: 0.214276\tvalid_1's multi_error: 0.0814015\tvalid_1's multi_logloss: 0.230217\n",
            "[169]\tvalid_0's multi_error: 0.0782958\tvalid_0's multi_logloss: 0.213871\tvalid_1's multi_error: 0.0812139\tvalid_1's multi_logloss: 0.229895\n",
            "[170]\tvalid_0's multi_error: 0.0781672\tvalid_0's multi_logloss: 0.213694\tvalid_1's multi_error: 0.0811389\tvalid_1's multi_logloss: 0.229821\n",
            "[171]\tvalid_0's multi_error: 0.0780225\tvalid_0's multi_logloss: 0.213388\tvalid_1's multi_error: 0.0810263\tvalid_1's multi_logloss: 0.229579\n",
            "[172]\tvalid_0's multi_error: 0.0778939\tvalid_0's multi_logloss: 0.213112\tvalid_1's multi_error: 0.0809513\tvalid_1's multi_logloss: 0.229386\n",
            "[173]\tvalid_0's multi_error: 0.0776206\tvalid_0's multi_logloss: 0.212495\tvalid_1's multi_error: 0.0805387\tvalid_1's multi_logloss: 0.228844\n",
            "[174]\tvalid_0's multi_error: 0.0773955\tvalid_0's multi_logloss: 0.212224\tvalid_1's multi_error: 0.0805012\tvalid_1's multi_logloss: 0.228633\n",
            "[175]\tvalid_0's multi_error: 0.0774116\tvalid_0's multi_logloss: 0.212034\tvalid_1's multi_error: 0.0803886\tvalid_1's multi_logloss: 0.228527\n",
            "[176]\tvalid_0's multi_error: 0.0773151\tvalid_0's multi_logloss: 0.211838\tvalid_1's multi_error: 0.0804261\tvalid_1's multi_logloss: 0.228418\n",
            "[177]\tvalid_0's multi_error: 0.0771865\tvalid_0's multi_logloss: 0.211618\tvalid_1's multi_error: 0.0805012\tvalid_1's multi_logloss: 0.228272\n",
            "[178]\tvalid_0's multi_error: 0.0771061\tvalid_0's multi_logloss: 0.211423\tvalid_1's multi_error: 0.0803511\tvalid_1's multi_logloss: 0.228173\n",
            "[179]\tvalid_0's multi_error: 0.0770579\tvalid_0's multi_logloss: 0.211232\tvalid_1's multi_error: 0.0803136\tvalid_1's multi_logloss: 0.228069\n",
            "[180]\tvalid_0's multi_error: 0.0769453\tvalid_0's multi_logloss: 0.21089\tvalid_1's multi_error: 0.0803136\tvalid_1's multi_logloss: 0.227807\n",
            "[181]\tvalid_0's multi_error: 0.0768328\tvalid_0's multi_logloss: 0.210715\tvalid_1's multi_error: 0.0801636\tvalid_1's multi_logloss: 0.22772\n",
            "[182]\tvalid_0's multi_error: 0.0766559\tvalid_0's multi_logloss: 0.210434\tvalid_1's multi_error: 0.0801636\tvalid_1's multi_logloss: 0.227522\n",
            "[183]\tvalid_0's multi_error: 0.0765756\tvalid_0's multi_logloss: 0.210209\tvalid_1's multi_error: 0.0801636\tvalid_1's multi_logloss: 0.227376\n",
            "[184]\tvalid_0's multi_error: 0.0765756\tvalid_0's multi_logloss: 0.210035\tvalid_1's multi_error: 0.080126\tvalid_1's multi_logloss: 0.227298\n",
            "[185]\tvalid_0's multi_error: 0.0765916\tvalid_0's multi_logloss: 0.209843\tvalid_1's multi_error: 0.0799385\tvalid_1's multi_logloss: 0.227188\n",
            "[186]\tvalid_0's multi_error: 0.0763023\tvalid_0's multi_logloss: 0.209457\tvalid_1's multi_error: 0.0798259\tvalid_1's multi_logloss: 0.226929\n",
            "[187]\tvalid_0's multi_error: 0.0760772\tvalid_0's multi_logloss: 0.209206\tvalid_1's multi_error: 0.0798259\tvalid_1's multi_logloss: 0.226767\n",
            "[188]\tvalid_0's multi_error: 0.0759968\tvalid_0's multi_logloss: 0.209033\tvalid_1's multi_error: 0.0797884\tvalid_1's multi_logloss: 0.226693\n",
            "[189]\tvalid_0's multi_error: 0.0757717\tvalid_0's multi_logloss: 0.208278\tvalid_1's multi_error: 0.0795634\tvalid_1's multi_logloss: 0.226009\n",
            "[190]\tvalid_0's multi_error: 0.0755788\tvalid_0's multi_logloss: 0.207943\tvalid_1's multi_error: 0.0794508\tvalid_1's multi_logloss: 0.225763\n",
            "[191]\tvalid_0's multi_error: 0.075418\tvalid_0's multi_logloss: 0.207711\tvalid_1's multi_error: 0.0795634\tvalid_1's multi_logloss: 0.225587\n",
            "[192]\tvalid_0's multi_error: 0.0752894\tvalid_0's multi_logloss: 0.207465\tvalid_1's multi_error: 0.0793758\tvalid_1's multi_logloss: 0.225414\n",
            "[193]\tvalid_0's multi_error: 0.0751608\tvalid_0's multi_logloss: 0.207235\tvalid_1's multi_error: 0.0793008\tvalid_1's multi_logloss: 0.225281\n",
            "[194]\tvalid_0's multi_error: 0.0751286\tvalid_0's multi_logloss: 0.207093\tvalid_1's multi_error: 0.0792257\tvalid_1's multi_logloss: 0.225231\n",
            "[195]\tvalid_0's multi_error: 0.0751125\tvalid_0's multi_logloss: 0.20683\tvalid_1's multi_error: 0.0791132\tvalid_1's multi_logloss: 0.225042\n",
            "[196]\tvalid_0's multi_error: 0.0748875\tvalid_0's multi_logloss: 0.206482\tvalid_1's multi_error: 0.0789632\tvalid_1's multi_logloss: 0.224806\n",
            "[197]\tvalid_0's multi_error: 0.0749196\tvalid_0's multi_logloss: 0.206311\tvalid_1's multi_error: 0.0790757\tvalid_1's multi_logloss: 0.224723\n",
            "[198]\tvalid_0's multi_error: 0.0748875\tvalid_0's multi_logloss: 0.20607\tvalid_1's multi_error: 0.0790757\tvalid_1's multi_logloss: 0.224566\n",
            "[199]\tvalid_0's multi_error: 0.0748232\tvalid_0's multi_logloss: 0.205827\tvalid_1's multi_error: 0.0789257\tvalid_1's multi_logloss: 0.224403\n",
            "[200]\tvalid_0's multi_error: 0.0747106\tvalid_0's multi_logloss: 0.205659\tvalid_1's multi_error: 0.0789632\tvalid_1's multi_logloss: 0.224326\n",
            "[201]\tvalid_0's multi_error: 0.0746785\tvalid_0's multi_logloss: 0.205395\tvalid_1's multi_error: 0.0790007\tvalid_1's multi_logloss: 0.224131\n",
            "[202]\tvalid_0's multi_error: 0.0747428\tvalid_0's multi_logloss: 0.205199\tvalid_1's multi_error: 0.0790382\tvalid_1's multi_logloss: 0.224019\n",
            "[203]\tvalid_0's multi_error: 0.0744855\tvalid_0's multi_logloss: 0.204697\tvalid_1's multi_error: 0.0788506\tvalid_1's multi_logloss: 0.223571\n",
            "[204]\tvalid_0's multi_error: 0.0744051\tvalid_0's multi_logloss: 0.204372\tvalid_1's multi_error: 0.0788506\tvalid_1's multi_logloss: 0.223288\n",
            "[205]\tvalid_0's multi_error: 0.0743087\tvalid_0's multi_logloss: 0.204198\tvalid_1's multi_error: 0.0788506\tvalid_1's multi_logloss: 0.223204\n",
            "[206]\tvalid_0's multi_error: 0.0740675\tvalid_0's multi_logloss: 0.203942\tvalid_1's multi_error: 0.0787381\tvalid_1's multi_logloss: 0.223018\n",
            "[207]\tvalid_0's multi_error: 0.0740514\tvalid_0's multi_logloss: 0.203683\tvalid_1's multi_error: 0.0787006\tvalid_1's multi_logloss: 0.222827\n",
            "[208]\tvalid_0's multi_error: 0.073955\tvalid_0's multi_logloss: 0.203397\tvalid_1's multi_error: 0.0786631\tvalid_1's multi_logloss: 0.22262\n",
            "[209]\tvalid_0's multi_error: 0.0738746\tvalid_0's multi_logloss: 0.203201\tvalid_1's multi_error: 0.0787381\tvalid_1's multi_logloss: 0.222516\n",
            "[210]\tvalid_0's multi_error: 0.0737621\tvalid_0's multi_logloss: 0.202937\tvalid_1's multi_error: 0.0786256\tvalid_1's multi_logloss: 0.222327\n",
            "[211]\tvalid_0's multi_error: 0.0733601\tvalid_0's multi_logloss: 0.20239\tvalid_1's multi_error: 0.078588\tvalid_1's multi_logloss: 0.221837\n",
            "[212]\tvalid_0's multi_error: 0.0733601\tvalid_0's multi_logloss: 0.202181\tvalid_1's multi_error: 0.078438\tvalid_1's multi_logloss: 0.221701\n",
            "[213]\tvalid_0's multi_error: 0.0732797\tvalid_0's multi_logloss: 0.201966\tvalid_1's multi_error: 0.0784005\tvalid_1's multi_logloss: 0.221577\n",
            "[214]\tvalid_0's multi_error: 0.0732958\tvalid_0's multi_logloss: 0.201735\tvalid_1's multi_error: 0.078438\tvalid_1's multi_logloss: 0.221401\n",
            "[215]\tvalid_0's multi_error: 0.0730707\tvalid_0's multi_logloss: 0.201399\tvalid_1's multi_error: 0.0784005\tvalid_1's multi_logloss: 0.221183\n",
            "[216]\tvalid_0's multi_error: 0.0729743\tvalid_0's multi_logloss: 0.201226\tvalid_1's multi_error: 0.0784755\tvalid_1's multi_logloss: 0.221123\n",
            "[217]\tvalid_0's multi_error: 0.0727814\tvalid_0's multi_logloss: 0.201012\tvalid_1's multi_error: 0.078438\tvalid_1's multi_logloss: 0.221001\n",
            "[218]\tvalid_0's multi_error: 0.0727653\tvalid_0's multi_logloss: 0.200711\tvalid_1's multi_error: 0.078363\tvalid_1's multi_logloss: 0.220796\n",
            "[219]\tvalid_0's multi_error: 0.0726849\tvalid_0's multi_logloss: 0.200441\tvalid_1's multi_error: 0.0782879\tvalid_1's multi_logloss: 0.220571\n",
            "[220]\tvalid_0's multi_error: 0.0725402\tvalid_0's multi_logloss: 0.200229\tvalid_1's multi_error: 0.0782504\tvalid_1's multi_logloss: 0.220436\n",
            "[221]\tvalid_0's multi_error: 0.0723955\tvalid_0's multi_logloss: 0.199949\tvalid_1's multi_error: 0.0782504\tvalid_1's multi_logloss: 0.220238\n",
            "[222]\tvalid_0's multi_error: 0.0724277\tvalid_0's multi_logloss: 0.19979\tvalid_1's multi_error: 0.0780629\tvalid_1's multi_logloss: 0.220165\n",
            "[223]\tvalid_0's multi_error: 0.0723151\tvalid_0's multi_logloss: 0.199612\tvalid_1's multi_error: 0.0780629\tvalid_1's multi_logloss: 0.220085\n",
            "[224]\tvalid_0's multi_error: 0.0719453\tvalid_0's multi_logloss: 0.199034\tvalid_1's multi_error: 0.0779503\tvalid_1's multi_logloss: 0.219557\n",
            "[225]\tvalid_0's multi_error: 0.0719614\tvalid_0's multi_logloss: 0.198799\tvalid_1's multi_error: 0.0779503\tvalid_1's multi_logloss: 0.219381\n",
            "[226]\tvalid_0's multi_error: 0.0718489\tvalid_0's multi_logloss: 0.198574\tvalid_1's multi_error: 0.0778378\tvalid_1's multi_logloss: 0.219213\n",
            "[227]\tvalid_0's multi_error: 0.0716881\tvalid_0's multi_logloss: 0.198296\tvalid_1's multi_error: 0.0776877\tvalid_1's multi_logloss: 0.219012\n",
            "[228]\tvalid_0's multi_error: 0.0716399\tvalid_0's multi_logloss: 0.19808\tvalid_1's multi_error: 0.0775752\tvalid_1's multi_logloss: 0.21889\n",
            "[229]\tvalid_0's multi_error: 0.0715434\tvalid_0's multi_logloss: 0.197637\tvalid_1's multi_error: 0.0775377\tvalid_1's multi_logloss: 0.218505\n",
            "[230]\tvalid_0's multi_error: 0.0713987\tvalid_0's multi_logloss: 0.197373\tvalid_1's multi_error: 0.0773877\tvalid_1's multi_logloss: 0.218336\n",
            "[231]\tvalid_0's multi_error: 0.0712862\tvalid_0's multi_logloss: 0.197158\tvalid_1's multi_error: 0.0773501\tvalid_1's multi_logloss: 0.218225\n",
            "[232]\tvalid_0's multi_error: 0.0711736\tvalid_0's multi_logloss: 0.197\tvalid_1's multi_error: 0.0772376\tvalid_1's multi_logloss: 0.218152\n",
            "[233]\tvalid_0's multi_error: 0.0709646\tvalid_0's multi_logloss: 0.196703\tvalid_1's multi_error: 0.076975\tvalid_1's multi_logloss: 0.217906\n",
            "[234]\tvalid_0's multi_error: 0.0709003\tvalid_0's multi_logloss: 0.196511\tvalid_1's multi_error: 0.0771251\tvalid_1's multi_logloss: 0.217787\n",
            "[235]\tvalid_0's multi_error: 0.070836\tvalid_0's multi_logloss: 0.196328\tvalid_1's multi_error: 0.0771251\tvalid_1's multi_logloss: 0.217702\n",
            "[236]\tvalid_0's multi_error: 0.0706913\tvalid_0's multi_logloss: 0.196088\tvalid_1's multi_error: 0.0769375\tvalid_1's multi_logloss: 0.217556\n",
            "[237]\tvalid_0's multi_error: 0.0703537\tvalid_0's multi_logloss: 0.195581\tvalid_1's multi_error: 0.0767499\tvalid_1's multi_logloss: 0.217118\n",
            "[238]\tvalid_0's multi_error: 0.0703376\tvalid_0's multi_logloss: 0.195398\tvalid_1's multi_error: 0.0767499\tvalid_1's multi_logloss: 0.217027\n",
            "[239]\tvalid_0's multi_error: 0.0701768\tvalid_0's multi_logloss: 0.195168\tvalid_1's multi_error: 0.0765999\tvalid_1's multi_logloss: 0.216855\n",
            "[240]\tvalid_0's multi_error: 0.0699196\tvalid_0's multi_logloss: 0.194765\tvalid_1's multi_error: 0.0764874\tvalid_1's multi_logloss: 0.216509\n",
            "[241]\tvalid_0's multi_error: 0.0697588\tvalid_0's multi_logloss: 0.19446\tvalid_1's multi_error: 0.0764498\tvalid_1's multi_logloss: 0.216266\n",
            "[242]\tvalid_0's multi_error: 0.0696785\tvalid_0's multi_logloss: 0.194219\tvalid_1's multi_error: 0.0764498\tvalid_1's multi_logloss: 0.216113\n",
            "[243]\tvalid_0's multi_error: 0.0696463\tvalid_0's multi_logloss: 0.194025\tvalid_1's multi_error: 0.0762998\tvalid_1's multi_logloss: 0.215987\n",
            "[244]\tvalid_0's multi_error: 0.0695659\tvalid_0's multi_logloss: 0.193578\tvalid_1's multi_error: 0.0761873\tvalid_1's multi_logloss: 0.215597\n",
            "[245]\tvalid_0's multi_error: 0.0694212\tvalid_0's multi_logloss: 0.193302\tvalid_1's multi_error: 0.0760747\tvalid_1's multi_logloss: 0.215382\n",
            "[246]\tvalid_0's multi_error: 0.0694212\tvalid_0's multi_logloss: 0.193085\tvalid_1's multi_error: 0.0758872\tvalid_1's multi_logloss: 0.215229\n",
            "[247]\tvalid_0's multi_error: 0.0693569\tvalid_0's multi_logloss: 0.192861\tvalid_1's multi_error: 0.0758872\tvalid_1's multi_logloss: 0.215069\n",
            "[248]\tvalid_0's multi_error: 0.0692926\tvalid_0's multi_logloss: 0.19263\tvalid_1's multi_error: 0.0758497\tvalid_1's multi_logloss: 0.214883\n",
            "[249]\tvalid_0's multi_error: 0.0692605\tvalid_0's multi_logloss: 0.192412\tvalid_1's multi_error: 0.0757371\tvalid_1's multi_logloss: 0.21471\n",
            "[250]\tvalid_0's multi_error: 0.0692283\tvalid_0's multi_logloss: 0.192266\tvalid_1's multi_error: 0.0756621\tvalid_1's multi_logloss: 0.214665\n",
            "[251]\tvalid_0's multi_error: 0.0690675\tvalid_0's multi_logloss: 0.192034\tvalid_1's multi_error: 0.0756996\tvalid_1's multi_logloss: 0.214501\n",
            "[252]\tvalid_0's multi_error: 0.0690514\tvalid_0's multi_logloss: 0.191808\tvalid_1's multi_error: 0.0756996\tvalid_1's multi_logloss: 0.214395\n",
            "[253]\tvalid_0's multi_error: 0.0690675\tvalid_0's multi_logloss: 0.191626\tvalid_1's multi_error: 0.0756996\tvalid_1's multi_logloss: 0.214285\n",
            "[254]\tvalid_0's multi_error: 0.0689389\tvalid_0's multi_logloss: 0.191272\tvalid_1's multi_error: 0.0755871\tvalid_1's multi_logloss: 0.213993\n",
            "[255]\tvalid_0's multi_error: 0.0685691\tvalid_0's multi_logloss: 0.190688\tvalid_1's multi_error: 0.075362\tvalid_1's multi_logloss: 0.21349\n",
            "[256]\tvalid_0's multi_error: 0.0684084\tvalid_0's multi_logloss: 0.190499\tvalid_1's multi_error: 0.0753245\tvalid_1's multi_logloss: 0.213367\n",
            "[257]\tvalid_0's multi_error: 0.0684244\tvalid_0's multi_logloss: 0.190348\tvalid_1's multi_error: 0.075437\tvalid_1's multi_logloss: 0.213288\n",
            "[258]\tvalid_0's multi_error: 0.0681994\tvalid_0's multi_logloss: 0.189887\tvalid_1's multi_error: 0.0751744\tvalid_1's multi_logloss: 0.212904\n",
            "[259]\tvalid_0's multi_error: 0.0682154\tvalid_0's multi_logloss: 0.189669\tvalid_1's multi_error: 0.0751744\tvalid_1's multi_logloss: 0.212764\n",
            "[260]\tvalid_0's multi_error: 0.068119\tvalid_0's multi_logloss: 0.189482\tvalid_1's multi_error: 0.0751369\tvalid_1's multi_logloss: 0.212672\n",
            "[261]\tvalid_0's multi_error: 0.0680386\tvalid_0's multi_logloss: 0.18928\tvalid_1's multi_error: 0.0752495\tvalid_1's multi_logloss: 0.212557\n",
            "[262]\tvalid_0's multi_error: 0.0678939\tvalid_0's multi_logloss: 0.189153\tvalid_1's multi_error: 0.0751744\tvalid_1's multi_logloss: 0.212538\n",
            "[263]\tvalid_0's multi_error: 0.0675241\tvalid_0's multi_logloss: 0.188674\tvalid_1's multi_error: 0.0748743\tvalid_1's multi_logloss: 0.21211\n",
            "[264]\tvalid_0's multi_error: 0.0675723\tvalid_0's multi_logloss: 0.188517\tvalid_1's multi_error: 0.0748743\tvalid_1's multi_logloss: 0.212031\n",
            "[265]\tvalid_0's multi_error: 0.0675723\tvalid_0's multi_logloss: 0.188302\tvalid_1's multi_error: 0.0747618\tvalid_1's multi_logloss: 0.211904\n",
            "[266]\tvalid_0's multi_error: 0.0675241\tvalid_0's multi_logloss: 0.188139\tvalid_1's multi_error: 0.0747618\tvalid_1's multi_logloss: 0.211822\n",
            "[267]\tvalid_0's multi_error: 0.0671543\tvalid_0's multi_logloss: 0.187764\tvalid_1's multi_error: 0.0746117\tvalid_1's multi_logloss: 0.21153\n",
            "[268]\tvalid_0's multi_error: 0.0672186\tvalid_0's multi_logloss: 0.187597\tvalid_1's multi_error: 0.0746493\tvalid_1's multi_logloss: 0.211438\n",
            "[269]\tvalid_0's multi_error: 0.0671383\tvalid_0's multi_logloss: 0.187432\tvalid_1's multi_error: 0.0746117\tvalid_1's multi_logloss: 0.211335\n",
            "[270]\tvalid_0's multi_error: 0.0671543\tvalid_0's multi_logloss: 0.187297\tvalid_1's multi_error: 0.0744992\tvalid_1's multi_logloss: 0.211297\n",
            "[271]\tvalid_0's multi_error: 0.0671222\tvalid_0's multi_logloss: 0.187136\tvalid_1's multi_error: 0.0745742\tvalid_1's multi_logloss: 0.211205\n",
            "[272]\tvalid_0's multi_error: 0.0671865\tvalid_0's multi_logloss: 0.186967\tvalid_1's multi_error: 0.0745367\tvalid_1's multi_logloss: 0.211115\n",
            "[273]\tvalid_0's multi_error: 0.0670096\tvalid_0's multi_logloss: 0.186565\tvalid_1's multi_error: 0.0742741\tvalid_1's multi_logloss: 0.210751\n",
            "[274]\tvalid_0's multi_error: 0.0668489\tvalid_0's multi_logloss: 0.186397\tvalid_1's multi_error: 0.0742741\tvalid_1's multi_logloss: 0.210661\n",
            "[275]\tvalid_0's multi_error: 0.0666881\tvalid_0's multi_logloss: 0.185938\tvalid_1's multi_error: 0.0739365\tvalid_1's multi_logloss: 0.210273\n",
            "[276]\tvalid_0's multi_error: 0.0666238\tvalid_0's multi_logloss: 0.185798\tvalid_1's multi_error: 0.073824\tvalid_1's multi_logloss: 0.210213\n",
            "[277]\tvalid_0's multi_error: 0.0665113\tvalid_0's multi_logloss: 0.185583\tvalid_1's multi_error: 0.073899\tvalid_1's multi_logloss: 0.210069\n",
            "[278]\tvalid_0's multi_error: 0.066463\tvalid_0's multi_logloss: 0.185454\tvalid_1's multi_error: 0.0739365\tvalid_1's multi_logloss: 0.210031\n",
            "[279]\tvalid_0's multi_error: 0.0663666\tvalid_0's multi_logloss: 0.185261\tvalid_1's multi_error: 0.0738615\tvalid_1's multi_logloss: 0.209939\n",
            "[280]\tvalid_0's multi_error: 0.0663023\tvalid_0's multi_logloss: 0.185144\tvalid_1's multi_error: 0.0738615\tvalid_1's multi_logloss: 0.209913\n",
            "[281]\tvalid_0's multi_error: 0.066254\tvalid_0's multi_logloss: 0.185025\tvalid_1's multi_error: 0.073824\tvalid_1's multi_logloss: 0.209892\n",
            "[282]\tvalid_0's multi_error: 0.0661254\tvalid_0's multi_logloss: 0.18482\tvalid_1's multi_error: 0.0737865\tvalid_1's multi_logloss: 0.209757\n",
            "[283]\tvalid_0's multi_error: 0.0659325\tvalid_0's multi_logloss: 0.18444\tvalid_1's multi_error: 0.0737865\tvalid_1's multi_logloss: 0.209424\n",
            "[284]\tvalid_0's multi_error: 0.0659164\tvalid_0's multi_logloss: 0.184282\tvalid_1's multi_error: 0.0736364\tvalid_1's multi_logloss: 0.209339\n",
            "[285]\tvalid_0's multi_error: 0.0658039\tvalid_0's multi_logloss: 0.184181\tvalid_1's multi_error: 0.0735989\tvalid_1's multi_logloss: 0.209323\n",
            "[286]\tvalid_0's multi_error: 0.0657395\tvalid_0's multi_logloss: 0.184006\tvalid_1's multi_error: 0.0735614\tvalid_1's multi_logloss: 0.20924\n",
            "[287]\tvalid_0's multi_error: 0.0656913\tvalid_0's multi_logloss: 0.183828\tvalid_1's multi_error: 0.0734864\tvalid_1's multi_logloss: 0.209154\n",
            "[288]\tvalid_0's multi_error: 0.065627\tvalid_0's multi_logloss: 0.183593\tvalid_1's multi_error: 0.0735239\tvalid_1's multi_logloss: 0.208988\n",
            "[289]\tvalid_0's multi_error: 0.0656592\tvalid_0's multi_logloss: 0.183466\tvalid_1's multi_error: 0.0736364\tvalid_1's multi_logloss: 0.208954\n",
            "[290]\tvalid_0's multi_error: 0.0654019\tvalid_0's multi_logloss: 0.183146\tvalid_1's multi_error: 0.0734114\tvalid_1's multi_logloss: 0.208693\n",
            "[291]\tvalid_0's multi_error: 0.0653215\tvalid_0's multi_logloss: 0.182979\tvalid_1's multi_error: 0.0733363\tvalid_1's multi_logloss: 0.208596\n",
            "[292]\tvalid_0's multi_error: 0.0653215\tvalid_0's multi_logloss: 0.182846\tvalid_1's multi_error: 0.0732988\tvalid_1's multi_logloss: 0.208545\n",
            "[293]\tvalid_0's multi_error: 0.0653055\tvalid_0's multi_logloss: 0.182725\tvalid_1's multi_error: 0.0732988\tvalid_1's multi_logloss: 0.208499\n",
            "[294]\tvalid_0's multi_error: 0.0652733\tvalid_0's multi_logloss: 0.182572\tvalid_1's multi_error: 0.0732988\tvalid_1's multi_logloss: 0.20843\n",
            "[295]\tvalid_0's multi_error: 0.0650965\tvalid_0's multi_logloss: 0.18225\tvalid_1's multi_error: 0.0731113\tvalid_1's multi_logloss: 0.208179\n",
            "[296]\tvalid_0's multi_error: 0.0651125\tvalid_0's multi_logloss: 0.1821\tvalid_1's multi_error: 0.0732613\tvalid_1's multi_logloss: 0.208123\n",
            "[297]\tvalid_0's multi_error: 0.0650643\tvalid_0's multi_logloss: 0.181871\tvalid_1's multi_error: 0.0732988\tvalid_1's multi_logloss: 0.20799\n",
            "[298]\tvalid_0's multi_error: 0.0649035\tvalid_0's multi_logloss: 0.181725\tvalid_1's multi_error: 0.0732238\tvalid_1's multi_logloss: 0.207906\n",
            "[299]\tvalid_0's multi_error: 0.0648392\tvalid_0's multi_logloss: 0.181576\tvalid_1's multi_error: 0.0731863\tvalid_1's multi_logloss: 0.207824\n",
            "[300]\tvalid_0's multi_error: 0.064791\tvalid_0's multi_logloss: 0.181389\tvalid_1's multi_error: 0.0730737\tvalid_1's multi_logloss: 0.207724\n",
            "[301]\tvalid_0's multi_error: 0.0648553\tvalid_0's multi_logloss: 0.181253\tvalid_1's multi_error: 0.0730362\tvalid_1's multi_logloss: 0.207668\n",
            "[302]\tvalid_0's multi_error: 0.0647588\tvalid_0's multi_logloss: 0.180984\tvalid_1's multi_error: 0.0728487\tvalid_1's multi_logloss: 0.207465\n",
            "[303]\tvalid_0's multi_error: 0.0646302\tvalid_0's multi_logloss: 0.180768\tvalid_1's multi_error: 0.0730362\tvalid_1's multi_logloss: 0.207321\n",
            "[304]\tvalid_0's multi_error: 0.0645338\tvalid_0's multi_logloss: 0.180531\tvalid_1's multi_error: 0.0727737\tvalid_1's multi_logloss: 0.207174\n",
            "[305]\tvalid_0's multi_error: 0.0645016\tvalid_0's multi_logloss: 0.180369\tvalid_1's multi_error: 0.0728862\tvalid_1's multi_logloss: 0.207099\n",
            "[306]\tvalid_0's multi_error: 0.0645177\tvalid_0's multi_logloss: 0.180199\tvalid_1's multi_error: 0.0729612\tvalid_1's multi_logloss: 0.207018\n",
            "[307]\tvalid_0's multi_error: 0.0644695\tvalid_0's multi_logloss: 0.180031\tvalid_1's multi_error: 0.0728487\tvalid_1's multi_logloss: 0.206933\n",
            "[308]\tvalid_0's multi_error: 0.0644534\tvalid_0's multi_logloss: 0.179885\tvalid_1's multi_error: 0.0728112\tvalid_1's multi_logloss: 0.206859\n",
            "[309]\tvalid_0's multi_error: 0.064373\tvalid_0's multi_logloss: 0.179774\tvalid_1's multi_error: 0.0727737\tvalid_1's multi_logloss: 0.206807\n",
            "[310]\tvalid_0's multi_error: 0.0643248\tvalid_0's multi_logloss: 0.179614\tvalid_1's multi_error: 0.0728487\tvalid_1's multi_logloss: 0.20672\n",
            "[311]\tvalid_0's multi_error: 0.0641318\tvalid_0's multi_logloss: 0.179432\tvalid_1's multi_error: 0.0728487\tvalid_1's multi_logloss: 0.206609\n",
            "[312]\tvalid_0's multi_error: 0.0640514\tvalid_0's multi_logloss: 0.179297\tvalid_1's multi_error: 0.0728862\tvalid_1's multi_logloss: 0.206539\n",
            "[313]\tvalid_0's multi_error: 0.063955\tvalid_0's multi_logloss: 0.179158\tvalid_1's multi_error: 0.0727361\tvalid_1's multi_logloss: 0.206462\n",
            "[314]\tvalid_0's multi_error: 0.0639711\tvalid_0's multi_logloss: 0.178995\tvalid_1's multi_error: 0.0726236\tvalid_1's multi_logloss: 0.206361\n",
            "[315]\tvalid_0's multi_error: 0.0639228\tvalid_0's multi_logloss: 0.178858\tvalid_1's multi_error: 0.0726986\tvalid_1's multi_logloss: 0.20629\n",
            "[316]\tvalid_0's multi_error: 0.0638264\tvalid_0's multi_logloss: 0.17866\tvalid_1's multi_error: 0.0726986\tvalid_1's multi_logloss: 0.206162\n",
            "[317]\tvalid_0's multi_error: 0.0637942\tvalid_0's multi_logloss: 0.178514\tvalid_1's multi_error: 0.0726611\tvalid_1's multi_logloss: 0.206087\n",
            "[318]\tvalid_0's multi_error: 0.0636817\tvalid_0's multi_logloss: 0.178304\tvalid_1's multi_error: 0.0725861\tvalid_1's multi_logloss: 0.205939\n",
            "[319]\tvalid_0's multi_error: 0.0637299\tvalid_0's multi_logloss: 0.178179\tvalid_1's multi_error: 0.0726611\tvalid_1's multi_logloss: 0.205895\n",
            "[320]\tvalid_0's multi_error: 0.0635209\tvalid_0's multi_logloss: 0.178007\tvalid_1's multi_error: 0.0726236\tvalid_1's multi_logloss: 0.205788\n",
            "[321]\tvalid_0's multi_error: 0.0634405\tvalid_0's multi_logloss: 0.177885\tvalid_1's multi_error: 0.0725111\tvalid_1's multi_logloss: 0.205731\n",
            "[322]\tvalid_0's multi_error: 0.0633441\tvalid_0's multi_logloss: 0.17762\tvalid_1's multi_error: 0.072361\tvalid_1's multi_logloss: 0.20554\n",
            "[323]\tvalid_0's multi_error: 0.0631994\tvalid_0's multi_logloss: 0.177474\tvalid_1's multi_error: 0.0723985\tvalid_1's multi_logloss: 0.205454\n",
            "[324]\tvalid_0's multi_error: 0.0630707\tvalid_0's multi_logloss: 0.177302\tvalid_1's multi_error: 0.0722485\tvalid_1's multi_logloss: 0.20535\n",
            "[325]\tvalid_0's multi_error: 0.0630386\tvalid_0's multi_logloss: 0.177127\tvalid_1's multi_error: 0.0721735\tvalid_1's multi_logloss: 0.205218\n",
            "[326]\tvalid_0's multi_error: 0.0629904\tvalid_0's multi_logloss: 0.176994\tvalid_1's multi_error: 0.0721359\tvalid_1's multi_logloss: 0.205169\n",
            "[327]\tvalid_0's multi_error: 0.0628939\tvalid_0's multi_logloss: 0.176815\tvalid_1's multi_error: 0.0720609\tvalid_1's multi_logloss: 0.205048\n",
            "[328]\tvalid_0's multi_error: 0.0628617\tvalid_0's multi_logloss: 0.176657\tvalid_1's multi_error: 0.0720234\tvalid_1's multi_logloss: 0.204963\n",
            "[329]\tvalid_0's multi_error: 0.0627331\tvalid_0's multi_logloss: 0.176536\tvalid_1's multi_error: 0.0719484\tvalid_1's multi_logloss: 0.204907\n",
            "[330]\tvalid_0's multi_error: 0.0627492\tvalid_0's multi_logloss: 0.176413\tvalid_1's multi_error: 0.0719859\tvalid_1's multi_logloss: 0.204851\n",
            "[331]\tvalid_0's multi_error: 0.062717\tvalid_0's multi_logloss: 0.176231\tvalid_1's multi_error: 0.0719484\tvalid_1's multi_logloss: 0.204741\n",
            "[332]\tvalid_0's multi_error: 0.0626527\tvalid_0's multi_logloss: 0.176081\tvalid_1's multi_error: 0.0719109\tvalid_1's multi_logloss: 0.204649\n",
            "[333]\tvalid_0's multi_error: 0.0626045\tvalid_0's multi_logloss: 0.175943\tvalid_1's multi_error: 0.0718358\tvalid_1's multi_logloss: 0.204569\n",
            "[334]\tvalid_0's multi_error: 0.0625723\tvalid_0's multi_logloss: 0.175811\tvalid_1's multi_error: 0.0719484\tvalid_1's multi_logloss: 0.204505\n",
            "[335]\tvalid_0's multi_error: 0.062508\tvalid_0's multi_logloss: 0.175707\tvalid_1's multi_error: 0.0719109\tvalid_1's multi_logloss: 0.204488\n",
            "[336]\tvalid_0's multi_error: 0.062508\tvalid_0's multi_logloss: 0.175588\tvalid_1's multi_error: 0.0720234\tvalid_1's multi_logloss: 0.204454\n",
            "[337]\tvalid_0's multi_error: 0.0623473\tvalid_0's multi_logloss: 0.175425\tvalid_1's multi_error: 0.0719109\tvalid_1's multi_logloss: 0.204353\n",
            "[338]\tvalid_0's multi_error: 0.062283\tvalid_0's multi_logloss: 0.175302\tvalid_1's multi_error: 0.0719484\tvalid_1's multi_logloss: 0.204296\n",
            "[339]\tvalid_0's multi_error: 0.062283\tvalid_0's multi_logloss: 0.175174\tvalid_1's multi_error: 0.0719109\tvalid_1's multi_logloss: 0.204234\n",
            "[340]\tvalid_0's multi_error: 0.0622669\tvalid_0's multi_logloss: 0.175037\tvalid_1's multi_error: 0.0717983\tvalid_1's multi_logloss: 0.204149\n",
            "[341]\tvalid_0's multi_error: 0.0622186\tvalid_0's multi_logloss: 0.174894\tvalid_1's multi_error: 0.0716858\tvalid_1's multi_logloss: 0.20407\n",
            "[342]\tvalid_0's multi_error: 0.0621543\tvalid_0's multi_logloss: 0.174751\tvalid_1's multi_error: 0.0716108\tvalid_1's multi_logloss: 0.204\n",
            "[343]\tvalid_0's multi_error: 0.0621543\tvalid_0's multi_logloss: 0.174639\tvalid_1's multi_error: 0.0716108\tvalid_1's multi_logloss: 0.203957\n",
            "[344]\tvalid_0's multi_error: 0.06209\tvalid_0's multi_logloss: 0.174511\tvalid_1's multi_error: 0.0716483\tvalid_1's multi_logloss: 0.203913\n",
            "[345]\tvalid_0's multi_error: 0.0621061\tvalid_0's multi_logloss: 0.174396\tvalid_1's multi_error: 0.0717233\tvalid_1's multi_logloss: 0.20389\n",
            "[346]\tvalid_0's multi_error: 0.06209\tvalid_0's multi_logloss: 0.174248\tvalid_1's multi_error: 0.0716483\tvalid_1's multi_logloss: 0.203813\n",
            "[347]\tvalid_0's multi_error: 0.0619936\tvalid_0's multi_logloss: 0.174068\tvalid_1's multi_error: 0.0714982\tvalid_1's multi_logloss: 0.203697\n",
            "[348]\tvalid_0's multi_error: 0.0619614\tvalid_0's multi_logloss: 0.173933\tvalid_1's multi_error: 0.0714982\tvalid_1's multi_logloss: 0.203648\n",
            "[349]\tvalid_0's multi_error: 0.0618489\tvalid_0's multi_logloss: 0.173819\tvalid_1's multi_error: 0.0714607\tvalid_1's multi_logloss: 0.203593\n",
            "[350]\tvalid_0's multi_error: 0.0617042\tvalid_0's multi_logloss: 0.17368\tvalid_1's multi_error: 0.0714982\tvalid_1's multi_logloss: 0.203519\n",
            "[351]\tvalid_0's multi_error: 0.0616238\tvalid_0's multi_logloss: 0.173584\tvalid_1's multi_error: 0.0714607\tvalid_1's multi_logloss: 0.203497\n",
            "[352]\tvalid_0's multi_error: 0.0614791\tvalid_0's multi_logloss: 0.173469\tvalid_1's multi_error: 0.0714982\tvalid_1's multi_logloss: 0.20347\n",
            "[353]\tvalid_0's multi_error: 0.0613505\tvalid_0's multi_logloss: 0.173234\tvalid_1's multi_error: 0.0715733\tvalid_1's multi_logloss: 0.203289\n",
            "[354]\tvalid_0's multi_error: 0.0612701\tvalid_0's multi_logloss: 0.173079\tvalid_1's multi_error: 0.0713857\tvalid_1's multi_logloss: 0.203211\n",
            "[355]\tvalid_0's multi_error: 0.0611897\tvalid_0's multi_logloss: 0.172943\tvalid_1's multi_error: 0.0713857\tvalid_1's multi_logloss: 0.203176\n",
            "[356]\tvalid_0's multi_error: 0.0610932\tvalid_0's multi_logloss: 0.172824\tvalid_1's multi_error: 0.0714607\tvalid_1's multi_logloss: 0.203128\n",
            "[357]\tvalid_0's multi_error: 0.0609325\tvalid_0's multi_logloss: 0.172686\tvalid_1's multi_error: 0.0715357\tvalid_1's multi_logloss: 0.203063\n",
            "[358]\tvalid_0's multi_error: 0.0609164\tvalid_0's multi_logloss: 0.172558\tvalid_1's multi_error: 0.0714982\tvalid_1's multi_logloss: 0.203032\n",
            "[359]\tvalid_0's multi_error: 0.0609646\tvalid_0's multi_logloss: 0.172446\tvalid_1's multi_error: 0.0714607\tvalid_1's multi_logloss: 0.203001\n",
            "[360]\tvalid_0's multi_error: 0.0608039\tvalid_0's multi_logloss: 0.172246\tvalid_1's multi_error: 0.0714607\tvalid_1's multi_logloss: 0.202863\n",
            "[361]\tvalid_0's multi_error: 0.0607717\tvalid_0's multi_logloss: 0.172143\tvalid_1's multi_error: 0.0714607\tvalid_1's multi_logloss: 0.202839\n",
            "[362]\tvalid_0's multi_error: 0.0606752\tvalid_0's multi_logloss: 0.172041\tvalid_1's multi_error: 0.0714607\tvalid_1's multi_logloss: 0.20282\n",
            "[363]\tvalid_0's multi_error: 0.0606752\tvalid_0's multi_logloss: 0.171925\tvalid_1's multi_error: 0.0713107\tvalid_1's multi_logloss: 0.202749\n",
            "[364]\tvalid_0's multi_error: 0.0607074\tvalid_0's multi_logloss: 0.171805\tvalid_1's multi_error: 0.0713857\tvalid_1's multi_logloss: 0.202707\n",
            "[365]\tvalid_0's multi_error: 0.0606592\tvalid_0's multi_logloss: 0.171672\tvalid_1's multi_error: 0.0714607\tvalid_1's multi_logloss: 0.202661\n",
            "[366]\tvalid_0's multi_error: 0.0606592\tvalid_0's multi_logloss: 0.171567\tvalid_1's multi_error: 0.0714607\tvalid_1's multi_logloss: 0.202618\n",
            "[367]\tvalid_0's multi_error: 0.0605788\tvalid_0's multi_logloss: 0.171436\tvalid_1's multi_error: 0.0713857\tvalid_1's multi_logloss: 0.202556\n",
            "[368]\tvalid_0's multi_error: 0.0606109\tvalid_0's multi_logloss: 0.171342\tvalid_1's multi_error: 0.0713857\tvalid_1's multi_logloss: 0.202544\n",
            "[369]\tvalid_0's multi_error: 0.0604502\tvalid_0's multi_logloss: 0.171003\tvalid_1's multi_error: 0.0714607\tvalid_1's multi_logloss: 0.202261\n",
            "[370]\tvalid_0's multi_error: 0.0603698\tvalid_0's multi_logloss: 0.170906\tvalid_1's multi_error: 0.0713482\tvalid_1's multi_logloss: 0.202232\n",
            "[371]\tvalid_0's multi_error: 0.0603215\tvalid_0's multi_logloss: 0.170793\tvalid_1's multi_error: 0.0713107\tvalid_1's multi_logloss: 0.202173\n",
            "[372]\tvalid_0's multi_error: 0.0602733\tvalid_0's multi_logloss: 0.170688\tvalid_1's multi_error: 0.0712732\tvalid_1's multi_logloss: 0.202137\n",
            "[373]\tvalid_0's multi_error: 0.0602733\tvalid_0's multi_logloss: 0.170523\tvalid_1's multi_error: 0.0712732\tvalid_1's multi_logloss: 0.202044\n",
            "[374]\tvalid_0's multi_error: 0.0602412\tvalid_0's multi_logloss: 0.170367\tvalid_1's multi_error: 0.0711606\tvalid_1's multi_logloss: 0.201973\n",
            "[375]\tvalid_0's multi_error: 0.0601929\tvalid_0's multi_logloss: 0.170236\tvalid_1's multi_error: 0.0712357\tvalid_1's multi_logloss: 0.201911\n",
            "[376]\tvalid_0's multi_error: 0.0600322\tvalid_0's multi_logloss: 0.169953\tvalid_1's multi_error: 0.0711231\tvalid_1's multi_logloss: 0.201671\n",
            "[377]\tvalid_0's multi_error: 0.0600161\tvalid_0's multi_logloss: 0.169845\tvalid_1's multi_error: 0.0710481\tvalid_1's multi_logloss: 0.201619\n",
            "[378]\tvalid_0's multi_error: 0.0598553\tvalid_0's multi_logloss: 0.169527\tvalid_1's multi_error: 0.0710106\tvalid_1's multi_logloss: 0.201358\n",
            "[379]\tvalid_0's multi_error: 0.0599035\tvalid_0's multi_logloss: 0.169343\tvalid_1's multi_error: 0.0710481\tvalid_1's multi_logloss: 0.201248\n",
            "[380]\tvalid_0's multi_error: 0.0598875\tvalid_0's multi_logloss: 0.169219\tvalid_1's multi_error: 0.0709731\tvalid_1's multi_logloss: 0.201187\n",
            "[381]\tvalid_0's multi_error: 0.0598875\tvalid_0's multi_logloss: 0.169048\tvalid_1's multi_error: 0.0709731\tvalid_1's multi_logloss: 0.20109\n",
            "[382]\tvalid_0's multi_error: 0.0596302\tvalid_0's multi_logloss: 0.168891\tvalid_1's multi_error: 0.0709731\tvalid_1's multi_logloss: 0.20102\n",
            "[383]\tvalid_0's multi_error: 0.0596624\tvalid_0's multi_logloss: 0.168752\tvalid_1's multi_error: 0.0709731\tvalid_1's multi_logloss: 0.200945\n",
            "[384]\tvalid_0's multi_error: 0.0595981\tvalid_0's multi_logloss: 0.168632\tvalid_1's multi_error: 0.070898\tvalid_1's multi_logloss: 0.200884\n",
            "[385]\tvalid_0's multi_error: 0.0595016\tvalid_0's multi_logloss: 0.168307\tvalid_1's multi_error: 0.070823\tvalid_1's multi_logloss: 0.200621\n",
            "[386]\tvalid_0's multi_error: 0.0594695\tvalid_0's multi_logloss: 0.168193\tvalid_1's multi_error: 0.0707855\tvalid_1's multi_logloss: 0.200568\n",
            "[387]\tvalid_0's multi_error: 0.0593891\tvalid_0's multi_logloss: 0.168116\tvalid_1's multi_error: 0.070823\tvalid_1's multi_logloss: 0.200564\n",
            "[388]\tvalid_0's multi_error: 0.0593248\tvalid_0's multi_logloss: 0.167958\tvalid_1's multi_error: 0.070748\tvalid_1's multi_logloss: 0.20046\n",
            "[389]\tvalid_0's multi_error: 0.0592605\tvalid_0's multi_logloss: 0.16786\tvalid_1's multi_error: 0.0707855\tvalid_1's multi_logloss: 0.200413\n",
            "[390]\tvalid_0's multi_error: 0.0591961\tvalid_0's multi_logloss: 0.167724\tvalid_1's multi_error: 0.0707855\tvalid_1's multi_logloss: 0.200343\n",
            "[391]\tvalid_0's multi_error: 0.0592283\tvalid_0's multi_logloss: 0.167612\tvalid_1's multi_error: 0.0707105\tvalid_1's multi_logloss: 0.20028\n",
            "[392]\tvalid_0's multi_error: 0.0591318\tvalid_0's multi_logloss: 0.167444\tvalid_1's multi_error: 0.0707105\tvalid_1's multi_logloss: 0.200185\n",
            "[393]\tvalid_0's multi_error: 0.0590675\tvalid_0's multi_logloss: 0.167329\tvalid_1's multi_error: 0.0707105\tvalid_1's multi_logloss: 0.200124\n",
            "[394]\tvalid_0's multi_error: 0.0590193\tvalid_0's multi_logloss: 0.167212\tvalid_1's multi_error: 0.070673\tvalid_1's multi_logloss: 0.200083\n",
            "[395]\tvalid_0's multi_error: 0.0590354\tvalid_0's multi_logloss: 0.167035\tvalid_1's multi_error: 0.0706355\tvalid_1's multi_logloss: 0.199973\n",
            "[396]\tvalid_0's multi_error: 0.0590032\tvalid_0's multi_logloss: 0.166897\tvalid_1's multi_error: 0.0706355\tvalid_1's multi_logloss: 0.199895\n",
            "[397]\tvalid_0's multi_error: 0.0589389\tvalid_0's multi_logloss: 0.166803\tvalid_1's multi_error: 0.0706355\tvalid_1's multi_logloss: 0.199882\n",
            "[398]\tvalid_0's multi_error: 0.0588746\tvalid_0's multi_logloss: 0.166694\tvalid_1's multi_error: 0.0707105\tvalid_1's multi_logloss: 0.199838\n",
            "[399]\tvalid_0's multi_error: 0.0588585\tvalid_0's multi_logloss: 0.166559\tvalid_1's multi_error: 0.070673\tvalid_1's multi_logloss: 0.199779\n",
            "[400]\tvalid_0's multi_error: 0.058746\tvalid_0's multi_logloss: 0.166244\tvalid_1's multi_error: 0.0705229\tvalid_1's multi_logloss: 0.1995\n",
            "[401]\tvalid_0's multi_error: 0.0586977\tvalid_0's multi_logloss: 0.166122\tvalid_1's multi_error: 0.0705979\tvalid_1's multi_logloss: 0.199439\n",
            "[402]\tvalid_0's multi_error: 0.0586817\tvalid_0's multi_logloss: 0.166017\tvalid_1's multi_error: 0.0705604\tvalid_1's multi_logloss: 0.199403\n",
            "[403]\tvalid_0's multi_error: 0.0586013\tvalid_0's multi_logloss: 0.165884\tvalid_1's multi_error: 0.0705979\tvalid_1's multi_logloss: 0.199352\n",
            "[404]\tvalid_0's multi_error: 0.0584727\tvalid_0's multi_logloss: 0.165567\tvalid_1's multi_error: 0.0704479\tvalid_1's multi_logloss: 0.199088\n",
            "[405]\tvalid_0's multi_error: 0.0584566\tvalid_0's multi_logloss: 0.165478\tvalid_1's multi_error: 0.0704854\tvalid_1's multi_logloss: 0.199081\n",
            "[406]\tvalid_0's multi_error: 0.0584084\tvalid_0's multi_logloss: 0.165382\tvalid_1's multi_error: 0.0704479\tvalid_1's multi_logloss: 0.199031\n",
            "[407]\tvalid_0's multi_error: 0.058328\tvalid_0's multi_logloss: 0.165269\tvalid_1's multi_error: 0.0704854\tvalid_1's multi_logloss: 0.198999\n",
            "[408]\tvalid_0's multi_error: 0.0582637\tvalid_0's multi_logloss: 0.165165\tvalid_1's multi_error: 0.0704104\tvalid_1's multi_logloss: 0.198971\n",
            "[409]\tvalid_0's multi_error: 0.0582315\tvalid_0's multi_logloss: 0.165065\tvalid_1's multi_error: 0.0704104\tvalid_1's multi_logloss: 0.198951\n",
            "[410]\tvalid_0's multi_error: 0.0581833\tvalid_0's multi_logloss: 0.16496\tvalid_1's multi_error: 0.0704104\tvalid_1's multi_logloss: 0.19892\n",
            "[411]\tvalid_0's multi_error: 0.0581511\tvalid_0's multi_logloss: 0.164848\tvalid_1's multi_error: 0.0703354\tvalid_1's multi_logloss: 0.198874\n",
            "[412]\tvalid_0's multi_error: 0.0581511\tvalid_0's multi_logloss: 0.16477\tvalid_1's multi_error: 0.0702978\tvalid_1's multi_logloss: 0.198867\n",
            "[413]\tvalid_0's multi_error: 0.0580064\tvalid_0's multi_logloss: 0.164675\tvalid_1's multi_error: 0.0702978\tvalid_1's multi_logloss: 0.198831\n",
            "[414]\tvalid_0's multi_error: 0.057926\tvalid_0's multi_logloss: 0.16454\tvalid_1's multi_error: 0.0702228\tvalid_1's multi_logloss: 0.198758\n",
            "[415]\tvalid_0's multi_error: 0.057926\tvalid_0's multi_logloss: 0.164457\tvalid_1's multi_error: 0.0702603\tvalid_1's multi_logloss: 0.198752\n",
            "[416]\tvalid_0's multi_error: 0.0578939\tvalid_0's multi_logloss: 0.164357\tvalid_1's multi_error: 0.0702603\tvalid_1's multi_logloss: 0.198734\n",
            "[417]\tvalid_0's multi_error: 0.0578296\tvalid_0's multi_logloss: 0.164236\tvalid_1's multi_error: 0.0701853\tvalid_1's multi_logloss: 0.198663\n",
            "[418]\tvalid_0's multi_error: 0.057717\tvalid_0's multi_logloss: 0.164116\tvalid_1's multi_error: 0.0701853\tvalid_1's multi_logloss: 0.198596\n",
            "[419]\tvalid_0's multi_error: 0.057701\tvalid_0's multi_logloss: 0.163985\tvalid_1's multi_error: 0.0702228\tvalid_1's multi_logloss: 0.198538\n",
            "[420]\tvalid_0's multi_error: 0.0576206\tvalid_0's multi_logloss: 0.163641\tvalid_1's multi_error: 0.0701103\tvalid_1's multi_logloss: 0.198207\n",
            "[421]\tvalid_0's multi_error: 0.0575723\tvalid_0's multi_logloss: 0.163557\tvalid_1's multi_error: 0.0701478\tvalid_1's multi_logloss: 0.198204\n",
            "[422]\tvalid_0's multi_error: 0.0575563\tvalid_0's multi_logloss: 0.16347\tvalid_1's multi_error: 0.0701478\tvalid_1's multi_logloss: 0.19818\n",
            "[423]\tvalid_0's multi_error: 0.0575241\tvalid_0's multi_logloss: 0.163187\tvalid_1's multi_error: 0.0699977\tvalid_1's multi_logloss: 0.197961\n",
            "[424]\tvalid_0's multi_error: 0.0574277\tvalid_0's multi_logloss: 0.163099\tvalid_1's multi_error: 0.0699602\tvalid_1's multi_logloss: 0.197948\n",
            "[425]\tvalid_0's multi_error: 0.0574277\tvalid_0's multi_logloss: 0.163021\tvalid_1's multi_error: 0.0699602\tvalid_1's multi_logloss: 0.197931\n",
            "[426]\tvalid_0's multi_error: 0.0573633\tvalid_0's multi_logloss: 0.162738\tvalid_1's multi_error: 0.0699227\tvalid_1's multi_logloss: 0.197686\n",
            "[427]\tvalid_0's multi_error: 0.0572347\tvalid_0's multi_logloss: 0.162617\tvalid_1's multi_error: 0.0698102\tvalid_1's multi_logloss: 0.197604\n",
            "[428]\tvalid_0's multi_error: 0.0571704\tvalid_0's multi_logloss: 0.162491\tvalid_1's multi_error: 0.0697727\tvalid_1's multi_logloss: 0.197535\n",
            "[429]\tvalid_0's multi_error: 0.057074\tvalid_0's multi_logloss: 0.162412\tvalid_1's multi_error: 0.0696601\tvalid_1's multi_logloss: 0.197505\n",
            "[430]\tvalid_0's multi_error: 0.0570096\tvalid_0's multi_logloss: 0.162307\tvalid_1's multi_error: 0.0696601\tvalid_1's multi_logloss: 0.197447\n",
            "[431]\tvalid_0's multi_error: 0.0569775\tvalid_0's multi_logloss: 0.162187\tvalid_1's multi_error: 0.0696601\tvalid_1's multi_logloss: 0.19741\n",
            "[432]\tvalid_0's multi_error: 0.0569614\tvalid_0's multi_logloss: 0.162029\tvalid_1's multi_error: 0.0696226\tvalid_1's multi_logloss: 0.19731\n",
            "[433]\tvalid_0's multi_error: 0.056881\tvalid_0's multi_logloss: 0.161767\tvalid_1's multi_error: 0.0695851\tvalid_1's multi_logloss: 0.197084\n",
            "[434]\tvalid_0's multi_error: 0.056881\tvalid_0's multi_logloss: 0.161661\tvalid_1's multi_error: 0.0696601\tvalid_1's multi_logloss: 0.197041\n",
            "[435]\tvalid_0's multi_error: 0.0568328\tvalid_0's multi_logloss: 0.161432\tvalid_1's multi_error: 0.0695851\tvalid_1's multi_logloss: 0.196838\n",
            "[436]\tvalid_0's multi_error: 0.0568328\tvalid_0's multi_logloss: 0.161313\tvalid_1's multi_error: 0.0695101\tvalid_1's multi_logloss: 0.196787\n",
            "[437]\tvalid_0's multi_error: 0.0567042\tvalid_0's multi_logloss: 0.161113\tvalid_1's multi_error: 0.0694726\tvalid_1's multi_logloss: 0.196627\n",
            "[438]\tvalid_0's multi_error: 0.0567042\tvalid_0's multi_logloss: 0.16099\tvalid_1's multi_error: 0.0694351\tvalid_1's multi_logloss: 0.196573\n",
            "[439]\tvalid_0's multi_error: 0.0567042\tvalid_0's multi_logloss: 0.160882\tvalid_1's multi_error: 0.0693976\tvalid_1's multi_logloss: 0.196523\n",
            "[440]\tvalid_0's multi_error: 0.056672\tvalid_0's multi_logloss: 0.160772\tvalid_1's multi_error: 0.06936\tvalid_1's multi_logloss: 0.196463\n",
            "[441]\tvalid_0's multi_error: 0.0566399\tvalid_0's multi_logloss: 0.160697\tvalid_1's multi_error: 0.06936\tvalid_1's multi_logloss: 0.196452\n",
            "[442]\tvalid_0's multi_error: 0.0565595\tvalid_0's multi_logloss: 0.160573\tvalid_1's multi_error: 0.0692475\tvalid_1's multi_logloss: 0.196373\n",
            "[443]\tvalid_0's multi_error: 0.0565916\tvalid_0's multi_logloss: 0.160447\tvalid_1's multi_error: 0.069285\tvalid_1's multi_logloss: 0.196308\n",
            "[444]\tvalid_0's multi_error: 0.0564952\tvalid_0's multi_logloss: 0.160323\tvalid_1's multi_error: 0.0693225\tvalid_1's multi_logloss: 0.19625\n",
            "[445]\tvalid_0's multi_error: 0.056463\tvalid_0's multi_logloss: 0.160237\tvalid_1's multi_error: 0.06936\tvalid_1's multi_logloss: 0.196235\n",
            "[446]\tvalid_0's multi_error: 0.0563505\tvalid_0's multi_logloss: 0.160145\tvalid_1's multi_error: 0.06936\tvalid_1's multi_logloss: 0.196221\n",
            "[447]\tvalid_0's multi_error: 0.056254\tvalid_0's multi_logloss: 0.160069\tvalid_1's multi_error: 0.0693976\tvalid_1's multi_logloss: 0.19622\n",
            "[448]\tvalid_0's multi_error: 0.0562379\tvalid_0's multi_logloss: 0.159968\tvalid_1's multi_error: 0.0693976\tvalid_1's multi_logloss: 0.196182\n",
            "[449]\tvalid_0's multi_error: 0.0561736\tvalid_0's multi_logloss: 0.159882\tvalid_1's multi_error: 0.0694351\tvalid_1's multi_logloss: 0.196169\n",
            "[450]\tvalid_0's multi_error: 0.0561576\tvalid_0's multi_logloss: 0.159755\tvalid_1's multi_error: 0.069285\tvalid_1's multi_logloss: 0.196113\n",
            "[451]\tvalid_0's multi_error: 0.0560772\tvalid_0's multi_logloss: 0.159658\tvalid_1's multi_error: 0.069285\tvalid_1's multi_logloss: 0.196104\n",
            "[452]\tvalid_0's multi_error: 0.0560932\tvalid_0's multi_logloss: 0.159552\tvalid_1's multi_error: 0.0693225\tvalid_1's multi_logloss: 0.196048\n",
            "[453]\tvalid_0's multi_error: 0.0559646\tvalid_0's multi_logloss: 0.159453\tvalid_1's multi_error: 0.069285\tvalid_1's multi_logloss: 0.196009\n",
            "[454]\tvalid_0's multi_error: 0.0559968\tvalid_0's multi_logloss: 0.159379\tvalid_1's multi_error: 0.069285\tvalid_1's multi_logloss: 0.195996\n",
            "[455]\tvalid_0's multi_error: 0.0560289\tvalid_0's multi_logloss: 0.159252\tvalid_1's multi_error: 0.0692475\tvalid_1's multi_logloss: 0.19594\n",
            "[456]\tvalid_0's multi_error: 0.0559325\tvalid_0's multi_logloss: 0.159165\tvalid_1's multi_error: 0.06936\tvalid_1's multi_logloss: 0.195917\n",
            "[457]\tvalid_0's multi_error: 0.0557074\tvalid_0's multi_logloss: 0.158897\tvalid_1's multi_error: 0.0693225\tvalid_1's multi_logloss: 0.195671\n",
            "[458]\tvalid_0's multi_error: 0.0556913\tvalid_0's multi_logloss: 0.158814\tvalid_1's multi_error: 0.0693225\tvalid_1's multi_logloss: 0.195646\n",
            "[459]\tvalid_0's multi_error: 0.0556752\tvalid_0's multi_logloss: 0.15874\tvalid_1's multi_error: 0.069285\tvalid_1's multi_logloss: 0.195639\n",
            "[460]\tvalid_0's multi_error: 0.0556592\tvalid_0's multi_logloss: 0.158645\tvalid_1's multi_error: 0.0693225\tvalid_1's multi_logloss: 0.195597\n",
            "[461]\tvalid_0's multi_error: 0.055627\tvalid_0's multi_logloss: 0.158531\tvalid_1's multi_error: 0.0693225\tvalid_1's multi_logloss: 0.195543\n",
            "[462]\tvalid_0's multi_error: 0.0555627\tvalid_0's multi_logloss: 0.158389\tvalid_1's multi_error: 0.069285\tvalid_1's multi_logloss: 0.19545\n",
            "[463]\tvalid_0's multi_error: 0.0554502\tvalid_0's multi_logloss: 0.158297\tvalid_1's multi_error: 0.0693225\tvalid_1's multi_logloss: 0.195423\n",
            "[464]\tvalid_0's multi_error: 0.0553537\tvalid_0's multi_logloss: 0.1582\tvalid_1's multi_error: 0.0692475\tvalid_1's multi_logloss: 0.195371\n",
            "[465]\tvalid_0's multi_error: 0.0553537\tvalid_0's multi_logloss: 0.158098\tvalid_1's multi_error: 0.069285\tvalid_1's multi_logloss: 0.195322\n",
            "[466]\tvalid_0's multi_error: 0.0553537\tvalid_0's multi_logloss: 0.157994\tvalid_1's multi_error: 0.0692475\tvalid_1's multi_logloss: 0.195281\n",
            "[467]\tvalid_0's multi_error: 0.0553215\tvalid_0's multi_logloss: 0.157907\tvalid_1's multi_error: 0.069285\tvalid_1's multi_logloss: 0.195252\n",
            "[468]\tvalid_0's multi_error: 0.0552894\tvalid_0's multi_logloss: 0.157781\tvalid_1's multi_error: 0.06921\tvalid_1's multi_logloss: 0.195193\n",
            "[469]\tvalid_0's multi_error: 0.0551286\tvalid_0's multi_logloss: 0.157515\tvalid_1's multi_error: 0.0690224\tvalid_1's multi_logloss: 0.194972\n",
            "[470]\tvalid_0's multi_error: 0.0550643\tvalid_0's multi_logloss: 0.157406\tvalid_1's multi_error: 0.0690224\tvalid_1's multi_logloss: 0.194919\n",
            "[471]\tvalid_0's multi_error: 0.0549839\tvalid_0's multi_logloss: 0.157153\tvalid_1's multi_error: 0.0689099\tvalid_1's multi_logloss: 0.194721\n",
            "[472]\tvalid_0's multi_error: 0.0549678\tvalid_0's multi_logloss: 0.157075\tvalid_1's multi_error: 0.0688724\tvalid_1's multi_logloss: 0.194714\n",
            "[473]\tvalid_0's multi_error: 0.0549035\tvalid_0's multi_logloss: 0.156966\tvalid_1's multi_error: 0.0688724\tvalid_1's multi_logloss: 0.194672\n",
            "[474]\tvalid_0's multi_error: 0.0549196\tvalid_0's multi_logloss: 0.156878\tvalid_1's multi_error: 0.0688724\tvalid_1's multi_logloss: 0.194656\n",
            "[475]\tvalid_0's multi_error: 0.0548553\tvalid_0's multi_logloss: 0.156796\tvalid_1's multi_error: 0.0688724\tvalid_1's multi_logloss: 0.194641\n",
            "[476]\tvalid_0's multi_error: 0.0548714\tvalid_0's multi_logloss: 0.156725\tvalid_1's multi_error: 0.0688724\tvalid_1's multi_logloss: 0.194612\n",
            "[477]\tvalid_0's multi_error: 0.0548392\tvalid_0's multi_logloss: 0.156637\tvalid_1's multi_error: 0.0689849\tvalid_1's multi_logloss: 0.194602\n",
            "[478]\tvalid_0's multi_error: 0.0547428\tvalid_0's multi_logloss: 0.156386\tvalid_1's multi_error: 0.0688349\tvalid_1's multi_logloss: 0.194402\n",
            "[479]\tvalid_0's multi_error: 0.0546302\tvalid_0's multi_logloss: 0.156287\tvalid_1's multi_error: 0.0687223\tvalid_1's multi_logloss: 0.194377\n",
            "[480]\tvalid_0's multi_error: 0.0546463\tvalid_0's multi_logloss: 0.156177\tvalid_1's multi_error: 0.0687223\tvalid_1's multi_logloss: 0.194332\n",
            "[481]\tvalid_0's multi_error: 0.054582\tvalid_0's multi_logloss: 0.156084\tvalid_1's multi_error: 0.0686848\tvalid_1's multi_logloss: 0.194318\n",
            "[482]\tvalid_0's multi_error: 0.0545659\tvalid_0's multi_logloss: 0.156015\tvalid_1's multi_error: 0.0687974\tvalid_1's multi_logloss: 0.194324\n",
            "[483]\tvalid_0's multi_error: 0.0545016\tvalid_0's multi_logloss: 0.155929\tvalid_1's multi_error: 0.0687223\tvalid_1's multi_logloss: 0.194306\n",
            "[484]\tvalid_0's multi_error: 0.0545338\tvalid_0's multi_logloss: 0.155859\tvalid_1's multi_error: 0.0687223\tvalid_1's multi_logloss: 0.194299\n",
            "[485]\tvalid_0's multi_error: 0.0544855\tvalid_0's multi_logloss: 0.15576\tvalid_1's multi_error: 0.0686473\tvalid_1's multi_logloss: 0.194263\n",
            "[486]\tvalid_0's multi_error: 0.0544695\tvalid_0's multi_logloss: 0.155665\tvalid_1's multi_error: 0.0687223\tvalid_1's multi_logloss: 0.194241\n",
            "[487]\tvalid_0's multi_error: 0.0544695\tvalid_0's multi_logloss: 0.155568\tvalid_1's multi_error: 0.0686473\tvalid_1's multi_logloss: 0.194225\n",
            "[488]\tvalid_0's multi_error: 0.0544534\tvalid_0's multi_logloss: 0.155488\tvalid_1's multi_error: 0.0686473\tvalid_1's multi_logloss: 0.194224\n",
            "[489]\tvalid_0's multi_error: 0.0544212\tvalid_0's multi_logloss: 0.155423\tvalid_1's multi_error: 0.0686473\tvalid_1's multi_logloss: 0.194234\n",
            "[490]\tvalid_0's multi_error: 0.054373\tvalid_0's multi_logloss: 0.155333\tvalid_1's multi_error: 0.0686473\tvalid_1's multi_logloss: 0.194219\n",
            "[491]\tvalid_0's multi_error: 0.0543408\tvalid_0's multi_logloss: 0.155269\tvalid_1's multi_error: 0.0687598\tvalid_1's multi_logloss: 0.194223\n",
            "[492]\tvalid_0's multi_error: 0.0542926\tvalid_0's multi_logloss: 0.155181\tvalid_1's multi_error: 0.0686473\tvalid_1's multi_logloss: 0.19417\n",
            "[493]\tvalid_0's multi_error: 0.0542283\tvalid_0's multi_logloss: 0.155048\tvalid_1's multi_error: 0.0687223\tvalid_1's multi_logloss: 0.194094\n",
            "[494]\tvalid_0's multi_error: 0.0541961\tvalid_0's multi_logloss: 0.154956\tvalid_1's multi_error: 0.0687223\tvalid_1's multi_logloss: 0.194068\n",
            "[495]\tvalid_0's multi_error: 0.0541961\tvalid_0's multi_logloss: 0.15487\tvalid_1's multi_error: 0.0686848\tvalid_1's multi_logloss: 0.194031\n",
            "[496]\tvalid_0's multi_error: 0.0541318\tvalid_0's multi_logloss: 0.154777\tvalid_1's multi_error: 0.0686473\tvalid_1's multi_logloss: 0.194004\n",
            "[497]\tvalid_0's multi_error: 0.0540193\tvalid_0's multi_logloss: 0.154659\tvalid_1's multi_error: 0.0686473\tvalid_1's multi_logloss: 0.193956\n",
            "[498]\tvalid_0's multi_error: 0.0539389\tvalid_0's multi_logloss: 0.154465\tvalid_1's multi_error: 0.0685348\tvalid_1's multi_logloss: 0.193822\n",
            "[499]\tvalid_0's multi_error: 0.0539068\tvalid_0's multi_logloss: 0.154391\tvalid_1's multi_error: 0.0685348\tvalid_1's multi_logloss: 0.193813\n",
            "[500]\tvalid_0's multi_error: 0.0537781\tvalid_0's multi_logloss: 0.154272\tvalid_1's multi_error: 0.0684973\tvalid_1's multi_logloss: 0.193754\n",
            "[501]\tvalid_0's multi_error: 0.0537299\tvalid_0's multi_logloss: 0.154161\tvalid_1's multi_error: 0.0684973\tvalid_1's multi_logloss: 0.19369\n",
            "[502]\tvalid_0's multi_error: 0.0536656\tvalid_0's multi_logloss: 0.154032\tvalid_1's multi_error: 0.0684597\tvalid_1's multi_logloss: 0.193591\n",
            "[503]\tvalid_0's multi_error: 0.0536977\tvalid_0's multi_logloss: 0.153932\tvalid_1's multi_error: 0.0684973\tvalid_1's multi_logloss: 0.193552\n",
            "[504]\tvalid_0's multi_error: 0.0536334\tvalid_0's multi_logloss: 0.153814\tvalid_1's multi_error: 0.0684973\tvalid_1's multi_logloss: 0.193494\n",
            "[505]\tvalid_0's multi_error: 0.0535691\tvalid_0's multi_logloss: 0.153664\tvalid_1's multi_error: 0.0685723\tvalid_1's multi_logloss: 0.193407\n",
            "[506]\tvalid_0's multi_error: 0.053537\tvalid_0's multi_logloss: 0.153586\tvalid_1's multi_error: 0.0686098\tvalid_1's multi_logloss: 0.193386\n",
            "[507]\tvalid_0's multi_error: 0.0534887\tvalid_0's multi_logloss: 0.153473\tvalid_1's multi_error: 0.0686098\tvalid_1's multi_logloss: 0.193313\n",
            "[508]\tvalid_0's multi_error: 0.0534405\tvalid_0's multi_logloss: 0.153391\tvalid_1's multi_error: 0.0685723\tvalid_1's multi_logloss: 0.193296\n",
            "[509]\tvalid_0's multi_error: 0.0533601\tvalid_0's multi_logloss: 0.153262\tvalid_1's multi_error: 0.0685723\tvalid_1's multi_logloss: 0.193235\n",
            "[510]\tvalid_0's multi_error: 0.053328\tvalid_0's multi_logloss: 0.153182\tvalid_1's multi_error: 0.0685723\tvalid_1's multi_logloss: 0.193187\n",
            "[511]\tvalid_0's multi_error: 0.0532637\tvalid_0's multi_logloss: 0.153073\tvalid_1's multi_error: 0.0684973\tvalid_1's multi_logloss: 0.193121\n",
            "[512]\tvalid_0's multi_error: 0.0532476\tvalid_0's multi_logloss: 0.152977\tvalid_1's multi_error: 0.0684597\tvalid_1's multi_logloss: 0.193077\n",
            "[513]\tvalid_0's multi_error: 0.0532637\tvalid_0's multi_logloss: 0.152858\tvalid_1's multi_error: 0.0684973\tvalid_1's multi_logloss: 0.193025\n",
            "[514]\tvalid_0's multi_error: 0.0532154\tvalid_0's multi_logloss: 0.152743\tvalid_1's multi_error: 0.0684973\tvalid_1's multi_logloss: 0.192959\n",
            "[515]\tvalid_0's multi_error: 0.0531672\tvalid_0's multi_logloss: 0.152653\tvalid_1's multi_error: 0.0684597\tvalid_1's multi_logloss: 0.192945\n",
            "[516]\tvalid_0's multi_error: 0.0531511\tvalid_0's multi_logloss: 0.152558\tvalid_1's multi_error: 0.0684597\tvalid_1's multi_logloss: 0.19289\n",
            "[517]\tvalid_0's multi_error: 0.053135\tvalid_0's multi_logloss: 0.152473\tvalid_1's multi_error: 0.0684597\tvalid_1's multi_logloss: 0.192875\n",
            "[518]\tvalid_0's multi_error: 0.053119\tvalid_0's multi_logloss: 0.152384\tvalid_1's multi_error: 0.0683847\tvalid_1's multi_logloss: 0.192846\n",
            "[519]\tvalid_0's multi_error: 0.0530707\tvalid_0's multi_logloss: 0.152252\tvalid_1's multi_error: 0.0683847\tvalid_1's multi_logloss: 0.192768\n",
            "[520]\tvalid_0's multi_error: 0.0529904\tvalid_0's multi_logloss: 0.15217\tvalid_1's multi_error: 0.0683472\tvalid_1's multi_logloss: 0.192752\n",
            "[521]\tvalid_0's multi_error: 0.05291\tvalid_0's multi_logloss: 0.152043\tvalid_1's multi_error: 0.0683097\tvalid_1's multi_logloss: 0.192676\n",
            "[522]\tvalid_0's multi_error: 0.0528778\tvalid_0's multi_logloss: 0.151951\tvalid_1's multi_error: 0.0682722\tvalid_1's multi_logloss: 0.192649\n",
            "[523]\tvalid_0's multi_error: 0.0528457\tvalid_0's multi_logloss: 0.151729\tvalid_1's multi_error: 0.0681972\tvalid_1's multi_logloss: 0.19246\n",
            "[524]\tvalid_0's multi_error: 0.0527814\tvalid_0's multi_logloss: 0.151633\tvalid_1's multi_error: 0.0681221\tvalid_1's multi_logloss: 0.192423\n",
            "[525]\tvalid_0's multi_error: 0.0527814\tvalid_0's multi_logloss: 0.15156\tvalid_1's multi_error: 0.0681221\tvalid_1's multi_logloss: 0.192425\n",
            "[526]\tvalid_0's multi_error: 0.0526849\tvalid_0's multi_logloss: 0.151353\tvalid_1's multi_error: 0.0680096\tvalid_1's multi_logloss: 0.192236\n",
            "[527]\tvalid_0's multi_error: 0.0525241\tvalid_0's multi_logloss: 0.151221\tvalid_1's multi_error: 0.0679346\tvalid_1's multi_logloss: 0.192128\n",
            "[528]\tvalid_0's multi_error: 0.052492\tvalid_0's multi_logloss: 0.151139\tvalid_1's multi_error: 0.0680096\tvalid_1's multi_logloss: 0.192081\n",
            "[529]\tvalid_0's multi_error: 0.0524437\tvalid_0's multi_logloss: 0.151025\tvalid_1's multi_error: 0.0678971\tvalid_1's multi_logloss: 0.192025\n",
            "[530]\tvalid_0's multi_error: 0.0524116\tvalid_0's multi_logloss: 0.15094\tvalid_1's multi_error: 0.0678971\tvalid_1's multi_logloss: 0.192005\n",
            "[531]\tvalid_0's multi_error: 0.0523633\tvalid_0's multi_logloss: 0.150856\tvalid_1's multi_error: 0.0678971\tvalid_1's multi_logloss: 0.191979\n",
            "[532]\tvalid_0's multi_error: 0.0523312\tvalid_0's multi_logloss: 0.150778\tvalid_1's multi_error: 0.0678596\tvalid_1's multi_logloss: 0.191977\n",
            "[533]\tvalid_0's multi_error: 0.0523151\tvalid_0's multi_logloss: 0.150678\tvalid_1's multi_error: 0.0678596\tvalid_1's multi_logloss: 0.191933\n",
            "[534]\tvalid_0's multi_error: 0.0522669\tvalid_0's multi_logloss: 0.150573\tvalid_1's multi_error: 0.067822\tvalid_1's multi_logloss: 0.19188\n",
            "[535]\tvalid_0's multi_error: 0.0522026\tvalid_0's multi_logloss: 0.150455\tvalid_1's multi_error: 0.0677845\tvalid_1's multi_logloss: 0.191823\n",
            "[536]\tvalid_0's multi_error: 0.0521865\tvalid_0's multi_logloss: 0.150384\tvalid_1's multi_error: 0.0677845\tvalid_1's multi_logloss: 0.19183\n",
            "[537]\tvalid_0's multi_error: 0.0521865\tvalid_0's multi_logloss: 0.150306\tvalid_1's multi_error: 0.067747\tvalid_1's multi_logloss: 0.191804\n",
            "[538]\tvalid_0's multi_error: 0.0521061\tvalid_0's multi_logloss: 0.150192\tvalid_1's multi_error: 0.0677845\tvalid_1's multi_logloss: 0.191747\n",
            "[539]\tvalid_0's multi_error: 0.05209\tvalid_0's multi_logloss: 0.150092\tvalid_1's multi_error: 0.0677845\tvalid_1's multi_logloss: 0.191704\n",
            "[540]\tvalid_0's multi_error: 0.052074\tvalid_0's multi_logloss: 0.150025\tvalid_1's multi_error: 0.0677845\tvalid_1's multi_logloss: 0.191711\n",
            "[541]\tvalid_0's multi_error: 0.0521222\tvalid_0's multi_logloss: 0.149942\tvalid_1's multi_error: 0.0676345\tvalid_1's multi_logloss: 0.191679\n",
            "[542]\tvalid_0's multi_error: 0.0521061\tvalid_0's multi_logloss: 0.149841\tvalid_1's multi_error: 0.067597\tvalid_1's multi_logloss: 0.191625\n",
            "[543]\tvalid_0's multi_error: 0.052074\tvalid_0's multi_logloss: 0.149684\tvalid_1's multi_error: 0.0675595\tvalid_1's multi_logloss: 0.191528\n",
            "[544]\tvalid_0's multi_error: 0.0520579\tvalid_0's multi_logloss: 0.149557\tvalid_1's multi_error: 0.0675219\tvalid_1's multi_logloss: 0.191454\n",
            "[545]\tvalid_0's multi_error: 0.0519936\tvalid_0's multi_logloss: 0.149467\tvalid_1's multi_error: 0.0674844\tvalid_1's multi_logloss: 0.191404\n",
            "[546]\tvalid_0's multi_error: 0.0518328\tvalid_0's multi_logloss: 0.149353\tvalid_1's multi_error: 0.0674844\tvalid_1's multi_logloss: 0.191346\n",
            "[547]\tvalid_0's multi_error: 0.051865\tvalid_0's multi_logloss: 0.149255\tvalid_1's multi_error: 0.0675219\tvalid_1's multi_logloss: 0.191311\n",
            "[548]\tvalid_0's multi_error: 0.0518489\tvalid_0's multi_logloss: 0.1492\tvalid_1's multi_error: 0.0675219\tvalid_1's multi_logloss: 0.19131\n",
            "[549]\tvalid_0's multi_error: 0.0518006\tvalid_0's multi_logloss: 0.149078\tvalid_1's multi_error: 0.0674469\tvalid_1's multi_logloss: 0.19125\n",
            "[550]\tvalid_0's multi_error: 0.0517203\tvalid_0's multi_logloss: 0.148965\tvalid_1's multi_error: 0.067597\tvalid_1's multi_logloss: 0.191195\n",
            "[551]\tvalid_0's multi_error: 0.051672\tvalid_0's multi_logloss: 0.148857\tvalid_1's multi_error: 0.0674844\tvalid_1's multi_logloss: 0.191138\n",
            "[552]\tvalid_0's multi_error: 0.0515595\tvalid_0's multi_logloss: 0.148757\tvalid_1's multi_error: 0.0674844\tvalid_1's multi_logloss: 0.191077\n",
            "[553]\tvalid_0's multi_error: 0.0515273\tvalid_0's multi_logloss: 0.148661\tvalid_1's multi_error: 0.0673344\tvalid_1's multi_logloss: 0.191039\n",
            "[554]\tvalid_0's multi_error: 0.051463\tvalid_0's multi_logloss: 0.148556\tvalid_1's multi_error: 0.0672969\tvalid_1's multi_logloss: 0.190966\n",
            "[555]\tvalid_0's multi_error: 0.0514791\tvalid_0's multi_logloss: 0.148455\tvalid_1's multi_error: 0.0672594\tvalid_1's multi_logloss: 0.190927\n",
            "[556]\tvalid_0's multi_error: 0.0513987\tvalid_0's multi_logloss: 0.148374\tvalid_1's multi_error: 0.0672594\tvalid_1's multi_logloss: 0.190903\n",
            "[557]\tvalid_0's multi_error: 0.0514148\tvalid_0's multi_logloss: 0.14827\tvalid_1's multi_error: 0.0672218\tvalid_1's multi_logloss: 0.19085\n",
            "[558]\tvalid_0's multi_error: 0.0513344\tvalid_0's multi_logloss: 0.14817\tvalid_1's multi_error: 0.0672218\tvalid_1's multi_logloss: 0.190774\n",
            "[559]\tvalid_0's multi_error: 0.0513183\tvalid_0's multi_logloss: 0.148102\tvalid_1's multi_error: 0.0672969\tvalid_1's multi_logloss: 0.190756\n",
            "[560]\tvalid_0's multi_error: 0.0512862\tvalid_0's multi_logloss: 0.148038\tvalid_1's multi_error: 0.0672969\tvalid_1's multi_logloss: 0.190755\n",
            "[561]\tvalid_0's multi_error: 0.0512219\tvalid_0's multi_logloss: 0.147949\tvalid_1's multi_error: 0.0671843\tvalid_1's multi_logloss: 0.190752\n",
            "[562]\tvalid_0's multi_error: 0.0511897\tvalid_0's multi_logloss: 0.147881\tvalid_1's multi_error: 0.0671468\tvalid_1's multi_logloss: 0.190755\n",
            "[563]\tvalid_0's multi_error: 0.0511093\tvalid_0's multi_logloss: 0.147669\tvalid_1's multi_error: 0.0669593\tvalid_1's multi_logloss: 0.190562\n",
            "[564]\tvalid_0's multi_error: 0.0510772\tvalid_0's multi_logloss: 0.147603\tvalid_1's multi_error: 0.0669968\tvalid_1's multi_logloss: 0.19056\n",
            "[565]\tvalid_0's multi_error: 0.0510772\tvalid_0's multi_logloss: 0.147502\tvalid_1's multi_error: 0.0669217\tvalid_1's multi_logloss: 0.190524\n",
            "[566]\tvalid_0's multi_error: 0.0510611\tvalid_0's multi_logloss: 0.147421\tvalid_1's multi_error: 0.0669968\tvalid_1's multi_logloss: 0.190503\n",
            "[567]\tvalid_0's multi_error: 0.0510289\tvalid_0's multi_logloss: 0.147336\tvalid_1's multi_error: 0.0669968\tvalid_1's multi_logloss: 0.190482\n",
            "[568]\tvalid_0's multi_error: 0.0510611\tvalid_0's multi_logloss: 0.147263\tvalid_1's multi_error: 0.0669968\tvalid_1's multi_logloss: 0.190464\n",
            "[569]\tvalid_0's multi_error: 0.0510289\tvalid_0's multi_logloss: 0.1472\tvalid_1's multi_error: 0.0669593\tvalid_1's multi_logloss: 0.190455\n",
            "[570]\tvalid_0's multi_error: 0.0509968\tvalid_0's multi_logloss: 0.147068\tvalid_1's multi_error: 0.0669593\tvalid_1's multi_logloss: 0.19038\n",
            "[571]\tvalid_0's multi_error: 0.0509325\tvalid_0's multi_logloss: 0.147\tvalid_1's multi_error: 0.0669968\tvalid_1's multi_logloss: 0.190334\n",
            "[572]\tvalid_0's multi_error: 0.0508682\tvalid_0's multi_logloss: 0.146835\tvalid_1's multi_error: 0.0668467\tvalid_1's multi_logloss: 0.190197\n",
            "[573]\tvalid_0's multi_error: 0.0508521\tvalid_0's multi_logloss: 0.146775\tvalid_1's multi_error: 0.0668467\tvalid_1's multi_logloss: 0.190189\n",
            "[574]\tvalid_0's multi_error: 0.0507717\tvalid_0's multi_logloss: 0.14667\tvalid_1's multi_error: 0.0667342\tvalid_1's multi_logloss: 0.190129\n",
            "[575]\tvalid_0's multi_error: 0.0507395\tvalid_0's multi_logloss: 0.146605\tvalid_1's multi_error: 0.0666592\tvalid_1's multi_logloss: 0.190128\n",
            "[576]\tvalid_0's multi_error: 0.0507235\tvalid_0's multi_logloss: 0.146545\tvalid_1's multi_error: 0.0666592\tvalid_1's multi_logloss: 0.190122\n",
            "[577]\tvalid_0's multi_error: 0.0507235\tvalid_0's multi_logloss: 0.146481\tvalid_1's multi_error: 0.0666592\tvalid_1's multi_logloss: 0.190118\n",
            "[578]\tvalid_0's multi_error: 0.0506913\tvalid_0's multi_logloss: 0.14642\tvalid_1's multi_error: 0.0668092\tvalid_1's multi_logloss: 0.190109\n",
            "[579]\tvalid_0's multi_error: 0.050627\tvalid_0's multi_logloss: 0.146316\tvalid_1's multi_error: 0.0668467\tvalid_1's multi_logloss: 0.190063\n",
            "[580]\tvalid_0's multi_error: 0.0506109\tvalid_0's multi_logloss: 0.146238\tvalid_1's multi_error: 0.0666967\tvalid_1's multi_logloss: 0.190061\n",
            "[581]\tvalid_0's multi_error: 0.0505788\tvalid_0's multi_logloss: 0.146139\tvalid_1's multi_error: 0.0666217\tvalid_1's multi_logloss: 0.190017\n",
            "[582]\tvalid_0's multi_error: 0.0506431\tvalid_0's multi_logloss: 0.14603\tvalid_1's multi_error: 0.0666592\tvalid_1's multi_logloss: 0.189954\n",
            "[583]\tvalid_0's multi_error: 0.0506109\tvalid_0's multi_logloss: 0.145851\tvalid_1's multi_error: 0.0666217\tvalid_1's multi_logloss: 0.189811\n",
            "[584]\tvalid_0's multi_error: 0.0505627\tvalid_0's multi_logloss: 0.145748\tvalid_1's multi_error: 0.0666217\tvalid_1's multi_logloss: 0.189769\n",
            "[585]\tvalid_0's multi_error: 0.0505305\tvalid_0's multi_logloss: 0.145643\tvalid_1's multi_error: 0.0665091\tvalid_1's multi_logloss: 0.189709\n",
            "[586]\tvalid_0's multi_error: 0.0504019\tvalid_0's multi_logloss: 0.145449\tvalid_1's multi_error: 0.0663591\tvalid_1's multi_logloss: 0.189566\n",
            "[587]\tvalid_0's multi_error: 0.0503859\tvalid_0's multi_logloss: 0.145365\tvalid_1's multi_error: 0.0663966\tvalid_1's multi_logloss: 0.189529\n",
            "[588]\tvalid_0's multi_error: 0.0504019\tvalid_0's multi_logloss: 0.145309\tvalid_1's multi_error: 0.0663591\tvalid_1's multi_logloss: 0.18952\n",
            "[589]\tvalid_0's multi_error: 0.0503537\tvalid_0's multi_logloss: 0.145208\tvalid_1's multi_error: 0.0663966\tvalid_1's multi_logloss: 0.189454\n",
            "[590]\tvalid_0's multi_error: 0.0502733\tvalid_0's multi_logloss: 0.145134\tvalid_1's multi_error: 0.0664341\tvalid_1's multi_logloss: 0.189444\n",
            "[591]\tvalid_0's multi_error: 0.050209\tvalid_0's multi_logloss: 0.145055\tvalid_1's multi_error: 0.0664341\tvalid_1's multi_logloss: 0.189428\n",
            "[592]\tvalid_0's multi_error: 0.0501608\tvalid_0's multi_logloss: 0.144901\tvalid_1's multi_error: 0.066284\tvalid_1's multi_logloss: 0.189315\n",
            "[593]\tvalid_0's multi_error: 0.0501286\tvalid_0's multi_logloss: 0.144818\tvalid_1's multi_error: 0.0662465\tvalid_1's multi_logloss: 0.189276\n",
            "[594]\tvalid_0's multi_error: 0.0500965\tvalid_0's multi_logloss: 0.144723\tvalid_1's multi_error: 0.0662465\tvalid_1's multi_logloss: 0.189246\n",
            "[595]\tvalid_0's multi_error: 0.0500322\tvalid_0's multi_logloss: 0.144643\tvalid_1's multi_error: 0.066284\tvalid_1's multi_logloss: 0.189216\n",
            "[596]\tvalid_0's multi_error: 0.0500161\tvalid_0's multi_logloss: 0.144579\tvalid_1's multi_error: 0.066209\tvalid_1's multi_logloss: 0.189211\n",
            "[597]\tvalid_0's multi_error: 0.0499518\tvalid_0's multi_logloss: 0.144473\tvalid_1's multi_error: 0.0661715\tvalid_1's multi_logloss: 0.189137\n",
            "[598]\tvalid_0's multi_error: 0.0499839\tvalid_0's multi_logloss: 0.144402\tvalid_1's multi_error: 0.066209\tvalid_1's multi_logloss: 0.189118\n",
            "[599]\tvalid_0's multi_error: 0.0499678\tvalid_0's multi_logloss: 0.144322\tvalid_1's multi_error: 0.066209\tvalid_1's multi_logloss: 0.189081\n",
            "[600]\tvalid_0's multi_error: 0.0499357\tvalid_0's multi_logloss: 0.144245\tvalid_1's multi_error: 0.0661715\tvalid_1's multi_logloss: 0.189052\n",
            "[601]\tvalid_0's multi_error: 0.0498714\tvalid_0's multi_logloss: 0.144141\tvalid_1's multi_error: 0.066134\tvalid_1's multi_logloss: 0.189013\n",
            "[602]\tvalid_0's multi_error: 0.0498071\tvalid_0's multi_logloss: 0.144036\tvalid_1's multi_error: 0.0660965\tvalid_1's multi_logloss: 0.188938\n",
            "[603]\tvalid_0's multi_error: 0.0496945\tvalid_0's multi_logloss: 0.143945\tvalid_1's multi_error: 0.0660215\tvalid_1's multi_logloss: 0.188909\n",
            "[604]\tvalid_0's multi_error: 0.0496785\tvalid_0's multi_logloss: 0.143774\tvalid_1's multi_error: 0.0658339\tvalid_1's multi_logloss: 0.188776\n",
            "[605]\tvalid_0's multi_error: 0.0496302\tvalid_0's multi_logloss: 0.143656\tvalid_1's multi_error: 0.0657589\tvalid_1's multi_logloss: 0.1887\n",
            "[606]\tvalid_0's multi_error: 0.0495659\tvalid_0's multi_logloss: 0.143567\tvalid_1's multi_error: 0.0657964\tvalid_1's multi_logloss: 0.188668\n",
            "[607]\tvalid_0's multi_error: 0.0495016\tvalid_0's multi_logloss: 0.143493\tvalid_1's multi_error: 0.0658339\tvalid_1's multi_logloss: 0.188668\n",
            "[608]\tvalid_0's multi_error: 0.0495177\tvalid_0's multi_logloss: 0.14333\tvalid_1's multi_error: 0.0656838\tvalid_1's multi_logloss: 0.188546\n",
            "[609]\tvalid_0's multi_error: 0.0494695\tvalid_0's multi_logloss: 0.143199\tvalid_1's multi_error: 0.0656463\tvalid_1's multi_logloss: 0.188487\n",
            "[610]\tvalid_0's multi_error: 0.0494855\tvalid_0's multi_logloss: 0.143076\tvalid_1's multi_error: 0.0656838\tvalid_1's multi_logloss: 0.188409\n",
            "[611]\tvalid_0's multi_error: 0.0494212\tvalid_0's multi_logloss: 0.142976\tvalid_1's multi_error: 0.0657589\tvalid_1's multi_logloss: 0.188359\n",
            "[612]\tvalid_0's multi_error: 0.0494212\tvalid_0's multi_logloss: 0.142872\tvalid_1's multi_error: 0.0656463\tvalid_1's multi_logloss: 0.18832\n",
            "[613]\tvalid_0's multi_error: 0.0494051\tvalid_0's multi_logloss: 0.1428\tvalid_1's multi_error: 0.0656088\tvalid_1's multi_logloss: 0.188301\n",
            "[614]\tvalid_0's multi_error: 0.0492765\tvalid_0's multi_logloss: 0.142706\tvalid_1's multi_error: 0.0656463\tvalid_1's multi_logloss: 0.188255\n",
            "[615]\tvalid_0's multi_error: 0.0491961\tvalid_0's multi_logloss: 0.142613\tvalid_1's multi_error: 0.0656088\tvalid_1's multi_logloss: 0.188226\n",
            "[616]\tvalid_0's multi_error: 0.0492283\tvalid_0's multi_logloss: 0.14253\tvalid_1's multi_error: 0.0655713\tvalid_1's multi_logloss: 0.188175\n",
            "[617]\tvalid_0's multi_error: 0.049164\tvalid_0's multi_logloss: 0.142444\tvalid_1's multi_error: 0.0655338\tvalid_1's multi_logloss: 0.188153\n",
            "[618]\tvalid_0's multi_error: 0.0490354\tvalid_0's multi_logloss: 0.142362\tvalid_1's multi_error: 0.0654588\tvalid_1's multi_logloss: 0.188123\n",
            "[619]\tvalid_0's multi_error: 0.048955\tvalid_0's multi_logloss: 0.142159\tvalid_1's multi_error: 0.0653462\tvalid_1's multi_logloss: 0.187945\n",
            "[620]\tvalid_0's multi_error: 0.0489389\tvalid_0's multi_logloss: 0.142068\tvalid_1's multi_error: 0.0653087\tvalid_1's multi_logloss: 0.187911\n",
            "[621]\tvalid_0's multi_error: 0.0488746\tvalid_0's multi_logloss: 0.141874\tvalid_1's multi_error: 0.0651962\tvalid_1's multi_logloss: 0.187756\n",
            "[622]\tvalid_0's multi_error: 0.0488585\tvalid_0's multi_logloss: 0.141807\tvalid_1's multi_error: 0.0652712\tvalid_1's multi_logloss: 0.187743\n",
            "[623]\tvalid_0's multi_error: 0.0487781\tvalid_0's multi_logloss: 0.141635\tvalid_1's multi_error: 0.0653087\tvalid_1's multi_logloss: 0.187596\n",
            "[624]\tvalid_0's multi_error: 0.048746\tvalid_0's multi_logloss: 0.141569\tvalid_1's multi_error: 0.0653087\tvalid_1's multi_logloss: 0.187581\n",
            "[625]\tvalid_0's multi_error: 0.0486817\tvalid_0's multi_logloss: 0.141505\tvalid_1's multi_error: 0.0653087\tvalid_1's multi_logloss: 0.187579\n",
            "[626]\tvalid_0's multi_error: 0.0486817\tvalid_0's multi_logloss: 0.141434\tvalid_1's multi_error: 0.0653087\tvalid_1's multi_logloss: 0.187553\n",
            "[627]\tvalid_0's multi_error: 0.0486334\tvalid_0's multi_logloss: 0.141364\tvalid_1's multi_error: 0.0653462\tvalid_1's multi_logloss: 0.187551\n",
            "[628]\tvalid_0's multi_error: 0.0486013\tvalid_0's multi_logloss: 0.141302\tvalid_1's multi_error: 0.0654213\tvalid_1's multi_logloss: 0.187548\n",
            "[629]\tvalid_0's multi_error: 0.0485691\tvalid_0's multi_logloss: 0.141232\tvalid_1's multi_error: 0.0653837\tvalid_1's multi_logloss: 0.187531\n",
            "[630]\tvalid_0's multi_error: 0.048537\tvalid_0's multi_logloss: 0.141178\tvalid_1's multi_error: 0.0653837\tvalid_1's multi_logloss: 0.18752\n",
            "[631]\tvalid_0's multi_error: 0.048537\tvalid_0's multi_logloss: 0.141115\tvalid_1's multi_error: 0.0654213\tvalid_1's multi_logloss: 0.187497\n",
            "[632]\tvalid_0's multi_error: 0.0484566\tvalid_0's multi_logloss: 0.141009\tvalid_1's multi_error: 0.0654213\tvalid_1's multi_logloss: 0.187443\n",
            "[633]\tvalid_0's multi_error: 0.0483601\tvalid_0's multi_logloss: 0.140912\tvalid_1's multi_error: 0.0654588\tvalid_1's multi_logloss: 0.18739\n",
            "[634]\tvalid_0's multi_error: 0.0483923\tvalid_0's multi_logloss: 0.14083\tvalid_1's multi_error: 0.0654588\tvalid_1's multi_logloss: 0.187352\n",
            "[635]\tvalid_0's multi_error: 0.0483601\tvalid_0's multi_logloss: 0.14067\tvalid_1's multi_error: 0.0652337\tvalid_1's multi_logloss: 0.187209\n",
            "[636]\tvalid_0's multi_error: 0.048328\tvalid_0's multi_logloss: 0.140594\tvalid_1's multi_error: 0.0652337\tvalid_1's multi_logloss: 0.187191\n",
            "[637]\tvalid_0's multi_error: 0.0482637\tvalid_0's multi_logloss: 0.140525\tvalid_1's multi_error: 0.0652712\tvalid_1's multi_logloss: 0.187171\n",
            "[638]\tvalid_0's multi_error: 0.048135\tvalid_0's multi_logloss: 0.140446\tvalid_1's multi_error: 0.0653087\tvalid_1's multi_logloss: 0.18715\n",
            "[639]\tvalid_0's multi_error: 0.0480225\tvalid_0's multi_logloss: 0.140339\tvalid_1's multi_error: 0.0653087\tvalid_1's multi_logloss: 0.18709\n",
            "[640]\tvalid_0's multi_error: 0.0479904\tvalid_0's multi_logloss: 0.140278\tvalid_1's multi_error: 0.0653087\tvalid_1's multi_logloss: 0.187083\n",
            "[641]\tvalid_0's multi_error: 0.0479904\tvalid_0's multi_logloss: 0.140201\tvalid_1's multi_error: 0.0652712\tvalid_1's multi_logloss: 0.187046\n",
            "[642]\tvalid_0's multi_error: 0.0478939\tvalid_0's multi_logloss: 0.140129\tvalid_1's multi_error: 0.0653462\tvalid_1's multi_logloss: 0.187042\n",
            "[643]\tvalid_0's multi_error: 0.04791\tvalid_0's multi_logloss: 0.140018\tvalid_1's multi_error: 0.0653087\tvalid_1's multi_logloss: 0.186981\n",
            "[644]\tvalid_0's multi_error: 0.047926\tvalid_0's multi_logloss: 0.13995\tvalid_1's multi_error: 0.0653087\tvalid_1's multi_logloss: 0.186964\n",
            "[645]\tvalid_0's multi_error: 0.0478617\tvalid_0's multi_logloss: 0.139882\tvalid_1's multi_error: 0.0653837\tvalid_1's multi_logloss: 0.186957\n",
            "[646]\tvalid_0's multi_error: 0.0478296\tvalid_0's multi_logloss: 0.13981\tvalid_1's multi_error: 0.0653087\tvalid_1's multi_logloss: 0.186938\n",
            "[647]\tvalid_0's multi_error: 0.0477974\tvalid_0's multi_logloss: 0.139712\tvalid_1's multi_error: 0.0653837\tvalid_1's multi_logloss: 0.186896\n",
            "[648]\tvalid_0's multi_error: 0.0477653\tvalid_0's multi_logloss: 0.139619\tvalid_1's multi_error: 0.0654213\tvalid_1's multi_logloss: 0.186856\n",
            "[649]\tvalid_0's multi_error: 0.047717\tvalid_0's multi_logloss: 0.139548\tvalid_1's multi_error: 0.0653462\tvalid_1's multi_logloss: 0.186845\n",
            "[650]\tvalid_0's multi_error: 0.0476527\tvalid_0's multi_logloss: 0.139469\tvalid_1's multi_error: 0.0652337\tvalid_1's multi_logloss: 0.186817\n",
            "[651]\tvalid_0's multi_error: 0.0475884\tvalid_0's multi_logloss: 0.139375\tvalid_1's multi_error: 0.0652337\tvalid_1's multi_logloss: 0.186777\n",
            "[652]\tvalid_0's multi_error: 0.0475241\tvalid_0's multi_logloss: 0.139275\tvalid_1's multi_error: 0.0652337\tvalid_1's multi_logloss: 0.186746\n",
            "[653]\tvalid_0's multi_error: 0.047508\tvalid_0's multi_logloss: 0.139207\tvalid_1's multi_error: 0.0653087\tvalid_1's multi_logloss: 0.18674\n",
            "[654]\tvalid_0's multi_error: 0.0474598\tvalid_0's multi_logloss: 0.139111\tvalid_1's multi_error: 0.0653837\tvalid_1's multi_logloss: 0.186693\n",
            "[655]\tvalid_0's multi_error: 0.0473633\tvalid_0's multi_logloss: 0.13901\tvalid_1's multi_error: 0.0653462\tvalid_1's multi_logloss: 0.186638\n",
            "[656]\tvalid_0's multi_error: 0.0473473\tvalid_0's multi_logloss: 0.138921\tvalid_1's multi_error: 0.0652712\tvalid_1's multi_logloss: 0.186599\n",
            "[657]\tvalid_0's multi_error: 0.0473633\tvalid_0's multi_logloss: 0.138837\tvalid_1's multi_error: 0.0652712\tvalid_1's multi_logloss: 0.18656\n",
            "[658]\tvalid_0's multi_error: 0.0472669\tvalid_0's multi_logloss: 0.138776\tvalid_1's multi_error: 0.0652712\tvalid_1's multi_logloss: 0.186542\n",
            "[659]\tvalid_0's multi_error: 0.0472186\tvalid_0's multi_logloss: 0.138718\tvalid_1's multi_error: 0.0652337\tvalid_1's multi_logloss: 0.186541\n",
            "[660]\tvalid_0's multi_error: 0.0472347\tvalid_0's multi_logloss: 0.138649\tvalid_1's multi_error: 0.0651212\tvalid_1's multi_logloss: 0.186517\n",
            "[661]\tvalid_0's multi_error: 0.0471865\tvalid_0's multi_logloss: 0.138571\tvalid_1's multi_error: 0.0650837\tvalid_1's multi_logloss: 0.186495\n",
            "[662]\tvalid_0's multi_error: 0.0471222\tvalid_0's multi_logloss: 0.138486\tvalid_1's multi_error: 0.0650461\tvalid_1's multi_logloss: 0.18645\n",
            "[663]\tvalid_0's multi_error: 0.047074\tvalid_0's multi_logloss: 0.138407\tvalid_1's multi_error: 0.0650461\tvalid_1's multi_logloss: 0.186427\n",
            "[664]\tvalid_0's multi_error: 0.04709\tvalid_0's multi_logloss: 0.138349\tvalid_1's multi_error: 0.0650461\tvalid_1's multi_logloss: 0.18642\n",
            "[665]\tvalid_0's multi_error: 0.0470257\tvalid_0's multi_logloss: 0.138193\tvalid_1's multi_error: 0.0648961\tvalid_1's multi_logloss: 0.186306\n",
            "[666]\tvalid_0's multi_error: 0.0469936\tvalid_0's multi_logloss: 0.13803\tvalid_1's multi_error: 0.0648586\tvalid_1's multi_logloss: 0.186164\n",
            "[667]\tvalid_0's multi_error: 0.0468971\tvalid_0's multi_logloss: 0.137962\tvalid_1's multi_error: 0.0648961\tvalid_1's multi_logloss: 0.186133\n",
            "[668]\tvalid_0's multi_error: 0.0468971\tvalid_0's multi_logloss: 0.137889\tvalid_1's multi_error: 0.0648586\tvalid_1's multi_logloss: 0.186114\n",
            "[669]\tvalid_0's multi_error: 0.0468328\tvalid_0's multi_logloss: 0.137826\tvalid_1's multi_error: 0.0648586\tvalid_1's multi_logloss: 0.186112\n",
            "[670]\tvalid_0's multi_error: 0.046865\tvalid_0's multi_logloss: 0.137712\tvalid_1's multi_error: 0.064746\tvalid_1's multi_logloss: 0.186051\n",
            "[671]\tvalid_0's multi_error: 0.046881\tvalid_0's multi_logloss: 0.137656\tvalid_1's multi_error: 0.0648211\tvalid_1's multi_logloss: 0.18604\n",
            "[672]\tvalid_0's multi_error: 0.0468489\tvalid_0's multi_logloss: 0.137583\tvalid_1's multi_error: 0.0647836\tvalid_1's multi_logloss: 0.186031\n",
            "[673]\tvalid_0's multi_error: 0.0468006\tvalid_0's multi_logloss: 0.137509\tvalid_1's multi_error: 0.0648211\tvalid_1's multi_logloss: 0.186003\n",
            "[674]\tvalid_0's multi_error: 0.0467363\tvalid_0's multi_logloss: 0.137426\tvalid_1's multi_error: 0.0648211\tvalid_1's multi_logloss: 0.185969\n",
            "[675]\tvalid_0's multi_error: 0.0467203\tvalid_0's multi_logloss: 0.137374\tvalid_1's multi_error: 0.0648211\tvalid_1's multi_logloss: 0.185961\n",
            "[676]\tvalid_0's multi_error: 0.0466559\tvalid_0's multi_logloss: 0.137271\tvalid_1's multi_error: 0.0649711\tvalid_1's multi_logloss: 0.185896\n",
            "[677]\tvalid_0's multi_error: 0.0465916\tvalid_0's multi_logloss: 0.137105\tvalid_1's multi_error: 0.0649711\tvalid_1's multi_logloss: 0.185733\n",
            "[678]\tvalid_0's multi_error: 0.0465916\tvalid_0's multi_logloss: 0.137026\tvalid_1's multi_error: 0.0650086\tvalid_1's multi_logloss: 0.185718\n",
            "[679]\tvalid_0's multi_error: 0.0465916\tvalid_0's multi_logloss: 0.136963\tvalid_1's multi_error: 0.0648961\tvalid_1's multi_logloss: 0.185713\n",
            "[680]\tvalid_0's multi_error: 0.0465756\tvalid_0's multi_logloss: 0.136887\tvalid_1's multi_error: 0.0648586\tvalid_1's multi_logloss: 0.185695\n",
            "[681]\tvalid_0's multi_error: 0.0464791\tvalid_0's multi_logloss: 0.136803\tvalid_1's multi_error: 0.0648961\tvalid_1's multi_logloss: 0.185671\n",
            "[682]\tvalid_0's multi_error: 0.0464309\tvalid_0's multi_logloss: 0.136724\tvalid_1's multi_error: 0.0650461\tvalid_1's multi_logloss: 0.185637\n",
            "[683]\tvalid_0's multi_error: 0.0463987\tvalid_0's multi_logloss: 0.136641\tvalid_1's multi_error: 0.0650086\tvalid_1's multi_logloss: 0.185619\n",
            "[684]\tvalid_0's multi_error: 0.0463666\tvalid_0's multi_logloss: 0.136561\tvalid_1's multi_error: 0.0650086\tvalid_1's multi_logloss: 0.185588\n",
            "[685]\tvalid_0's multi_error: 0.0463183\tvalid_0's multi_logloss: 0.136478\tvalid_1's multi_error: 0.0650086\tvalid_1's multi_logloss: 0.185566\n",
            "[686]\tvalid_0's multi_error: 0.0463344\tvalid_0's multi_logloss: 0.136424\tvalid_1's multi_error: 0.0649336\tvalid_1's multi_logloss: 0.18557\n",
            "[687]\tvalid_0's multi_error: 0.046254\tvalid_0's multi_logloss: 0.136322\tvalid_1's multi_error: 0.0648586\tvalid_1's multi_logloss: 0.18552\n",
            "[688]\tvalid_0's multi_error: 0.0462058\tvalid_0's multi_logloss: 0.136229\tvalid_1's multi_error: 0.0648211\tvalid_1's multi_logloss: 0.185485\n",
            "[689]\tvalid_0's multi_error: 0.0461897\tvalid_0's multi_logloss: 0.136166\tvalid_1's multi_error: 0.0648211\tvalid_1's multi_logloss: 0.185467\n",
            "[690]\tvalid_0's multi_error: 0.0461736\tvalid_0's multi_logloss: 0.136113\tvalid_1's multi_error: 0.0648211\tvalid_1's multi_logloss: 0.185467\n",
            "[691]\tvalid_0's multi_error: 0.0460932\tvalid_0's multi_logloss: 0.136043\tvalid_1's multi_error: 0.0647836\tvalid_1's multi_logloss: 0.185432\n",
            "[692]\tvalid_0's multi_error: 0.0460772\tvalid_0's multi_logloss: 0.135983\tvalid_1's multi_error: 0.0648211\tvalid_1's multi_logloss: 0.185433\n",
            "[693]\tvalid_0's multi_error: 0.0460611\tvalid_0's multi_logloss: 0.135942\tvalid_1's multi_error: 0.0648211\tvalid_1's multi_logloss: 0.185436\n",
            "[694]\tvalid_0's multi_error: 0.0460129\tvalid_0's multi_logloss: 0.135862\tvalid_1's multi_error: 0.0647836\tvalid_1's multi_logloss: 0.185411\n",
            "[695]\tvalid_0's multi_error: 0.0459968\tvalid_0's multi_logloss: 0.135805\tvalid_1's multi_error: 0.0648211\tvalid_1's multi_logloss: 0.185407\n",
            "[696]\tvalid_0's multi_error: 0.0459968\tvalid_0's multi_logloss: 0.135743\tvalid_1's multi_error: 0.0648211\tvalid_1's multi_logloss: 0.185392\n",
            "[697]\tvalid_0's multi_error: 0.0459807\tvalid_0's multi_logloss: 0.135685\tvalid_1's multi_error: 0.0647836\tvalid_1's multi_logloss: 0.185386\n",
            "[698]\tvalid_0's multi_error: 0.0459486\tvalid_0's multi_logloss: 0.135624\tvalid_1's multi_error: 0.0647836\tvalid_1's multi_logloss: 0.185372\n",
            "[699]\tvalid_0's multi_error: 0.0459164\tvalid_0's multi_logloss: 0.135474\tvalid_1's multi_error: 0.0647836\tvalid_1's multi_logloss: 0.185267\n",
            "[700]\tvalid_0's multi_error: 0.0458842\tvalid_0's multi_logloss: 0.135372\tvalid_1's multi_error: 0.0647836\tvalid_1's multi_logloss: 0.185206\n",
            "[701]\tvalid_0's multi_error: 0.0458842\tvalid_0's multi_logloss: 0.135312\tvalid_1's multi_error: 0.064746\tvalid_1's multi_logloss: 0.185201\n",
            "[702]\tvalid_0's multi_error: 0.0458039\tvalid_0's multi_logloss: 0.135235\tvalid_1's multi_error: 0.0648211\tvalid_1's multi_logloss: 0.185177\n",
            "[703]\tvalid_0's multi_error: 0.0457074\tvalid_0's multi_logloss: 0.135153\tvalid_1's multi_error: 0.0648211\tvalid_1's multi_logloss: 0.185155\n",
            "[704]\tvalid_0's multi_error: 0.0456913\tvalid_0's multi_logloss: 0.135089\tvalid_1's multi_error: 0.0648211\tvalid_1's multi_logloss: 0.185154\n",
            "[705]\tvalid_0's multi_error: 0.0456109\tvalid_0's multi_logloss: 0.134887\tvalid_1's multi_error: 0.0647085\tvalid_1's multi_logloss: 0.184974\n",
            "[706]\tvalid_0's multi_error: 0.0455788\tvalid_0's multi_logloss: 0.134791\tvalid_1's multi_error: 0.0646335\tvalid_1's multi_logloss: 0.18492\n",
            "[707]\tvalid_0's multi_error: 0.0455627\tvalid_0's multi_logloss: 0.134725\tvalid_1's multi_error: 0.064671\tvalid_1's multi_logloss: 0.184907\n",
            "[708]\tvalid_0's multi_error: 0.0455466\tvalid_0's multi_logloss: 0.134657\tvalid_1's multi_error: 0.0646335\tvalid_1's multi_logloss: 0.184899\n",
            "[709]\tvalid_0's multi_error: 0.0455145\tvalid_0's multi_logloss: 0.134553\tvalid_1's multi_error: 0.064596\tvalid_1's multi_logloss: 0.184839\n",
            "[710]\tvalid_0's multi_error: 0.0454984\tvalid_0's multi_logloss: 0.134454\tvalid_1's multi_error: 0.064596\tvalid_1's multi_logloss: 0.184798\n",
            "[711]\tvalid_0's multi_error: 0.0454984\tvalid_0's multi_logloss: 0.13434\tvalid_1's multi_error: 0.0646335\tvalid_1's multi_logloss: 0.184744\n",
            "[712]\tvalid_0's multi_error: 0.0453859\tvalid_0's multi_logloss: 0.13427\tvalid_1's multi_error: 0.064671\tvalid_1's multi_logloss: 0.184724\n",
            "[713]\tvalid_0's multi_error: 0.0453859\tvalid_0's multi_logloss: 0.13421\tvalid_1's multi_error: 0.0646335\tvalid_1's multi_logloss: 0.184714\n",
            "[714]\tvalid_0's multi_error: 0.0452894\tvalid_0's multi_logloss: 0.134136\tvalid_1's multi_error: 0.064596\tvalid_1's multi_logloss: 0.184708\n",
            "[715]\tvalid_0's multi_error: 0.045209\tvalid_0's multi_logloss: 0.134072\tvalid_1's multi_error: 0.0647085\tvalid_1's multi_logloss: 0.184707\n",
            "[716]\tvalid_0's multi_error: 0.0451768\tvalid_0's multi_logloss: 0.133981\tvalid_1's multi_error: 0.064746\tvalid_1's multi_logloss: 0.184677\n",
            "[717]\tvalid_0's multi_error: 0.0451608\tvalid_0's multi_logloss: 0.133894\tvalid_1's multi_error: 0.064671\tvalid_1's multi_logloss: 0.184624\n",
            "[718]\tvalid_0's multi_error: 0.0450643\tvalid_0's multi_logloss: 0.133832\tvalid_1's multi_error: 0.0646335\tvalid_1's multi_logloss: 0.184629\n",
            "[719]\tvalid_0's multi_error: 0.0450161\tvalid_0's multi_logloss: 0.133771\tvalid_1's multi_error: 0.064671\tvalid_1's multi_logloss: 0.184624\n",
            "[720]\tvalid_0's multi_error: 0.045\tvalid_0's multi_logloss: 0.133647\tvalid_1's multi_error: 0.0645585\tvalid_1's multi_logloss: 0.184549\n",
            "[721]\tvalid_0's multi_error: 0.0450322\tvalid_0's multi_logloss: 0.133585\tvalid_1's multi_error: 0.0645585\tvalid_1's multi_logloss: 0.184538\n",
            "[722]\tvalid_0's multi_error: 0.0450161\tvalid_0's multi_logloss: 0.133513\tvalid_1's multi_error: 0.064596\tvalid_1's multi_logloss: 0.18451\n",
            "[723]\tvalid_0's multi_error: 0.0450161\tvalid_0's multi_logloss: 0.13339\tvalid_1's multi_error: 0.0645585\tvalid_1's multi_logloss: 0.184434\n",
            "[724]\tvalid_0's multi_error: 0.0449357\tvalid_0's multi_logloss: 0.13332\tvalid_1's multi_error: 0.064521\tvalid_1's multi_logloss: 0.184423\n",
            "[725]\tvalid_0's multi_error: 0.0449357\tvalid_0's multi_logloss: 0.133249\tvalid_1's multi_error: 0.0645585\tvalid_1's multi_logloss: 0.184389\n",
            "[726]\tvalid_0's multi_error: 0.0449196\tvalid_0's multi_logloss: 0.133166\tvalid_1's multi_error: 0.064521\tvalid_1's multi_logloss: 0.184358\n",
            "[727]\tvalid_0's multi_error: 0.0448714\tvalid_0's multi_logloss: 0.1331\tvalid_1's multi_error: 0.0645585\tvalid_1's multi_logloss: 0.184339\n",
            "[728]\tvalid_0's multi_error: 0.0448392\tvalid_0's multi_logloss: 0.132999\tvalid_1's multi_error: 0.064521\tvalid_1's multi_logloss: 0.184271\n",
            "[729]\tvalid_0's multi_error: 0.0448392\tvalid_0's multi_logloss: 0.13288\tvalid_1's multi_error: 0.0644459\tvalid_1's multi_logloss: 0.184203\n",
            "[730]\tvalid_0's multi_error: 0.0448232\tvalid_0's multi_logloss: 0.132815\tvalid_1's multi_error: 0.0644459\tvalid_1's multi_logloss: 0.184192\n",
            "[731]\tvalid_0's multi_error: 0.0447749\tvalid_0's multi_logloss: 0.132732\tvalid_1's multi_error: 0.0643709\tvalid_1's multi_logloss: 0.184138\n",
            "[732]\tvalid_0's multi_error: 0.0447749\tvalid_0's multi_logloss: 0.132665\tvalid_1's multi_error: 0.0643709\tvalid_1's multi_logloss: 0.184118\n",
            "[733]\tvalid_0's multi_error: 0.0447428\tvalid_0's multi_logloss: 0.132608\tvalid_1's multi_error: 0.0642959\tvalid_1's multi_logloss: 0.184117\n",
            "[734]\tvalid_0's multi_error: 0.0446945\tvalid_0's multi_logloss: 0.132542\tvalid_1's multi_error: 0.0643709\tvalid_1's multi_logloss: 0.184104\n",
            "[735]\tvalid_0's multi_error: 0.0446302\tvalid_0's multi_logloss: 0.132421\tvalid_1's multi_error: 0.0642959\tvalid_1's multi_logloss: 0.184016\n",
            "[736]\tvalid_0's multi_error: 0.0446463\tvalid_0's multi_logloss: 0.132296\tvalid_1's multi_error: 0.0643334\tvalid_1's multi_logloss: 0.183931\n",
            "[737]\tvalid_0's multi_error: 0.0445981\tvalid_0's multi_logloss: 0.132161\tvalid_1's multi_error: 0.0643334\tvalid_1's multi_logloss: 0.183863\n",
            "[738]\tvalid_0's multi_error: 0.0445498\tvalid_0's multi_logloss: 0.132095\tvalid_1's multi_error: 0.0643334\tvalid_1's multi_logloss: 0.183841\n",
            "[739]\tvalid_0's multi_error: 0.0445659\tvalid_0's multi_logloss: 0.132023\tvalid_1's multi_error: 0.0642959\tvalid_1's multi_logloss: 0.183829\n",
            "[740]\tvalid_0's multi_error: 0.0445016\tvalid_0's multi_logloss: 0.131937\tvalid_1's multi_error: 0.0642959\tvalid_1's multi_logloss: 0.183816\n",
            "[741]\tvalid_0's multi_error: 0.0445016\tvalid_0's multi_logloss: 0.131871\tvalid_1's multi_error: 0.0642959\tvalid_1's multi_logloss: 0.183802\n",
            "[742]\tvalid_0's multi_error: 0.0444695\tvalid_0's multi_logloss: 0.131787\tvalid_1's multi_error: 0.0641834\tvalid_1's multi_logloss: 0.183771\n",
            "[743]\tvalid_0's multi_error: 0.0444534\tvalid_0's multi_logloss: 0.1317\tvalid_1's multi_error: 0.0642209\tvalid_1's multi_logloss: 0.183734\n",
            "[744]\tvalid_0's multi_error: 0.0444373\tvalid_0's multi_logloss: 0.131621\tvalid_1's multi_error: 0.0642209\tvalid_1's multi_logloss: 0.183732\n",
            "[745]\tvalid_0's multi_error: 0.0443891\tvalid_0's multi_logloss: 0.131535\tvalid_1's multi_error: 0.0642584\tvalid_1's multi_logloss: 0.183699\n",
            "[746]\tvalid_0's multi_error: 0.0443248\tvalid_0's multi_logloss: 0.131472\tvalid_1's multi_error: 0.0641458\tvalid_1's multi_logloss: 0.183685\n",
            "[747]\tvalid_0's multi_error: 0.0443248\tvalid_0's multi_logloss: 0.131408\tvalid_1's multi_error: 0.0641083\tvalid_1's multi_logloss: 0.183659\n",
            "[748]\tvalid_0's multi_error: 0.0443087\tvalid_0's multi_logloss: 0.131326\tvalid_1's multi_error: 0.0640708\tvalid_1's multi_logloss: 0.183628\n",
            "[749]\tvalid_0's multi_error: 0.0442444\tvalid_0's multi_logloss: 0.131241\tvalid_1's multi_error: 0.0640333\tvalid_1's multi_logloss: 0.183597\n",
            "[750]\tvalid_0's multi_error: 0.0442283\tvalid_0's multi_logloss: 0.131176\tvalid_1's multi_error: 0.0639583\tvalid_1's multi_logloss: 0.183581\n",
            "[751]\tvalid_0's multi_error: 0.0442122\tvalid_0's multi_logloss: 0.131098\tvalid_1's multi_error: 0.0640333\tvalid_1's multi_logloss: 0.183565\n",
            "[752]\tvalid_0's multi_error: 0.0441318\tvalid_0's multi_logloss: 0.131025\tvalid_1's multi_error: 0.0639958\tvalid_1's multi_logloss: 0.18354\n",
            "[753]\tvalid_0's multi_error: 0.0441479\tvalid_0's multi_logloss: 0.130946\tvalid_1's multi_error: 0.0639583\tvalid_1's multi_logloss: 0.183506\n",
            "[754]\tvalid_0's multi_error: 0.0441158\tvalid_0's multi_logloss: 0.130878\tvalid_1's multi_error: 0.0639958\tvalid_1's multi_logloss: 0.183484\n",
            "[755]\tvalid_0's multi_error: 0.0440514\tvalid_0's multi_logloss: 0.130797\tvalid_1's multi_error: 0.0639958\tvalid_1's multi_logloss: 0.183463\n",
            "[756]\tvalid_0's multi_error: 0.0440354\tvalid_0's multi_logloss: 0.130724\tvalid_1's multi_error: 0.0639208\tvalid_1's multi_logloss: 0.183429\n",
            "[757]\tvalid_0's multi_error: 0.0440032\tvalid_0's multi_logloss: 0.130639\tvalid_1's multi_error: 0.0639583\tvalid_1's multi_logloss: 0.183379\n",
            "[758]\tvalid_0's multi_error: 0.0440354\tvalid_0's multi_logloss: 0.130587\tvalid_1's multi_error: 0.0639583\tvalid_1's multi_logloss: 0.183378\n",
            "[759]\tvalid_0's multi_error: 0.0439871\tvalid_0's multi_logloss: 0.130532\tvalid_1's multi_error: 0.0639583\tvalid_1's multi_logloss: 0.183373\n",
            "[760]\tvalid_0's multi_error: 0.0439389\tvalid_0's multi_logloss: 0.130443\tvalid_1's multi_error: 0.0639583\tvalid_1's multi_logloss: 0.18331\n",
            "[761]\tvalid_0's multi_error: 0.0438746\tvalid_0's multi_logloss: 0.130374\tvalid_1's multi_error: 0.0639583\tvalid_1's multi_logloss: 0.183286\n",
            "[762]\tvalid_0's multi_error: 0.0438424\tvalid_0's multi_logloss: 0.130294\tvalid_1's multi_error: 0.0639958\tvalid_1's multi_logloss: 0.183257\n",
            "[763]\tvalid_0's multi_error: 0.0438264\tvalid_0's multi_logloss: 0.130228\tvalid_1's multi_error: 0.0639958\tvalid_1's multi_logloss: 0.183233\n",
            "[764]\tvalid_0's multi_error: 0.0437781\tvalid_0's multi_logloss: 0.130158\tvalid_1's multi_error: 0.0639208\tvalid_1's multi_logloss: 0.183214\n",
            "[765]\tvalid_0's multi_error: 0.0437621\tvalid_0's multi_logloss: 0.130103\tvalid_1's multi_error: 0.0639208\tvalid_1's multi_logloss: 0.183205\n",
            "[766]\tvalid_0's multi_error: 0.0437781\tvalid_0's multi_logloss: 0.130041\tvalid_1's multi_error: 0.0639958\tvalid_1's multi_logloss: 0.183175\n",
            "[767]\tvalid_0's multi_error: 0.043746\tvalid_0's multi_logloss: 0.129989\tvalid_1's multi_error: 0.0639208\tvalid_1's multi_logloss: 0.183165\n",
            "[768]\tvalid_0's multi_error: 0.0436817\tvalid_0's multi_logloss: 0.129847\tvalid_1's multi_error: 0.0639208\tvalid_1's multi_logloss: 0.183052\n",
            "[769]\tvalid_0's multi_error: 0.0436817\tvalid_0's multi_logloss: 0.129731\tvalid_1's multi_error: 0.0638082\tvalid_1's multi_logloss: 0.182983\n",
            "[770]\tvalid_0's multi_error: 0.0436656\tvalid_0's multi_logloss: 0.129652\tvalid_1's multi_error: 0.0638082\tvalid_1's multi_logloss: 0.182955\n",
            "[771]\tvalid_0's multi_error: 0.0436013\tvalid_0's multi_logloss: 0.129589\tvalid_1's multi_error: 0.0639208\tvalid_1's multi_logloss: 0.18296\n",
            "[772]\tvalid_0's multi_error: 0.043537\tvalid_0's multi_logloss: 0.129529\tvalid_1's multi_error: 0.0638082\tvalid_1's multi_logloss: 0.182951\n",
            "[773]\tvalid_0's multi_error: 0.0435531\tvalid_0's multi_logloss: 0.129431\tvalid_1's multi_error: 0.0638082\tvalid_1's multi_logloss: 0.182895\n",
            "[774]\tvalid_0's multi_error: 0.0435691\tvalid_0's multi_logloss: 0.12938\tvalid_1's multi_error: 0.0638457\tvalid_1's multi_logloss: 0.18289\n",
            "[775]\tvalid_0's multi_error: 0.0435531\tvalid_0's multi_logloss: 0.129257\tvalid_1's multi_error: 0.0638082\tvalid_1's multi_logloss: 0.182807\n",
            "[776]\tvalid_0's multi_error: 0.0435048\tvalid_0's multi_logloss: 0.129181\tvalid_1's multi_error: 0.0638082\tvalid_1's multi_logloss: 0.182779\n",
            "[777]\tvalid_0's multi_error: 0.0434887\tvalid_0's multi_logloss: 0.129102\tvalid_1's multi_error: 0.0637332\tvalid_1's multi_logloss: 0.182752\n",
            "[778]\tvalid_0's multi_error: 0.0435209\tvalid_0's multi_logloss: 0.12898\tvalid_1's multi_error: 0.0636582\tvalid_1's multi_logloss: 0.182671\n",
            "[779]\tvalid_0's multi_error: 0.0435048\tvalid_0's multi_logloss: 0.128913\tvalid_1's multi_error: 0.0636582\tvalid_1's multi_logloss: 0.182669\n",
            "[780]\tvalid_0's multi_error: 0.0434566\tvalid_0's multi_logloss: 0.128842\tvalid_1's multi_error: 0.0636957\tvalid_1's multi_logloss: 0.182657\n",
            "[781]\tvalid_0's multi_error: 0.0434727\tvalid_0's multi_logloss: 0.128775\tvalid_1's multi_error: 0.0636957\tvalid_1's multi_logloss: 0.182617\n",
            "[782]\tvalid_0's multi_error: 0.0434244\tvalid_0's multi_logloss: 0.128648\tvalid_1's multi_error: 0.0637332\tvalid_1's multi_logloss: 0.182534\n",
            "[783]\tvalid_0's multi_error: 0.0433923\tvalid_0's multi_logloss: 0.128563\tvalid_1's multi_error: 0.0637707\tvalid_1's multi_logloss: 0.182501\n",
            "[784]\tvalid_0's multi_error: 0.043328\tvalid_0's multi_logloss: 0.128484\tvalid_1's multi_error: 0.0636957\tvalid_1's multi_logloss: 0.182443\n",
            "[785]\tvalid_0's multi_error: 0.0432476\tvalid_0's multi_logloss: 0.128428\tvalid_1's multi_error: 0.0636957\tvalid_1's multi_logloss: 0.182429\n",
            "[786]\tvalid_0's multi_error: 0.0432637\tvalid_0's multi_logloss: 0.128372\tvalid_1's multi_error: 0.0637332\tvalid_1's multi_logloss: 0.182419\n",
            "[787]\tvalid_0's multi_error: 0.0432315\tvalid_0's multi_logloss: 0.128282\tvalid_1's multi_error: 0.0638082\tvalid_1's multi_logloss: 0.182369\n",
            "[788]\tvalid_0's multi_error: 0.043135\tvalid_0's multi_logloss: 0.128214\tvalid_1's multi_error: 0.0637707\tvalid_1's multi_logloss: 0.182364\n",
            "[789]\tvalid_0's multi_error: 0.043119\tvalid_0's multi_logloss: 0.128149\tvalid_1's multi_error: 0.0637332\tvalid_1's multi_logloss: 0.182351\n",
            "[790]\tvalid_0's multi_error: 0.0431029\tvalid_0's multi_logloss: 0.128074\tvalid_1's multi_error: 0.0636957\tvalid_1's multi_logloss: 0.182336\n",
            "[791]\tvalid_0's multi_error: 0.0430386\tvalid_0's multi_logloss: 0.128011\tvalid_1's multi_error: 0.0636957\tvalid_1's multi_logloss: 0.182322\n",
            "[792]\tvalid_0's multi_error: 0.0430064\tvalid_0's multi_logloss: 0.127951\tvalid_1's multi_error: 0.0636957\tvalid_1's multi_logloss: 0.182316\n",
            "[793]\tvalid_0's multi_error: 0.0430225\tvalid_0's multi_logloss: 0.127873\tvalid_1's multi_error: 0.0636957\tvalid_1's multi_logloss: 0.18229\n",
            "[794]\tvalid_0's multi_error: 0.0429904\tvalid_0's multi_logloss: 0.127813\tvalid_1's multi_error: 0.0637332\tvalid_1's multi_logloss: 0.18228\n",
            "[795]\tvalid_0's multi_error: 0.0429421\tvalid_0's multi_logloss: 0.127756\tvalid_1's multi_error: 0.0637332\tvalid_1's multi_logloss: 0.182257\n",
            "[796]\tvalid_0's multi_error: 0.0429421\tvalid_0's multi_logloss: 0.127693\tvalid_1's multi_error: 0.0636957\tvalid_1's multi_logloss: 0.182233\n",
            "[797]\tvalid_0's multi_error: 0.042926\tvalid_0's multi_logloss: 0.127628\tvalid_1's multi_error: 0.0636957\tvalid_1's multi_logloss: 0.182218\n",
            "[798]\tvalid_0's multi_error: 0.0429421\tvalid_0's multi_logloss: 0.127566\tvalid_1's multi_error: 0.0637332\tvalid_1's multi_logloss: 0.182215\n",
            "[799]\tvalid_0's multi_error: 0.0428939\tvalid_0's multi_logloss: 0.127494\tvalid_1's multi_error: 0.0637332\tvalid_1's multi_logloss: 0.182194\n",
            "[800]\tvalid_0's multi_error: 0.042926\tvalid_0's multi_logloss: 0.127425\tvalid_1's multi_error: 0.0636957\tvalid_1's multi_logloss: 0.18218\n",
            "[801]\tvalid_0's multi_error: 0.04291\tvalid_0's multi_logloss: 0.127364\tvalid_1's multi_error: 0.0635832\tvalid_1's multi_logloss: 0.182159\n",
            "[802]\tvalid_0's multi_error: 0.0428135\tvalid_0's multi_logloss: 0.127298\tvalid_1's multi_error: 0.0635832\tvalid_1's multi_logloss: 0.182144\n",
            "[803]\tvalid_0's multi_error: 0.0428135\tvalid_0's multi_logloss: 0.127241\tvalid_1's multi_error: 0.0636207\tvalid_1's multi_logloss: 0.182136\n",
            "[804]\tvalid_0's multi_error: 0.0427492\tvalid_0's multi_logloss: 0.127173\tvalid_1's multi_error: 0.0635457\tvalid_1's multi_logloss: 0.182124\n",
            "[805]\tvalid_0's multi_error: 0.0427331\tvalid_0's multi_logloss: 0.127099\tvalid_1's multi_error: 0.0636582\tvalid_1's multi_logloss: 0.182102\n",
            "[806]\tvalid_0's multi_error: 0.0427331\tvalid_0's multi_logloss: 0.127025\tvalid_1's multi_error: 0.0636207\tvalid_1's multi_logloss: 0.182077\n",
            "[807]\tvalid_0's multi_error: 0.0426849\tvalid_0's multi_logloss: 0.126957\tvalid_1's multi_error: 0.0636582\tvalid_1's multi_logloss: 0.18205\n",
            "[808]\tvalid_0's multi_error: 0.0426849\tvalid_0's multi_logloss: 0.126881\tvalid_1's multi_error: 0.0637332\tvalid_1's multi_logloss: 0.182045\n",
            "[809]\tvalid_0's multi_error: 0.0426688\tvalid_0's multi_logloss: 0.126809\tvalid_1's multi_error: 0.0637332\tvalid_1's multi_logloss: 0.182016\n",
            "[810]\tvalid_0's multi_error: 0.0425884\tvalid_0's multi_logloss: 0.126752\tvalid_1's multi_error: 0.0637332\tvalid_1's multi_logloss: 0.182002\n",
            "[811]\tvalid_0's multi_error: 0.0426045\tvalid_0's multi_logloss: 0.126688\tvalid_1's multi_error: 0.0636957\tvalid_1's multi_logloss: 0.181996\n",
            "[812]\tvalid_0's multi_error: 0.0425884\tvalid_0's multi_logloss: 0.126621\tvalid_1's multi_error: 0.0636957\tvalid_1's multi_logloss: 0.181984\n",
            "[813]\tvalid_0's multi_error: 0.042508\tvalid_0's multi_logloss: 0.12654\tvalid_1's multi_error: 0.0636582\tvalid_1's multi_logloss: 0.18194\n",
            "[814]\tvalid_0's multi_error: 0.0424759\tvalid_0's multi_logloss: 0.126458\tvalid_1's multi_error: 0.0636207\tvalid_1's multi_logloss: 0.181886\n",
            "[815]\tvalid_0's multi_error: 0.0424277\tvalid_0's multi_logloss: 0.126385\tvalid_1's multi_error: 0.0635832\tvalid_1's multi_logloss: 0.181864\n",
            "[816]\tvalid_0's multi_error: 0.0423794\tvalid_0's multi_logloss: 0.126331\tvalid_1's multi_error: 0.0635832\tvalid_1's multi_logloss: 0.18184\n",
            "[817]\tvalid_0's multi_error: 0.0423955\tvalid_0's multi_logloss: 0.12628\tvalid_1's multi_error: 0.0635832\tvalid_1's multi_logloss: 0.181843\n",
            "[818]\tvalid_0's multi_error: 0.0423473\tvalid_0's multi_logloss: 0.126226\tvalid_1's multi_error: 0.0635457\tvalid_1's multi_logloss: 0.18184\n",
            "[819]\tvalid_0's multi_error: 0.0423151\tvalid_0's multi_logloss: 0.126136\tvalid_1's multi_error: 0.0635081\tvalid_1's multi_logloss: 0.181805\n",
            "[820]\tvalid_0's multi_error: 0.0422347\tvalid_0's multi_logloss: 0.12608\tvalid_1's multi_error: 0.0635457\tvalid_1's multi_logloss: 0.181803\n",
            "[821]\tvalid_0's multi_error: 0.0422347\tvalid_0's multi_logloss: 0.126033\tvalid_1's multi_error: 0.0635832\tvalid_1's multi_logloss: 0.18179\n",
            "[822]\tvalid_0's multi_error: 0.0422186\tvalid_0's multi_logloss: 0.125948\tvalid_1's multi_error: 0.0634706\tvalid_1's multi_logloss: 0.18175\n",
            "[823]\tvalid_0's multi_error: 0.0421865\tvalid_0's multi_logloss: 0.125883\tvalid_1's multi_error: 0.0634706\tvalid_1's multi_logloss: 0.181726\n",
            "[824]\tvalid_0's multi_error: 0.0421865\tvalid_0's multi_logloss: 0.125834\tvalid_1's multi_error: 0.0634706\tvalid_1's multi_logloss: 0.18171\n",
            "[825]\tvalid_0's multi_error: 0.0421865\tvalid_0's multi_logloss: 0.125778\tvalid_1's multi_error: 0.0633956\tvalid_1's multi_logloss: 0.181703\n",
            "[826]\tvalid_0's multi_error: 0.0421543\tvalid_0's multi_logloss: 0.125718\tvalid_1's multi_error: 0.0633956\tvalid_1's multi_logloss: 0.181691\n",
            "[827]\tvalid_0's multi_error: 0.0421061\tvalid_0's multi_logloss: 0.125628\tvalid_1's multi_error: 0.0632831\tvalid_1's multi_logloss: 0.181639\n",
            "[828]\tvalid_0's multi_error: 0.0421061\tvalid_0's multi_logloss: 0.125569\tvalid_1's multi_error: 0.063208\tvalid_1's multi_logloss: 0.181645\n",
            "[829]\tvalid_0's multi_error: 0.0420579\tvalid_0's multi_logloss: 0.125516\tvalid_1's multi_error: 0.0632456\tvalid_1's multi_logloss: 0.181628\n",
            "[830]\tvalid_0's multi_error: 0.0419936\tvalid_0's multi_logloss: 0.125461\tvalid_1's multi_error: 0.0632456\tvalid_1's multi_logloss: 0.18161\n",
            "[831]\tvalid_0's multi_error: 0.0419614\tvalid_0's multi_logloss: 0.125397\tvalid_1's multi_error: 0.063208\tvalid_1's multi_logloss: 0.181581\n",
            "[832]\tvalid_0's multi_error: 0.0419453\tvalid_0's multi_logloss: 0.12533\tvalid_1's multi_error: 0.063208\tvalid_1's multi_logloss: 0.181575\n",
            "[833]\tvalid_0's multi_error: 0.041881\tvalid_0's multi_logloss: 0.125278\tvalid_1's multi_error: 0.0632456\tvalid_1's multi_logloss: 0.181566\n",
            "[834]\tvalid_0's multi_error: 0.0417524\tvalid_0's multi_logloss: 0.125218\tvalid_1's multi_error: 0.0632456\tvalid_1's multi_logloss: 0.181578\n",
            "[835]\tvalid_0's multi_error: 0.0417685\tvalid_0's multi_logloss: 0.125158\tvalid_1's multi_error: 0.0632456\tvalid_1's multi_logloss: 0.18157\n",
            "[836]\tvalid_0's multi_error: 0.0417363\tvalid_0's multi_logloss: 0.125101\tvalid_1's multi_error: 0.0632456\tvalid_1's multi_logloss: 0.181569\n",
            "[837]\tvalid_0's multi_error: 0.0416881\tvalid_0's multi_logloss: 0.125028\tvalid_1's multi_error: 0.0632456\tvalid_1's multi_logloss: 0.181561\n",
            "[838]\tvalid_0's multi_error: 0.0416077\tvalid_0's multi_logloss: 0.124947\tvalid_1's multi_error: 0.0632831\tvalid_1's multi_logloss: 0.181524\n",
            "[839]\tvalid_0's multi_error: 0.0415595\tvalid_0's multi_logloss: 0.124884\tvalid_1's multi_error: 0.0632456\tvalid_1's multi_logloss: 0.1815\n",
            "[840]\tvalid_0's multi_error: 0.0415595\tvalid_0's multi_logloss: 0.124826\tvalid_1's multi_error: 0.063208\tvalid_1's multi_logloss: 0.181485\n",
            "[841]\tvalid_0's multi_error: 0.0414952\tvalid_0's multi_logloss: 0.124721\tvalid_1's multi_error: 0.063058\tvalid_1's multi_logloss: 0.181414\n",
            "[842]\tvalid_0's multi_error: 0.041463\tvalid_0's multi_logloss: 0.124655\tvalid_1's multi_error: 0.063058\tvalid_1's multi_logloss: 0.181399\n",
            "[843]\tvalid_0's multi_error: 0.0413987\tvalid_0's multi_logloss: 0.124603\tvalid_1's multi_error: 0.063058\tvalid_1's multi_logloss: 0.181398\n",
            "[844]\tvalid_0's multi_error: 0.0414148\tvalid_0's multi_logloss: 0.124546\tvalid_1's multi_error: 0.063058\tvalid_1's multi_logloss: 0.181392\n",
            "[845]\tvalid_0's multi_error: 0.0414148\tvalid_0's multi_logloss: 0.124492\tvalid_1's multi_error: 0.0630955\tvalid_1's multi_logloss: 0.181384\n",
            "[846]\tvalid_0's multi_error: 0.0413826\tvalid_0's multi_logloss: 0.124424\tvalid_1's multi_error: 0.063058\tvalid_1's multi_logloss: 0.181359\n",
            "[847]\tvalid_0's multi_error: 0.0413344\tvalid_0's multi_logloss: 0.124351\tvalid_1's multi_error: 0.0630955\tvalid_1's multi_logloss: 0.181335\n",
            "[848]\tvalid_0's multi_error: 0.0413023\tvalid_0's multi_logloss: 0.124278\tvalid_1's multi_error: 0.0630955\tvalid_1's multi_logloss: 0.181297\n",
            "[849]\tvalid_0's multi_error: 0.0412219\tvalid_0's multi_logloss: 0.124202\tvalid_1's multi_error: 0.0631705\tvalid_1's multi_logloss: 0.181285\n",
            "[850]\tvalid_0's multi_error: 0.0412219\tvalid_0's multi_logloss: 0.124141\tvalid_1's multi_error: 0.0630955\tvalid_1's multi_logloss: 0.181254\n",
            "[851]\tvalid_0's multi_error: 0.0412058\tvalid_0's multi_logloss: 0.124085\tvalid_1's multi_error: 0.0630955\tvalid_1's multi_logloss: 0.181251\n",
            "[852]\tvalid_0's multi_error: 0.0412058\tvalid_0's multi_logloss: 0.124017\tvalid_1's multi_error: 0.0630955\tvalid_1's multi_logloss: 0.181212\n",
            "[853]\tvalid_0's multi_error: 0.0410932\tvalid_0's multi_logloss: 0.123958\tvalid_1's multi_error: 0.0630955\tvalid_1's multi_logloss: 0.181191\n",
            "[854]\tvalid_0's multi_error: 0.0410932\tvalid_0's multi_logloss: 0.123897\tvalid_1's multi_error: 0.063058\tvalid_1's multi_logloss: 0.181174\n",
            "[855]\tvalid_0's multi_error: 0.0410932\tvalid_0's multi_logloss: 0.12383\tvalid_1's multi_error: 0.063058\tvalid_1's multi_logloss: 0.181151\n",
            "[856]\tvalid_0's multi_error: 0.0410289\tvalid_0's multi_logloss: 0.123753\tvalid_1's multi_error: 0.0630955\tvalid_1's multi_logloss: 0.181134\n",
            "[857]\tvalid_0's multi_error: 0.0409807\tvalid_0's multi_logloss: 0.123691\tvalid_1's multi_error: 0.0630955\tvalid_1's multi_logloss: 0.18112\n",
            "[858]\tvalid_0's multi_error: 0.0409807\tvalid_0's multi_logloss: 0.123629\tvalid_1's multi_error: 0.0630955\tvalid_1's multi_logloss: 0.181076\n",
            "[859]\tvalid_0's multi_error: 0.0409807\tvalid_0's multi_logloss: 0.123558\tvalid_1's multi_error: 0.063058\tvalid_1's multi_logloss: 0.181058\n",
            "[860]\tvalid_0's multi_error: 0.0409486\tvalid_0's multi_logloss: 0.123483\tvalid_1's multi_error: 0.0630955\tvalid_1's multi_logloss: 0.18104\n",
            "[861]\tvalid_0's multi_error: 0.0409164\tvalid_0's multi_logloss: 0.123365\tvalid_1's multi_error: 0.063058\tvalid_1's multi_logloss: 0.18094\n",
            "[862]\tvalid_0's multi_error: 0.0408682\tvalid_0's multi_logloss: 0.1233\tvalid_1's multi_error: 0.0630955\tvalid_1's multi_logloss: 0.180943\n",
            "[863]\tvalid_0's multi_error: 0.0409164\tvalid_0's multi_logloss: 0.123223\tvalid_1's multi_error: 0.063058\tvalid_1's multi_logloss: 0.180928\n",
            "[864]\tvalid_0's multi_error: 0.0409164\tvalid_0's multi_logloss: 0.123151\tvalid_1's multi_error: 0.0630955\tvalid_1's multi_logloss: 0.180923\n",
            "[865]\tvalid_0's multi_error: 0.0409003\tvalid_0's multi_logloss: 0.12307\tvalid_1's multi_error: 0.063058\tvalid_1's multi_logloss: 0.18089\n",
            "[866]\tvalid_0's multi_error: 0.0408682\tvalid_0's multi_logloss: 0.123007\tvalid_1's multi_error: 0.063058\tvalid_1's multi_logloss: 0.180884\n",
            "[867]\tvalid_0's multi_error: 0.0408521\tvalid_0's multi_logloss: 0.122929\tvalid_1's multi_error: 0.0630955\tvalid_1's multi_logloss: 0.180857\n",
            "[868]\tvalid_0's multi_error: 0.0408521\tvalid_0's multi_logloss: 0.122858\tvalid_1's multi_error: 0.0630955\tvalid_1's multi_logloss: 0.180839\n",
            "[869]\tvalid_0's multi_error: 0.0408199\tvalid_0's multi_logloss: 0.122809\tvalid_1's multi_error: 0.0630955\tvalid_1's multi_logloss: 0.180832\n",
            "[870]\tvalid_0's multi_error: 0.0407878\tvalid_0's multi_logloss: 0.122753\tvalid_1's multi_error: 0.063058\tvalid_1's multi_logloss: 0.180811\n",
            "[871]\tvalid_0's multi_error: 0.0407556\tvalid_0's multi_logloss: 0.122705\tvalid_1's multi_error: 0.063058\tvalid_1's multi_logloss: 0.18081\n",
            "[872]\tvalid_0's multi_error: 0.0407395\tvalid_0's multi_logloss: 0.122653\tvalid_1's multi_error: 0.0630205\tvalid_1's multi_logloss: 0.180807\n",
            "[873]\tvalid_0's multi_error: 0.0407235\tvalid_0's multi_logloss: 0.122582\tvalid_1's multi_error: 0.063058\tvalid_1's multi_logloss: 0.180778\n",
            "[874]\tvalid_0's multi_error: 0.0406913\tvalid_0's multi_logloss: 0.12248\tvalid_1's multi_error: 0.062983\tvalid_1's multi_logloss: 0.180702\n",
            "[875]\tvalid_0's multi_error: 0.0406752\tvalid_0's multi_logloss: 0.122411\tvalid_1's multi_error: 0.062983\tvalid_1's multi_logloss: 0.180674\n",
            "[876]\tvalid_0's multi_error: 0.0406752\tvalid_0's multi_logloss: 0.122329\tvalid_1's multi_error: 0.063058\tvalid_1's multi_logloss: 0.180651\n",
            "[877]\tvalid_0's multi_error: 0.0406431\tvalid_0's multi_logloss: 0.122273\tvalid_1's multi_error: 0.0630205\tvalid_1's multi_logloss: 0.180644\n",
            "[878]\tvalid_0's multi_error: 0.0406109\tvalid_0's multi_logloss: 0.12221\tvalid_1's multi_error: 0.0629455\tvalid_1's multi_logloss: 0.180634\n",
            "[879]\tvalid_0's multi_error: 0.0406431\tvalid_0's multi_logloss: 0.122135\tvalid_1's multi_error: 0.0629079\tvalid_1's multi_logloss: 0.180621\n",
            "[880]\tvalid_0's multi_error: 0.0406431\tvalid_0's multi_logloss: 0.122071\tvalid_1's multi_error: 0.0629079\tvalid_1's multi_logloss: 0.180588\n",
            "[881]\tvalid_0's multi_error: 0.0405145\tvalid_0's multi_logloss: 0.121997\tvalid_1's multi_error: 0.0629079\tvalid_1's multi_logloss: 0.180559\n",
            "[882]\tvalid_0's multi_error: 0.0405305\tvalid_0's multi_logloss: 0.121947\tvalid_1's multi_error: 0.0629455\tvalid_1's multi_logloss: 0.18056\n",
            "[883]\tvalid_0's multi_error: 0.0404502\tvalid_0's multi_logloss: 0.121889\tvalid_1's multi_error: 0.0629079\tvalid_1's multi_logloss: 0.18055\n",
            "[884]\tvalid_0's multi_error: 0.0404823\tvalid_0's multi_logloss: 0.121828\tvalid_1's multi_error: 0.0629079\tvalid_1's multi_logloss: 0.180534\n",
            "[885]\tvalid_0's multi_error: 0.040418\tvalid_0's multi_logloss: 0.121773\tvalid_1's multi_error: 0.0628704\tvalid_1's multi_logloss: 0.180523\n",
            "[886]\tvalid_0's multi_error: 0.0403698\tvalid_0's multi_logloss: 0.12171\tvalid_1's multi_error: 0.0628329\tvalid_1's multi_logloss: 0.180516\n",
            "[887]\tvalid_0's multi_error: 0.0403537\tvalid_0's multi_logloss: 0.121658\tvalid_1's multi_error: 0.0627954\tvalid_1's multi_logloss: 0.180513\n",
            "[888]\tvalid_0's multi_error: 0.0402251\tvalid_0's multi_logloss: 0.121587\tvalid_1's multi_error: 0.0628329\tvalid_1's multi_logloss: 0.180466\n",
            "[889]\tvalid_0's multi_error: 0.0402251\tvalid_0's multi_logloss: 0.121524\tvalid_1's multi_error: 0.0628704\tvalid_1's multi_logloss: 0.180454\n",
            "[890]\tvalid_0's multi_error: 0.0401929\tvalid_0's multi_logloss: 0.12148\tvalid_1's multi_error: 0.0628704\tvalid_1's multi_logloss: 0.180455\n",
            "[891]\tvalid_0's multi_error: 0.0401768\tvalid_0's multi_logloss: 0.121383\tvalid_1's multi_error: 0.0628704\tvalid_1's multi_logloss: 0.180402\n",
            "[892]\tvalid_0's multi_error: 0.0401768\tvalid_0's multi_logloss: 0.121337\tvalid_1's multi_error: 0.0628329\tvalid_1's multi_logloss: 0.1804\n",
            "[893]\tvalid_0's multi_error: 0.0401286\tvalid_0's multi_logloss: 0.121267\tvalid_1's multi_error: 0.0627954\tvalid_1's multi_logloss: 0.18036\n",
            "[894]\tvalid_0's multi_error: 0.0400965\tvalid_0's multi_logloss: 0.121195\tvalid_1's multi_error: 0.0627204\tvalid_1's multi_logloss: 0.180316\n",
            "[895]\tvalid_0's multi_error: 0.0400322\tvalid_0's multi_logloss: 0.12113\tvalid_1's multi_error: 0.0627579\tvalid_1's multi_logloss: 0.180287\n",
            "[896]\tvalid_0's multi_error: 0.04\tvalid_0's multi_logloss: 0.121034\tvalid_1's multi_error: 0.0627204\tvalid_1's multi_logloss: 0.18026\n",
            "[897]\tvalid_0's multi_error: 0.0399678\tvalid_0's multi_logloss: 0.120976\tvalid_1's multi_error: 0.0627579\tvalid_1's multi_logloss: 0.180247\n",
            "[898]\tvalid_0's multi_error: 0.0399357\tvalid_0's multi_logloss: 0.120927\tvalid_1's multi_error: 0.0627204\tvalid_1's multi_logloss: 0.180233\n",
            "[899]\tvalid_0's multi_error: 0.0399035\tvalid_0's multi_logloss: 0.120823\tvalid_1's multi_error: 0.0627204\tvalid_1's multi_logloss: 0.180173\n",
            "[900]\tvalid_0's multi_error: 0.0398392\tvalid_0's multi_logloss: 0.120766\tvalid_1's multi_error: 0.0627204\tvalid_1's multi_logloss: 0.180164\n",
            "[901]\tvalid_0's multi_error: 0.0398553\tvalid_0's multi_logloss: 0.120671\tvalid_1's multi_error: 0.0626829\tvalid_1's multi_logloss: 0.18011\n",
            "[902]\tvalid_0's multi_error: 0.0398232\tvalid_0's multi_logloss: 0.120558\tvalid_1's multi_error: 0.0626454\tvalid_1's multi_logloss: 0.18002\n",
            "[903]\tvalid_0's multi_error: 0.0397588\tvalid_0's multi_logloss: 0.120508\tvalid_1's multi_error: 0.0626078\tvalid_1's multi_logloss: 0.180011\n",
            "[904]\tvalid_0's multi_error: 0.0397428\tvalid_0's multi_logloss: 0.120447\tvalid_1's multi_error: 0.0625703\tvalid_1's multi_logloss: 0.179989\n",
            "[905]\tvalid_0's multi_error: 0.0397267\tvalid_0's multi_logloss: 0.120383\tvalid_1's multi_error: 0.0626454\tvalid_1's multi_logloss: 0.17997\n",
            "[906]\tvalid_0's multi_error: 0.0396945\tvalid_0's multi_logloss: 0.120304\tvalid_1's multi_error: 0.0626454\tvalid_1's multi_logloss: 0.179934\n",
            "[907]\tvalid_0's multi_error: 0.0396141\tvalid_0's multi_logloss: 0.120243\tvalid_1's multi_error: 0.0626829\tvalid_1's multi_logloss: 0.179935\n",
            "[908]\tvalid_0's multi_error: 0.0395659\tvalid_0's multi_logloss: 0.120139\tvalid_1's multi_error: 0.0626829\tvalid_1's multi_logloss: 0.179874\n",
            "[909]\tvalid_0's multi_error: 0.0396141\tvalid_0's multi_logloss: 0.120075\tvalid_1's multi_error: 0.0626829\tvalid_1's multi_logloss: 0.179858\n",
            "[910]\tvalid_0's multi_error: 0.0395659\tvalid_0's multi_logloss: 0.120007\tvalid_1's multi_error: 0.0626078\tvalid_1's multi_logloss: 0.179824\n",
            "[911]\tvalid_0's multi_error: 0.0395498\tvalid_0's multi_logloss: 0.1199\tvalid_1's multi_error: 0.0626078\tvalid_1's multi_logloss: 0.179741\n",
            "[912]\tvalid_0's multi_error: 0.0394855\tvalid_0's multi_logloss: 0.119836\tvalid_1's multi_error: 0.0626078\tvalid_1's multi_logloss: 0.179726\n",
            "[913]\tvalid_0's multi_error: 0.0394534\tvalid_0's multi_logloss: 0.11977\tvalid_1's multi_error: 0.0625703\tvalid_1's multi_logloss: 0.179706\n",
            "[914]\tvalid_0's multi_error: 0.0394695\tvalid_0's multi_logloss: 0.119683\tvalid_1's multi_error: 0.0626078\tvalid_1's multi_logloss: 0.179656\n",
            "[915]\tvalid_0's multi_error: 0.0394373\tvalid_0's multi_logloss: 0.119619\tvalid_1's multi_error: 0.0626078\tvalid_1's multi_logloss: 0.179612\n",
            "[916]\tvalid_0's multi_error: 0.0394051\tvalid_0's multi_logloss: 0.11955\tvalid_1's multi_error: 0.0625328\tvalid_1's multi_logloss: 0.179571\n",
            "[917]\tvalid_0's multi_error: 0.0393569\tvalid_0's multi_logloss: 0.119477\tvalid_1's multi_error: 0.0625703\tvalid_1's multi_logloss: 0.179562\n",
            "[918]\tvalid_0's multi_error: 0.0393248\tvalid_0's multi_logloss: 0.119402\tvalid_1's multi_error: 0.0625328\tvalid_1's multi_logloss: 0.179518\n",
            "[919]\tvalid_0's multi_error: 0.0393087\tvalid_0's multi_logloss: 0.119343\tvalid_1's multi_error: 0.0625703\tvalid_1's multi_logloss: 0.179501\n",
            "[920]\tvalid_0's multi_error: 0.0393408\tvalid_0's multi_logloss: 0.119281\tvalid_1's multi_error: 0.0625328\tvalid_1's multi_logloss: 0.179484\n",
            "[921]\tvalid_0's multi_error: 0.0392444\tvalid_0's multi_logloss: 0.119207\tvalid_1's multi_error: 0.0624953\tvalid_1's multi_logloss: 0.179446\n",
            "[922]\tvalid_0's multi_error: 0.0391801\tvalid_0's multi_logloss: 0.119153\tvalid_1's multi_error: 0.0625328\tvalid_1's multi_logloss: 0.17945\n",
            "[923]\tvalid_0's multi_error: 0.0391479\tvalid_0's multi_logloss: 0.119086\tvalid_1's multi_error: 0.0624578\tvalid_1's multi_logloss: 0.179434\n",
            "[924]\tvalid_0's multi_error: 0.039164\tvalid_0's multi_logloss: 0.11902\tvalid_1's multi_error: 0.0624953\tvalid_1's multi_logloss: 0.179427\n",
            "[925]\tvalid_0's multi_error: 0.0391158\tvalid_0's multi_logloss: 0.118956\tvalid_1's multi_error: 0.0624953\tvalid_1's multi_logloss: 0.179426\n",
            "[926]\tvalid_0's multi_error: 0.0391158\tvalid_0's multi_logloss: 0.118904\tvalid_1's multi_error: 0.0625328\tvalid_1's multi_logloss: 0.179415\n",
            "[927]\tvalid_0's multi_error: 0.0390836\tvalid_0's multi_logloss: 0.118837\tvalid_1's multi_error: 0.0625703\tvalid_1's multi_logloss: 0.179398\n",
            "[928]\tvalid_0's multi_error: 0.0390354\tvalid_0's multi_logloss: 0.118772\tvalid_1's multi_error: 0.0625703\tvalid_1's multi_logloss: 0.179365\n",
            "[929]\tvalid_0's multi_error: 0.0389711\tvalid_0's multi_logloss: 0.118696\tvalid_1's multi_error: 0.0626078\tvalid_1's multi_logloss: 0.179326\n",
            "[930]\tvalid_0's multi_error: 0.038955\tvalid_0's multi_logloss: 0.118639\tvalid_1's multi_error: 0.0626454\tvalid_1's multi_logloss: 0.179337\n",
            "[931]\tvalid_0's multi_error: 0.0389068\tvalid_0's multi_logloss: 0.118599\tvalid_1's multi_error: 0.0626454\tvalid_1's multi_logloss: 0.179342\n",
            "[932]\tvalid_0's multi_error: 0.0389068\tvalid_0's multi_logloss: 0.118527\tvalid_1's multi_error: 0.0626078\tvalid_1's multi_logloss: 0.179325\n",
            "[933]\tvalid_0's multi_error: 0.0388907\tvalid_0's multi_logloss: 0.118463\tvalid_1's multi_error: 0.0626078\tvalid_1's multi_logloss: 0.179316\n",
            "[934]\tvalid_0's multi_error: 0.0388103\tvalid_0's multi_logloss: 0.1184\tvalid_1's multi_error: 0.0625703\tvalid_1's multi_logloss: 0.17931\n",
            "[935]\tvalid_0's multi_error: 0.0387942\tvalid_0's multi_logloss: 0.118351\tvalid_1's multi_error: 0.0626078\tvalid_1's multi_logloss: 0.179306\n",
            "[936]\tvalid_0's multi_error: 0.0387942\tvalid_0's multi_logloss: 0.118306\tvalid_1's multi_error: 0.0626078\tvalid_1's multi_logloss: 0.179305\n",
            "[937]\tvalid_0's multi_error: 0.0387781\tvalid_0's multi_logloss: 0.118239\tvalid_1's multi_error: 0.0626078\tvalid_1's multi_logloss: 0.179282\n",
            "[938]\tvalid_0's multi_error: 0.0387781\tvalid_0's multi_logloss: 0.118178\tvalid_1's multi_error: 0.0626078\tvalid_1's multi_logloss: 0.17927\n",
            "[939]\tvalid_0's multi_error: 0.0387299\tvalid_0's multi_logloss: 0.11812\tvalid_1's multi_error: 0.0626454\tvalid_1's multi_logloss: 0.179256\n",
            "[940]\tvalid_0's multi_error: 0.0386977\tvalid_0's multi_logloss: 0.118056\tvalid_1's multi_error: 0.0626829\tvalid_1's multi_logloss: 0.179226\n",
            "[941]\tvalid_0's multi_error: 0.0386656\tvalid_0's multi_logloss: 0.117996\tvalid_1's multi_error: 0.0626829\tvalid_1's multi_logloss: 0.179211\n",
            "[942]\tvalid_0's multi_error: 0.0386495\tvalid_0's multi_logloss: 0.117932\tvalid_1's multi_error: 0.0626454\tvalid_1's multi_logloss: 0.179187\n",
            "[943]\tvalid_0's multi_error: 0.0385209\tvalid_0's multi_logloss: 0.117874\tvalid_1's multi_error: 0.0626078\tvalid_1's multi_logloss: 0.179175\n",
            "[944]\tvalid_0's multi_error: 0.0384887\tvalid_0's multi_logloss: 0.1178\tvalid_1's multi_error: 0.0625328\tvalid_1's multi_logloss: 0.179154\n",
            "[945]\tvalid_0's multi_error: 0.0384244\tvalid_0's multi_logloss: 0.117692\tvalid_1's multi_error: 0.0624953\tvalid_1's multi_logloss: 0.179096\n",
            "[946]\tvalid_0's multi_error: 0.0383923\tvalid_0's multi_logloss: 0.117629\tvalid_1's multi_error: 0.0624953\tvalid_1's multi_logloss: 0.179086\n",
            "[947]\tvalid_0's multi_error: 0.0383762\tvalid_0's multi_logloss: 0.117578\tvalid_1's multi_error: 0.0625328\tvalid_1's multi_logloss: 0.179085\n",
            "[948]\tvalid_0's multi_error: 0.0383923\tvalid_0's multi_logloss: 0.117523\tvalid_1's multi_error: 0.0624953\tvalid_1's multi_logloss: 0.179072\n",
            "[949]\tvalid_0's multi_error: 0.0384084\tvalid_0's multi_logloss: 0.117474\tvalid_1's multi_error: 0.0624578\tvalid_1's multi_logloss: 0.179079\n",
            "[950]\tvalid_0's multi_error: 0.0383601\tvalid_0's multi_logloss: 0.117403\tvalid_1's multi_error: 0.0624578\tvalid_1's multi_logloss: 0.179056\n",
            "[951]\tvalid_0's multi_error: 0.0383441\tvalid_0's multi_logloss: 0.117336\tvalid_1's multi_error: 0.0624578\tvalid_1's multi_logloss: 0.179035\n",
            "[952]\tvalid_0's multi_error: 0.0382797\tvalid_0's multi_logloss: 0.117286\tvalid_1's multi_error: 0.0624578\tvalid_1's multi_logloss: 0.179031\n",
            "[953]\tvalid_0's multi_error: 0.0382958\tvalid_0's multi_logloss: 0.117236\tvalid_1's multi_error: 0.0624578\tvalid_1's multi_logloss: 0.179024\n",
            "[954]\tvalid_0's multi_error: 0.0382958\tvalid_0's multi_logloss: 0.117188\tvalid_1's multi_error: 0.0624578\tvalid_1's multi_logloss: 0.179027\n",
            "[955]\tvalid_0's multi_error: 0.0382797\tvalid_0's multi_logloss: 0.117066\tvalid_1's multi_error: 0.0624203\tvalid_1's multi_logloss: 0.178935\n",
            "[956]\tvalid_0's multi_error: 0.0381994\tvalid_0's multi_logloss: 0.117004\tvalid_1's multi_error: 0.0623828\tvalid_1's multi_logloss: 0.178926\n",
            "[957]\tvalid_0's multi_error: 0.0381672\tvalid_0's multi_logloss: 0.116941\tvalid_1's multi_error: 0.0624203\tvalid_1's multi_logloss: 0.178921\n",
            "[958]\tvalid_0's multi_error: 0.0381029\tvalid_0's multi_logloss: 0.116882\tvalid_1's multi_error: 0.0624203\tvalid_1's multi_logloss: 0.178916\n",
            "[959]\tvalid_0's multi_error: 0.0380868\tvalid_0's multi_logloss: 0.11682\tvalid_1's multi_error: 0.0624203\tvalid_1's multi_logloss: 0.178903\n",
            "[960]\tvalid_0's multi_error: 0.0380868\tvalid_0's multi_logloss: 0.116746\tvalid_1's multi_error: 0.0623828\tvalid_1's multi_logloss: 0.178881\n",
            "[961]\tvalid_0's multi_error: 0.0380225\tvalid_0's multi_logloss: 0.116701\tvalid_1's multi_error: 0.0623828\tvalid_1's multi_logloss: 0.178877\n",
            "[962]\tvalid_0's multi_error: 0.037926\tvalid_0's multi_logloss: 0.116651\tvalid_1's multi_error: 0.0623828\tvalid_1's multi_logloss: 0.17886\n",
            "[963]\tvalid_0's multi_error: 0.0378939\tvalid_0's multi_logloss: 0.116596\tvalid_1's multi_error: 0.0623453\tvalid_1's multi_logloss: 0.178837\n",
            "[964]\tvalid_0's multi_error: 0.0378778\tvalid_0's multi_logloss: 0.11653\tvalid_1's multi_error: 0.0623453\tvalid_1's multi_logloss: 0.178824\n",
            "[965]\tvalid_0's multi_error: 0.0378617\tvalid_0's multi_logloss: 0.116471\tvalid_1's multi_error: 0.0623078\tvalid_1's multi_logloss: 0.178823\n",
            "[966]\tvalid_0's multi_error: 0.0378135\tvalid_0's multi_logloss: 0.116418\tvalid_1's multi_error: 0.0622327\tvalid_1's multi_logloss: 0.178816\n",
            "[967]\tvalid_0's multi_error: 0.0377653\tvalid_0's multi_logloss: 0.116366\tvalid_1's multi_error: 0.0622327\tvalid_1's multi_logloss: 0.178815\n",
            "[968]\tvalid_0's multi_error: 0.0377974\tvalid_0's multi_logloss: 0.116324\tvalid_1's multi_error: 0.0622327\tvalid_1's multi_logloss: 0.178817\n",
            "[969]\tvalid_0's multi_error: 0.0378135\tvalid_0's multi_logloss: 0.116263\tvalid_1's multi_error: 0.0622327\tvalid_1's multi_logloss: 0.178808\n",
            "[970]\tvalid_0's multi_error: 0.0377331\tvalid_0's multi_logloss: 0.116212\tvalid_1's multi_error: 0.0622327\tvalid_1's multi_logloss: 0.178813\n",
            "[971]\tvalid_0's multi_error: 0.0377331\tvalid_0's multi_logloss: 0.11617\tvalid_1's multi_error: 0.0622702\tvalid_1's multi_logloss: 0.178801\n",
            "[972]\tvalid_0's multi_error: 0.037701\tvalid_0's multi_logloss: 0.116118\tvalid_1's multi_error: 0.0621952\tvalid_1's multi_logloss: 0.178795\n",
            "[973]\tvalid_0's multi_error: 0.0376527\tvalid_0's multi_logloss: 0.116054\tvalid_1's multi_error: 0.0621577\tvalid_1's multi_logloss: 0.178781\n",
            "[974]\tvalid_0's multi_error: 0.0376367\tvalid_0's multi_logloss: 0.116009\tvalid_1's multi_error: 0.0622327\tvalid_1's multi_logloss: 0.178777\n",
            "[975]\tvalid_0's multi_error: 0.0376527\tvalid_0's multi_logloss: 0.11596\tvalid_1's multi_error: 0.0622327\tvalid_1's multi_logloss: 0.178762\n",
            "[976]\tvalid_0's multi_error: 0.0375723\tvalid_0's multi_logloss: 0.115886\tvalid_1's multi_error: 0.0622327\tvalid_1's multi_logloss: 0.178727\n",
            "[977]\tvalid_0's multi_error: 0.0375402\tvalid_0's multi_logloss: 0.115835\tvalid_1's multi_error: 0.0621952\tvalid_1's multi_logloss: 0.178712\n",
            "[978]\tvalid_0's multi_error: 0.0375723\tvalid_0's multi_logloss: 0.11579\tvalid_1's multi_error: 0.0621952\tvalid_1's multi_logloss: 0.178709\n",
            "[979]\tvalid_0's multi_error: 0.037492\tvalid_0's multi_logloss: 0.115731\tvalid_1's multi_error: 0.0621952\tvalid_1's multi_logloss: 0.178677\n",
            "[980]\tvalid_0's multi_error: 0.0374598\tvalid_0's multi_logloss: 0.115638\tvalid_1's multi_error: 0.0621577\tvalid_1's multi_logloss: 0.178624\n",
            "[981]\tvalid_0's multi_error: 0.0374116\tvalid_0's multi_logloss: 0.11559\tvalid_1's multi_error: 0.0620827\tvalid_1's multi_logloss: 0.178618\n",
            "[982]\tvalid_0's multi_error: 0.0374116\tvalid_0's multi_logloss: 0.115525\tvalid_1's multi_error: 0.0621202\tvalid_1's multi_logloss: 0.178606\n",
            "[983]\tvalid_0's multi_error: 0.0373794\tvalid_0's multi_logloss: 0.115468\tvalid_1's multi_error: 0.0620827\tvalid_1's multi_logloss: 0.178603\n",
            "[984]\tvalid_0's multi_error: 0.0373633\tvalid_0's multi_logloss: 0.115418\tvalid_1's multi_error: 0.0620077\tvalid_1's multi_logloss: 0.178591\n",
            "[985]\tvalid_0's multi_error: 0.0373312\tvalid_0's multi_logloss: 0.115369\tvalid_1's multi_error: 0.0619326\tvalid_1's multi_logloss: 0.17857\n",
            "[986]\tvalid_0's multi_error: 0.0373151\tvalid_0's multi_logloss: 0.115318\tvalid_1's multi_error: 0.0619701\tvalid_1's multi_logloss: 0.178559\n",
            "[987]\tvalid_0's multi_error: 0.0373151\tvalid_0's multi_logloss: 0.115264\tvalid_1's multi_error: 0.0619701\tvalid_1's multi_logloss: 0.178547\n",
            "[988]\tvalid_0's multi_error: 0.0373312\tvalid_0's multi_logloss: 0.115207\tvalid_1's multi_error: 0.0618951\tvalid_1's multi_logloss: 0.17854\n",
            "[989]\tvalid_0's multi_error: 0.0373473\tvalid_0's multi_logloss: 0.115157\tvalid_1's multi_error: 0.0618951\tvalid_1's multi_logloss: 0.178538\n",
            "[990]\tvalid_0's multi_error: 0.0372508\tvalid_0's multi_logloss: 0.115092\tvalid_1's multi_error: 0.0618576\tvalid_1's multi_logloss: 0.178514\n",
            "[991]\tvalid_0's multi_error: 0.0372347\tvalid_0's multi_logloss: 0.115037\tvalid_1's multi_error: 0.0618951\tvalid_1's multi_logloss: 0.178505\n",
            "[992]\tvalid_0's multi_error: 0.0372186\tvalid_0's multi_logloss: 0.114973\tvalid_1's multi_error: 0.0618576\tvalid_1's multi_logloss: 0.1785\n",
            "[993]\tvalid_0's multi_error: 0.0372186\tvalid_0's multi_logloss: 0.114899\tvalid_1's multi_error: 0.0618201\tvalid_1's multi_logloss: 0.178449\n",
            "[994]\tvalid_0's multi_error: 0.0372026\tvalid_0's multi_logloss: 0.11484\tvalid_1's multi_error: 0.0618201\tvalid_1's multi_logloss: 0.178424\n",
            "[995]\tvalid_0's multi_error: 0.0370579\tvalid_0's multi_logloss: 0.114793\tvalid_1's multi_error: 0.0618201\tvalid_1's multi_logloss: 0.178412\n",
            "[996]\tvalid_0's multi_error: 0.0370579\tvalid_0's multi_logloss: 0.114739\tvalid_1's multi_error: 0.0618201\tvalid_1's multi_logloss: 0.178407\n",
            "[997]\tvalid_0's multi_error: 0.0370418\tvalid_0's multi_logloss: 0.114675\tvalid_1's multi_error: 0.0617451\tvalid_1's multi_logloss: 0.178375\n",
            "[998]\tvalid_0's multi_error: 0.0370096\tvalid_0's multi_logloss: 0.114635\tvalid_1's multi_error: 0.0617451\tvalid_1's multi_logloss: 0.178376\n",
            "[999]\tvalid_0's multi_error: 0.0369614\tvalid_0's multi_logloss: 0.11458\tvalid_1's multi_error: 0.0617826\tvalid_1's multi_logloss: 0.178363\n",
            "[1000]\tvalid_0's multi_error: 0.0369936\tvalid_0's multi_logloss: 0.114496\tvalid_1's multi_error: 0.0617826\tvalid_1's multi_logloss: 0.178312\n",
            "[1001]\tvalid_0's multi_error: 0.0369936\tvalid_0's multi_logloss: 0.114439\tvalid_1's multi_error: 0.0617076\tvalid_1's multi_logloss: 0.178277\n",
            "[1002]\tvalid_0's multi_error: 0.0369775\tvalid_0's multi_logloss: 0.114387\tvalid_1's multi_error: 0.0617451\tvalid_1's multi_logloss: 0.178257\n",
            "[1003]\tvalid_0's multi_error: 0.0369775\tvalid_0's multi_logloss: 0.114296\tvalid_1's multi_error: 0.0617076\tvalid_1's multi_logloss: 0.178195\n",
            "[1004]\tvalid_0's multi_error: 0.0369453\tvalid_0's multi_logloss: 0.114239\tvalid_1's multi_error: 0.0617076\tvalid_1's multi_logloss: 0.178169\n",
            "[1005]\tvalid_0's multi_error: 0.0369293\tvalid_0's multi_logloss: 0.114151\tvalid_1's multi_error: 0.0617451\tvalid_1's multi_logloss: 0.178111\n",
            "[1006]\tvalid_0's multi_error: 0.0368971\tvalid_0's multi_logloss: 0.114031\tvalid_1's multi_error: 0.0617076\tvalid_1's multi_logloss: 0.17802\n",
            "[1007]\tvalid_0's multi_error: 0.036881\tvalid_0's multi_logloss: 0.113952\tvalid_1's multi_error: 0.06167\tvalid_1's multi_logloss: 0.177968\n",
            "[1008]\tvalid_0's multi_error: 0.0368971\tvalid_0's multi_logloss: 0.113909\tvalid_1's multi_error: 0.0616325\tvalid_1's multi_logloss: 0.177954\n",
            "[1009]\tvalid_0's multi_error: 0.0368328\tvalid_0's multi_logloss: 0.113861\tvalid_1's multi_error: 0.06167\tvalid_1's multi_logloss: 0.177939\n",
            "[1010]\tvalid_0's multi_error: 0.0368006\tvalid_0's multi_logloss: 0.113815\tvalid_1's multi_error: 0.06167\tvalid_1's multi_logloss: 0.177929\n",
            "[1011]\tvalid_0's multi_error: 0.0367203\tvalid_0's multi_logloss: 0.113755\tvalid_1's multi_error: 0.0617826\tvalid_1's multi_logloss: 0.177906\n",
            "[1012]\tvalid_0's multi_error: 0.0366881\tvalid_0's multi_logloss: 0.113715\tvalid_1's multi_error: 0.0618201\tvalid_1's multi_logloss: 0.177906\n",
            "[1013]\tvalid_0's multi_error: 0.0366399\tvalid_0's multi_logloss: 0.113661\tvalid_1's multi_error: 0.0616325\tvalid_1's multi_logloss: 0.1779\n",
            "[1014]\tvalid_0's multi_error: 0.0365916\tvalid_0's multi_logloss: 0.113605\tvalid_1's multi_error: 0.061595\tvalid_1's multi_logloss: 0.177885\n",
            "[1015]\tvalid_0's multi_error: 0.0365595\tvalid_0's multi_logloss: 0.11355\tvalid_1's multi_error: 0.0615575\tvalid_1's multi_logloss: 0.177881\n",
            "[1016]\tvalid_0's multi_error: 0.0365273\tvalid_0's multi_logloss: 0.113486\tvalid_1's multi_error: 0.061595\tvalid_1's multi_logloss: 0.177853\n",
            "[1017]\tvalid_0's multi_error: 0.0365113\tvalid_0's multi_logloss: 0.113405\tvalid_1's multi_error: 0.061595\tvalid_1's multi_logloss: 0.177824\n",
            "[1018]\tvalid_0's multi_error: 0.0364791\tvalid_0's multi_logloss: 0.113303\tvalid_1's multi_error: 0.0616325\tvalid_1's multi_logloss: 0.177751\n",
            "[1019]\tvalid_0's multi_error: 0.0364469\tvalid_0's multi_logloss: 0.113254\tvalid_1's multi_error: 0.06167\tvalid_1's multi_logloss: 0.177747\n",
            "[1020]\tvalid_0's multi_error: 0.0363987\tvalid_0's multi_logloss: 0.113169\tvalid_1's multi_error: 0.06167\tvalid_1's multi_logloss: 0.177698\n",
            "[1021]\tvalid_0's multi_error: 0.0363666\tvalid_0's multi_logloss: 0.113122\tvalid_1's multi_error: 0.0617076\tvalid_1's multi_logloss: 0.177689\n",
            "[1022]\tvalid_0's multi_error: 0.0363344\tvalid_0's multi_logloss: 0.113032\tvalid_1's multi_error: 0.06167\tvalid_1's multi_logloss: 0.177629\n",
            "[1023]\tvalid_0's multi_error: 0.0363666\tvalid_0's multi_logloss: 0.112932\tvalid_1's multi_error: 0.0615575\tvalid_1's multi_logloss: 0.177554\n",
            "[1024]\tvalid_0's multi_error: 0.0363183\tvalid_0's multi_logloss: 0.112889\tvalid_1's multi_error: 0.061595\tvalid_1's multi_logloss: 0.17755\n",
            "[1025]\tvalid_0's multi_error: 0.0363183\tvalid_0's multi_logloss: 0.112835\tvalid_1's multi_error: 0.061595\tvalid_1's multi_logloss: 0.177544\n",
            "[1026]\tvalid_0's multi_error: 0.0362701\tvalid_0's multi_logloss: 0.11278\tvalid_1's multi_error: 0.0615575\tvalid_1's multi_logloss: 0.17754\n",
            "[1027]\tvalid_0's multi_error: 0.0362219\tvalid_0's multi_logloss: 0.112728\tvalid_1's multi_error: 0.0615575\tvalid_1's multi_logloss: 0.177518\n",
            "[1028]\tvalid_0's multi_error: 0.0362379\tvalid_0's multi_logloss: 0.112684\tvalid_1's multi_error: 0.061595\tvalid_1's multi_logloss: 0.177499\n",
            "[1029]\tvalid_0's multi_error: 0.0362379\tvalid_0's multi_logloss: 0.112631\tvalid_1's multi_error: 0.06167\tvalid_1's multi_logloss: 0.177499\n",
            "[1030]\tvalid_0's multi_error: 0.0362058\tvalid_0's multi_logloss: 0.112579\tvalid_1's multi_error: 0.0616325\tvalid_1's multi_logloss: 0.177484\n",
            "[1031]\tvalid_0's multi_error: 0.0361576\tvalid_0's multi_logloss: 0.112529\tvalid_1's multi_error: 0.0616325\tvalid_1's multi_logloss: 0.177479\n",
            "[1032]\tvalid_0's multi_error: 0.0360772\tvalid_0's multi_logloss: 0.112476\tvalid_1's multi_error: 0.06167\tvalid_1's multi_logloss: 0.177475\n",
            "[1033]\tvalid_0's multi_error: 0.0359968\tvalid_0's multi_logloss: 0.112417\tvalid_1's multi_error: 0.06167\tvalid_1's multi_logloss: 0.177449\n",
            "[1034]\tvalid_0's multi_error: 0.0359968\tvalid_0's multi_logloss: 0.112373\tvalid_1's multi_error: 0.0616325\tvalid_1's multi_logloss: 0.177453\n",
            "[1035]\tvalid_0's multi_error: 0.0359325\tvalid_0's multi_logloss: 0.112317\tvalid_1's multi_error: 0.06167\tvalid_1's multi_logloss: 0.177445\n",
            "[1036]\tvalid_0's multi_error: 0.0359486\tvalid_0's multi_logloss: 0.112273\tvalid_1's multi_error: 0.0616325\tvalid_1's multi_logloss: 0.177437\n",
            "[1037]\tvalid_0's multi_error: 0.0359646\tvalid_0's multi_logloss: 0.112219\tvalid_1's multi_error: 0.0615575\tvalid_1's multi_logloss: 0.177417\n",
            "[1038]\tvalid_0's multi_error: 0.0359486\tvalid_0's multi_logloss: 0.112172\tvalid_1's multi_error: 0.06152\tvalid_1's multi_logloss: 0.177406\n",
            "[1039]\tvalid_0's multi_error: 0.0359486\tvalid_0's multi_logloss: 0.112117\tvalid_1's multi_error: 0.06152\tvalid_1's multi_logloss: 0.177392\n",
            "[1040]\tvalid_0's multi_error: 0.0358682\tvalid_0's multi_logloss: 0.112062\tvalid_1's multi_error: 0.061445\tvalid_1's multi_logloss: 0.17739\n",
            "[1041]\tvalid_0's multi_error: 0.0358682\tvalid_0's multi_logloss: 0.112003\tvalid_1's multi_error: 0.061445\tvalid_1's multi_logloss: 0.177375\n",
            "[1042]\tvalid_0's multi_error: 0.0358682\tvalid_0's multi_logloss: 0.111956\tvalid_1's multi_error: 0.061445\tvalid_1's multi_logloss: 0.177374\n",
            "[1043]\tvalid_0's multi_error: 0.0358682\tvalid_0's multi_logloss: 0.111908\tvalid_1's multi_error: 0.061445\tvalid_1's multi_logloss: 0.177361\n",
            "[1044]\tvalid_0's multi_error: 0.0357878\tvalid_0's multi_logloss: 0.111838\tvalid_1's multi_error: 0.061445\tvalid_1's multi_logloss: 0.177338\n",
            "[1045]\tvalid_0's multi_error: 0.0357556\tvalid_0's multi_logloss: 0.111792\tvalid_1's multi_error: 0.0613699\tvalid_1's multi_logloss: 0.177325\n",
            "[1046]\tvalid_0's multi_error: 0.0357395\tvalid_0's multi_logloss: 0.111737\tvalid_1's multi_error: 0.0613699\tvalid_1's multi_logloss: 0.177315\n",
            "[1047]\tvalid_0's multi_error: 0.0356913\tvalid_0's multi_logloss: 0.111679\tvalid_1's multi_error: 0.0614075\tvalid_1's multi_logloss: 0.1773\n",
            "[1048]\tvalid_0's multi_error: 0.0356913\tvalid_0's multi_logloss: 0.111624\tvalid_1's multi_error: 0.0614075\tvalid_1's multi_logloss: 0.177292\n",
            "[1049]\tvalid_0's multi_error: 0.0356431\tvalid_0's multi_logloss: 0.111547\tvalid_1's multi_error: 0.0613699\tvalid_1's multi_logloss: 0.177253\n",
            "[1050]\tvalid_0's multi_error: 0.035627\tvalid_0's multi_logloss: 0.111496\tvalid_1's multi_error: 0.0613699\tvalid_1's multi_logloss: 0.177242\n",
            "[1051]\tvalid_0's multi_error: 0.0356431\tvalid_0's multi_logloss: 0.111457\tvalid_1's multi_error: 0.0613699\tvalid_1's multi_logloss: 0.177235\n",
            "[1052]\tvalid_0's multi_error: 0.0356431\tvalid_0's multi_logloss: 0.111407\tvalid_1's multi_error: 0.0614075\tvalid_1's multi_logloss: 0.177232\n",
            "[1053]\tvalid_0's multi_error: 0.0355305\tvalid_0's multi_logloss: 0.111358\tvalid_1's multi_error: 0.0613699\tvalid_1's multi_logloss: 0.177232\n",
            "[1054]\tvalid_0's multi_error: 0.0354984\tvalid_0's multi_logloss: 0.111299\tvalid_1's multi_error: 0.0613324\tvalid_1's multi_logloss: 0.177215\n",
            "[1055]\tvalid_0's multi_error: 0.0354823\tvalid_0's multi_logloss: 0.111209\tvalid_1's multi_error: 0.0613324\tvalid_1's multi_logloss: 0.177145\n",
            "[1056]\tvalid_0's multi_error: 0.0354502\tvalid_0's multi_logloss: 0.111163\tvalid_1's multi_error: 0.0613324\tvalid_1's multi_logloss: 0.177151\n",
            "[1057]\tvalid_0's multi_error: 0.035418\tvalid_0's multi_logloss: 0.111109\tvalid_1's multi_error: 0.0613699\tvalid_1's multi_logloss: 0.177146\n",
            "[1058]\tvalid_0's multi_error: 0.0353698\tvalid_0's multi_logloss: 0.11106\tvalid_1's multi_error: 0.0613699\tvalid_1's multi_logloss: 0.177132\n",
            "[1059]\tvalid_0's multi_error: 0.0353537\tvalid_0's multi_logloss: 0.111016\tvalid_1's multi_error: 0.0613324\tvalid_1's multi_logloss: 0.177129\n",
            "[1060]\tvalid_0's multi_error: 0.0353859\tvalid_0's multi_logloss: 0.110954\tvalid_1's multi_error: 0.0613324\tvalid_1's multi_logloss: 0.177125\n",
            "[1061]\tvalid_0's multi_error: 0.0353376\tvalid_0's multi_logloss: 0.110889\tvalid_1's multi_error: 0.0612199\tvalid_1's multi_logloss: 0.17711\n",
            "[1062]\tvalid_0's multi_error: 0.0352412\tvalid_0's multi_logloss: 0.110837\tvalid_1's multi_error: 0.0612199\tvalid_1's multi_logloss: 0.177108\n",
            "[1063]\tvalid_0's multi_error: 0.0351608\tvalid_0's multi_logloss: 0.110777\tvalid_1's multi_error: 0.0612199\tvalid_1's multi_logloss: 0.177105\n",
            "[1064]\tvalid_0's multi_error: 0.0351125\tvalid_0's multi_logloss: 0.11072\tvalid_1's multi_error: 0.0612949\tvalid_1's multi_logloss: 0.177095\n",
            "[1065]\tvalid_0's multi_error: 0.0350804\tvalid_0's multi_logloss: 0.110625\tvalid_1's multi_error: 0.0612199\tvalid_1's multi_logloss: 0.177016\n",
            "[1066]\tvalid_0's multi_error: 0.0350643\tvalid_0's multi_logloss: 0.110573\tvalid_1's multi_error: 0.0612199\tvalid_1's multi_logloss: 0.177013\n",
            "[1067]\tvalid_0's multi_error: 0.0350643\tvalid_0's multi_logloss: 0.110518\tvalid_1's multi_error: 0.0612199\tvalid_1's multi_logloss: 0.177013\n",
            "[1068]\tvalid_0's multi_error: 0.0350161\tvalid_0's multi_logloss: 0.110473\tvalid_1's multi_error: 0.0612199\tvalid_1's multi_logloss: 0.177027\n",
            "[1069]\tvalid_0's multi_error: 0.0349678\tvalid_0's multi_logloss: 0.110427\tvalid_1's multi_error: 0.0612199\tvalid_1's multi_logloss: 0.177016\n",
            "[1070]\tvalid_0's multi_error: 0.0349678\tvalid_0's multi_logloss: 0.110367\tvalid_1's multi_error: 0.0612949\tvalid_1's multi_logloss: 0.176985\n",
            "[1071]\tvalid_0's multi_error: 0.0349678\tvalid_0's multi_logloss: 0.110323\tvalid_1's multi_error: 0.0612574\tvalid_1's multi_logloss: 0.176976\n",
            "[1072]\tvalid_0's multi_error: 0.0349518\tvalid_0's multi_logloss: 0.110285\tvalid_1's multi_error: 0.0612574\tvalid_1's multi_logloss: 0.176972\n",
            "[1073]\tvalid_0's multi_error: 0.0348714\tvalid_0's multi_logloss: 0.110224\tvalid_1's multi_error: 0.0612199\tvalid_1's multi_logloss: 0.176963\n",
            "[1074]\tvalid_0's multi_error: 0.034791\tvalid_0's multi_logloss: 0.110164\tvalid_1's multi_error: 0.0612574\tvalid_1's multi_logloss: 0.17694\n",
            "[1075]\tvalid_0's multi_error: 0.034791\tvalid_0's multi_logloss: 0.110109\tvalid_1's multi_error: 0.0613324\tvalid_1's multi_logloss: 0.176933\n",
            "[1076]\tvalid_0's multi_error: 0.0347749\tvalid_0's multi_logloss: 0.110068\tvalid_1's multi_error: 0.0613324\tvalid_1's multi_logloss: 0.176917\n",
            "[1077]\tvalid_0's multi_error: 0.0347749\tvalid_0's multi_logloss: 0.110005\tvalid_1's multi_error: 0.0613324\tvalid_1's multi_logloss: 0.176895\n",
            "[1078]\tvalid_0's multi_error: 0.0347428\tvalid_0's multi_logloss: 0.109954\tvalid_1's multi_error: 0.0613324\tvalid_1's multi_logloss: 0.176894\n",
            "[1079]\tvalid_0's multi_error: 0.0347106\tvalid_0's multi_logloss: 0.109916\tvalid_1's multi_error: 0.0613324\tvalid_1's multi_logloss: 0.176886\n",
            "[1080]\tvalid_0's multi_error: 0.0347106\tvalid_0's multi_logloss: 0.10987\tvalid_1's multi_error: 0.0612949\tvalid_1's multi_logloss: 0.17689\n",
            "[1081]\tvalid_0's multi_error: 0.0347267\tvalid_0's multi_logloss: 0.109817\tvalid_1's multi_error: 0.0613324\tvalid_1's multi_logloss: 0.176876\n",
            "[1082]\tvalid_0's multi_error: 0.0347267\tvalid_0's multi_logloss: 0.109726\tvalid_1's multi_error: 0.0613324\tvalid_1's multi_logloss: 0.176801\n",
            "[1083]\tvalid_0's multi_error: 0.0347106\tvalid_0's multi_logloss: 0.109676\tvalid_1's multi_error: 0.0613324\tvalid_1's multi_logloss: 0.176798\n",
            "[1084]\tvalid_0's multi_error: 0.0346785\tvalid_0's multi_logloss: 0.109619\tvalid_1's multi_error: 0.0613699\tvalid_1's multi_logloss: 0.176785\n",
            "[1085]\tvalid_0's multi_error: 0.0346463\tvalid_0's multi_logloss: 0.109573\tvalid_1's multi_error: 0.0614075\tvalid_1's multi_logloss: 0.176795\n",
            "[1086]\tvalid_0's multi_error: 0.0346141\tvalid_0's multi_logloss: 0.109519\tvalid_1's multi_error: 0.0612949\tvalid_1's multi_logloss: 0.176786\n",
            "[1087]\tvalid_0's multi_error: 0.034582\tvalid_0's multi_logloss: 0.109471\tvalid_1's multi_error: 0.0612574\tvalid_1's multi_logloss: 0.176785\n",
            "[1088]\tvalid_0's multi_error: 0.0345659\tvalid_0's multi_logloss: 0.109415\tvalid_1's multi_error: 0.0612199\tvalid_1's multi_logloss: 0.176767\n",
            "[1089]\tvalid_0's multi_error: 0.0345177\tvalid_0's multi_logloss: 0.109379\tvalid_1's multi_error: 0.0612199\tvalid_1's multi_logloss: 0.17677\n",
            "[1090]\tvalid_0's multi_error: 0.0344855\tvalid_0's multi_logloss: 0.109322\tvalid_1's multi_error: 0.0612574\tvalid_1's multi_logloss: 0.176767\n",
            "[1091]\tvalid_0's multi_error: 0.0344695\tvalid_0's multi_logloss: 0.10927\tvalid_1's multi_error: 0.0613699\tvalid_1's multi_logloss: 0.17676\n",
            "[1092]\tvalid_0's multi_error: 0.0344212\tvalid_0's multi_logloss: 0.109206\tvalid_1's multi_error: 0.0611449\tvalid_1's multi_logloss: 0.176728\n",
            "[1093]\tvalid_0's multi_error: 0.0344051\tvalid_0's multi_logloss: 0.10913\tvalid_1's multi_error: 0.0610698\tvalid_1's multi_logloss: 0.176692\n",
            "[1094]\tvalid_0's multi_error: 0.0343408\tvalid_0's multi_logloss: 0.109081\tvalid_1's multi_error: 0.0610698\tvalid_1's multi_logloss: 0.176701\n",
            "[1095]\tvalid_0's multi_error: 0.0343408\tvalid_0's multi_logloss: 0.109014\tvalid_1's multi_error: 0.0610698\tvalid_1's multi_logloss: 0.176685\n",
            "[1096]\tvalid_0's multi_error: 0.0342765\tvalid_0's multi_logloss: 0.108961\tvalid_1's multi_error: 0.0611074\tvalid_1's multi_logloss: 0.176687\n",
            "[1097]\tvalid_0's multi_error: 0.0342605\tvalid_0's multi_logloss: 0.108914\tvalid_1's multi_error: 0.0611074\tvalid_1's multi_logloss: 0.176697\n",
            "[1098]\tvalid_0's multi_error: 0.0342605\tvalid_0's multi_logloss: 0.108848\tvalid_1's multi_error: 0.0611074\tvalid_1's multi_logloss: 0.176685\n",
            "[1099]\tvalid_0's multi_error: 0.0342283\tvalid_0's multi_logloss: 0.108796\tvalid_1's multi_error: 0.0610323\tvalid_1's multi_logloss: 0.176684\n",
            "[1100]\tvalid_0's multi_error: 0.0341961\tvalid_0's multi_logloss: 0.108739\tvalid_1's multi_error: 0.0611074\tvalid_1's multi_logloss: 0.17667\n",
            "[1101]\tvalid_0's multi_error: 0.0341961\tvalid_0's multi_logloss: 0.108696\tvalid_1's multi_error: 0.0611449\tvalid_1's multi_logloss: 0.176662\n",
            "[1102]\tvalid_0's multi_error: 0.0341961\tvalid_0's multi_logloss: 0.10864\tvalid_1's multi_error: 0.0611824\tvalid_1's multi_logloss: 0.176654\n",
            "[1103]\tvalid_0's multi_error: 0.0341801\tvalid_0's multi_logloss: 0.108588\tvalid_1's multi_error: 0.0611449\tvalid_1's multi_logloss: 0.176638\n",
            "[1104]\tvalid_0's multi_error: 0.0341479\tvalid_0's multi_logloss: 0.108539\tvalid_1's multi_error: 0.0611074\tvalid_1's multi_logloss: 0.176637\n",
            "[1105]\tvalid_0's multi_error: 0.0341158\tvalid_0's multi_logloss: 0.108491\tvalid_1's multi_error: 0.0611074\tvalid_1's multi_logloss: 0.176637\n",
            "[1106]\tvalid_0's multi_error: 0.0340997\tvalid_0's multi_logloss: 0.108436\tvalid_1's multi_error: 0.0611449\tvalid_1's multi_logloss: 0.176625\n",
            "[1107]\tvalid_0's multi_error: 0.0340032\tvalid_0's multi_logloss: 0.108386\tvalid_1's multi_error: 0.0611824\tvalid_1's multi_logloss: 0.176629\n",
            "[1108]\tvalid_0's multi_error: 0.0339711\tvalid_0's multi_logloss: 0.108328\tvalid_1's multi_error: 0.0611824\tvalid_1's multi_logloss: 0.176619\n",
            "[1109]\tvalid_0's multi_error: 0.0339389\tvalid_0's multi_logloss: 0.108282\tvalid_1's multi_error: 0.0611824\tvalid_1's multi_logloss: 0.176597\n",
            "[1110]\tvalid_0's multi_error: 0.033955\tvalid_0's multi_logloss: 0.108217\tvalid_1's multi_error: 0.0611824\tvalid_1's multi_logloss: 0.176584\n",
            "[1111]\tvalid_0's multi_error: 0.0339389\tvalid_0's multi_logloss: 0.108149\tvalid_1's multi_error: 0.0611824\tvalid_1's multi_logloss: 0.176567\n",
            "[1112]\tvalid_0's multi_error: 0.0339228\tvalid_0's multi_logloss: 0.108102\tvalid_1's multi_error: 0.0611449\tvalid_1's multi_logloss: 0.176567\n",
            "[1113]\tvalid_0's multi_error: 0.0339068\tvalid_0's multi_logloss: 0.108056\tvalid_1's multi_error: 0.0612199\tvalid_1's multi_logloss: 0.176562\n",
            "[1114]\tvalid_0's multi_error: 0.0338907\tvalid_0's multi_logloss: 0.108016\tvalid_1's multi_error: 0.0612574\tvalid_1's multi_logloss: 0.176556\n",
            "[1115]\tvalid_0's multi_error: 0.0338746\tvalid_0's multi_logloss: 0.107976\tvalid_1's multi_error: 0.0612574\tvalid_1's multi_logloss: 0.176557\n",
            "[1116]\tvalid_0's multi_error: 0.0337942\tvalid_0's multi_logloss: 0.107919\tvalid_1's multi_error: 0.0612574\tvalid_1's multi_logloss: 0.176556\n",
            "[1117]\tvalid_0's multi_error: 0.0337621\tvalid_0's multi_logloss: 0.107853\tvalid_1's multi_error: 0.0612574\tvalid_1's multi_logloss: 0.176537\n",
            "[1118]\tvalid_0's multi_error: 0.0337781\tvalid_0's multi_logloss: 0.1078\tvalid_1's multi_error: 0.0611824\tvalid_1's multi_logloss: 0.176534\n",
            "[1119]\tvalid_0's multi_error: 0.0337621\tvalid_0's multi_logloss: 0.107755\tvalid_1's multi_error: 0.0610698\tvalid_1's multi_logloss: 0.176526\n",
            "[1120]\tvalid_0's multi_error: 0.033746\tvalid_0's multi_logloss: 0.107712\tvalid_1's multi_error: 0.0610698\tvalid_1's multi_logloss: 0.176514\n",
            "[1121]\tvalid_0's multi_error: 0.0338103\tvalid_0's multi_logloss: 0.107666\tvalid_1's multi_error: 0.0611449\tvalid_1's multi_logloss: 0.17652\n",
            "[1122]\tvalid_0's multi_error: 0.0337621\tvalid_0's multi_logloss: 0.107612\tvalid_1's multi_error: 0.0610698\tvalid_1's multi_logloss: 0.176512\n",
            "[1123]\tvalid_0's multi_error: 0.0336817\tvalid_0's multi_logloss: 0.107559\tvalid_1's multi_error: 0.0611074\tvalid_1's multi_logloss: 0.17652\n",
            "[1124]\tvalid_0's multi_error: 0.0336656\tvalid_0's multi_logloss: 0.107506\tvalid_1's multi_error: 0.0611074\tvalid_1's multi_logloss: 0.176518\n",
            "[1125]\tvalid_0's multi_error: 0.0336495\tvalid_0's multi_logloss: 0.107464\tvalid_1's multi_error: 0.0611074\tvalid_1's multi_logloss: 0.176507\n",
            "[1126]\tvalid_0's multi_error: 0.0336656\tvalid_0's multi_logloss: 0.107388\tvalid_1's multi_error: 0.0610323\tvalid_1's multi_logloss: 0.176449\n",
            "[1127]\tvalid_0's multi_error: 0.0336334\tvalid_0's multi_logloss: 0.107333\tvalid_1's multi_error: 0.0610323\tvalid_1's multi_logloss: 0.176436\n",
            "[1128]\tvalid_0's multi_error: 0.0335531\tvalid_0's multi_logloss: 0.107282\tvalid_1's multi_error: 0.0610323\tvalid_1's multi_logloss: 0.176437\n",
            "[1129]\tvalid_0's multi_error: 0.0334244\tvalid_0's multi_logloss: 0.107206\tvalid_1's multi_error: 0.0609198\tvalid_1's multi_logloss: 0.176392\n",
            "[1130]\tvalid_0's multi_error: 0.0333762\tvalid_0's multi_logloss: 0.10717\tvalid_1's multi_error: 0.0608823\tvalid_1's multi_logloss: 0.176385\n",
            "[1131]\tvalid_0's multi_error: 0.033328\tvalid_0's multi_logloss: 0.107125\tvalid_1's multi_error: 0.0608823\tvalid_1's multi_logloss: 0.176383\n",
            "[1132]\tvalid_0's multi_error: 0.0333119\tvalid_0's multi_logloss: 0.107079\tvalid_1's multi_error: 0.0608823\tvalid_1's multi_logloss: 0.176382\n",
            "[1133]\tvalid_0's multi_error: 0.0332637\tvalid_0's multi_logloss: 0.107025\tvalid_1's multi_error: 0.0608823\tvalid_1's multi_logloss: 0.176373\n",
            "[1134]\tvalid_0's multi_error: 0.0332797\tvalid_0's multi_logloss: 0.106973\tvalid_1's multi_error: 0.0608823\tvalid_1's multi_logloss: 0.176374\n",
            "[1135]\tvalid_0's multi_error: 0.0332476\tvalid_0's multi_logloss: 0.106925\tvalid_1's multi_error: 0.0608823\tvalid_1's multi_logloss: 0.176378\n",
            "[1136]\tvalid_0's multi_error: 0.0332154\tvalid_0's multi_logloss: 0.106869\tvalid_1's multi_error: 0.0609948\tvalid_1's multi_logloss: 0.176373\n",
            "[1137]\tvalid_0's multi_error: 0.0332154\tvalid_0's multi_logloss: 0.106816\tvalid_1's multi_error: 0.0609573\tvalid_1's multi_logloss: 0.176352\n",
            "[1138]\tvalid_0's multi_error: 0.0331833\tvalid_0's multi_logloss: 0.106761\tvalid_1's multi_error: 0.0610323\tvalid_1's multi_logloss: 0.176339\n",
            "[1139]\tvalid_0's multi_error: 0.0331672\tvalid_0's multi_logloss: 0.106721\tvalid_1's multi_error: 0.0609198\tvalid_1's multi_logloss: 0.176336\n",
            "[1140]\tvalid_0's multi_error: 0.0331672\tvalid_0's multi_logloss: 0.106675\tvalid_1's multi_error: 0.0607698\tvalid_1's multi_logloss: 0.176321\n",
            "[1141]\tvalid_0's multi_error: 0.0331994\tvalid_0's multi_logloss: 0.106621\tvalid_1's multi_error: 0.0608073\tvalid_1's multi_logloss: 0.176289\n",
            "[1142]\tvalid_0's multi_error: 0.0331833\tvalid_0's multi_logloss: 0.106535\tvalid_1's multi_error: 0.0607698\tvalid_1's multi_logloss: 0.176226\n",
            "[1143]\tvalid_0's multi_error: 0.0331833\tvalid_0's multi_logloss: 0.106487\tvalid_1's multi_error: 0.0608073\tvalid_1's multi_logloss: 0.17622\n",
            "[1144]\tvalid_0's multi_error: 0.0331511\tvalid_0's multi_logloss: 0.106424\tvalid_1's multi_error: 0.0607322\tvalid_1's multi_logloss: 0.176203\n",
            "[1145]\tvalid_0's multi_error: 0.033135\tvalid_0's multi_logloss: 0.106365\tvalid_1's multi_error: 0.0608073\tvalid_1's multi_logloss: 0.176185\n",
            "[1146]\tvalid_0's multi_error: 0.0330868\tvalid_0's multi_logloss: 0.106315\tvalid_1's multi_error: 0.0608073\tvalid_1's multi_logloss: 0.176183\n",
            "[1147]\tvalid_0's multi_error: 0.033119\tvalid_0's multi_logloss: 0.106269\tvalid_1's multi_error: 0.0608448\tvalid_1's multi_logloss: 0.17618\n",
            "[1148]\tvalid_0's multi_error: 0.0331029\tvalid_0's multi_logloss: 0.106209\tvalid_1's multi_error: 0.0608073\tvalid_1's multi_logloss: 0.176159\n",
            "[1149]\tvalid_0's multi_error: 0.0330547\tvalid_0's multi_logloss: 0.10616\tvalid_1's multi_error: 0.0608073\tvalid_1's multi_logloss: 0.176148\n",
            "[1150]\tvalid_0's multi_error: 0.0330064\tvalid_0's multi_logloss: 0.106114\tvalid_1's multi_error: 0.0608448\tvalid_1's multi_logloss: 0.176137\n",
            "[1151]\tvalid_0's multi_error: 0.0329421\tvalid_0's multi_logloss: 0.106049\tvalid_1's multi_error: 0.0608448\tvalid_1's multi_logloss: 0.176101\n",
            "[1152]\tvalid_0's multi_error: 0.0329421\tvalid_0's multi_logloss: 0.106005\tvalid_1's multi_error: 0.0609198\tvalid_1's multi_logloss: 0.176103\n",
            "[1153]\tvalid_0's multi_error: 0.0328617\tvalid_0's multi_logloss: 0.105944\tvalid_1's multi_error: 0.0608448\tvalid_1's multi_logloss: 0.176096\n",
            "[1154]\tvalid_0's multi_error: 0.0328296\tvalid_0's multi_logloss: 0.105908\tvalid_1's multi_error: 0.0607698\tvalid_1's multi_logloss: 0.176096\n",
            "[1155]\tvalid_0's multi_error: 0.0327974\tvalid_0's multi_logloss: 0.10586\tvalid_1's multi_error: 0.0606947\tvalid_1's multi_logloss: 0.176089\n",
            "[1156]\tvalid_0's multi_error: 0.0327814\tvalid_0's multi_logloss: 0.105819\tvalid_1's multi_error: 0.0606947\tvalid_1's multi_logloss: 0.176081\n",
            "[1157]\tvalid_0's multi_error: 0.0327331\tvalid_0's multi_logloss: 0.10577\tvalid_1's multi_error: 0.0606572\tvalid_1's multi_logloss: 0.176057\n",
            "[1158]\tvalid_0's multi_error: 0.032701\tvalid_0's multi_logloss: 0.105671\tvalid_1's multi_error: 0.0605822\tvalid_1's multi_logloss: 0.175988\n",
            "[1159]\tvalid_0's multi_error: 0.0326367\tvalid_0's multi_logloss: 0.105609\tvalid_1's multi_error: 0.0606197\tvalid_1's multi_logloss: 0.175971\n",
            "[1160]\tvalid_0's multi_error: 0.0326045\tvalid_0's multi_logloss: 0.105517\tvalid_1's multi_error: 0.0607698\tvalid_1's multi_logloss: 0.175923\n",
            "[1161]\tvalid_0's multi_error: 0.0325884\tvalid_0's multi_logloss: 0.105465\tvalid_1's multi_error: 0.0607698\tvalid_1's multi_logloss: 0.175905\n",
            "[1162]\tvalid_0's multi_error: 0.0325563\tvalid_0's multi_logloss: 0.105386\tvalid_1's multi_error: 0.0607322\tvalid_1's multi_logloss: 0.175857\n",
            "[1163]\tvalid_0's multi_error: 0.032508\tvalid_0's multi_logloss: 0.105333\tvalid_1's multi_error: 0.0606947\tvalid_1's multi_logloss: 0.17586\n",
            "[1164]\tvalid_0's multi_error: 0.032492\tvalid_0's multi_logloss: 0.105276\tvalid_1's multi_error: 0.0606572\tvalid_1's multi_logloss: 0.175843\n",
            "[1165]\tvalid_0's multi_error: 0.032508\tvalid_0's multi_logloss: 0.105224\tvalid_1's multi_error: 0.0607322\tvalid_1's multi_logloss: 0.175846\n",
            "[1166]\tvalid_0's multi_error: 0.0324759\tvalid_0's multi_logloss: 0.10517\tvalid_1's multi_error: 0.0606572\tvalid_1's multi_logloss: 0.175836\n",
            "[1167]\tvalid_0's multi_error: 0.0323955\tvalid_0's multi_logloss: 0.105119\tvalid_1's multi_error: 0.0606572\tvalid_1's multi_logloss: 0.175827\n",
            "[1168]\tvalid_0's multi_error: 0.0324116\tvalid_0's multi_logloss: 0.105072\tvalid_1's multi_error: 0.0606947\tvalid_1's multi_logloss: 0.175827\n",
            "[1169]\tvalid_0's multi_error: 0.0324277\tvalid_0's multi_logloss: 0.10502\tvalid_1's multi_error: 0.0606947\tvalid_1's multi_logloss: 0.175815\n",
            "[1170]\tvalid_0's multi_error: 0.0324277\tvalid_0's multi_logloss: 0.104969\tvalid_1's multi_error: 0.0606947\tvalid_1's multi_logloss: 0.175815\n",
            "[1171]\tvalid_0's multi_error: 0.0323955\tvalid_0's multi_logloss: 0.104925\tvalid_1's multi_error: 0.0606197\tvalid_1's multi_logloss: 0.175804\n",
            "[1172]\tvalid_0's multi_error: 0.0323794\tvalid_0's multi_logloss: 0.104874\tvalid_1's multi_error: 0.0606947\tvalid_1's multi_logloss: 0.175793\n",
            "[1173]\tvalid_0's multi_error: 0.0323151\tvalid_0's multi_logloss: 0.104822\tvalid_1's multi_error: 0.0606572\tvalid_1's multi_logloss: 0.175789\n",
            "[1174]\tvalid_0's multi_error: 0.0323312\tvalid_0's multi_logloss: 0.104775\tvalid_1's multi_error: 0.0606197\tvalid_1's multi_logloss: 0.175798\n",
            "[1175]\tvalid_0's multi_error: 0.0323151\tvalid_0's multi_logloss: 0.104713\tvalid_1's multi_error: 0.0606572\tvalid_1's multi_logloss: 0.175776\n",
            "[1176]\tvalid_0's multi_error: 0.0322669\tvalid_0's multi_logloss: 0.104661\tvalid_1's multi_error: 0.0606947\tvalid_1's multi_logloss: 0.175777\n",
            "[1177]\tvalid_0's multi_error: 0.0322347\tvalid_0's multi_logloss: 0.104616\tvalid_1's multi_error: 0.0606947\tvalid_1's multi_logloss: 0.17577\n",
            "[1178]\tvalid_0's multi_error: 0.0322186\tvalid_0's multi_logloss: 0.104566\tvalid_1's multi_error: 0.0606572\tvalid_1's multi_logloss: 0.175771\n",
            "[1179]\tvalid_0's multi_error: 0.0322026\tvalid_0's multi_logloss: 0.104509\tvalid_1's multi_error: 0.0606947\tvalid_1's multi_logloss: 0.17576\n",
            "[1180]\tvalid_0's multi_error: 0.0321704\tvalid_0's multi_logloss: 0.104455\tvalid_1's multi_error: 0.0606197\tvalid_1's multi_logloss: 0.175745\n",
            "[1181]\tvalid_0's multi_error: 0.0321383\tvalid_0's multi_logloss: 0.104412\tvalid_1's multi_error: 0.0605822\tvalid_1's multi_logloss: 0.175747\n",
            "[1182]\tvalid_0's multi_error: 0.03209\tvalid_0's multi_logloss: 0.104351\tvalid_1's multi_error: 0.0605822\tvalid_1's multi_logloss: 0.175718\n",
            "[1183]\tvalid_0's multi_error: 0.0319775\tvalid_0's multi_logloss: 0.104271\tvalid_1's multi_error: 0.0605072\tvalid_1's multi_logloss: 0.175662\n",
            "[1184]\tvalid_0's multi_error: 0.0319293\tvalid_0's multi_logloss: 0.104222\tvalid_1's multi_error: 0.0605447\tvalid_1's multi_logloss: 0.175649\n",
            "[1185]\tvalid_0's multi_error: 0.0318971\tvalid_0's multi_logloss: 0.104164\tvalid_1's multi_error: 0.0605072\tvalid_1's multi_logloss: 0.175638\n",
            "[1186]\tvalid_0's multi_error: 0.031881\tvalid_0's multi_logloss: 0.104132\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.175639\n",
            "[1187]\tvalid_0's multi_error: 0.0318489\tvalid_0's multi_logloss: 0.104088\tvalid_1's multi_error: 0.0605072\tvalid_1's multi_logloss: 0.175635\n",
            "[1188]\tvalid_0's multi_error: 0.0318167\tvalid_0's multi_logloss: 0.104007\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.175584\n",
            "[1189]\tvalid_0's multi_error: 0.0318006\tvalid_0's multi_logloss: 0.103957\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.175567\n",
            "[1190]\tvalid_0's multi_error: 0.0318328\tvalid_0's multi_logloss: 0.103899\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.175557\n",
            "[1191]\tvalid_0's multi_error: 0.0317685\tvalid_0's multi_logloss: 0.103846\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.175538\n",
            "[1192]\tvalid_0's multi_error: 0.0317685\tvalid_0's multi_logloss: 0.10379\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.175537\n",
            "[1193]\tvalid_0's multi_error: 0.0317524\tvalid_0's multi_logloss: 0.103745\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.175537\n",
            "[1194]\tvalid_0's multi_error: 0.0317363\tvalid_0's multi_logloss: 0.103707\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.17554\n",
            "[1195]\tvalid_0's multi_error: 0.0316559\tvalid_0's multi_logloss: 0.103624\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.175484\n",
            "[1196]\tvalid_0's multi_error: 0.0316238\tvalid_0's multi_logloss: 0.103581\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.175487\n",
            "[1197]\tvalid_0's multi_error: 0.0316238\tvalid_0's multi_logloss: 0.103517\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.17544\n",
            "[1198]\tvalid_0's multi_error: 0.0316238\tvalid_0's multi_logloss: 0.103469\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.175435\n",
            "[1199]\tvalid_0's multi_error: 0.0316399\tvalid_0's multi_logloss: 0.10342\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.175421\n",
            "[1200]\tvalid_0's multi_error: 0.0316399\tvalid_0's multi_logloss: 0.103375\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.175405\n",
            "[1201]\tvalid_0's multi_error: 0.0316238\tvalid_0's multi_logloss: 0.103332\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.175398\n",
            "[1202]\tvalid_0's multi_error: 0.0315916\tvalid_0's multi_logloss: 0.103276\tvalid_1's multi_error: 0.0605072\tvalid_1's multi_logloss: 0.175369\n",
            "[1203]\tvalid_0's multi_error: 0.0315595\tvalid_0's multi_logloss: 0.103236\tvalid_1's multi_error: 0.0605447\tvalid_1's multi_logloss: 0.175362\n",
            "[1204]\tvalid_0's multi_error: 0.0315273\tvalid_0's multi_logloss: 0.103161\tvalid_1's multi_error: 0.0605447\tvalid_1's multi_logloss: 0.175314\n",
            "[1205]\tvalid_0's multi_error: 0.0314952\tvalid_0's multi_logloss: 0.103115\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.175319\n",
            "[1206]\tvalid_0's multi_error: 0.0314791\tvalid_0's multi_logloss: 0.10306\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.175306\n",
            "[1207]\tvalid_0's multi_error: 0.0314791\tvalid_0's multi_logloss: 0.102996\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.175271\n",
            "[1208]\tvalid_0's multi_error: 0.0315113\tvalid_0's multi_logloss: 0.102942\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.175259\n",
            "[1209]\tvalid_0's multi_error: 0.0314791\tvalid_0's multi_logloss: 0.102899\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.175256\n",
            "[1210]\tvalid_0's multi_error: 0.0314469\tvalid_0's multi_logloss: 0.102815\tvalid_1's multi_error: 0.0603196\tvalid_1's multi_logloss: 0.175207\n",
            "[1211]\tvalid_0's multi_error: 0.0314148\tvalid_0's multi_logloss: 0.102759\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.175196\n",
            "[1212]\tvalid_0's multi_error: 0.0314309\tvalid_0's multi_logloss: 0.102701\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.175192\n",
            "[1213]\tvalid_0's multi_error: 0.0313666\tvalid_0's multi_logloss: 0.102656\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.175182\n",
            "[1214]\tvalid_0's multi_error: 0.0313183\tvalid_0's multi_logloss: 0.102609\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.17519\n",
            "[1215]\tvalid_0's multi_error: 0.0313344\tvalid_0's multi_logloss: 0.102555\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.175181\n",
            "[1216]\tvalid_0's multi_error: 0.0313183\tvalid_0's multi_logloss: 0.102514\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.175167\n",
            "[1217]\tvalid_0's multi_error: 0.0313183\tvalid_0's multi_logloss: 0.102466\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.175152\n",
            "[1218]\tvalid_0's multi_error: 0.0312862\tvalid_0's multi_logloss: 0.10242\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.175133\n",
            "[1219]\tvalid_0's multi_error: 0.0312862\tvalid_0's multi_logloss: 0.102385\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.175132\n",
            "[1220]\tvalid_0's multi_error: 0.031254\tvalid_0's multi_logloss: 0.102339\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.175118\n",
            "[1221]\tvalid_0's multi_error: 0.0312379\tvalid_0's multi_logloss: 0.102307\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.175117\n",
            "[1222]\tvalid_0's multi_error: 0.031254\tvalid_0's multi_logloss: 0.102258\tvalid_1's multi_error: 0.0603196\tvalid_1's multi_logloss: 0.175109\n",
            "[1223]\tvalid_0's multi_error: 0.0312379\tvalid_0's multi_logloss: 0.102216\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.175102\n",
            "[1224]\tvalid_0's multi_error: 0.0312058\tvalid_0's multi_logloss: 0.102169\tvalid_1's multi_error: 0.0603196\tvalid_1's multi_logloss: 0.175099\n",
            "[1225]\tvalid_0's multi_error: 0.0311736\tvalid_0's multi_logloss: 0.102129\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.175105\n",
            "[1226]\tvalid_0's multi_error: 0.0311093\tvalid_0's multi_logloss: 0.102057\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.175067\n",
            "[1227]\tvalid_0's multi_error: 0.0310772\tvalid_0's multi_logloss: 0.102004\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.175059\n",
            "[1228]\tvalid_0's multi_error: 0.031045\tvalid_0's multi_logloss: 0.101962\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.175059\n",
            "[1229]\tvalid_0's multi_error: 0.0310289\tvalid_0's multi_logloss: 0.101908\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.175045\n",
            "[1230]\tvalid_0's multi_error: 0.0310289\tvalid_0's multi_logloss: 0.101865\tvalid_1's multi_error: 0.0602821\tvalid_1's multi_logloss: 0.175046\n",
            "[1231]\tvalid_0's multi_error: 0.031045\tvalid_0's multi_logloss: 0.1018\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.175027\n",
            "[1232]\tvalid_0's multi_error: 0.0310129\tvalid_0's multi_logloss: 0.101756\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.175021\n",
            "[1233]\tvalid_0's multi_error: 0.0309646\tvalid_0's multi_logloss: 0.101714\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.175004\n",
            "[1234]\tvalid_0's multi_error: 0.0309486\tvalid_0's multi_logloss: 0.101664\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.174995\n",
            "[1235]\tvalid_0's multi_error: 0.0309164\tvalid_0's multi_logloss: 0.101593\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.174955\n",
            "[1236]\tvalid_0's multi_error: 0.0308842\tvalid_0's multi_logloss: 0.10155\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.174946\n",
            "[1237]\tvalid_0's multi_error: 0.0308842\tvalid_0's multi_logloss: 0.101506\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.174945\n",
            "[1238]\tvalid_0's multi_error: 0.0309003\tvalid_0's multi_logloss: 0.101459\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.174932\n",
            "[1239]\tvalid_0's multi_error: 0.0308682\tvalid_0's multi_logloss: 0.101411\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.174925\n",
            "[1240]\tvalid_0's multi_error: 0.0308521\tvalid_0's multi_logloss: 0.101363\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.174917\n",
            "[1241]\tvalid_0's multi_error: 0.0308199\tvalid_0's multi_logloss: 0.101311\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.174889\n",
            "[1242]\tvalid_0's multi_error: 0.0307717\tvalid_0's multi_logloss: 0.101268\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.174879\n",
            "[1243]\tvalid_0's multi_error: 0.0307717\tvalid_0's multi_logloss: 0.10123\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.174885\n",
            "[1244]\tvalid_0's multi_error: 0.0306913\tvalid_0's multi_logloss: 0.101188\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.174872\n",
            "[1245]\tvalid_0's multi_error: 0.0306752\tvalid_0's multi_logloss: 0.101147\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.174863\n",
            "[1246]\tvalid_0's multi_error: 0.0306592\tvalid_0's multi_logloss: 0.1011\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.174862\n",
            "[1247]\tvalid_0's multi_error: 0.0306431\tvalid_0's multi_logloss: 0.101024\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.174795\n",
            "[1248]\tvalid_0's multi_error: 0.0306913\tvalid_0's multi_logloss: 0.100981\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.174786\n",
            "[1249]\tvalid_0's multi_error: 0.0306431\tvalid_0's multi_logloss: 0.100932\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.174763\n",
            "[1250]\tvalid_0's multi_error: 0.030627\tvalid_0's multi_logloss: 0.100885\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.174769\n",
            "[1251]\tvalid_0's multi_error: 0.0305949\tvalid_0's multi_logloss: 0.100804\tvalid_1's multi_error: 0.0602821\tvalid_1's multi_logloss: 0.174717\n",
            "[1252]\tvalid_0's multi_error: 0.0305466\tvalid_0's multi_logloss: 0.100761\tvalid_1's multi_error: 0.0602821\tvalid_1's multi_logloss: 0.174715\n",
            "[1253]\tvalid_0's multi_error: 0.0305305\tvalid_0's multi_logloss: 0.100717\tvalid_1's multi_error: 0.0602821\tvalid_1's multi_logloss: 0.174717\n",
            "[1254]\tvalid_0's multi_error: 0.0305305\tvalid_0's multi_logloss: 0.100674\tvalid_1's multi_error: 0.0602821\tvalid_1's multi_logloss: 0.174713\n",
            "[1255]\tvalid_0's multi_error: 0.0305305\tvalid_0's multi_logloss: 0.10063\tvalid_1's multi_error: 0.0603196\tvalid_1's multi_logloss: 0.174717\n",
            "[1256]\tvalid_0's multi_error: 0.0305305\tvalid_0's multi_logloss: 0.100588\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.174722\n",
            "[1257]\tvalid_0's multi_error: 0.0305145\tvalid_0's multi_logloss: 0.100544\tvalid_1's multi_error: 0.0603196\tvalid_1's multi_logloss: 0.174723\n",
            "[1258]\tvalid_0's multi_error: 0.0305145\tvalid_0's multi_logloss: 0.100497\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.17472\n",
            "[1259]\tvalid_0's multi_error: 0.0305466\tvalid_0's multi_logloss: 0.100419\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.174651\n",
            "[1260]\tvalid_0's multi_error: 0.030418\tvalid_0's multi_logloss: 0.100374\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.174649\n",
            "[1261]\tvalid_0's multi_error: 0.0304019\tvalid_0's multi_logloss: 0.100329\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.174641\n",
            "[1262]\tvalid_0's multi_error: 0.0303537\tvalid_0's multi_logloss: 0.100269\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.174601\n",
            "[1263]\tvalid_0's multi_error: 0.0303537\tvalid_0's multi_logloss: 0.100225\tvalid_1's multi_error: 0.0602821\tvalid_1's multi_logloss: 0.174585\n",
            "[1264]\tvalid_0's multi_error: 0.0303376\tvalid_0's multi_logloss: 0.10018\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.174587\n",
            "[1265]\tvalid_0's multi_error: 0.0303376\tvalid_0's multi_logloss: 0.100132\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.174588\n",
            "[1266]\tvalid_0's multi_error: 0.0303376\tvalid_0's multi_logloss: 0.10009\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.174589\n",
            "[1267]\tvalid_0's multi_error: 0.0303215\tvalid_0's multi_logloss: 0.100036\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.174562\n",
            "[1268]\tvalid_0's multi_error: 0.0303055\tvalid_0's multi_logloss: 0.0999943\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.17455\n",
            "[1269]\tvalid_0's multi_error: 0.0302894\tvalid_0's multi_logloss: 0.0999406\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.174537\n",
            "[1270]\tvalid_0's multi_error: 0.0302251\tvalid_0's multi_logloss: 0.0998911\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.174523\n",
            "[1271]\tvalid_0's multi_error: 0.030209\tvalid_0's multi_logloss: 0.0998173\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.174472\n",
            "[1272]\tvalid_0's multi_error: 0.0301768\tvalid_0's multi_logloss: 0.0997772\tvalid_1's multi_error: 0.0602821\tvalid_1's multi_logloss: 0.174467\n",
            "[1273]\tvalid_0's multi_error: 0.0301447\tvalid_0's multi_logloss: 0.0997076\tvalid_1's multi_error: 0.0602821\tvalid_1's multi_logloss: 0.174425\n",
            "[1274]\tvalid_0's multi_error: 0.0301768\tvalid_0's multi_logloss: 0.0996657\tvalid_1's multi_error: 0.0602821\tvalid_1's multi_logloss: 0.174415\n",
            "[1275]\tvalid_0's multi_error: 0.0301447\tvalid_0's multi_logloss: 0.0996292\tvalid_1's multi_error: 0.0602446\tvalid_1's multi_logloss: 0.174406\n",
            "[1276]\tvalid_0's multi_error: 0.0301286\tvalid_0's multi_logloss: 0.0995837\tvalid_1's multi_error: 0.0602821\tvalid_1's multi_logloss: 0.174413\n",
            "[1277]\tvalid_0's multi_error: 0.0300482\tvalid_0's multi_logloss: 0.0995298\tvalid_1's multi_error: 0.0602446\tvalid_1's multi_logloss: 0.174397\n",
            "[1278]\tvalid_0's multi_error: 0.0300322\tvalid_0's multi_logloss: 0.0994867\tvalid_1's multi_error: 0.0602446\tvalid_1's multi_logloss: 0.174389\n",
            "[1279]\tvalid_0's multi_error: 0.0300322\tvalid_0's multi_logloss: 0.0994355\tvalid_1's multi_error: 0.0602071\tvalid_1's multi_logloss: 0.17437\n",
            "[1280]\tvalid_0's multi_error: 0.03\tvalid_0's multi_logloss: 0.0993935\tvalid_1's multi_error: 0.0602446\tvalid_1's multi_logloss: 0.174375\n",
            "[1281]\tvalid_0's multi_error: 0.0299678\tvalid_0's multi_logloss: 0.0993471\tvalid_1's multi_error: 0.0602071\tvalid_1's multi_logloss: 0.174371\n",
            "[1282]\tvalid_0's multi_error: 0.0299518\tvalid_0's multi_logloss: 0.0993013\tvalid_1's multi_error: 0.0602821\tvalid_1's multi_logloss: 0.174366\n",
            "[1283]\tvalid_0's multi_error: 0.0299839\tvalid_0's multi_logloss: 0.0992616\tvalid_1's multi_error: 0.0602821\tvalid_1's multi_logloss: 0.174365\n",
            "[1284]\tvalid_0's multi_error: 0.0299196\tvalid_0's multi_logloss: 0.0992295\tvalid_1's multi_error: 0.0602446\tvalid_1's multi_logloss: 0.174364\n",
            "[1285]\tvalid_0's multi_error: 0.0298714\tvalid_0's multi_logloss: 0.0991925\tvalid_1's multi_error: 0.0603196\tvalid_1's multi_logloss: 0.174365\n",
            "[1286]\tvalid_0's multi_error: 0.0298714\tvalid_0's multi_logloss: 0.0991412\tvalid_1's multi_error: 0.0603196\tvalid_1's multi_logloss: 0.174355\n",
            "[1287]\tvalid_0's multi_error: 0.0298553\tvalid_0's multi_logloss: 0.0991023\tvalid_1's multi_error: 0.0603196\tvalid_1's multi_logloss: 0.174358\n",
            "[1288]\tvalid_0's multi_error: 0.0298071\tvalid_0's multi_logloss: 0.099057\tvalid_1's multi_error: 0.0603196\tvalid_1's multi_logloss: 0.174354\n",
            "[1289]\tvalid_0's multi_error: 0.029791\tvalid_0's multi_logloss: 0.0990111\tvalid_1's multi_error: 0.0602446\tvalid_1's multi_logloss: 0.174351\n",
            "[1290]\tvalid_0's multi_error: 0.0297588\tvalid_0's multi_logloss: 0.0989662\tvalid_1's multi_error: 0.0601696\tvalid_1's multi_logloss: 0.174334\n",
            "[1291]\tvalid_0's multi_error: 0.0297749\tvalid_0's multi_logloss: 0.0989262\tvalid_1's multi_error: 0.0601696\tvalid_1's multi_logloss: 0.174326\n",
            "[1292]\tvalid_0's multi_error: 0.0297749\tvalid_0's multi_logloss: 0.0988811\tvalid_1's multi_error: 0.060132\tvalid_1's multi_logloss: 0.17433\n",
            "[1293]\tvalid_0's multi_error: 0.0297106\tvalid_0's multi_logloss: 0.0988429\tvalid_1's multi_error: 0.0601696\tvalid_1's multi_logloss: 0.174323\n",
            "[1294]\tvalid_0's multi_error: 0.0297267\tvalid_0's multi_logloss: 0.0988074\tvalid_1's multi_error: 0.0602071\tvalid_1's multi_logloss: 0.17432\n",
            "[1295]\tvalid_0's multi_error: 0.0297267\tvalid_0's multi_logloss: 0.098775\tvalid_1's multi_error: 0.060132\tvalid_1's multi_logloss: 0.174314\n",
            "[1296]\tvalid_0's multi_error: 0.0296945\tvalid_0's multi_logloss: 0.0987392\tvalid_1's multi_error: 0.060132\tvalid_1's multi_logloss: 0.174317\n",
            "[1297]\tvalid_0's multi_error: 0.0296624\tvalid_0's multi_logloss: 0.0986991\tvalid_1's multi_error: 0.060132\tvalid_1's multi_logloss: 0.174311\n",
            "[1298]\tvalid_0's multi_error: 0.0296785\tvalid_0's multi_logloss: 0.0986593\tvalid_1's multi_error: 0.0602071\tvalid_1's multi_logloss: 0.174313\n",
            "[1299]\tvalid_0's multi_error: 0.0296302\tvalid_0's multi_logloss: 0.0986244\tvalid_1's multi_error: 0.0602071\tvalid_1's multi_logloss: 0.174314\n",
            "[1300]\tvalid_0's multi_error: 0.0296141\tvalid_0's multi_logloss: 0.0985873\tvalid_1's multi_error: 0.0602071\tvalid_1's multi_logloss: 0.174311\n",
            "[1301]\tvalid_0's multi_error: 0.0295338\tvalid_0's multi_logloss: 0.0985517\tvalid_1's multi_error: 0.0602071\tvalid_1's multi_logloss: 0.174308\n",
            "[1302]\tvalid_0's multi_error: 0.0294695\tvalid_0's multi_logloss: 0.0984961\tvalid_1's multi_error: 0.0602821\tvalid_1's multi_logloss: 0.174286\n",
            "[1303]\tvalid_0's multi_error: 0.0295016\tvalid_0's multi_logloss: 0.0984425\tvalid_1's multi_error: 0.0603196\tvalid_1's multi_logloss: 0.174265\n",
            "[1304]\tvalid_0's multi_error: 0.0294373\tvalid_0's multi_logloss: 0.0983992\tvalid_1's multi_error: 0.0602821\tvalid_1's multi_logloss: 0.174254\n",
            "[1305]\tvalid_0's multi_error: 0.0294373\tvalid_0's multi_logloss: 0.0983369\tvalid_1's multi_error: 0.0603196\tvalid_1's multi_logloss: 0.174235\n",
            "[1306]\tvalid_0's multi_error: 0.0294051\tvalid_0's multi_logloss: 0.0983031\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.174238\n",
            "[1307]\tvalid_0's multi_error: 0.0293248\tvalid_0's multi_logloss: 0.098249\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.174223\n",
            "[1308]\tvalid_0's multi_error: 0.029373\tvalid_0's multi_logloss: 0.098196\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.174202\n",
            "[1309]\tvalid_0's multi_error: 0.0293248\tvalid_0's multi_logloss: 0.098145\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.174183\n",
            "[1310]\tvalid_0's multi_error: 0.0293569\tvalid_0's multi_logloss: 0.0981067\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.174176\n",
            "[1311]\tvalid_0's multi_error: 0.0293248\tvalid_0's multi_logloss: 0.0980694\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.174171\n",
            "[1312]\tvalid_0's multi_error: 0.0293248\tvalid_0's multi_logloss: 0.0980267\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.174157\n",
            "[1313]\tvalid_0's multi_error: 0.0292926\tvalid_0's multi_logloss: 0.0979795\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.17415\n",
            "[1314]\tvalid_0's multi_error: 0.0292444\tvalid_0's multi_logloss: 0.097945\tvalid_1's multi_error: 0.0603196\tvalid_1's multi_logloss: 0.174138\n",
            "[1315]\tvalid_0's multi_error: 0.0292122\tvalid_0's multi_logloss: 0.0979017\tvalid_1's multi_error: 0.0603196\tvalid_1's multi_logloss: 0.174138\n",
            "[1316]\tvalid_0's multi_error: 0.0292122\tvalid_0's multi_logloss: 0.0978618\tvalid_1's multi_error: 0.0602446\tvalid_1's multi_logloss: 0.174127\n",
            "[1317]\tvalid_0's multi_error: 0.0291479\tvalid_0's multi_logloss: 0.0978318\tvalid_1's multi_error: 0.0602446\tvalid_1's multi_logloss: 0.174126\n",
            "[1318]\tvalid_0's multi_error: 0.0290997\tvalid_0's multi_logloss: 0.0977864\tvalid_1's multi_error: 0.0602821\tvalid_1's multi_logloss: 0.174123\n",
            "[1319]\tvalid_0's multi_error: 0.0290032\tvalid_0's multi_logloss: 0.0977493\tvalid_1's multi_error: 0.0603196\tvalid_1's multi_logloss: 0.174119\n",
            "[1320]\tvalid_0's multi_error: 0.0289389\tvalid_0's multi_logloss: 0.0976902\tvalid_1's multi_error: 0.0603196\tvalid_1's multi_logloss: 0.174078\n",
            "[1321]\tvalid_0's multi_error: 0.0289228\tvalid_0's multi_logloss: 0.0976525\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.174076\n",
            "[1322]\tvalid_0's multi_error: 0.0289068\tvalid_0's multi_logloss: 0.097604\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.174046\n",
            "[1323]\tvalid_0's multi_error: 0.0289068\tvalid_0's multi_logloss: 0.0975679\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.174034\n",
            "[1324]\tvalid_0's multi_error: 0.0288907\tvalid_0's multi_logloss: 0.0975291\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.174033\n",
            "[1325]\tvalid_0's multi_error: 0.0288746\tvalid_0's multi_logloss: 0.0974862\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.174011\n",
            "[1326]\tvalid_0's multi_error: 0.0288424\tvalid_0's multi_logloss: 0.0974385\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.173994\n",
            "[1327]\tvalid_0's multi_error: 0.0288264\tvalid_0's multi_logloss: 0.0974013\tvalid_1's multi_error: 0.0605072\tvalid_1's multi_logloss: 0.173991\n",
            "[1328]\tvalid_0's multi_error: 0.0288424\tvalid_0's multi_logloss: 0.0973623\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.173992\n",
            "[1329]\tvalid_0's multi_error: 0.0288585\tvalid_0's multi_logloss: 0.0973149\tvalid_1's multi_error: 0.0605447\tvalid_1's multi_logloss: 0.173992\n",
            "[1330]\tvalid_0's multi_error: 0.0288907\tvalid_0's multi_logloss: 0.0972654\tvalid_1's multi_error: 0.0605072\tvalid_1's multi_logloss: 0.173966\n",
            "[1331]\tvalid_0's multi_error: 0.0288907\tvalid_0's multi_logloss: 0.0972343\tvalid_1's multi_error: 0.0605822\tvalid_1's multi_logloss: 0.173973\n",
            "[1332]\tvalid_0's multi_error: 0.0288746\tvalid_0's multi_logloss: 0.0971922\tvalid_1's multi_error: 0.0605822\tvalid_1's multi_logloss: 0.173974\n",
            "[1333]\tvalid_0's multi_error: 0.0288585\tvalid_0's multi_logloss: 0.0971565\tvalid_1's multi_error: 0.0605447\tvalid_1's multi_logloss: 0.173972\n",
            "[1334]\tvalid_0's multi_error: 0.0288264\tvalid_0's multi_logloss: 0.0971065\tvalid_1's multi_error: 0.0605447\tvalid_1's multi_logloss: 0.173951\n",
            "[1335]\tvalid_0's multi_error: 0.0288103\tvalid_0's multi_logloss: 0.0970749\tvalid_1's multi_error: 0.0605447\tvalid_1's multi_logloss: 0.173954\n",
            "[1336]\tvalid_0's multi_error: 0.0287781\tvalid_0's multi_logloss: 0.0970019\tvalid_1's multi_error: 0.0605822\tvalid_1's multi_logloss: 0.173901\n",
            "[1337]\tvalid_0's multi_error: 0.0287621\tvalid_0's multi_logloss: 0.0969486\tvalid_1's multi_error: 0.0605447\tvalid_1's multi_logloss: 0.173883\n",
            "[1338]\tvalid_0's multi_error: 0.0286977\tvalid_0's multi_logloss: 0.0969061\tvalid_1's multi_error: 0.0605447\tvalid_1's multi_logloss: 0.173879\n",
            "[1339]\tvalid_0's multi_error: 0.0286013\tvalid_0's multi_logloss: 0.0968572\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.173862\n",
            "[1340]\tvalid_0's multi_error: 0.0285852\tvalid_0's multi_logloss: 0.0968205\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.173862\n",
            "[1341]\tvalid_0's multi_error: 0.0286013\tvalid_0's multi_logloss: 0.0967514\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.173821\n",
            "[1342]\tvalid_0's multi_error: 0.0285691\tvalid_0's multi_logloss: 0.0967066\tvalid_1's multi_error: 0.0605447\tvalid_1's multi_logloss: 0.173817\n",
            "[1343]\tvalid_0's multi_error: 0.0285852\tvalid_0's multi_logloss: 0.0966667\tvalid_1's multi_error: 0.0605447\tvalid_1's multi_logloss: 0.173813\n",
            "[1344]\tvalid_0's multi_error: 0.0285209\tvalid_0's multi_logloss: 0.0965934\tvalid_1's multi_error: 0.0605822\tvalid_1's multi_logloss: 0.173784\n",
            "[1345]\tvalid_0's multi_error: 0.0285209\tvalid_0's multi_logloss: 0.096546\tvalid_1's multi_error: 0.0606197\tvalid_1's multi_logloss: 0.173761\n",
            "[1346]\tvalid_0's multi_error: 0.0284566\tvalid_0's multi_logloss: 0.0965031\tvalid_1's multi_error: 0.0606197\tvalid_1's multi_logloss: 0.173746\n",
            "[1347]\tvalid_0's multi_error: 0.0284244\tvalid_0's multi_logloss: 0.0964585\tvalid_1's multi_error: 0.0606572\tvalid_1's multi_logloss: 0.173746\n",
            "[1348]\tvalid_0's multi_error: 0.0284084\tvalid_0's multi_logloss: 0.0964059\tvalid_1's multi_error: 0.0606572\tvalid_1's multi_logloss: 0.173713\n",
            "[1349]\tvalid_0's multi_error: 0.0283441\tvalid_0's multi_logloss: 0.0963647\tvalid_1's multi_error: 0.0606197\tvalid_1's multi_logloss: 0.173707\n",
            "[1350]\tvalid_0's multi_error: 0.0283119\tvalid_0's multi_logloss: 0.0962983\tvalid_1's multi_error: 0.0605822\tvalid_1's multi_logloss: 0.173675\n",
            "[1351]\tvalid_0's multi_error: 0.0282958\tvalid_0's multi_logloss: 0.0962426\tvalid_1's multi_error: 0.0605822\tvalid_1's multi_logloss: 0.173671\n",
            "[1352]\tvalid_0's multi_error: 0.0283762\tvalid_0's multi_logloss: 0.096193\tvalid_1's multi_error: 0.0605447\tvalid_1's multi_logloss: 0.173652\n",
            "[1353]\tvalid_0's multi_error: 0.0283601\tvalid_0's multi_logloss: 0.0961413\tvalid_1's multi_error: 0.0605822\tvalid_1's multi_logloss: 0.173642\n",
            "[1354]\tvalid_0's multi_error: 0.028328\tvalid_0's multi_logloss: 0.0960584\tvalid_1's multi_error: 0.0605447\tvalid_1's multi_logloss: 0.173586\n",
            "[1355]\tvalid_0's multi_error: 0.028328\tvalid_0's multi_logloss: 0.0960136\tvalid_1's multi_error: 0.0605822\tvalid_1's multi_logloss: 0.173582\n",
            "[1356]\tvalid_0's multi_error: 0.0282958\tvalid_0's multi_logloss: 0.0959708\tvalid_1's multi_error: 0.0605822\tvalid_1's multi_logloss: 0.17358\n",
            "[1357]\tvalid_0's multi_error: 0.0282958\tvalid_0's multi_logloss: 0.0959125\tvalid_1's multi_error: 0.0605447\tvalid_1's multi_logloss: 0.173561\n",
            "[1358]\tvalid_0's multi_error: 0.0283119\tvalid_0's multi_logloss: 0.0958623\tvalid_1's multi_error: 0.0605072\tvalid_1's multi_logloss: 0.17354\n",
            "[1359]\tvalid_0's multi_error: 0.028328\tvalid_0's multi_logloss: 0.0958068\tvalid_1's multi_error: 0.0605447\tvalid_1's multi_logloss: 0.173505\n",
            "[1360]\tvalid_0's multi_error: 0.0283119\tvalid_0's multi_logloss: 0.0957707\tvalid_1's multi_error: 0.0605447\tvalid_1's multi_logloss: 0.173494\n",
            "[1361]\tvalid_0's multi_error: 0.0282476\tvalid_0's multi_logloss: 0.095732\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.173497\n",
            "[1362]\tvalid_0's multi_error: 0.0281833\tvalid_0's multi_logloss: 0.0956971\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.173497\n",
            "[1363]\tvalid_0's multi_error: 0.0281833\tvalid_0's multi_logloss: 0.0956612\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.173492\n",
            "[1364]\tvalid_0's multi_error: 0.0281511\tvalid_0's multi_logloss: 0.0956229\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.173489\n",
            "[1365]\tvalid_0's multi_error: 0.028119\tvalid_0's multi_logloss: 0.0955855\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.173497\n",
            "[1366]\tvalid_0's multi_error: 0.0281029\tvalid_0's multi_logloss: 0.0955517\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.173496\n",
            "[1367]\tvalid_0's multi_error: 0.0280868\tvalid_0's multi_logloss: 0.095516\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.173498\n",
            "[1368]\tvalid_0's multi_error: 0.0280547\tvalid_0's multi_logloss: 0.0954768\tvalid_1's multi_error: 0.0605072\tvalid_1's multi_logloss: 0.1735\n",
            "[1369]\tvalid_0's multi_error: 0.0280547\tvalid_0's multi_logloss: 0.0954312\tvalid_1's multi_error: 0.0605072\tvalid_1's multi_logloss: 0.173493\n",
            "[1370]\tvalid_0's multi_error: 0.0280225\tvalid_0's multi_logloss: 0.0953793\tvalid_1's multi_error: 0.0605072\tvalid_1's multi_logloss: 0.173474\n",
            "[1371]\tvalid_0's multi_error: 0.0279904\tvalid_0's multi_logloss: 0.0952998\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.17341\n",
            "[1372]\tvalid_0's multi_error: 0.0279743\tvalid_0's multi_logloss: 0.0952618\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.173415\n",
            "[1373]\tvalid_0's multi_error: 0.0279421\tvalid_0's multi_logloss: 0.0952158\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.173425\n",
            "[1374]\tvalid_0's multi_error: 0.0279421\tvalid_0's multi_logloss: 0.0951725\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.173425\n",
            "[1375]\tvalid_0's multi_error: 0.027926\tvalid_0's multi_logloss: 0.0951354\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.173413\n",
            "[1376]\tvalid_0's multi_error: 0.0278778\tvalid_0's multi_logloss: 0.0950925\tvalid_1's multi_error: 0.0605072\tvalid_1's multi_logloss: 0.173394\n",
            "[1377]\tvalid_0's multi_error: 0.0278457\tvalid_0's multi_logloss: 0.0950473\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.17338\n",
            "[1378]\tvalid_0's multi_error: 0.0278135\tvalid_0's multi_logloss: 0.0950025\tvalid_1's multi_error: 0.0604697\tvalid_1's multi_logloss: 0.17337\n",
            "[1379]\tvalid_0's multi_error: 0.0278296\tvalid_0's multi_logloss: 0.0949278\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.173301\n",
            "[1380]\tvalid_0's multi_error: 0.0278296\tvalid_0's multi_logloss: 0.0948656\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.173244\n",
            "[1381]\tvalid_0's multi_error: 0.0278135\tvalid_0's multi_logloss: 0.0948352\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.173236\n",
            "[1382]\tvalid_0's multi_error: 0.0278135\tvalid_0's multi_logloss: 0.094802\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.173234\n",
            "[1383]\tvalid_0's multi_error: 0.0277974\tvalid_0's multi_logloss: 0.0947667\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.173239\n",
            "[1384]\tvalid_0's multi_error: 0.0277492\tvalid_0's multi_logloss: 0.0946999\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.173188\n",
            "[1385]\tvalid_0's multi_error: 0.0277653\tvalid_0's multi_logloss: 0.0946643\tvalid_1's multi_error: 0.0604321\tvalid_1's multi_logloss: 0.173183\n",
            "[1386]\tvalid_0's multi_error: 0.0277974\tvalid_0's multi_logloss: 0.0945921\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.173124\n",
            "[1387]\tvalid_0's multi_error: 0.0277814\tvalid_0's multi_logloss: 0.0945568\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.173123\n",
            "[1388]\tvalid_0's multi_error: 0.0277653\tvalid_0's multi_logloss: 0.0945235\tvalid_1's multi_error: 0.0603946\tvalid_1's multi_logloss: 0.17313\n",
            "[1389]\tvalid_0's multi_error: 0.0277653\tvalid_0's multi_logloss: 0.0944499\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.173081\n",
            "[1390]\tvalid_0's multi_error: 0.0277492\tvalid_0's multi_logloss: 0.0944161\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.173076\n",
            "[1391]\tvalid_0's multi_error: 0.0277492\tvalid_0's multi_logloss: 0.0943788\tvalid_1's multi_error: 0.0603571\tvalid_1's multi_logloss: 0.17308\n",
            "[1392]\tvalid_0's multi_error: 0.0277492\tvalid_0's multi_logloss: 0.0943455\tvalid_1's multi_error: 0.0603196\tvalid_1's multi_logloss: 0.173081\n",
            "Early stopping, best iteration is:\n",
            "[1292]\tvalid_0's multi_error: 0.0297749\tvalid_0's multi_logloss: 0.0988811\tvalid_1's multi_error: 0.060132\tvalid_1's multi_logloss: 0.17433\n",
            "Eval ACC: 0.939867957086053\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGZUuuwY_dkj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "1d8b5b02-8569-48f6-f121-53b694ffdf1c"
      },
      "source": [
        "best_iter = clf.best_iteration_\n",
        "params['n_estimators'] = best_iter\n",
        "print(params)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'learning_rate': 0.04, 'max_depth': 18, 'n_estimators': 1292, 'objective': 'multiclass', 'boosting_type': 'gbdt', 'subsample': 0.7, 'random_state': 42, 'colsample_bytree': 0.7, 'min_data_in_leaf': 55, 'reg_alpha': 1.7, 'reg_lambda': 1.11}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bCp9pcOD_h53",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_train = pd.concat((df_train, df_valid))"
      ],
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u6Px3Mol_n2O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "56e226a1-4514-4fe5-efb7-cdbcd1f247a2"
      },
      "source": [
        "clf = LGBMClassifier(**params)\n",
        "\n",
        "clf.fit(df_train[feature_cols], df_train[label_col], eval_metric='multi_error', verbose=False, categorical_feature=cat_cols)\n",
        "\n",
        "eval_score_acc = accuracy_score(df_train[label_col], clf.predict(df_train[feature_cols]))\n",
        "\n",
        "print('ACC: {}'.format(eval_score_acc))\n",
        "print('AUC: {}'.format(eval_score_auc))"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/lightgbm/basic.py:1209: UserWarning: categorical_feature in Dataset is overridden.\n",
            "New categorical_feature is ['Crop_Type', 'Crop_Type_Lag_1', 'Crop_Type_Lag_2', 'Pesticide_Use_Category', 'Pesticide_Use_Category_Lag_1', 'Pesticide_Use_Category_Lag_2', 'Season', 'Season_Lag_1', 'Season_Lag_2', 'Soil_Type', 'Soil_Type_Lag_1', 'Soil_Type_Lag_2']\n",
            "  'New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ACC: 0.9633797744716289\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-106-97790fd71dfe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ACC: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meval_score_acc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'AUC: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meval_score_auc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'eval_score_auc' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVZZ42S0Aw1P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds = clf.predict(df_test[feature_cols])"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WuaT-_YUcGqa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ce829803-3638-41f7-857f-3bdb52285718"
      },
      "source": [
        "from collections import Counter\n",
        "Counter(df_train['Crop_Damage'])\n",
        "Counter(preds)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({0: 50707, 1: 8234, 2: 369})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bncf1QOjeMs4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "submission = pd.DataFrame({'ID':df_test['ID'], 'Crop_Damage':preds})"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_iyph9cyVBhS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "778baddd-77fb-4544-b4e8-84d28b779cef"
      },
      "source": [
        "import lightgbm as lgb\n",
        "plt.rcParams['figure.figsize'] = (12, 6)\n",
        "lgb.plot_importance(clf)\n",
        "plt.show()"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nD3Xr1mXKlCm0aNGCBx54gEmTJlGrVi0aNWrEY489xvHHHw/A1KlTCwtu33HHHfz0pz+tytsqN0mlZo/0RCTOOeecc865KpWdnc348ePJyspi6dKl5OfnM336dK6//nqefPJJFi1axOWXX14YjHXu3JmsrCyWLFlC3759+eUvfwnApk2byMzM5O2332b+/PlkZmayefPmqry1faLGBW2STNKYpO3hkkbto7anSCp5oezetZsvaZGkZZIWS7o1afngPifpu/F6xX/OjEsjC44bIClX0mFxu4OkCi8GltRT0qwy9k9I6sNnktZLGly5uyuzH60lvSVpV6yZ55xzzjnnqom8vDxyc3PJy8sjJyeHJk2aIImtW0Oy8S1bttCkSRMAevXqRZ06dQA47bTTWLNmDQBz5szhnHPOoUGDBhx99NGcc845zJ49u2puaB+qie+07QIukTTazDZUdWcKSKplZnml7M41s07xuGOAvxDeU7trf/TFzDYCnUro4yFAuqTvmNk2Qrr9DwhZFufH7TeLn7cP+nNjUh8GAd8zs8mln1FpmwilFyryjp9zzjnnnNvPmjZtyvDhw0lPTyctLY2MjAwyMjKYNGkSP/rRj0hLS+Ooo45i3rx53zj30Ucf5dxzzwXCjN1xxx1XuK9Zs2ZkZ1cqJ1+1UhODtjxCDa5fEAovF5I0BZhlZs/E7e1mVjemgc8kZBXsQMjo+B5wM5AG9Ik1wQB6S7qdEFTdYmazYvHpewkZDo8AJpjZn2O7dwObCTXUWqbqfMy0OIRQ32xUbO9h4Hvx3m4xs9cktSOUEDicMGN6qZmtlHQlITA5nFCf7IbY9KOxDQMeM7OxJVx7j6QsoDvwMiG75QRCsFYQtL0s6UhCYer2hIQlo8xsZmnPodh3cCrh++mb9ExTkvQwIf1/GvCMmd0VP/8RIVHKDkLSkhPN7PyS2jCzL4AvJJ1X3uvm7s6n+e0vlPdwdxC6tUMeg3yMuDL4GHGp+BhxqdT0MbL63vPYvHkzM2fOZNWqVdSvX59+/foxbdo0nn32WV588UW6d+/O/fffzy233MKkSZMKz502bRpZWVn861//qsI72P9qYtAGIdBYIum+CpxzCqHo8iZCAehJZtZN0s3AUEKaeQgZCbsBJwGvSWoBDAS2mNmpko4A5koqKIjdBWhfkaLWZvZJDICOAa4MH1kHSa0J2RhbEmqcPWhmT0o6HDhUUhvgx0CPmOb+j4QU/cuApmbWHkBS/TIuPxc4Q9JbhLpwCWA0IUPjGcB/EYLhV83sqtjWfEkvx2uV9hyQdAYh2LuoEkWtR8aMlIcCr0jqCHwI/Bk408xWSXqqgm2WKAbNQwAaNmzEnR1KmyB1Do5NC3+ZOlcaHyMuFR8jLpWaPkYSiQSJRILatWuzbNkyANq0acOMGTPIysoiNzeXRCJBeno6EyZMIJFIALBw4ULGjx/PuHHjeOutUMJ3y5YtLFq0qPCY+fPn06lTp8Ltb6saGbSZ2VZJjxNmnHLLedoCM1sHIOljQp0xCDNuvZKOe9rM9gArJX1CmEHLADomve9WDzgZ+AqYX5GArQTfJwQ6mNlySZ8SZuzeAkZKagY8G2fZzibMji0IWe5JIxTF/jtwoqQ/AC8k3VtJ3gRuBd4gPJOPJbWINdvqxu0M4MKk98JqE2q2lfUc2hBm2DLMrDIFrfvHYKoW0BhoS5hh/CTp+T5FDLb2hpk9EvtKq1atbOgVF+1tk64GSyQS9D8IMnq5yvMx4lLxMeJSORjGSFpaGjNmzKBbt26kpaUxefJkevfuzdy5c2nSpAktW7bk0UcfpWvXrvTs2ZN3332XP/7xj7z88sucfPLJhe107NiRrl27csoppwCwdOlSpk6dSoMGDarq1vaJGhm0ReOAdwhLCAvkEZOvxPe3Dk/atyvp9z1J23so+pyK10gwQMBQM5uTvCMuj6xw/TWFYtX5hICrRGb2F0lvA+cBL0r6WezHVDP7Ru0zSacA/0mYoesPXFVK0/MIyxB7EAJDgDXAZUnbIizHLJILP9ZDK+05rCMEd52BCgVtkk4AhgOnmtnmuMy1dkXacM4555xz1Vf37t3p27cvXbp0oVatWnTu3JkhQ4bQrFkzLr30Ug455BCOPvpoHnvsMQBGjBjB9u3b6devHwDp6ek8//zzNGjQgN/85jeceuqpANx5553f+oANanDQFpfSPQ1cDTwWP15NmIl6GriQ8D5WRfWTNBU4ATgRWAHMAa6X9GpcltgSqNQbj3FG60/AQ2Zmkt4gLDt8NbabDqyIgd0nZjZeUjrQkTCDNlPS2PhuXAPgO4TA8Ssz+x9JK4BppV3fzLZJ+hwYTHg3DUKwNoxQXJt4v0MlDY197Gxm76Z4Dl8SvouXJO0ws0QFHstR8R62SDoWOJewbHMFYQaxuZmtJiwNdc4555xz30KZmZlkZmYW+eziiy/m4osv/saxL7/8cqntXHXVVVx1VWnzE99ONTZoi8YAP0/ankgIahYDs6nELBjwGSEpx1HAdWa2U9Ikwrtu78TZpvVULENhWky1fxhhNvAJQnINCIHSw5Lei/sGmdkuSf2Bn0jaDfwf8LsYqN5BeO/tEGA3cCNhiejkpDIC35iJK2Yu4b2zz+P2W8Dv+Dpz5N2Emcwlsc1VwPlAmc/BzP4t6XzgH5KuMrO3S7n+IEnJz+804F1gOfB57B9mlivpBmC2pB3AgrJuStL/A7II390eScOAtma2NcXzcM4555xzrsrIrPhqP+e+PSTVNbPtMUicAKwsKTNmZbVq1cpWrFiR+kB30EokEvSs4e8ZuL3jY8Sl4mPEpeJj5OAgaaGZfa+kfTWuuLY76FwbZymXERKf/DnF8c4555xzrpzGjh1Lu3btaN++PQMGDGDnzp2F+2666Sbq1q1buP3AAw/Qtm1bOnbsyNlnn82nn35apK2tW7fSrFkzfv7zn+MqxoO2A0TSdyUtKuHnu1XUnwkl9GXwAbz+4BKuP6Gi7ZjZWDPrZGZtzewKM8vZV20755xzzh3MsrOzGT9+PFlZWSxdupT8/HymT58OQFZWFps3by5yfOfOncnKymLJkiX07duXX/7yl0X2/+Y3v+HMM888YP2vSTxo248kmaQxAGa2kZAA5G8xyCj42VjJtqckpdavMDO7sVg/OhHS9RfUo0PSnPi+XsH2GEm37Iu+mtnk4tc3sxvLe76k7WXcW7naltRT0qyK3o9zzjnn3MEiLy+P3Nxc8vLyyMnJoUmTJuTn5zNixAjuu69oSeRevXpRp04dAE477TTWrFlTuG/hwoX8+9//JiMj44D2v6ao6YlIqtou4BJJo81sQ1V3poCkWmZWUoXGuYRyAONigpGGhKQdBc4AfnEAulht5O7Op/ntL1R1N1w1dmuHPAb5GHFl8DHiUvEx4lKpijGy+t7zaNq0KcOHDyc9PZ20tDQyMjLIyMjgwQcf5MILL6Rx48alnv/oo49y7rnnArBnzx5uvfVWpk2bVmbWR1c6D9r2rzxCkeZfACOTd8RaY7PM7Jm4vd3M6saaZpmEFPkdCOUJ3gNuJhTL7mNmH8dmeku6nRBY3WJmsyQdCtxLSNd/BDDBzP4c270b2EwoCN6yhP6+CRQk8WgHLAUaSzoayCEUyH5HUldCdsu6wAZCRst1kk4iJANpFI+/1syWF7vvu4HjCOn/7yGUXsgD/mlmw6kgSY2Bv8ZnUAu43szeiAXAM+Mz+BgYHBOW/JCQ+TIH+N9S2hxCLNLdsGEj7uxQUnzrXHBsWvjL1LnS+BhxqfgYcalUxRhJJBJs27aNqVOnMm3aNOrWrcuoUaP41a9+xaxZsxg3bhyJRIL8/HwSiUSRc1966SVeffXVwmOee+45WrVqxUcffcTy5cvJzs7+xjmubB607X8TCKnx70t55NdOIQRIm4BPgElm1k3SzcBQQs00COn1uwEnAa9JagEMBLaY2amSjgDmSvpnPL4L0N7MVpV0UTNbKykv1n07g5DqvylwOrCFEDwa8AdCSYD1kn5MCL6uIgSo15nZSkndCeUKflDQvqT7CXXjBgMNgIuB1rHWW/0KPJ9klwNzzOyeGLDWkdQQuAPobWY7JN0G3BK/g4mxTx8Rgr2SnsMj8V5o1aqVDb3iokp2zR0MEokE/T2jlyuDjxGXio8Rl0pVjZEZM2bQuXNn+vQJlZjWrl3LXXfdRW5uLldffTUAu3bt4pprruGjjz4CQv20Z599ln/9618cc8wxAEycOJE33niDOXPmsH37dr766itatWrFvffee8Dv6dvKg7b9zMy2SnocuIlQL608FpjZOgBJHxOKZkMImnolHfe0me0BVkr6hDCDlkF4N63gHbB6wMnAV8D80gK2JG8SArYzCLNpTePvWwjLJ1sB7QlFsgEOBdZJqhuPmxE/hzDLVeA3wNtmNiTe1xZgJ/BofK+srHfLSqpLUfDZAuAxSYcR3hdcJOksoC0hYAU4nBCAtgZWmdnK2IdpxBk155xzzjlXVHp6OvPmzSMnJ4e0tDReeeUVbrnlFoYOHVp4TN26dQsDtnfffZef/exnzJ49uzBgA3jyyScLf58yZQpZWVkesFWQB20HxjjgHWBy0md5xEQw8f2xw5P27Ur6fU/S9h6KfmfFgxkDBAw1sznJO+LyyPIUE59LCL46EJZHfg7cCmyN/RewzMxOL9b+UcCXMaFJSRYAXSU1MLNNZpYnqRtwNtCXUAT9B6WcuxE4OulaDQjLMjGz1yWdCZwHTJH0AGEJ6EtmNqBYH0vrm3POOeecK6Z79+707duXLl26UKtWLTp37syQIaX//+4RI0awfft2+vXrB4Sg7/nnnz9Q3a3RPGg7AMxsk6SnCe9xPRY/Xg10JbyzdiFwWCWa7idpKnACcCKwApgDXC/pVTPbLaklkF2BNt8EhgOfmFk+sCkuXWwHXEsI3hpJOt3M3oozXC3NbJmkVZL6mdmMWOy6o5ktju3Ojn17Ib5vZkAdM3tR0lzCMtDSJIBhkqaa2VfAIOA1AEnHA2vMbGJcDtqFsFxzgqQWZvaRpCMJM4bLgeaSTorvBQ4o4VrOOeeccy7KzMwkMzOz1P3bt3+d0Ls8SUYGDRrEoEGD9kXXDioetB04YwizSQUmAjMlLSYENOWZBSvuM2A+IQnHdWa2M6bob05IGCJgPdCnAm2+R8ga+Zdin9UtyIAZl16Ol1SPMIbGEYpbXwE8LOkOQhA6HSgI2ojB3HeA5wnvos2UVJswe1dqKYGYYKUrsFBSPiGxyHVxd09ghKTdwHZgYHzXbhDwVAzkAO4wsw9jkpEXJOUAbxDesXPOOeecc67akllJrws55yAkIlmxYkVVd8NVY4lEgp6eQMCVwceIS8XHiEulqsbI2LFjmTRpEpLo0KEDkydPpnbt2gDcdNNNPPbYY4Uzba+//jrDhg1jyZIlTJ8+nb59Q3qFRYsWcf3117N161YOPfRQRo4cyY9//OMDfi/fBpIWmtn3StrnxbWdc84555xzRWRnZzN+/HiysrJYunQp+fn5TJ8+HYCsrCw2b95c5Pj09HSmTJnC5ZdfXuTzOnXq8Pjjj7Ns2TJmz57NsGHD+PLLLw/YfdQUHrSVQFK+pEVJP7eXcWwfSW2Ttv9LUu990If6km6oxHmjJJVa70zSFEmDit1fwc93967XRa5T5LmU85yRsR8fS8qRtFPSWklj9lW/kq41TFKdfd2uc84551xNkZeXR25uLnl5eeTk5NCkSRPy8/MZMWIE991XtJpV8+bN6dixI4ccUjS8aNmyJSeffDIATZo04ZhjjmH9+vUH7B5qCg/aSpZrZp2SfsrKSdqHkF4eADO708z2Ran3+kCFg7Zy2l7s/gp+Nu7DaxR5LuVhZvcAV8bNLmZWm1CI+6N92K8CwwAP2pxzzjnnStC0aVOGDx9Oeno6jRs3pl69emRkZPDQQw9x4YUX0rhx4wq3OX/+fL766itOOumk/dDjms0TkVSApHsJmR7zCLXTno3bZ8XkG5cS6pHNMrNnJK0GngLOjecMAUYDLYD7zexPsb7ZTEJK+8MICTNmAvcCJ0laREhfP0LSCKA/of7Zc2Z2V+zXSOCnwBeEFP0Ly3k/q4GpwAXx2v3MbHmsc7J0d44AACAASURBVPZgPMyAM81sWxnXH0jIOGnAEuDhEp7LeYTkIXnA+2Z2WSnd+iVwj5ktB4gZLB+O12lOyL7ZkJBgZbCZfSZpSsEzj8dtN7O6sczBKEJ5gPbxuVxJKFDehFCQfIOZJde+KyJ3dz7Nb38h5bN0B69bO+QxyMeIK4OPEZeKjxGXyoEeI6vvPY/Nmzczc+ZMVq1aRf369enXrx+PP/44M2bMIJFIVLjNdevW8ZOf/ISpU6d+YzbOpeZBW8nSYrBUYDTwMnAx0NrMTFJ9M/tS0vMUDRiKt/WZmXWSNBaYAvQAahNqoP2JUGD64liEuyEwL7Z5O9C+oO5ZTJN/MtCNkG3x+VifbAdwGdCJ8H2+QzmDtmiDmXWJSzGHA9fEP280s7kxqNxZxvU3AncAZ5jZhoI6bCU8l9uBE8xsVywhUJr2hEybJfkDMNXMpkq6ChhP6syYnQnlCtYSatD1MLPxkm4BehVkxEwWM0wOAWjYsBF3dshLcQl3MDs2Lfxl6lxpfIy4VHyMuFQO9BhJJBIkEglq167NsmXLAGjTpg233XYbu3btolmzZgDk5OTQtGnTIsWz/+///o9ly5bRsGHDws927NjBL37xC6644gp27txZqaDvYOdBW8lyixeJllSLEGA9KmkWMKucbRVUFCxIm78N2CapIHjZAfwuBkB7CPXEji2hnYz4827crksIor5DmPXKif2saAXDZ+OfC4FL4u9zgQckPQk8a2ZrYtBW0vVPAWYUBD9mtqmU6ywBnpT0N+BvFexjgdOT+vgEcF8ZxxaYb2ZrAGIg3hz437JOMLNHgEcgZI8cesVFleyuOxgkEgn6e9Y3VwYfIy4VHyMulaoYI2lpacyYMYNu3bqRlpbG5MmT+fWvf83QoUMLj6lbty7Z2UXLAU+ZMoV27doVZrv86quvOPfcc7nhhhsYNmzYgbyFGsXnJsvJzPIIs0zPAOcTaquVx674556k3wu2axFqmzUCusZA8d+EmbjiBIxOev+shZk9WvE7KbV/+bE/xHf4rgHSgLmSWu+D658HTCAUv14Qg+CSLCMUHa+IPOJYlnQIcHjSvuRnXniPzjnnnHOudN27d6dv37506dKFDh06sGfPHoYMGVLq8QsWLKBZs2bMmDGDn/3sZ7Rr1w6Ap59+mtdff50pU6bQqVMnOnXqxKJFi0ptx5XMg7ZyissE65nZi8AvCDNMANvYuwLN9YAvzGy3pF7A8aW0Owe4KvYDSU0lHQO8DvSRlBYLV1+wF30htn2Smb1nZr8HFgCty7j+q0C/gsyTkhoU738MpI4zs9eA2+I91y3l8vcDv5bUsuBcSQWFtN8kLAWFEOy+EX9fzdeB3oWE9/NS2dvvzTnnnHOuRsvMzGT58uUsXbqUJ554giOOOKLI/oIabQCnnnoqa9asYceOHWzcuLFwWeWVV17J7t27WbRoUeFPp05FFrS5cvBZh5IVf6dtNiExx0xJtQmzTrfEfdOBiZJuAvpW4lpPAn+X9B6QBRQk4Ngoaa6kpcA/YiKSNsBb8b257cCVZvaOpL8CiwmJSBZUog/FDYsB5B7CzNc/4rtoJV1/maR7gH9JyicsnxxE0edyGWFZaT3CsxtvZiUW6DCzJZKGAU/FlPzG10tRhwKTY0KU9cDg+PlEwnezmPBd7SjHPT4CzJa0tqxEJM4555xzzlU1mVlV98G5aqtVq1a2YsWKqu6Gq8YSiUThun3nSuJjxKXiY8Sl4mPk4CBpoZl9r6R9vjzSOeecc845V2js2LG0a9eO9u3bM2DAAHbu3MnVV1/NKaecQseOHenbt2/h0sjPPvuMXr160blzZzp27MiLL75Y2M7o0aNp0aIFrVq1Ys6cOVV1OzWCB201lKQJkhYV+xmc+swDQ9LgEvo3oar75Zxzzjl3MMvOzmb8+PFkZWWxdOlS8vPzmT59OmPHjmXx4sUsWbKE9PR0HnroIQB++9vf0r9/f959912mT5/ODTfcAMD777/P9OnTWbZsGbNnz+aGG24gPz+/Km/tW63GBW2STNKYpO3hkkbto7anSKrMe2tltTk2vsNVsD1H0qSk7TGxplhFHQn8NinbYyczm1yBfn3jXiVtL+34CrTbU9IsM5tcrG+dzOxGSYMkPbS31ynl2q0lvRXLLQzfH9dwzjnnnPu2y8vLIzc3l7y8PHJycmjSpAlHHXUUAGZGbm5uYW1iSWzduhWALVu20KRJEwBmzpzJZZddxhFHHMEJJ5xAixYtmD9/ftXcUA1QExOR7AIukTS6pMLJVUVSrVg2oLi5QH9gXMyy2BA4Kmn/GYRslW7vbQJuInVB7kK5u/NpfvsL+69H7lvv1g55DPIx4srgY8Sl4mPEpXKgxsjqe8+jadOmDB8+nPT0dNLS0sjIyCAjIwOAwYMH8+KLL9K2bVvGjAlzJKNGjSIjI4M//OEP7Nixg5dffhkIM3annXZaYdvNmjX7Rk03V341MWjLI2QG/AUwMnmHpCnALDN7Jm5vN7O6knoCmcCXQAfgaUIx7JsJtcr6mNnHsZnekm4nBFa3mNksSYcC9wI9gSOACWb259ju3cBmQtr8liX0901gbPy9HbAUaCzpaCAHaAO8I6kr8AAhVf4GYJCZrZN0EqH+WaN4/LVmtrzYfd8NHAdcDdxDSIufB/zTzCo84ySpMfDX+AxqAdeb2RuxAHdmfAYfA4PNbLukHwLjYv/KLGxdxjUfBk4lfB/PmNld8fMfEZ7LDkIAfKKZnV9SG2b2BfCFpPNSXGsIMASgYcNG3NmhpFjbueDYtPCXqXOl8THiUvEx4lI5UGMkkUiwbds2pk6dyrRp06hbty6jRo1i5MiRnHPOOfz0pz/lyiuvZPz48WRmZnLuuefy9NNP8x//8R/079+fZcuWcemll/LYY4+RnZ3NBx98QCKRAGDdunUsW7aMhg0b7vf7qIlqYtAGIYhZIum+CpxzCiFA2gR8Akwys26Sbiakmi9YwticUGT7JOA1SS2AgcAWMztV0hGEgtT/jMd3Adqb2aqSLmpmayXlSUonzKq9BTQFTge2EIJHA/4AXGRm6yX9mBB8XUUIUK8zs5WSugN/BH5Q0L6k+wn1yAYDDYCLgdZmZpLqV+D5JLscmGNm98SAtY6khsAdQG8z2yHpNuCW+B1MjH36iBDsVcZIM9sUr/eKpI7Ah8CfgTPNbJWkpyrZdhFm9gjhudKqVSsbesVF+6JZV0MlEgn6e0YvVwYfIy4VHyMulQM5RmbMmEHnzp3p0ycsTFq7di3z5s0rkr3ysMMO47777uP3v/89N954I7Nnz+a4446jZ8+ejBkzhvbt29OtWzeAwvNGjx5NRkYGp59++gG5j5qmxr3TBmBmW4HHCUvhymuBma0zs12EWaKCoOs9QqBW4Gkz22NmKwnBXWsgAxgYa7u9DXwXODkeP7+0gC3Jm4SArSBoeytpey7QCmgPvBSvcQfQLBa6PgOYET//M9A4qd3fEAqCX2ehtsMWYCehZtolhJmv0pRUC6LgswXA4PiuYAcz2wacBrQlBKyLgJ8SCoW3BlaZ2crYh2kpnkVp+kt6h1AHrl28Vmvgk6Tnu0+CNuecc865g1V6ejrz5s0jJycHM+OVV16hTZs2fPTRR0B4p+3555+ndevWhce/8sorAHzwwQfs3LmTRo0aceGFFzJ9+nR27drFqlWrWLlyZWEg5yqups60QViO9w6QnHwjjxioxvfHDk/atyvp9z1J23so+pyKBzNGKBg91MyK5DKNyyPLU+h5LiH46kBYHvk5cCuwNfZfwDIzK/K/JiQdBXxpZqWVlV8AdJXUwMw2mVmepG7A2YRC4D8naVaumI3A0UnXakBYlomZvS7pTOA8YIqkBwhLQF8yswHF+rjXJe8lnQAMB041s81xmWvtvW3XOeecc84V1b17d/r27UuXLl2oVasWnTt3ZsiQIfzgBz9g69atmBmnnHIKDz/8MABjxozh2muvZezYsUhiypQpSKJdu3b079+ftm3bUqtWLSZMmMChhx5axXf37VVjg7a4lO5pwntcj8WPVwNdCe+sXQgcVomm+0maCpwAnAisAOYA10t61cx2S2oJVORNyzcJQcknZpYPbIpLF9sB1xKCt0aSTjeztyQdBrQ0s2WSVknqZ2YzFNL4dDSzxbHd2bFvL8T3zQyoY2YvSppLmCksTQIYJmmqmX0FDAJeA5B0PLDGzCbG5aBdCMs1J0hqYWYfSTqSsMxzOdBc0knxvcABJVwrlaMIwe8WSccC58b+rQBOlNTczFYDP65E284555xzLklmZiaZmZlFPps7d26Jx7Zt27bUfSNHjmTkyJEl7nMVU2ODtmgMYTapwERgpqTFhICmPLNgxX0GzCcEEteZ2c6Yor85IWGIgPVUIEMhYQlmQ+AvxT6rW5ABM6bfHy+pHuF7GwcsA64AHpZ0ByEInQ4UBG3EYO47wPOEd9FmSqpNmL0rtZRATLDSFVgoKZ+wZPS6uLsnMELSbmA7MDC+azcIeCoGcgB3mNmHMbHHC5JygDcI79iVZZCk5Od3GmFZ5HLCLOTc2MdcSTcAsyXtIMwslkrS/wOyCN/dnlhqoW1cTuucc84551y1pPCakXPfTpLqxgyVIiSgWWlmY1OdV16tWrWyFStW7KvmXA2USCSKvJztXHE+RlwqPkZcKpUdI2PHjmXSpElIokOHDkyePJlJkyYxbtw4Pv74Y9avX1+YzfH+++/nySefBEKdtg8++ID169fToEEDHnzwQSZOnIiZce211zJs2LCyLusqSdJCM/teSftqZCISd1C5NiY+WQbUIyRjcc4555w7qGVnZzN+/HiysrJYunQp+fn5TJ8+nR49evDyyy9z/PHHFzl+xIgRLFq0iEWLFjF69GjOOussGjRowNKlS5k4cSLz589n8eLFzJo1qzApiTtwqlXQJilf0qKkn9vLOLaPpLZJ2/8lqfc+6EP9uOSuoueNklRqzTNJT8X3zxYV+/nu3vX4G9cp8lzKcfzIpL58LClH0heS3pU0Zl/2LV5vmKQ6kgaX8CwmxGNWxxICKZnZWDPrZGZtzewKM8spre143RckLZe0TNK9+/r+nHPOOeeqi7y8PHJzc8nLyyMnJ4cmTZrQuXNnmjdvXuZ5Tz31FAMGhDQEH3zwAd27d6dOnTrUqlWLs846i2efffYA9N4lq1ZBG5Ab/wFe8FPWP6r7ENK+A2Bmd5rZy/ugD/WBCgdt5bALGFHs/jqZ2cZ9fJ0izyUVM7snZp+8Mn7UxcyOAb5HqKu2rw0jJEOZXMKzuHFfXCBF2/9tZq2BzkAPSefui2s655xzzlUnTZs2Zfjw4aSnp9O4cWPq1atHRkZGyvNycnKYPXs2l156KQDt27fnjTfeYOPGjeTk5PDiiy/y+eef7+/uu2K+FYlI4ozIhYSU/f8Eno3bZ8UEHJcSapLNMrNnJK0m1Ow6N54zBBgNtADuN7M/xRpnMwlp7Q8jJM2YCdwLnBSX3L1kZiMkjQD6A0cAz5nZXbFfIwn1yL4gJMhYWM77WQ1MBS6I1+5nZsslnQU8GA8zQtHobWVcfyAh66QBS4CHS3gu5xESiOQB75vZZaV065fAPWa2HCBmsXw4Xqc5IQNnQ0KSlcFm9llMvT/LzJ6Jx203s7oKpQ5GEUoEtI/P5UpCkfImhKLkG8ysV3meV2y7W3w2tYHc2IcVkuoAU+J1VsT2bzSzrOJtmFkOMQOmmX2lUPetWVnXzd2dT/PbXyhvN91B6NYOeQzyMeLK4GPEpeJjxKVS0TGy+t7z2Lx5MzNnzmTVqlXUr1+ffv36MW3aNK688soyz/373/9Ojx49aNCgAQBt2rThtttuIyMjgyOPPJJOnTp56v4qUN2CtrQYLBUYDbwMXAy0NjOTVN/MvpT0PEUDhuJtfWZmnSSNJfyjvgfhH/xLgT8RikxfbGZb41K8ebHN24H2BbXPYqr8k4FuhIyLzyvUKNsBXAZ0IjzHdyhn0BZtMLMucSnmcOCa+OeNZjY3BpU7y7j+RkKR7TPMbINiLbYSnsvtwAlmtkuhjEBp2hOybZbkD8BUM5sq6SpgPKmzY3YmlCxYS8j22MPMxku6BehVkBWzApYD/xFrzfUGfkcISm8ANptZW0ntgUVlNVIgPosL+DpITt43hBDo07BhI+7skFfBrrqDybFp4S9T50rjY8Sl4mPEpVLRMZJIJEgkEtSuXZtly5YBIfiaMWMGzZqF/1+9c+dO5s6dS7169Yqc+9BDD3HWWWeRSCQKPzvppJMYMyb8M3HixIk0atSoyH63/1W3oC23eKFoSbUIAdajkmYBs8rZ1vPxz4LU+duAbZIKgpcdwO9iALSHUFPs2BLayYg/78btuoQg6juEWa+c2M/nSzi3LAWLgRcCl8Tf5wIPSHoSeNbM1sSgraTrnwLMKAh+zGxTKddZAjwp6W/A3yrYxwKnJ/XxCeC+cpwz38zWAMRAvDnwv5W8PoQkI1MlnUyYWSyosfd9YuBlZkslLUnVUBxTTwHjzewbterM7BHgEQjZI4decdFedNvVdIlEgv6e9c2VwceIS8XHiEulMmMkLS2NGTNm0K1bN9LS0pg8eTK9e/cuzEJZu3ZtevToUZg9EmDLli0sW7aM2bNnc+SRRxZ+/sUXX3DMMcfw2WefsXDhQubNm0f9+mXNBbh9rbq90/YNZpZHmGV6BjifUF+tPHbFP/ck/V6wXYtQ36wR0DUGiv8mzMQVJ2B00rtRLczs0YrfSan9y4/9Ib7Ddw2QBsyV1HofXP88Qir8LsCCGLCUZBmh8HhF5BHHkKRDgMOT9iU/88J73At3A6+ZWXvCDFlJ31V5PUIoDTBuL/vknHPOOVctde/enb59+9KlSxc6dOjAnj17GDJkCOPHj6dZs2asWbOGjh07cs011xSe89xzzxUug0x26aWX0rZtWy644AImTJjgAVsVqPZBW1wmWM/MXgR+QZhhAthG6iLNZakHfGFmuyX1AgrynhZvdw5wVewHkppKOgZ4HegjKU2hePUFe9EXYtsnmdl7ZvZ7QqHo1mVc/1WgX0H2SUkNivc/BlLHmdlrwG3xnuuWcvn7gV9LallwrqSCYtpvEpaCQgh234i/r+brQO9Cvp79Kktlv7d6QHb8fVDS53MJ7/sRs2Z2KKsRSb+NbXmBEeecc87VaJmZmSxfvpylS5fyxBNPcMQRR3DTTTexZs0a8vLyWLt2LZMmTSo8ftCgQUyfPv0b7bzxxhu8//77LF68mLPPPvtA3oKLqtvyyOLvtM0mLH2bKak2YdbplrhvOjBR0k1A30pc60ng75LeA7II70xhZhslzZW0FPhHTETSBngrvje3HbjSzN6R9FdgMSERyYJK9KG4YTGA3EOY+fpHfBetpOsvk3QP8C9J+YTlk4Mo+lwuIywrrUd4duPN7MuSLmxmSyQNA56KyT2Mr5eiDgUmx4Qo64HB8fOJhO9mMeG72lGOe3wEmC1pbYpEJEsk7Ym/P01Ykjk1JlhJfhP3j/Hz9wnf4TJgS0kNSmoGjIzHvROf50NmNqmk451zzjnnnKsOZGZV3QfnKk3SocBhZrZT0kmExDWtzOyrfdF+q1atbMWKFfuiKVdDJRKJwvcDnCuJjxGXio8Rl4qPkYODpIVm9r2S9lX75ZHOpVAH+N842/cccMO+Cticc845574txo4dS7t27Wjfvj0DBgxg586dPPTQQ7Ro0QJJbNjwdeJuM+Omm26iRYsWdOzYkXfeeQeA1157jU6dOhX+1K5dm7/9rbJ57Ny+VN2WR37rSZpAKC+Q7EEzm1wV/SlO0mDg5mIfz91Xha0r2Je3CbXnkv3EzN4rbxsxK+g3/o/EvmjbOeecc+7bIDs7m/Hjx/P++++TlpZG//79mT59Oj169OD888//xizdP/7xD1auXMnKlSt5++23uf7663n77bfp1asXixaFN5U2bdpEixYtylWQ2+1/NS5ok2TAA2Z2a9weTkj5P2oftD2FpBpoJalo8BPryH1akMlQ0hzgczO7Jm6PAbLN7IF90dcYPKYMIEs6v6B4dkX6UUK7PYHhZna+mXUvYf8gST8zs5/vzXVKafuKWE5BhIQo15vZ4r25jnPOOedcdZCXl0dubi6HHXYYOTk5NGnShM6dO5d47MyZMxk4cCCSOO200/jyyy9Zt24djRs3LjzmmWee4dxzz6VOnToH6hZcGWpc0EZINX+JpNGVKOC830iqFcsXFFeQ/XBczPbYEDgqaf8ZhKyZbu+tAs4ys82SziUkRflGcJcsd3c+zW9/oaxD3EHu1g55DPIx4srgY8Sl4mPEpVLWGFl973k0bdqU4cOHk56eTlpaGhkZGWXOkGVnZ3PccccVbjdr1ozs7OwiQdv06dO55ZZbSjrdVYGaGLTlEf4x/gtCpsBCxWePCmaO4uxPJvAlIWX804Si3DcTaqb1MbOPYzO9Jd1OCKxuMbNZMRnGvUBPwpK8CWb259ju3cBmQvr+liX0901gbPy9HbAUaCzpaCAHaEPIdNgVeICQsn8DMMjM1sXkGxMINedygGvNbHmx+74bOA64GriHkJ4/D/inmQ0vxzMtQlJj4K/xGdQizFi9EQuBZ8Zn8DEw2My2S/ohMC72r1IFtiU9DJxK+D6eMbO74uc/IjyXHYQA+EQzO7+kNszszaTNeUCzUq41BBgC0LBhI+7sUFKs7VxwbFr4y9S50vgYcan4GHGplDVGEokE27ZtY+rUqUybNo26desyatQoRo4cyTnnnAPAzp07mTt3LvXq1QNg48aNvPvuu+TlhTY3b97MwoUL2b59e+H+d955h9q1a5NIJPb/DbqUamLQBiGIWSLpvgqccwohQNoEfAJMMrNukm4mpLwvqOvVnFDs+yTgNUktgIHAFjM7VdIRhMLY/4zHdwHam9mqki5qZmsl5UlKJ8yqvQU0BU4npK5/j5B+/w/ARWa2XtKPCcHXVYQA9TozWympOyEF/g8K2pd0P6Eu2mCgAXAx0NrMTFJlKyNeDswxs3tiwFpHUkPgDqC3me2QdBtwS/wOJsY+fUQI9ipjpJltitd7RVJH4EPgz8CZZrZK0lMVaO9q4B8l7TCzRwjPlVatWtnQKy6qZJfdwSCRSNDfM3q5MvgYcan4GHGppBojM2bMoHPnzvTp0weAtWvXMm/evMJ32WrXrk2PHj1o2LAhAB07dqRhw4aF+3fs2MGFF15YONP24IMP0r9/f3r37r3f7slVTI3MHmlmW4HHgZsqcNoCM1tnZrsIs0QFQdd7hECtwNNmtsfMVhKCu9ZABjAw1ph7G/gucHI8fn5pAVuSNwkBW0HQ9lbS9lygFdAeeCle4w6gWSy4fQYwI37+Z6BxUru/IRQmv85CbYctwE5C7bZLCDNfpSmpFkTBZwuAwZJGAR1iMpDTgLaEgHUR8FNCwfLWwCozWxn7MC3FsyhNf0nvEOrRtYvXag18kvR8yxW0xVp4VxMKjjvnnHPOfaulp6czb948cnJyMDNeeeUV2rRpU+rxF154IY8//jhmxrx586hXr16RpZFPPfUUAwYMOBBdd+VUI4O2aBzhH+ZHJn2WR7zn+P7Y4Un7diX9vidpew9FZySLBzNGSGwx1Mw6xZ8TzKwg6CtPwem5hOCrA2F55DzCTNsZhIBOwLKk9juYWUa8ly+TPu9kZsn/hS4AukpqABDfqesGPAOcTyiIXZqNwNEFG7GNDbGd14EzgWxgiqSBsY8vJfWjrZldXY57T0nSCcBw4Gwz60gorl27km11BCYRZi037ov+Oeecc85Vpe7du9O3b1+6/H/2zju+yiL7/+8PRSyIqKA/LIhYIj0CigUV1hVFsCAs+l1UQBErYgFlV0GssLosKmJFwWWxr4ptrRALooAQqqAoKKiL4EoJBkg5vz9mEm7CTUNayHm/XveVp8wzc2aeEe+5Z+ZzmjenSZMm5Obm0rt3bx588EEOOuggli5dStOmTenVqxcAZ555JvXr1+fwww/nsssu4+GHH86va/HixSxZsoRTTjlle3XHScJO67SZ2f8Ie9MSHYfFQIt4fDZQdTOq/pOkSnEvWX1gAfAOcKWkqgCSjpS0R3GVFOJTghP1PzPLibbXJDhun8Y2aks6PtZfVVKjGFFcJOlP8bokNUuo923CXrs3Je0ZI3N7mdlbhD1/iWULkwacLynPse0BTIztHAIsM7MnCA5Qc4KjeWJcLoqkPSQdCcwH6sXxAticn21qEJzfVZL2B9rH6wuA+pLqxfPzi6skLkF9mSD9/9Vm2OE4juM4jrNDcvvttzN//nzmzJnD2LFjqVatGtdeey1Lly4lOzubH3/8kVGjRgEgiZEjR/LNN98we/ZsWrbcmD2pXr16/PDDD1SqtNO6CeWSnXVPWx7DgETp+CeA8TER89uULgpWmO+BKQRH4gozWydpFGEJ5XRJApYD55ahztkE1chnCl2rnqeAKakL8KCkvQjv7X5gLtANeETSrQQn9DkgX8bezF6UtCfwGmEv2nhJuxIiY0VKAkWBlRbAF5JyCEtGr4i32wD9JWUBGcDFca9dD+DZuK8P4FYz+yoKe7wp6TfgY8Ieu+LoISlx/I4jLIucDywhRCYxs0xJVwFvS1pLiCwWxyDC0tWHw2siu6is847jOI7jOI6zo6CwzchxyieSqkeFShEEaL42s+ElPVdaUlJSbMGCBVuqOmcnJC0tbZOkpY6TiM8RpyR8jpQPFixYwPnnb1zU8+2333LHHXewcuVKnnjiCWrXrg3APffcw5lnnplf7vvvv6dhw4YMHjyYfv36sW7dOk4++WTWr19PdnY2Xbp04fbbby+2bZ8jFQNJXxQVUNjZI23Ozs9lkroT9ifOIIixOI7jOI7jbFFSUlJIT08HICcnhwMPPJBOnToxevRorr/+evr1S55F6YYbbqB9+/b559WqVWPChAlUs/kZUgAAIABJREFUr16drKwsWrduTfv27TnuuOO2ST+c8slOt1hVkkkalnDeL6ocbom6x8Rlipvz7L6S0pN89pWUE4/nSpop6cYolLLVkXRLgi3zJWVKuiXe+794nrdXr4mkWZvRRhtJbxS61jPJWIyU1EPSQ6Wt28yGJwifdDOz34qpu5ukWZJmS/q00P4/x3Ecx3GcUvHBBx9w2GGHccghhxRb7tVXX+XQQw+lUaNG+dckUb16dQCysrLIysoibttwnCLZ6Zw2gurjeTFv2I7EqkIqj3mfX4DMeNwIOI0gtHHbtjDKzO7Os4Ugo58JPBhvnwB8CRydcP7pprVsVrujk4zF1Vu57kXAKWbWhJD0/PEt0Z7jOI7jOBWL5557roAk/kMPPUTTpk255JJL+PXXXwHIyMjgb3/7G7fdtulXupycHFJTU9lvv/047bTTaNWq1Taz3Smf7IzLI7MJX8avB25JvCFpDPCGmb0UzzPMrLqkNsDtwEqC7P4LBCGQvsBuwLlm9k2s5o+SBhCESG6Igh2VCSqNbYBqwEgzeyzWeyfwKyGn2JElGW9mP0fhjqkxQlgNeARoGft2g5lNlNQIGE1YFlgJ6BwTbF9IyE+3CyFn3FWx6idjHQY8lWzfl5nlSpoGtALeJyhtjiQ4a1Pi3/ejMuYIQu64qsBgMxtf1DgUegfHEN5Pl4QxLRFJjwDHEN7HS2Z2W7x+JvAPgqjMJKC+mXVMVoeZJTqcnwEHldRuZlYO9Qa8WVoznQrIjU2y6eFzxCkGnyNOSfgcKR8sHtoBgA0bNvDaa68xZMgQAK688koGDhyIJAYOHMiNN97IU089xeDBg7n++uvzo2qJVK5cmfT0dFauXEmnTp2YM2cOjRs33qb9ccoXO6PTBsHRmCXp3jI80wxoAPyPkDR7lJkdK6kv0Ae4LparR8h1dhgwMUrcX0yIpB0TlRMnScrL09YcaFyKBNv5mNm30QHaD7gwXLImko4C3o1S+lcAD5jZuCjLX1lSA4Ls/YlmliXpYYK65FzgQDNrDCCpZjHNTwJOkDSZkKMuDRhCUKs8AbiD4AxPMLNLYl1TJL0f2ypqHJB0AsHZO8fMvi/teERuMbP/xXH5QCHf2leEPWwnm9kiSaVKrh25FPhPshvRae4NUKtWbQY1yS6jqU5FYv/dwhcuxykKnyNOSfgcKR+kpaUB8Mknn3DooYfy5Zdf8uWXXxYo06RJE5555hnS0tJ49913+de//sW1115LRkYGlSpVYsmSJXTq1KnAM/Xq1WPkyJEFRE4Kk5GRkd++UzHZKZ02M1st6Z+EiFNmKR+bamY/AUj6BshzNmYDbRPKvWBmucDXkr4lRNDaAU0T9rvtBRwBbACmlMVhS0JrgqODmc2X9B0hYjcZuEXSQcDLMcp2KiE6NjWujd4N+Bl4nZDPbAQhMfW7mzaTz6fAjQRp/qlm9o2kwyXVJqQg+EZSO+BsSXk7bncF6pYwDg0IEbZ2ZvbjZoxD1+hMVQHqEJZyVgK+TRjfZ4nOVnFIaktw2lonu29mj0dbSUlJsT7dztkMc52KQlpaGl1d0cspBp8jTkn4HClfPProo1x11VX5ao4//fQTderUAWD48OG0atWKNm3aMGvWRhmAwYMHU716dfr168fy5cupWrUqNWvWJDMzk4EDB3LzzTcXqw7p6pHOTum0Re4HphOWEOaRTdzHF4U+dkm4tz7hODfhPJeC41Q4R4IRcp71MbN3Em/E5ZFlzgUnqT6QQ3C4kmJmz0j6HOgAvCXp8mjH02b2lyR1NgNOJ0TougKXFFH1Z4RliCcSHEOApcAFCeciLMcsoIUfZfeLGoefCM7d0UCZnDZJhwL9gGPM7Ne4zHXXstSRUFdTQkLw9nE/oeM4juM4TqlYu3Yt7733Ho89tnH3x0033UR6ejqSqFevXoF7yfjpp5/o3r07OTk55Obm0rVrVzp2TLqzw3Hy2WmdtriU7gVCROWpeHkxIRL1AnA2YT9WWfmTpKeBQ4H6wALgHeBKSRPissQjgR82x+4Y0XoUeMjMTNLHhGWHE2K9dYEF0bH71swelFQXaEqIoI2XNDzujduHkMh6LbDBzP4taQHwr6LaN7M1kpYAPQl70yA4a9cBD8fzd4A+kvpEG482sxkljMNKwrt4T9JaM0srw7DUiH1YJWl/glBLGmHs60uqZ2aLCUtDiySO08vARWb2VRnadxzHcRzHYY899uCXXwr+5jt27NgSnxs8eHD+cdOmTZkxY8aWNs3ZydlpnbbIMOCahPMnCE7NTOBtNiMKBnxPEOWoAVxhZuskjSLsdZseo03LgXPLUOduktIJTmQ2MJYgrgHBUXpE0ux4r4eZrZfUFbhIUhbwX+Ce6KjeStj3VgnIAq4mLBEdrY1pBDaJxBViEmHf2ZJ4Phm4h43KkXcSIpmzYp2LgI6ECFaR42BmyyR1BP4j6RIz+7yI9ntIShy/4wg52OYDS6J9mFmmpKuAtyWtBaaW0K9BwL7Aw3H5aHZRCQwdx3Ecx3EcZ0dBZoVX+zlO+UFSdTPLiE7iSODrZMqYm0tKSootWLCg5IJOhcX3GTgl4XPEKQmfI05J+BypGEj6oqiAws6Yp82pWFwWo5RzCcInxS8kdxzHcSoE9erVo0mTJqSmptKyZfgONHDgQJo2bUpqairt2rXjxx8LbrGeOnUqVapU4aWXXsq/dsYZZ1CzZk3fc+Q4znbFnbZthKR9JaUn+ey7newZmcSWntuw/Z5J2h9Z1nrMbHhMnt3QzLqZ2W9bqm7HcRynfDNx4kTS09OZNm0aAP3792fWrFmkp6fTsWNH7rjjjvyyOTk53HzzzbRr165AHf379y/VniXHcZytyQ61p01SDkFiP4/nzGxoEWXPBb4ys3nx/A7gIzN7/3faUBP4s5k9XGLhgs8NBjLM7O9FFBkG3JWX2HtrUXhcisLMrk7ybHuF5Nq7E9QzJ5jZjVvYvuuAx81sNAWVPRPLLAZamtmKzWmjhLrvJuTV29vMNs126TiO4+y01KhRI/947dq1xP3NAIwYMYLOnTszdWrB7dGnnnqq58dyHGe7s0M5bUCmmaWWsuy5wBvAPAAzG7SFbKgJXMVGpcTyRoFxKS2SGgMPAR1iPrjKlCLn2WZwHUG98retUHdpeJ3Qz69LUzgzK4d6A97cuhY55Zobm2TTw+eIUww+R7Yti4d2AEAS7dq1QxKXX345vXuH/6Xdcsst/POf/2SvvfZi4sSJAPzwww+88sorTJw4cROnzXEcZ0dghxIikZSRLPohaShBoj+bIGv/MsExWRU/nYGBwBtm9lKM1DxLkIbPJjgfQ4DDgfvM7FFJ1YHxwN4E1cZbzWy8pOeAcwhy8u+ZWX9J/Qm5zaoBr5jZbdGuW4DuhHxqS4Avioq0xdxiifY9DZwV2/5TdJROAR6IjxhwcpTgL6r9iwn5ywyYBTySZFw6EHKzZQPzzOyCIuz7J5BmZk8luVePkDahFkERsqeZfZ/Yp1guw8yqx7xsg4EVQGPgC+BCoA/w9zi2K8ysLUlIFmmTdGwcm10Japg9zWyBpN2BMbGdBcABwNVmNi1Z3Qn1JZ1r8V5vosNaq1btFoPuf6K4qpwKzv67wbLM7W2FsyPjc2Tb0uTAvQBYvnw5tWvX5tdff6Vfv35ce+21NGvWLL/cuHHj2LBhAz179mTw4MF07dqVhg0bMnToUI4//nhOOeWU/LLp6ek8//zzDBkyZKvYnJGRQfXqvvjDKRqfIxWDtm3bFilEsqNF2vKk7/MYArwPdAKOijnBaprZSkmvUdBhKFzX92aWKmk44Uv9iYQv/HMIedDWAZ3MbLWkWsBnsc4BQOO8iJ+kdsARwLGEpNKvSTqZkC7gAiCVMI7TCc5JaVlhZs2jZH0/oFf8e7WZTYpO5bpi2v8FuBU4wcxWSNonSv4XHpcBwKExTUDNYuxpTFjCmYwRhKTdT0u6BHiQklMaHA00IiTSngScGHPK3QC03Yylj/OBk8wsW9IfCSkIOhOior+aWcMYLUwvrpLSYGaPA49DUI/s0+2c31ulsxOTlpZGV1f0corB58j2Z+bMmWRlZRVQ36tfvz5nnnkmTz/9NN999x333nsvACtWrGD69Ok0a9aMc8/d+L+6999/f6up97kyoFMSPkecHc1p22R5pKQqBAfrSUlvECJJpeG1+Hc2UN3M1gBrJOU5L2uBe6IDlAscCOyfpJ528ZOXBbE6wYnakxD1+i3a+VqSZ4vj5fj3C+C8eDwJ+IekccDLZrY0Om3J2m8GvJjn/JjZ/4poZxYwTtKrwKtltDGP4xNsHAvcW4pnppjZUoDoiNcDPtnM9iEoQz4t6QhCZDEvMXprYnTSzOZImvU72nAcx3F2AtauXUtubi577rkna9eu5d1332XQoEF8/fXXHHHEEQCMHz+eo446CoBFixblP9ujRw86duxYwGFzHMfZ3uxoTtsmxMjKscCpQBdCsuw/lOLR9fFvbsJx3nkVoBtQG2hhZllxSd6uSeoRMMTMCkjJR0GN30OeTTnRHsxsqKQ3gTOBSZJOL6b9PqVspwNwMmEp5i2SmphZdpJyc4EWwMwy9CGbqEAak2zvknAvcczz+/g7uBOYaGad4nLNtN9Zn+M4jrOTsmzZMjp16gRAdnY2f/7znznjjDPo3LkzCxYsoFKlShxyyCE8+uijJdZ10kknMX/+fDIyMjjooIN48sknOf3007d2FxzHcQqwwzttcZng7mb2lqRJwLfx1hpCtGtz2Qv4OTpsbYFDiqj3HeBOSeNiEucDgSzgI2CMpCGEcTyL35kjTNJhZjYbmC3pGOCoYtqfALwi6R9m9kve8shE+6MjdbCZTZT0CWE5Z3VgZZLm7wNelvSJmX0Vn+1tZo8Cn8ZnxxKc3Y/jM4sJjt4LhD2HVTepdVPy7Cvr8si9gB/icY+E65MI+/0mSmoINCljvY7jOM5ORv369Zk5c9PfIP/973+X+OyYMWMKnH/88cfJCzqO42xDdjSnrfCetrcJS9/GS9qVEHW6Id57DnhC0rWECFxZGQe8Lmk2MI2wZ4roAE2SNAf4TxQiaQBMjvvmMoALzWy6pOcJkamfgS0hN3VddCBzCZGv/8S9aMnanxvl6z+MqRJmEJyZxHG5gLCsdC/C2D1oZskcNsxsVowePhvFPYyNS1H7AKOjIMpyIC+f2xOEdzOT8K7WlqKPjwNvS/qxKCGSyCxJufH4BcKSzKcl3QokyrA9HK/PI7zDuQQRlqRIuhf4M7C7pKXAKDMbXAq7HcdxHMdxHGe7sEOpRzpOWYmpCaqa2TpJhxGEa1LMbMOWqD8lJcUWLFiwJapydlJ8c7hTEuV9juTk5NCyZUsOPPBA3njjDcyMW2+9lRdffJHKlStz5ZVXcu211wKhr9dddx1ZWVnUqlWLDz/8EICVK1fSq1cv5syZgySeeuopjj/++O3ZrR2K8j5HnK2Pz5GKgaRyox7pOGVld8LSyKqEaOJVW8phcxzHceCBBx6gQYMGrF69GgjLB5csWcL8+fOpVKkSP//8MxAcs6uuuoq3336bunXr5l8H6Nu3L2eccQYvvfQSGzZs4LfftleqTsdxnPJJpe1twM6GpJGS0gt9esZ7JmlYQtl+kgZvoXbHSCpxmaiknknsG1lE2eGJgiuS3pE0KuF8WJTw3yxbJX2exJZS7UmT1FvSfDYuS+1rZk3N7D/xfrK6+8QUCEg6N+6BcxzHcYpg6dKlvPnmm/Tq1Sv/2iOPPMKgQYOoVCl8hdhvv/0AeOaZZzjvvPOoW7dugeurVq3io48+4tJLLwVgl112oWbN4jLQOI7jOIXxSNsWxsyuLub2euA8SUM2I0/ZFsHMRgOjE6/FtArJyBP5uD8Kk9QCaiTcPwG4/nfY0mpznpPUEbgcaB1z1DUn5K9rZWY/lLLucwl79uZtjg2O4zgVgeuuu457772XNWvW5F/75ptveP7553nllVeoXbs2Dz74IEcccQRfffVVfi60NWvW0LdvXy6++GIWLVpE7dq16dmzJzNnzqRFixY88MAD7LHHHtuxZ47jOOULd9q2LdkEIY7rgVsSb0gaQ8Gk2BlmVl1SG+B2guJjE4Iox2ygL7AbcK6ZfROr+WOMJNUAbjCzN+Ker6FAG6AaMNLMHov13gn8SlCpPDKJvZ8Cw+NxI0Ji8jqS9gZ+AxoA0yW1AP5BUKZcAfQws5/iHrORhNQKvwGXmdn8Qv2+EzgYuBS4m6BCmQ28a2b9ihjHm4H+CTnqpksaDVwN/DWmb2gZHbqWwN/NrI2kHkBL4JnYzilR2KRzwhgWIDMrh3oD3kx2y3EAuLFJNj18jjjFUF7nyEOtjf32248WLVqQlpaWf339+vXsuuuuTJs2jZdffplLLrmEjz/+mOzsbL744gs++OADMjMzOf744znuuOPIzs5m+vTpjBgxglatWtG3b1+GDh3KnXfeuf065ziOU85wp23bM5KgjFiaBNV5NCM4SP8jpDwYZWbHSupLUHbMW8JYDzgWOIywz+tw4GJglZkdI6kaIf/bu7F8c6CxmW3MKpqAmf0oKVtSXUJUbTIhCfnxBIXG2QSVyRHAOWa2XNL5BOfrEoKDeoWZfS2pFUHpMT/HnqT7CPL/PYF9gE7AUWZmCgnQi6IRISl5ItPYqGpZLGb2qUIy9HwnORFJvYHeALVq1WZQk2Rp7RwnsP9u4Uu54xRFeZ0jzz77Au+++y4vv/xy/j600047jX322YcDDjiAtLQ09t57b2bMmEFaWhobNmwgJSWFqVPDqvUjjjiCZ555hqZNm1KrVi0yMzNJS0vjsMMO45lnnuHUU0/dzj3cccjIyCjgGDtOYXyOOO60bWPMbLWkfwLXApmlfGyqmf0EIOkbIM/pmg0kyua/YGa5wNeSviVE0NoBTRP2u+0FHAFsAKYU5bAl8CnBYTuBEE07MB6vIiyfTAEaA+/FlASVgZ8U8uudALwYr0OI9OUxEPjczHrHfq0C1hFSFLzBxnQD2xwze5zgcJKSkmJ9up2zvUxxygFpaWl0dUUvpxjK6xxJ/LcvLS2Nv//977zxxhsMGDCAzMxM2rRpQ1paGg0aNKBNmzbsv//+XHPNNbRu3ZoNGzbw/fffc++999K4cWOGDx9OnTp1SElJIS0tjZNOOsmV8BJwZUCnJHyOOO60bR/uB6ZTcG9ZNlEYJu4f2yXh3vqE49yE81wKvsPC+RuMoKjYx8zeSbwRl0eWJq/aJILz1YSwPHIJcCOwOtovYK6ZFdBullQDWGlmqUXUOxVooZgU3MyyJR0LnErIu3cNCVG5QswjJPWekHCtBSHaBgljCexaij46juM4pWTAgAF069aN4cOHU716dUaNCvpUDRo04IwzzqBp06ZUqlSJXr160bhxYwBGjBhBt27d2LBhA/Xr12f06NHFNeE4juMUwp227YCZ/U/SC4R9XE/Fy4sJjscLhP1WVTej6j9Jeho4FKgPLADeAa6UNMHMsiQdCfxQhjo/BfoB35pZDvC/uHSxEXAZwXmrLel4M5scpfePjMm/F0n6k5m9qBBua2pmM2O9b0fb3pTUjuBg7m5mb0maRFgGWhT3An+TdEZMhp5KWFqZ5+QtJozlf4DORdSxhrA003EcxymBNm3a5P/KX7NmTd58M/kevf79+9O/f/9NrqempjJt2rQkTziO4zilwSX/tx/DCGqMeTxBEMaYSdgzVpooWGG+B6YQnJUrzGwdMIoQmZouaQ7wGGVz1mdHOz8rdG2Vma2IOdG6EJyomUA6ITIH0A24NF6fCxRYZ2hmLxL6/RrBgXpD0izgE6DIVAJm9hrwJGF/3sJY/lwzWx6L3A48IGkakFNENc8B/SXNiIIpjuM4juM4jrNDIrPCK+ocp/wQ0xWMJvwAcaFt4QmdkpJiCxYs2JJVOjsZvs/AKQmfI05J+BxxSsLnSMVA0hdm1jLZPY+0OeUaM8s2s4vMrNuWdtgcx3HKKzk5ORx99NF07NgRgEsvvZRmzZrRtGlTunTpQkZGBgDff/89bdu25eijj6Zp06a89dZbAIwbN47U1NT8T6VKlUhPT99u/XEcx6nouNPmIGlfSelJPvtuZ7tuSWLTLSU/6TiOU7F54IEHaNCgQf758OHDmTlzJrNmzaJu3bo89NBDANx111107dqVGTNm8Nxzz3HVVVcB0K1bN9LT00lPT2fs2LEceuihpKYWpSvlOI7jbG22mtMmKafQl+0BxZQ9V1LDhPM7JP1xC9hQU9JVm/HcYElFJXZG0pgECf2tRuFxKeOz7SVNkzQv7tsaVlRZM/vFzFKTfH4poY3rJO1eQpnFkmbHzzxJd0kqlaKjmd2dxKa7S/Os4zhORWXp0qW8+eab9OrVK/9ajRo1ADAzMjMzyUvFIonVq1cDsGrVKg444IBN6nv22We54IILtoHljuM4TlFsTfXIzGLk3gtzLiEv1zwAMxu0hWyoCVxFSOpcHikwLqVFUmPgIaCDmc2XVJmYLHoLcx3wL+C3Esq1NbMVMXfb4wQxlO5bwZ4tTmZWDvUGJFdJcxwISZN7+BxximFbzpHFQztw3XXXce+997JmzZoC93r27Mlbb71Fw4YNGTYs/I43ePBg2rVrx4gRI1i7di3vv//+JnU+//zzjB8/fpvY7ziO4yRnm0v+SxpKkLTPJiSJfjmenyLpVoJE+0DgDTN7SdJi4FmgfXymNzAEOBy4z8wejc7AeGBvglT+rWY2HhgKHCYpHXjPzPpL6g90JSR6fsXMbot23UJwJH4m5CL7opT9WQw8DZwV2/5TdJROAR6IxQw42czWFNP+xQRpfQNmAY8kGZcOwBVxHOaZWVE/fd4E3G1m8wGiVP8jsZ16hDQDtYDlQE8z+17SmLwxj+UyzKx6zOc2GFhBSKL9BXAh0Ac4AJgoaYWZJSb5ToqZZUi6AlgiaR9Cgu9N3lu08W2CYuUJhJxuowmqkPsB3cxsSszr9gAhF1tm7MuCGP0bE+1dEO282symxfQCt8fx/yY+k5Fop6TeRCe3Vq3aDGqSXVLXnArM/ruFL+WOUxTbco4MGTKErKws1qxZQ3p6Or/88gtpaWkAdO/enQsvvJAHH3yQ22+/nfbt2/PCCy9w0kkn0bVrV+bOnUvnzp156qmnqFQpLMSZN28eZsaKFSvy63G2PBkZGT6+TrH4HHEws63yIUitpyd8zgf2JXyJzlOtrBn/jgG6JDybf07IuXVlPB5OcGj2BGoDy+L1KkCNeFwLWEhI+lwPmJNQbztCpEeEpaFvACcTcnrNBnYHasTn+xXTt8L29YnHVwGj4vHrwInxuHq0saj2GwFfAbVi+X2KGJcfgWqJY1eEfdOBZkXcex3oHo8vAV4toq2M+LcNsAo4KNo8GWid0PdaJcyDTcrE+dCqhPeWTUjoXYngKD4V752TYHMNoEo8/iPw73jcD3gsHjeOdbWMbXwE7BHv3QwMKs7+I4880hynOCZOnLi9TXB2cLblHBkwYIAdeOCBdsghh9j+++9vu+22m3Xr1q1AmQ8//NA6dOhgZmYNGza077//Pv/eoYceasuWLcs/v+666+zuu+/eNsZXYPzfEackfI5UDIBpVsR30q0pRJJpBfciPU/48r8OeFLSeZS8rC6P1+Lf2cDnZrbGQk6u9THRs4B7Yo6v94EDgf2T1NMufmYQHJujgCOAkwhRr9/MbHVCe6Xl5fj3C4LDATAJ+IekawkOVnYx7f8BeNHMVkBIvl1EO7OAcZIuJDgim8PxwDPxeCzQuhTPTDGzpWaWS3C46m1m23ko4W9R722Rmc2Obc4FPoiTeXZC+3sBL8b8c8MJzi+EPj0HYGZzCOMGcBzQkJDfLZ0QWT3kd/bFcRxnh2HIkCEsXbqUxYsX89xzz/GHP/yBsWPHsnDhQiD8UPvaa69x1FFHAVC3bl0++OADAL788kvWrVtH7dq1AcjNzeWFF17w/WyO4zg7ANt0eaSZZcclbacSEjJfQ3BYSmJ9/JubcJx3XoWQxLk20MLMsuKSxWRiFwKGmNljBS5K15WlH8XYlxPtwcyGSnoTOJPgJJxeTPt9StlOB0Jk7izgFklNojNYmLmE6OHMMvQhmyhMI6kSsEvCvcQxz+/j5iBpT4LT9RXFv7fC7zlxDuS1fycw0cw6xSWVaSU1T1gm+3+ba7/jOE55w8zo3r07q1evxsxo1qwZjzzyCADDhg3jsssuY/jw4UhizJgx+SIlH330EQcffDD169ffnuY7juM4bGOnLe49293M3pI0Cfg23lpDWPK4uewF/By/+LdlY/SkcL3vAHdKGmdhf9WBQBZhydwYSUMIY3IWQSxjs5F0mJnNBmZLOoYQVSuq/QnAK5L+YWa/SNonRtvy7Y+O1MFmNlHSJ8AFhGWXK5M0fx/wsqRPzOyr+GxvM3sU+DQ+O5bgNH0cn1lMcPReIOylq1qKbubZt6KUY1KdIArzqpn9Kqmo91Za9gJ+iMc9Eq5PIuwbnBjVN5vE658BIyUdbmYLJe0BHGhmX5WxXcdxnB2eNm3a5CfjnTRpUtIyDRs2LPJemzZt+Oyzz7aWeY7jOE4Z2JpO225xCVoebxNEI8ZHyXcBN8R7zwFPxKWEmyOlPw54XdJsYBqQJ8Dxi6RJcfncfywIkTQAJsdfEjOAC81suqTnCZGpnwnCF7+X66Ijkre87z9mtr6I9udKuhv4UFIOYflkDwqOywWEZaV7EcbuQTNL5rBhZrNi9PDZKMphhP1zEARERkdBlOVAz3j9CcK7mUl4V2tL0cfHgbcl/WjFC5FMVOhwJeAVQoQMinhvZeBe4Oko1JIozfZwvD4v1jkXWGVmyyX1IIxLtVj2VkLUz3Ecx3Ecx3F2SPIEQRxnpyGmOKhqZuskHUbYL5diZhvKWldKSootWLBgi9vo7DykpaXlRzOcism6des4+eSTWb9+PdnZ2XRQabVOAAAgAElEQVTp0oXbb7+dDz74gP79+7N69Wr+3//7f4wZM4bDDz+c66+/nokTJwLw22+/8fPPP7Ny5Uq+++47OnXqRG5uLllZWfTp04crrrhiO/fO2Rb4vyNOSfgcqRhI+sLMWia7t80l/x1nG7A7IbpXlRCVvGpzHDbHcZzSUK1aNSZMmED16tXJysqidevWtG/fniuvvJLx48ezbNky5s2bx1133cWYMWMYPnx4/rMjRoxgxowZANSpU4fJkydTrVo1MjIyaNy4MWeffXbShNeO4zhOxcKdtmKQNBI4sdDlB8xs9DZo+xbgzwThj1zgcjP7vFCZnkBf4GDCss71QH2gfp4SZULZfYEP4un/i/Uuj+fH/l6nRtLnhNxniTwA3E/Yu7g7sAy418zeYCtiZmsIEv+O4zhbHUlUr14dgKysLLKyspCEJFavXg3AqlWrkjpfzz77LLfffjsAu+yyUf9p/fr15ObmbgPrHcdxnPKAO23FYGZXb492JR0PdASax31wtSio5ghAdB5HF3p2cbI6zewXIDWWGUzIw/b3LWWzmbUqfC0m5v7YzDrG81TgVUmZZvZB4fKO4zjllZycHFq0aMHChQu5+uqradWqFaNGjeLMM8+kUqVK1K5dexNRj++++45Fixbxhz9sFFFesmQJHTp0YOHChdx3330eZXMcx3EAd9p2VOoAK8xsPUBe1EzSqcDfCe9tKiHp+HpJaYRk4NPK0kiU358FHBkVHGsQxFiOBN6Lx6fE9i4xsylRcXEEIWl1VWCwmY0vTXtmli7pDkKqhw8knUUQAtkF+AXoZmbLolN5KCFqWBe4npBjrT1BLfKsaO8ggtLnbgRVzMvNzKJa55OECOV7QHszaxz3ug0lJAyvBowsnH6hMJlZOdQb8GZxRZwKzo1Nsunhc6TCsnhoBwAqV65Meno6K1eupFOnTsyZM4fhw4fz1ltvkZmZydSpU7nhhhsYNWpU/rPPPfccXbp0oXLlyvnXDj74YGbNmsWPP/7IueeeS5cuXdh//2RpRx3HcZyKhDttOybvAoMkfUUQ0Xge+BwYA5waZfz/CVxJWH64WZjZmujwdQBeJShUvhwdIgjpGVIlnQw8RXDUbgEmmNklMbH5FEnvm1lp1CYhJBXvH48/AY6LjlYv4CbgxnjvMKAtIRn2ZKCzmd0k6ZUEex8yszsAJI0lRCdfJ0QfLzOzyZKGJrR9KUFF8pioHjlJ0rtmtijRQEm9gd4AtWrVZlCTzc1j7lQE9t8tOG5OxSQtLW2Ta/Xq1eOhhx7i888/JzMzk4yMDOrWrcvIkSMLlB81ahR9+/ZNWgfAvvvuy6OPPsopp5yydYx3dhgyMjKKnAeOAz5HHHfadkhiDrcWwEkEx+V5YAiwKCGn2NPA1fwOpy0yiuAsvUqQ/78s4d6z0Z6PJNWITlo74GxJ/WKZXQnRsC9L2Z4Sjg8CnpdUhxBtS3Se/hOdx9lAZUIaAoDZhOTcAG0l3UTYL7cPMFfSx8CeZjY5lnmG4MwRbW8qKS+txF7AEYXaxcweJ6QzICUlxfp0O6eUXXMqImlpaXR1Ra8KzfLly6latSo1a9YkMzOTgQMHcvPNN/PSSy9xwAEH8OOPP7Js2TJatGiRr/42f/58srKyuPrqq/OTWS9dupR9992X3XbbjV9//ZVvvvmGe++9lyZNmhTTurMz4MqATkn4HHHcadtBMbMcIA1Ii47LVtlfZ2aTJNWL+88qm9mcxNuFixOcrs5mtrk6+Eez0cEbAfzDzF6L7Q9OKJe3NDRXUpZtzE2RC1SJuf4eBlqa2ZK4pHLXEtoW0MfM3tlM2x3HcTbhp59+onv37uTk5JCbm0vXrl3p2LEjTzzxBJ07d+a3337j4IMP5qmnnsp/5rnnnuOCCy7Id9gAvvzyS2688UYkYWb069fPHTbHcRwHcKdth0RSCpBrZl/HS6nAN0A7SYeb2ULgIuDDLdTkPwkRqTsLXT+fIJ3fmrCscJWkd4A+kvrEZY1Hm9mM0jQiqSkwEOgVL+1F2KMG0L2MNuc5aCskVSckZX/JzFZKWiOpVVTbvCDhmXeAKyVNiFG8I4EfyrC003EcZxOaNm2aL9ufSKdOnejUqVPSX8gHDx68SfnTTjuNWbNmbSUrHcdxnPKMO207JtWBEXE5YjawkLDH6lngRUl5QiSPbqH2xgF3xfoTWSdpBkFw5JJ47U7CksxZkioRlhZ2pGhOinXsTkhLcG2CcuRgQn9+BSYQxEdKRXTOngDmAP8ljEcelwJPSMolOLar4vVRhKWV0xV+3l4OnFvaNh3HcRzHcRxne+BO2w6ImX0BnJDk1geE5YWFy7dJOK5XivoHF7rUmhilKnT9X2Z2XaFnM4HLS2ojlk0jRNOKuj8e2ER5srB9ZlY92T0zu5WgPlmYuWbWFEDSAGBaLJ8L/DV+HMdxHMdxHKdcUGl7G+BsXySNIMjgF14aWZ7pICld0hyCmMtd29sgx6lorFu3jmOPPZZmzZrRqFEjbrvtNgAWLVpEq1atOPzwwzn//PPZsGEDAB999BHNmzenSpUqvPTSSwXqOuOMM6hZsyYdOxYX1Hccx3GcnRd32nZCJO0bnZbCn30LlzWzPmZ2eIIqZd71NqXN+ybp9CRtvbKl+lNWzOx5M0s1s8Zm1sHMlm8vWxynolKtWjUmTJjAzJkzSU9P5+233+azzz7j5ptv5vrrr2fhwoXsvffePPnkkwDUrVuXMWPG8Oc//3mTuvr378/YsWO3dRccx3EcZ4dhh3faJJmkYQnn/aJS4Jaoe0yC/PsWQ1JOdFzmSpop6ca4/2ubYGa/mFkqUdwjOjCpBCGTTElVo51NJJV517ukNpLeSGjvnbw2Ej6dYtkekpZLmiHpa0nvSEq29NNxnJ0ISVSvHlY2Z2VlkZWVhSQmTJhAly7hn93u3bvz6quvAiG3WdOmTalUadN/Kk899VT23HPPbWe84ziO4+xglIc9beuB8yQNMbMV29uYPCRVMbOiMupmRicJSfsRlBlrALdtK/sis4G6kvY0szWEfXJfEvbFTYnnn24DO543s2sAJLUFXpbU1sxKm9ttu5GZlUO9AW9ubzOcHZgbm2TTw+dIARYP7QBATk4OLVq0YOHChVx99dUcdthh1KxZkypVwv96DjroIH744YfiqnIcx3Ech/LhtGUTEh1fD9ySeEPSGOANM3spnmeYWfWY8+t2YCXQBHiB4MD0BXYDzjWzb2I1f4xiFTWAG8zsDUmVCfu82gDVgJFm9lis907gV+Ao4MiSjDeznyX1BqbGCGE14BGgZezbDWY2UVIjYDQhyXQlQi60ryVdCFwbr38OXBWrfjLWYcBTZjY8Sdu5kqYBrYD3gRbASIKzlue0vS9pD0LOtMYEpcjBZja+qHEo9A6OIbyfLgljWtx4TJT0OEEN83pJl8XjXQgqmReZ2W/x3WYSHMz9COqVFwPHA5+bWY/Y/iPAMYT3+pKZ3Ravnwn8A1gLTALqm1nHovpaqE+9o03UqlWbQU2K8s0dB/bfLThuzkbS0tLyj++//34yMjIYOHAgBx10EJmZmfn3f/75Z9auXVug/H//+1/mzp1LrVq1CtSZnp7OL7/8UqBseSEjI6Nc2u1sO3yOOCXhc8QpD04bBEdjlqR7y/BMM6AB8D/gW2CUmR0rqS/QB8hTRawHHAscRshJdjjBOVhlZsdIqgZMkvRuLN8caGxmi0priJl9Gx2g/YALwyVrIuko4N2YL+wK4AEzGydpF6CypAaEXGknxrxiDwPdgLnAgWbWGCCmBiiKScAJkiYTElOnAUMIsv0nAHcQnOEJZnZJrGuKpPdjW0WNA3GZ4wjgHDP7vrTjAUxnowLly2b2RKzvLoJc/4h4b2+Ck3Y28BpwIiHH21RJqWaWDtxiZv+L4/tBzAX3FfAYcLKZLZKUmMogaV8Tc7WZ2eMER5SUlBTr0+2cMnTNqWikpaXRtVAOLmdTpk+fzrp161i/fj2tW7emSpUqTJ48mSOPPLJADrMxY8bQqFGjTfKaAbz//vtJr+/oJMvT5jiJ+BxxSsLniLPD72kDMLPVhATQ15bhsalm9pOZrSckps5zNmYTHLU8XjCzvETW3xIiaO2AiyWlE6Jb+wJHxPJTyuKwJaE18C8AM5sPfEeI2E0G/irpZuCQKK1/KiE6NjXacipQP9pZX9IISWcAq4tp71OCc3YsYUy+AQ6XVBuoHs/bAQNiG2mExNV1SxiHBgTH5qwyOmwASjhuLOljSbMJTmKjhHuvm5kR3tkyM5sdZfvnsvEddpU0HZgRn21IeIffJrynRKetqL46jrMFWb58OStXhiwimZmZvPfeezRo0IC2bdvmq0M+/fTTnHOO/yjiOI7jOCVRXiJtECJD0wlLCPPIJjqeUehjl4R76xOOcxPOcynYbyvUjhGcij5m9k7ijbg8ci1lRFJ9IIeQXDopZvaMpM+BDsBbki6PdjxtZn9JUmcz4HRChK4rG5NfF+YzwvLBEwmOIcBS4IKEcxGWYy4o1EZx4/ATweE5GvixqH4VwdGEvXUAYwjLVWdK6kFYiplH4jsr/D6rSDoU6AccY2a/xiWVu5bQdtK+Oo6zZfnpp5/o3r07OTk55Obm0rVrVzp27EjDhg254IILuPXWWzn66KO59NJLAZg6dSqdOnXi119/5fXXX+e2225j7ty5AJx00knMnz+fjIwMDjroIJ588klOP/307dk9x3Ecx9mmlBunLS6Be4GwfO6peHkxIRL1AmEJXdXNqPpPkp4GDiVEsRYA7wBXSpoQlyUeCWzWbvkY0XoUeMjMTNLHhIjShFhvXWBBdOy+NbMHJdUFmhKig+MlDY974/YB9iQ4jhvM7N+SFhAjd8kwszWSlgA92egQTSYsD304nr8D9JHUJ9p4tJnNKGEcVhLexXuS1sZE2qUZj1MI+8Xaxkt7Aj9FRctulG2caxDGYpWk/YH2hOjZAkIksp6ZLSYsMc2jqL46jrMFadq0KTNmbPqfVv369ZkyZcom14855hiWLl2atK6PP/54i9vnOI7jOOWJcuO0RYYB1yScP0FwamYCb7MZUTDge4IoRw3gCjNbJ2kUYfnd9BhtWg6cW4Y6d4vL76oSooFjCaIYEBylR+JywGygh5mtl9QVuEhSFvBf4J7oqN5K2PdWCcgCriYIdIxOSCOwSSSuEJMI+86WxPPJwD1sVI68kxDJnBXrXAR0BIodBzNbJqkj8B9Jl5jZ50W0f76k1sDuse7OCcqRAwlLL5fHv6XW9Y7RuRnAfGBJ7CdmlinpKuBtSWuBqQmPFdVXx3Ecx3Ecx9khUdgy5Dg7F5Kqm1lGdDZHAl8nU9gsiZSUFFuwwFdSOkXjm8NhyZIlXHzxxSxbtgxJ9O7dm759+zJz5kyuuOIKMjIyqFevHuPGjaNGjRpMmTKF3r17A2BmDB48mE6dOhVZT3nH54hTEj5HnJLwOVIxkPSFmbVMdq9cCJE4zmZwWYx2zgX2IqhJOo6zFahSpQrDhg1j3rx5fPbZZ4wcOZJ58+bRq1cvhg4dyuzZs+nUqRP33XcfAI0bN2batGmkp6fz9ttvc/nll5OdnV1kPY7jOI5T0SmV0ybpsCj5jqQ2kq4tQWZ+p0fSvpLSk3z23cZ23CJprqQVkjIlfZVgS89taEfPJGMxUtJiSbPjZ56kuySVJBbyuzGz4WaWamYNzaybmf22tdt0nIpKnTp1aN68OQB77rknDRo04IcffuCrr77i5JNPBuC0007j3//+NwC77757foLtdevWEQLiRdfjOI7jOBWd0u5p+zfQUiGH2ePAeOAZ4MytZdiOjpn9AqRuTxskHU/Yj9U87ourBexiZmVVc/zdmNloCip75tnYAWhrZiskVSfMn8eA7tvYRMdxtgGLFy9mxowZtGrVikaNGjF+/HjOPfdcXnzxRZYsWZJf7vPPP+eSSy7hu+++Y+zYsflOXLJ6HMdxHKeiU1qnLdfMsiV1AkaY2YgoAOFsX+oAK2IuOsxsBYCkFgThk+rACoLYyU+SLiMoN+4CLAQuMrPfJP0JuI2QlmCVmZ0co2GPAC0Jgik3mNnEKMt/NkFU5DDgFTO7qTTGxj1mVwBLohLmBsIPAHsTRFtuNbPxkuoRhGU+I+SYm0pwCG8nJCjvZmZTJB0LPECQ+c8EeprZAkm7E1IJNCYoSR4AXG1m0yS1i/VUI+Tv62lmGUXZnJmVQ70Bb5ame04F5cYm2fSowHNk8dAO+ccZGRl07tyZ+++/nxo1avDUU09x7bXXcuedd3L22Wezyy4bs7K0atWKuXPn8uWXX9K9e3fat2/PrrvumrQex3Ecx6nolNZpy5L0f4ToyFnx2ubI6ztblneBQZK+At4HnicoQo4gqEUul3Q+cDchj9vLZvYEgKS7CJL9I4BBwOlm9kPCsterATOzJpKOIihYHhnvpRJyra0npCsYkaBMWSxmtlrSIkKS7i+ATvFaLeAzSa/FoocDf4p2TwX+TEhMfjbwV4KK5XzgpPiDwh8JipidgauAX82soaTGQHrscy3gVuCPZrZWIZH5DcAdiTZK6k1wbqlVqzaDmmSXpmtOBWX/3YLjVlFJS0sDIDs7m7/85S+0atWKffbZJ//6X//6VyCIley333751xPJzs7m6aefJiUlpch6yjMZGRk7RT+crYfPEackfI44pXXaehKSON9tZotiUuOxW88spzTEyFUL4CRC3rPngbsIEab34j6RyoRE2ACNo7NWkxCFy0uaPQkYE/PgvRyvtSY4dJjZfEnfAXlO2wdmtgpA0jzgEILkfmlRwt97JJ1MSJh9ILB/vLfIzGbHNubGNi2mSqgXy+wFPC3pCEJS9LwfEloTInCY2RxJs+L144CGwKQ4NruwMcF4Pmb2OGEZJykpKdan2zll6JpT0UhLS6NrBVf0MjO6d+/OiSeeyP33359//eeff2a//fYjNzeXHj160L9/f9q0acOiRYs4+OCDqVKlCt999x3//e9/6dy5M/vuu2/Seso7rvrmlITPEackfI44pXLazGxejErUjeeLgL9tTcOc0mFmOYSE0mnRobkamGtmxycpPgY4N+Y360FMtm1mV0hqBXQAvoiOYHGsTzjOoQz5/iTtSXC6viIk064NtIjJuxcTljoWbiM34Tw3ob07gYlm1ikuqUwrqXngPTP7v9La6zhOyUyaNImxY8fSpEkTUlPDVt977rmHr7/+mpEjRwJw3nnn0bNn0Eb65JNPGDp0KFWrVqVSpUo8/PDD1KpVi08++SRpPWeeWWG3TzuO4zgOUMov25LOAv5OiEwcKikVuMPMzt6axjnFIymFsN/w63gpFfgSaCfpeDObLKkqcKSZzSUkrv4pXusG/BDrOSwmxv5cUnvgYODjWGZCXBZZl7A/rPnvsLc6Ibn4q2b2q6S9gJ+jw9aWELErC3vl9QHokXB9EtAVmCipIdAkXv8MGCnpcDNbKGkP4EAz+2ozu+Q4DtC6dWuKyvmZLM/aRRddxEUXXVSmehzHcRynIlPaCMlg4FhiJMPM0iXV30o2OaWnOjAi7kPLJoiL9CYs7XswOkVVgPsJ+coGAp8Dy+PfPWM998UlhgI+AGYS9os9EqN32QQxk/V50txlZGJMcl0JeIUQIQMYB7we25gW2ywL9xKWR94KJCpBPByvz4t1ziUIrCyPEcZn81JYEPa4udPmOI7jOI7j7LCUWojEzFYV+sKeuxXsccqAmX1BUFcszArg5CTlHyEoQha+fl6SOtYR9jIWLjuGsMwy77xjCTbWK+beCiDZMk4I+/LyyvVIOF6cd8/MJrNxnx0EByzP9gvNbJ2kwwgiLd/FZyYAxxRns+M4juM4juPsSJQquTYwV9KfgcqSjpA0gqBS6Dg7IrsDn0iaSYjsXWVmG7azTY6zU7FkyRLatm1Lw4YNadSoEQ888AAA6enpHHfccaSmptKyZUumTJkCwKpVqzjrrLNo1qwZjRo1YvTokNbxu+++o3nz5qSmptKoUSMeffTR7dYnx3Ecx9lRKW2krQ9wC0EM4hmC6uBdW8sop/wh6XNC7rNELspTgNyWmNkaQn45x3G2ElWqVGHYsGE0b96cNWvW0KJFC0477TRuuukmbrvtNtq3b89bb73FTTfdRFpaGiNHjqRhw4a8/vrrLF++nJSUFLp160adOnWYPHky1apVIyMjg8aNG3P22WdzwAEHbO8uOo7jOM4OQ4mRNkmVgTfN7BYzOyZ+bjWzdZvbqKQcSemS5kh6MSZDLsvzNSVdlXB+gKSXSnhmVBSlKHy9h6SHytJ+fG6wpH6Fri2OucC2CJL6SZofx2qqpItLKN9D0nb5pmNmrcwstdBntqQ2klZJmiFpgaSPJBW7pNJxnB2fOnXq0Lx50CXac889adCgAT/88AOSWL16NRCia3nOlyTWrFmDmZGRkcE+++xDlSpV2GWXXahWLfzes379enJzfeW94ziO4xSmxEibmeVIypW0V15uri1AppmlAkgaR8gB948yPF+TkED54Wjjj0CX4h4ws16bZ+r2QdIVwGnAsTH5dA2gUwmP9QDmAD9uRbuqmFlZMwl/nLf3LSqPviop08w+2PIWblkys3KoN+DNkgs6FZYbm2TTo4LNkcVDOxQ8X7yYGTNm0KpVK+6//35OP/10+vXrR25uLp9+GlbSX3PNNfkRtDVr1vD8889TqVL43XDJkiV06NCBhQsXct9993mUzXEcx3EKUdrlkRnAbEnvAWvzLprZtVvAho+BplF+fQRBZKIqMNjMxktqBIwmpBuoBHQmqA8eJikdeA8YCbxhZo1jZPBvwBkEsZQnzGyEpDSgn5lNk9QT+AuwkqCUuB5AUm3gUWI+OuA6M5tU1g7FvrwAHERIbn2nmT0f85/9g6D6uIKgyPhTEdX8FWhjZqsB4t+nY/2DgLOA3Qh7Cy+P49ISGCcpkyDw0TBZe5KOAZ6M4/Me0D6O3a4EoZKWBMXIG8xsYlRcPC/WUzkm2n7ZzF6N9owDXjCz8SWNTVQevQO4BvggppO4lfB+fwG6mdkySYOBQ4H6hPdxPSE5dnuCzP9ZMVXAJmMRk3AX1cfKwFBCjrpqwEgzeyzRRkm9CSqc1KpVm0FNyuqjOhWJ/XcLjltFIi0tLf84MzOTvn370qtXL6ZPn86DDz7IpZdeyimnnMLEiRM577zzGDZsGB9++CG1atXimWee4ccff6RXr16MGjWKPfbYA4AHH3yQFStWMHDgQOrUqcM+++yznXq35cnIyCgwZo5TGJ8jTkn4HHFK67S9HD9bFElVCF/C3ybsmZtgZpdECfspkt4nROEeMLNxknYhOEEDgMYJ0bp6CdX2JiRvTjWzbEkF/s8vqQ5wO9ACWAVMBGbE2w8Aw83sE0l1CXv3GmxG184AfjSzDrHNvWJutBHAOVF6/nzgbuCS/8/evcf5XKaPH39djArjsHJYJWQwGMOEDFYaOfRVUrLLWh2ESiurqOgnpSMqGytpHWqituhEyYowkooMYxyK2kw5lUORcZzh+v1x3zM+Mz5z0pjBXM/HYx59Pu/D/b7f73nv9zuX+76vK8hzKQuUUdXvs2j/RVV9wh87A+isqu+IyL2cDEyzu96rwJ2+jtvogHYHAKqqkSJSD1jga7SBq8/WSFV/EZGrcUHUbF9WoBVwex6ez2rgQf/5M6CFD7T6AQ8BQ/y+MKAtLvj8Auimqg+JyPu4QuCzgz0L4MNs7rEvLv3/lT7t/3IRWeALxoN7AJNxZROoXqu2jl2X69rhpggaEplKUXtHknrFAJCSkkLnzp3p378/gwcPBuDGG2/k3XffRUS4+uqreeGFF4iJieG5555j2LBhXHXVVQBMmzaNSpUq0bx58wxtz5s3jxMnThATE1OQt3RGxcXFnVf3Y/KfvSMmJ/aOmFz9paGqr+XzdUv6UTJwI23TcKMkXQLWiV2EG2H5AhguItVwozvf5lArrD3wctoUPlX9JdP+aCBOVXcDiMhMTqaNbw80CGi/rIiEqmpykOtkVQFWgXXAWBEZgxsBXCYiDXGjiAt9+8WBrEbZctJWRB7CZUmsgKtD9mGmY8KDXc8HxGV8unxwiWXS1pi1xgV6qOo3fkQt7dksTHuWqrpURF7yI5PdgHfzOGUy8BdYDZjpg+kLgC0B+/7rR9PW+f7P99vX4QLzoM9CRJZlc48dcSO7adNpywF1Ml03XckSxdmUaSqYMYHi4uLSg5iiRFXp27cv9evXTw/YAC655BKWLl1KTEwMixcvpk6dOgBUr16dRYsWcdVVV/Hzzz+zadMmatWqxbZt27j44ospWbIkv/76K5999hn3339/Yd2WMcYYc1bKVdAmIlsIEqSo6ukW2E5f0xZwDcGNpGzKdOzXPjPh9cA8EbkbyGoE6vcqhhv1yU2Slb1A1UzbygD7VHWviDQBrgOeEpFFuNTzG1Q1q7pk6fwatmQRqZV5tM1PYXwJaKaqW/00wouCNCPBrueDttNxMNP36cAtwF8JUs8tB1cAX/vPE4B/quoHIhKDK+Se5iiAqp4QkRRVTXsHTwAheXgWgQQYqKof57HPxpgAy5cvZ8aMGURGRhIV5f7P+TPPPMOUKVMYNGgQqampXHTRRUyePBmAESNG0Lt3byIjI1FVxowZQ8WKFVm4cCFDhgxBRFBVHnjgASIjIwvz1owxxpizTm7n9ASmT78I+AtuVCM/fQwMFJGBfqrcFaq6RkRqAd+r6r/8lMVGuHVoZbJoZyFwt4gsSZsemWm0bQUwXkQuBn7z97LW71uAK2/wHLikGaqaQHCf4taPjVbVAyJyM7DWJ265BPhFVV8XkX1AP9w6qkoi0tJP2SsB1FXVDVm0PwqYKCI9fBAXiltX9oHfv8dv+zOQljnzQMBz2ZTV9UTkgIhEq+oKXNCVZhnQC1jsp0VW9+00CdK/WGAl8JOqbsziHk4hInoF2wAAACAASURBVI2AEf6ZgBvp2u4/52WKJZwM0DI8C1Xdl809fgzcIyKL/SheXWC7qmYOSo0x2WjdujUn/x0lo/j4+FO2XXLJJSxYsOCU7R06dCAxMTHf+2eMMcacT3I7PXJvpk3jRCQeeDQf+/IkMA5IFJFiuOlqnYHuwK0ikgL8BDzj11UtF5H1wH9xiUjSTMVN6Uv050wB0lP6+0QcI3HTLvcBgUHZP3CBUiLu2XyKW1N3ClVNFFcq4DMRUWAXJwORSOA5ETkBpAD3qOoxPyXvX34dWIi/36yCtkm4xB9f+ftIAcb6gGQKLkvkT8BXAefEAi8HJCLJ6np9gSm+f0txa/vAjVpN8tMRU3GJS44Gm47qk4V8jVtXlpOrRGQNbgrjLuAfAZkjRwJvi8ivwGJc8pFcyeFZZHWPU3FTK1f70d3dwE25vaYxxhhjjDEFTbL6l9IMB7mpfmmK4Ube7lHVxmeqY+bMCVynJyLDgKqqOiiPbZTCrS1rko+lIPJNftwjQHh4uG7alHnGrjEn2eJwkxN7R0xO7B0xObF3pGgQkXhVbRZsX47Ftb2xAT+jcNPluudP90whuF58cXPgKuCpvJwsIu1xa9ImnI0Bm/e77tEY4+qntW3blgYNGhAREcH48eMB6NGjB1FRUURFRVGzZs30NW3Hjh3jjjvuIDIyksaNG2dITx0fH09kZCS1a9fmH//4R5ZTK40xxhhzqtyuaesbJCFGrqex/R5+6uE/VXWI//4AEKqqI/Oh7Vhcdsd3sjnmDiDzCM1yVR2QxfEvAD+o6jj//WNgq/ri3iIyFreG6p/++0TgT5maGa+qr+a1r9kJPF9VZ+IyNiarasO8tqWqnwA1fLsxwAO4hCJjMh26BZiDWyO4DTfd83vgcVX9/HTuIw99nAnMPJPXMOZ8FxISwtixY2nSpAkHDhygadOmdOjQgZkzT/5Pa8iQIZQrVw6AKVOmALBu3Tp27dpFp06d+OqrryhWrBj33HMPU6ZMITo6muuuu4758+fTqVOnQrkvY4wx5lyT25G2YIHCaQUPp+EocLOIVCyg62Wgqq+qalSmnwHiaswFsxxXtwy/Nq8iEBGwvxWuvEFa+wOCtJ8hYDsXqOrHQe6jq989U1WvUNU6uIQs74nI6dS/M8YUoKpVq9KkiZsdX6ZMGerXr8/27dvT96sqs2bNomfPngBs3LiRa665BoDKlStTvnx5Vq1axc6dO/ntt99o0aIFIsJtt93G7Nm5WQ5rjDHGGMhhpE1cgeUIoJzPjpimLDmnVs8vqbhCx/fjCnAH9i+WgNEnP3IU6kd/HsclGokEZuHWXw0CSgI3qer/fDPt/ZqnssBgVZ0rIsVxwUUMcCEwUVX/7dt9EvgVqMfJGmaBPgde8J8jcEkyqorIH4BDuGLdq0WkKfBP3OjTHlzSj50iEoZLrFLJH3+nqn6T6b6fBC7DJdt4Gujin9MCVX2APPI10mb6ZxCCW6+4TEQ6+ud4IfA/4A5VTRaR/8MlNTmEK46da6q6REQm44qg3y8id/rPFwDfAbeq6iH/uz2MKw9QGVcU/DZcgpUVqtrb930ScCXu9/qOqj7mt1+He74HcYF0LVXtLCKlcaOCDYESwEhVnZNVfw+nHKfmsI/ycoumiBkSmUrv8/AdScpUnzApKYk1a9YQHR2dvm3ZsmVUqVIlvRZb48aN+eCDD+jZsydbt24lPj6erVu3UqxYMapVq5Z+XrVq1TIEf8YYY4zJXk7TI8NxGRzLAzcEbD8A3HmmOhXERFw2yGfzcE5jXID0C25K3lRVbS4ig3Bp/e/zx9UEmgNhwBIRqY0LDvar6pUiciGwXETSclU3ARqqatBizKq6Q0RSfXmCVrgslZfigo39uOBRcYHDjaq6W0R64IKvPrgAtb+6IuLRuIyO16S1LyLP4dL634Eru9AVqOfLJJxuDba/AR+r6tM+YC3lRzYfAdqr6kERGQoM9r+DKb5P33F6UxBXA3f7z++p6hR/b0/hAtEJft8fcM+tC67UwZ9wGTq/CijHMNxnEy0OLPIlBTYD/wbaqOoWEXkz4NrDgcWq2sc/r5Ui8klgyn8RuQsXSFKxYiUejcxL3XBT1FQp6QK3803gerTDhw8zaNAg+vXrx+rVq9O3v/DCCzRv3jz92LCwMBYuXEi9evWoUqUK9erV4+uvv2bPnj38+uuv6cclJiayd+/eDNc4nyUnJxeZezWnx94RkxN7R0y2QZsfgZiTVuurgPoUrB+/ich0XEr+w7k87StV3QkgIv/D1WADFzS1DThulqqeAL4Vke9xI2gdgUY+RT+4WmJ1gGPAyqwCtgCf4wK2VrjRnkv95/24UZ9w3EjPQp9Ovziw09caa4VLgZ/W1oUB7Y7AjTLd5e9rP3AEmCYic4G52fQp2Kr/tG1fAa/4Wm6zVTVBRK4GGuACVnAjYV/gns8WVf3W9+F1fICTB4E1BBr6YK08btQxsOj1hz4YXQf8rKrr/DU34ILtBKC7D7JCcMXOG+Cm/X4f8Ht6M6CPHYEufm0kuBHj6pws9o2qTsYFz4SHh+vAXjfm8fZMURIXF0f38zijV0pKCp07d6Z///4MHjw4fXtqaio9evQgPj4+wyhau3bt0j+3atWKm2++mT/84Q+88MIL6ZnPdu7cSaNGjYpMJjTL+mZyYu+IyYm9Iya3iUjWiMgA3HS/9GmRqtrnjPQquHG4EZrA9V6p+HV5fv3YBQH7jgZ8PhHw/QQZ7ztzMKO4oGKgqgYGEGlJN3JThDltXVskbnrkVmAIrpj3q779DaraMlP7ZYF9qhqVRbtfAU3FFwz3xcObA+1wNdnuJWBULpO9uJGrtGtVwE3LRFU/FZE2wPVArIj8EzcFdKGq9szUx6z6lhdXcDJIisVNV10rIr1xU1LTBP7OMv8+Q3wynAeAK1X1Vz+lMqdpuwJ0U1XL429MDlSVvn37Ur9+/QwBG8Ann3xCvXr1MgRshw4dQlUpXbo0CxcuJCQkhAYNGgBQtmxZvvzyS6Kjo5k+fToDBw4s0HsxxhhjzmW5TUQyA/gjcC2uUHE13BTJAqOqv+DWpvUN2JwENPWfu+DWKOXVX0SkmF9LVgvYhBvtucePPCEidf1aqNz6HDet9BdVPe77Xh431e9zf41KItLSt19CRCJU9Tdgi4j8xW8XEQmshTcft9buIxEp40fmyqnqPNyav+zq5sUBPUQkLbDtDSzx16mBG8magis+3QT4EviTny6KiJQWkbrAN0BN/7wAMgR1OfEjeHfhpliCm+q50z/rXnlpC7cG7yCwX0SqAGmp6DYBtUSkpv/eI+Ccj4GB4ocPReSKPF7TmCJj+fLlzJgxg8WLF6en+J83bx4Ab731VnoCkjS7du2iSZMm1K9fnzFjxjBjxoz0fS+99BL9+vWjdu3ahIWFWeZIY4wxJg9yO9JWW1X/IiI3quprIvIfYNmZ7FgWxuJGk9JMwU3fXIsLaHIzCpbZj8BKXADQX1WPiMhU3PS71f6P+93ATXlocx0ua+R/Mm0LVdU9AH7q5b9EpBzu9zAO2IALXCaJyCO4IPQtYG1aI6r6toiUwa3x+hvu/i/CjSBl/KfwAD7BSlMgXkSO4xKL9Pe7Y4AHRSQFSAZu82vtegNv+nV9AI+o6mY/HfEjETmEew/K5PA8eohIa6AUrgxAN1VNG2kbAazAPeMVuWgr8J7WisgaXCC5FTfCiaoeFpG/A/NF5CBuhDLNk7hnnehHZ7fgAmxjTCatW7fOsp5abGzsKdtq1qxJVsXomzVrxvr16/Oze8YYY0yRIbkpcCoiK30Sj0+BvwM/4dZ21TrTHTTmdIhIqM90KbhENt+q6gs5nZdZeHi4ZvVHqDFg6wxMzuwdMTmxd8TkxN6RokFE4lW1WbB9uZ0eOdmnrB+BG+HZCOQlk6MxBe1OEUnAjV6Ww2WTNOa8snXrVtq2bUuDBg2IiIhg/PjxGfaPHTsWEWHPnj0AzJkzh0aNGhEVFUWzZs347DNXsSMhIYGWLVsSERFBo0aNMhTPNsYYY0zhy9X0SFWd6j8uxa37KvJE5GJgUZBd7VR1b0H3J42IDAf+kmnz26r69Bm+7h24OniBlqvqgGzOGY6b4nkcl1zkblVdkR/98aNqeR5ZM+ZcEhISwtixY2nSpAkHDhygadOmdOjQgQYNGrB161YWLFhA9erV049v164dXbp0QURITEyke/fufPPNN5QqVYrp06dTp04dduzYQdOmTbn22mspX/50q4gYY4wxJj/lKmjzSR6eAS5R1U4i0gBoqarTzmjvzmI+MMuPTIr5ygdnZzRAy+K6r5Ixs2e2fBKWzkATVT3q68JdkMNpxpgAVatWpWrVqgCUKVOG+vXrs337dho0aMD999/Ps88+y403nixZERoamv754MGDpJUWqVu3bvr2Sy65hMqVK7N7924L2owxxpizRG4TkcTi/iAf7r9vxhVVLrJBm/ndqgJ7VPUoQECClqa42nahuJIEvVV1p4jcics6eQGuqPetqnrIZ9p8DDdat19V2/jELJOAZriyEINVdYlPrNIFlxAlDHhfVR/KrpOHU45Tc9hH+Xzr5nwyJDKV3oXwjiSNvj7j96Qk1qxZQ3R0NHPmzOHSSy+lceNTE8q+//77PPzww+zatYuPPjq13ytXruTYsWOEhYWdss8YY4wxhSO3a9oqquos3BQ2VDUV90eyMadrAXCZiGwWkZdE5Gqf9n8C8GdVbQq8wslRw/dU9UpVbYyr8ZZW+uFR4Fq/vYvfNgBQVY3ElSR4zQdy4EZHe+Bq6PUQkcvO8H0ac8YlJyfTrVs3xo0bR0hICM888wxPPPFE0GO7du3KN998w+zZsxkxYkSGfTt37uTWW2/l1VdfpVix3P6/B2OMMcacabkdaTvo13ApgIi0APafsV6Z857P7NgUuApoixu5fQpoCCz007aKAzv9KQ1F5ClcvbtQXL01cGn+Y0VkFvCe39YaF/yhqt+IyA9A2vyvRaq6H0BENgI1cOUC0vmSBncBVKxYiUcjU/Pxzs35pkpJN9pW0OLi4gBITU3l4YcfJjo6mgoVKvDWW2+xefNmwsPDAdi9ezcRERFMmjSJChUqZGhj48aNzJkzh3LlynHw4EHuv/9+evXqxZEjR9LbN79fcnKyPU+TLXtHTE7sHTG5TfnfBPdHcENgPVAJNxqSeGa7Z4oKX7duAHCRqrYMsn8LcJOvzdYbiFHV3n5fNHA9cBuu2PpUYIKqLvb7l/m2mwDNVPVev30u8LyqxmXVL0v5b3JSmGmYVZXbb7+dChUqMG7cuKDH1KxZk1WrVlGxYkW+++47wsLCEBFWr17NDTfcwLZt20hJSaFTp07ccMMN3HfffQV8F+c/S9VtcmLviMmJvSNFw2mn/BeR6gCquhq4GmgF3A1EWMBmfg8RCReROgGbonDTHiv5JCWISAkRifD7ywA7/RTKXgHthKnqClV9FFeg+zJcwe9efn9doDpgkZc57yxfvpwZM2awePFioqKiiIqKYt68eVke/+6779KwYUOioqIYMGAAM2fORESYNWsWn376KbGxsentJCQkFOCdGGOMMSY7OU2PnI0bnQCYqardznB/TNERCkwQkfK4ZCHf4aYkTgb+JSLlcO/nOFyttRHAClxgtgIXxAE854M/wZVgWAt8A0wSkXW+7d4+Q2VB3ZsxBaJ169bkNFsiKSkp/fPQoUMZOnToKcfccsst3HLLLfndPWOMMcbkk5yCtsC/cq0+m8k3qhqPG7nNbA/QJsjxk3AZITNvvzlIG0eAO4IcG4vLhJr2vXOuO2yMMcYYY0whySk9mGbx2RhjjDHGGGNMAcgpaGssIr+JyAGgkf/8m4gcEJHfCqKDxhhTFGzdupW2bdvSoEEDIiIiGD9+PAAPPvgg9erVo1GjRnTt2pV9+/ZlOO/HH38kNDSU559/HoBNmzalr0uLioqibNmyWSYpMcYYY8y5IdugTVWLq2pZVS2jqiH+c9r3sgXVyfOdiPxRRN4Skf+JSLyIzPMJNPLzGheLSIL/+UlEtgd8vyA/rxVwzRgR2S8ia0Rkk4h8KiI2JdGYIEJCQhg7diwbN27kyy+/ZOLEiWzcuJEOHTqwfv16EhMTqVu3LqNGjcpw3uDBg+nUqVP69/DwcBISEkhISCA+Pp5SpUrRtWvXgr4dY4wxxuSj3NZpM2eIuOwY7wOvqepf/bbGQBVgs/8e4guanzZV3YvL0IiIjASSVfX539NmLi1LWzsmIlHAbBE5rKqLCuDaxpwzqlatStWqVQEoU6YM9evXZ/v27XTs2DH9mBYtWvDOO++kf589ezaXX345pUuXDtrmokWLCAsLo0aNGme288YYY4w5oyxoK3xtgRRVfTltg69FFuPri/0K1BORRrhEHM1wGREHq+oSX7OsK1AOuBR4XVUfz82FRaQMkAjUVdUUESmLy75YF1joP1+Ne0/6qOpKESnNyZp9JYCRqjonN9dT1QQReQK4F1gkIjcAjwAXAHuBXqr6sw8qL8clv6kO3A+0ADoB24EbfH8fBW4ASgKfA3erqorIlcA04IS/j06q2lBEigOjgRjgQmCiqv47uz4fTjlOzWEf5eb2TBE1JDKV3r/zHUkafX3G70lJrFmzhujo6AzbX3nlFXr06AG4Qqtjxoxh4cKF6VMjM3vrrbfo2bPn7+qbMcYYYwqfBW2FryEQn8W+JkBDVd0iIkMAVdVIEakHLAiYQtnct3MI+EpEPlLVVTldWFUPiEgcrjD1bOCvwHs+IAIopapRItIGeMVfYziwWFX7+HT9K0XkE1U9mMv7XQ086D9/BrTwgVY/4CFgiN8XhgtoGwBfAN1U9SEReT+gvy+q6hMAIjID6Ax8CLwK3KmqX4jI6IBr9wX2q+qVInIhsFxEFqjqlsAOishduPIDVKxYiUcjf9cgpznPVSnpArffIy4uLv3z4cOHGTRoEP369WP16tXp219//XX27dvHpZdeSlxcHJMmTaJjx46sWrWKpKQkSpYsmaGdlJQU3n33XTp37pxhuyl4ycnJ9jsw2bJ3xOTE3hFjQdvZbWVAQNEaN8KFqn4jIj/gRsQAFvrpj4jIe/7YHIM2byouWJqNS5N/Z8C+N/31PhWRsj5I6wh0EZEH/DEX4UbDvs7l9QLLSFQDZopIVdxoW2Dw9F8fPK4DigPz/fZ1QE3/ua2IPASUAioAG/zoZBlV/cIf8x9cMIfveyMR+bP/Xg6ok+m6qOpkXL04wsPDdWCvG3N5a6YoiouLo3tMTL60lZKSQufOnenfvz+DBw9O3x4bG8uGDRtYtGgRpUqVAmDEiBGsWLGC1157jX379lGsWDEiIiK49957AZgzZw7R0dHcfHOwqhimIMXFxRGTT++IOT/ZO2JyYu+IsaCt8G0A/pzFvtyOXmUux5Dr8gyqulxEaopIDFBcVdfn0K7gRr025fYamVzByQBvAvBPVf3AX39kwHFHff9OiEiKnqwgfAIIEZGLgJeAZqq61U+pvCiHawswUFU/Ps2+G3PGqCp9+/alfv36GQK2+fPn8+yzz7J06dL0gA1g2bJl6Z9HjhxJaGhoesAG8Oabb9rUSGOMMeY8kVPKf3PmLQYu9FPyAPDr167KdNwyoJffXxc3upUWOHUQkQoiUhK4CViexz5Mx41IvZppew9/vda4aYX7gY+BgT6BCiJyRW4v4u9rBDDRbyqHW6MGcHse+5wWoO0RkVB84Kuq+4ADIpK2GOivAed8DNwjIiV8f+r6NXrGFLrly5czY8YMFi9enJ6uf968edx7770cOHCADh06EBUVRf/+/XNs6+DBgyxcuNBG2YwxxpjzhI20FTK/nqsrME5EhgJHgCTcdMVALwGT/HTBVKC3qh71sdNK4F3cdMPXc7OeLZM3gKfw0yEDHBGRNbiEI338tieBcUCiiBTDTS3MLo3/Vb6NUsAu4B8BmSNHAm+LyK+44PXy3HZYVfeJyBRgPfAT8FXA7r7AFBE5ASwF9vvtU3FTK1f7oHM3Lsg1ptC1bt2akwPKJ1133XU5njty5MgM30uXLs3evXvzq2vGGGOMKWQWtJ0FVHUH0D3IrikBxxzBrTkLZpuq5jr4UNWRmTa1Bt7xo1SBXlfV+zKdexi4O5fXicONpmW1fw5wSubJzP1T1dBg+1T1EVz2ycw2qGojABEZhl/fp6ongP/nf4wxxhhjjDkn2PTIIk5EJuDS4D9Z2H3JR9f7ouHrcdNMnyrsDhkTzNatW2nbti0NGjQgIiKC8ePHA/Dggw9Sr149GjVqRNeuXdm37+S/p4waNYratWsTHh7Oxx+75ZlHjhyhefPmNG7cmIiICB577LFCuR9jjDHGnBk20naOU9VYIDZwm4hcDAQrXt0uLctkwPkDs2g3Jrd9EJFrgTGZNm9R1a65bSM/qepMYGZhXNuYvAgJCWHs2LE0adKEAwcO0LRpUzp06ECHDh0YNWoUISEhDB06lFGjRjFmzBg2btzIW2+9xYYNG9ixYwft27dn8+bNXHjhhSxevJjQ0FBSUlJo3bo1nTp1okWLFoV9i8YYY4zJB+fcSJuIqIiMDfj+gM8cmB9txwakg88XIvKCiNwX8P1jEZka8H2siAwOfna27WbZV1Xdq6pRQX72ZmrjLhH5xv+s8hkcc7puFz/lEBG5SUQaqOrHQa7VNaCfW0RkrYhsFpHpIlItr/drzPmoatWqNGnSBIAyZcpQv359tm/fTseOHQkJcf+m1qJFC7Zt2wa4NP5//etfufDCC7n88supXbs2K1euREQIDXWziFNSUkhJScGvdzXGGGPMeeBcHGk7CtwsIqNUdU9hdyaNiISoarAKu8tx69XG+cQdFYGyAftbAfcXQBczEJHOuLVprVV1j4g0AT4QkWhV3Z7Vear6AfCB/3oTMBfYmMPlHlTVd3zyj/uAxSLSUFWP/f47ObMOpxyn5rCPCrsb5iw2JDKV3qfxjiSNvj7j96Qk1qxZQ3R0dIbtr7zyCj169ABg+/btGUbPqlWrxvbt7n+ux48fp2nTpnz33XcMGDDglHaMMcYYc+4650bacJkTJxMk0Mk8+iQiyf6/MSKyVETmiMj3IjJaRHqJyEoRWSciYQHNtPejTpt9YIOIFBeR50TkKxFJFJG7A9pdJiIfkHXg8jnQ0n+OwGU7PCAifxCRC4H6uGyGTX0f4/1oXFV/jTARme+3LxORekHu+0l/78X9vW30/Xw+m+c4FBdM7QFQ1dW4lP8DfJtJIlLRf24mInH+c28ReVFEWgFdgOf8+rGwYBcJpM4LuGyPnXx7k/zz3iAijwfcU5KIjPJtrxKRJv65/E9E+vtjQkVkkYis9r/HGwPOHyEim0TkMxF5U3wx8Nw8T2MKWnJyMt26dWPcuHGULXvy33SefvppQkJC6NWrV45tFC9enISEBLZt28bKlStZv359jucYY4wx5txwLo60gavzlSgiz+bhnMa4AOkX4Htgqqo2F5FBwEDcCBC4lPDNgTBgiYjUBm7D1Sm70gday0VkgT++CdBQVbcEu6iq7hCRVBGpjhtV+wK4FBfI7QfW4YpWTwBuVNXdItIDeBqXZn8y0F9VvxVXe+wl4Jq09kXkOaAMLrNkBaArUM+XEiifzfOIAOIzbVtF1hkqM9/X5z5Ynauq7+TmnACrgXq4zJHDVfUXESkOLBKRRqqa6I/7UVWjROQF3Lq9P+Hqs60HXsaVR+iqqr/5APNL36dmQDfc77yEv17avWb7PMFNGwXuAqhYsRKPRgYbQDXGqVLSjbblVVxcHACpqak8/PDDREdHU6FChfTt8+fP58MPP2Ts2LEsXboUgKNHj7J06VKqVXMzjBMTE2nSpEn6OWlq1qzJxIkT00foTOFKTk4+5XdkTCB7R0xO7B0x52TQ5v9Inw78Azicy9O+UtWdACLyPyAt6FoHtA04bpZPDf+tiHyPCy46Ao0CRvHKAXWAY8DKrAK2AJ/jArZWwD9xQVsrXNC2HAgHGgIL/TqU4sBOcUWjW+FqmaW1dWFAuyOAFap6l7+v/bhAZpqIzMVNXTwbBS626e6DpBCgKtAASAva0qZhrgNCVfUAbpTyqA9IDwLPiEgb4ATuuVbBBXdzfJmEIyLyIbiRObJ/ngCo6mRccEd4eLgO7HVj5kOMSRcXF0f3mJjTOldVuf322/nTn/7EuHHj0rfPnz+fDz74gKVLl1KpUqX07ZUqVeJvf/sbL774Ijt27GDv3r3079+fX375hRIlSlC+fHkOHz7MiBEjGDp0KDGn2S+Tv+Li4ux3YbJl74jJib0j5pwM2rxxuBGUVwO2peKnfPr1YxcE7Dsa8PlEwPcTZHwOmavbKi7IGKiqHwfuEJe442Au+rocFyxE4kaJtgJDgN98/wVXW6xl4EkiUhbYp6pRWbT7FdBURCqo6i+qmioizYF2wJ+Be8k0ihRgI9AUV9Q6TVN8TTMCniVudCs/XYEbVbsceAC4UlV/FZHYTNcK/B1l/v2FAL2ASkBTVU0RkaQc+lqM7J+nMQVq+fLlzJgxg8jISKKi3Gv5zDPP8I9//IOjR4/SoUMHwCUjefnll4mIiKB79+40aNCAkJAQJk6cSPHixdm5cye33347x48f58SJE3Tv3p3OnbOreW+MMcaYc8k5G7T5KXWzgL7AK35zEi7wmIVbb1XiNJr+i4i8BlwO1AI2AR8D94jIYh8c1AWyTNYRxOe44OR7VT0O/OJHiiKAO3HBWyURaamqX4hICaCuqm4Ql3nxL6r6trjhoUaquta3O9/37SMR6YgLMEup6jwRWY6bBpqVZ4ExIvJ/qrpXRKJwUyvTgrwk3LP8L26qYTAHcFMzc8X3fyBuRG0+brrqQWC/iFTBrXOLy217uBHPXf530hao4bcvB/4tIqNw73hnYLIfoc3ueRpToFq3bo1q5n8nguuuuy7Lc4YPH87w4cMzKGvZkQAAIABJREFUbGvUqBFr1qzJ9/4ZY4wx5uxwLiYiCTQWl40xzRTgahFZi1szlptRsMx+BFbigpX+fordVNzI1GpxBZv/Td4C3nW+n19m2rZfVff4LIp/xgVRa4EE3MgcuNGkvn77BiDDXD1VfRt33x/gAqi5IpIIfAZkWUrAZ4Gchluf950//iZV3e0PeRwYLyKrgONZNPMW8KCIrMkhEclzvv+bgSuBtqp6zAdLa4BvgP/ggq28eANoJiLrcOsOv/H39hXueSTifo/rcFNRIYfnaYwxxhhjzNlGgv0rrylaRCQEN02zGHCLngcvhYiEqmqyiJQCPgXu8hky8yQ8PFw3bdqU/x005w1bZ2ByYu+IyYm9IyYn9o4UDSISr6rNgu0710faTD5Q1VRVvVVVe50PAZs3WUQScOse3z2dgM2Y/LB161batm1LgwYNiIiIYPz48QD88ssvdOjQgTp16tChQwd+/fVXAH799Ve6du1Ko0aNaN68+Smp+48fP84VV1xha9aMMcaYIsSCtnwiIhf7mmKZfy4u5H4ND9Kn4TmfmefrTAxynVyVDzgTVPVvqhqlqvVUdVRh9cOYkJAQxo4dy8aNG/nyyy+ZOHEiGzduZPTo0bRr145vv/2Wdu3aMXr0aMAlIomKiiIxMZHp06czaNCgDO2NHz+e+vXrF8atGGOMMaaQWNCWRyJy3Ack60XkbT/9DlXd64OEzD97M51fXkT+HvD9EhHJts6ZiEwVkQZBtvcWkRezO1dVn87cJ6BEWrHpgLbSi2mfDlUdkOkarwND/bP6SkRuy+58fy+XnO71jTlbVa1alSZNmgBQpkwZ6tevz/bt25kzZw633347ALfffjuzZ88GYOPGjVxzjcsHVK9ePZKSkvj5558B2LZtGx999BH9+vUrhDsxxhhjTGE5Z7NHFqLDaSnjReQNoD+u9lpulQf+jivqjKruwCUhyZKqnlN/oYlIf6AD0NxnbCyLy0yZnd64cgg7zmC/QlQ1T1WQD6ccp+awj85Ul8x5YEhkKr2zeEeSRl+f8XtSEmvWrCE6Opqff/6ZqlWrAvDHP/4xPTBr3Lgx7733HldddRUrV67khx9+YNu2bVSpUoX77ruPZ599lgMHDpzZmzLGGGPMWcWCtt9nGa7odmlgAq5AdglgpKrOEZEIXIKPC3Cjmt2AJ4Ewv95qITARmKuqDUWkODAG+D9cLbIpqjpBROKAB1R1lZ9y+DCwD1iLr18mIpWAl4Hqvm/3qWpeszHi72UWUA1X5PtJVZ0pIk1xwWkosAfonVasPIj/B8So6m/giqEDr/n2HwVuAEriSiHc7Z9LM+ANETmMy/zZINj1RORKXNbLE/75dfLP7iJgkm8nFRisqktEpDdws2+nuIj8ALynqrN9f97AFVSfE/AM7gLuAqhYsRKPRuYpzjNFTJWSLnALJi4uLv3z4cOHGTRoEP369WP16tWkpqZm2H/8+HHi4uL405/+xIsvvkjt2rWpVasWtWvXZs2aNSxYsICUlBQOHDhAQkICe/fuzXC+OXslJyfb78pky94RkxN7Rwyqaj95+AGS/X9DgDnAPcAzuKyL4EbSNgNpgVwvv/0CXKBSE1gf0F76d9/WO0CI/17B/zcOF4xUxZUkqOTbWw686I/5D9Daf64OfJ3NPYzEBYGB25JwZQm64YLFtO3lcIHo50Alv60H8EoWbZcFfs3m2hUCPs8Abgi8R/85y+vhRuNa+s+jA57dkIBj6vnndBFuBG9bwLO8GpgdcG9b0p53sJ+6deuqMdlZsmRJjsccO3ZMO3bsqGPHjk3fVrduXd2xY4eqqu7YsUODvWsnTpzQGjVq6P79+3XYsGF66aWXao0aNbRKlSpasmRJ7dWrV77dhzlzcvOOmKLN3hGTE3tHigZglWbxN6mtacu7kn6UbBUuMJgGdASG+e1xuGChOvAF8P9EZChQQ1UP59B2e+Df6qfwqeovmfZHA3GqultdbbeZmc590ffhA6CsiIRmcZ2sMkQqrqZZBxEZIyJXqep+IBw3irjQt/8IbiTudLQVkRW+tto1uALjmQW9ni9IXkZVv/DH/SfgnNa4dXSo6jfAD0Bdv29h2rNU1aVAHT8y2ROXWdKG0swZo6r07duX+vXrM3jwydKJXbp04bXXXgPgtdde48YbXcnAffv2cezYMQCmTp1KmzZtKFu2LKNGjWLbtm0kJSXx1ltvcc011/D6668X/A0ZY4wxpsDZ9Mi8S1/TlkZEBOimqpkLen0tIiuA64F5InI38P0Z6lcxoIW6YuA52YsbtQtUBtinqntFpAlwHfCUiCwC3gc2qGrLnBpWt4YtWURqqWqGe/VTGF/CjahtFZGRuAA3Mwl2PR+0nY7MRdanA7cAfwUKLcOlKRqWL1/OjBkziIyMJCrK/Z+OZ555hmHDhtG9e3emTZtGjRo1mDVrFgBff/01t99+OyJCREQE06ZNK8zuG2OMMeYsYEFb/vgYGCgiA1VVReQKVV0jIrWA71X1XyJSHWiEW4dWJot2FgJ3i8gSVU0VkQqZRttWAON9GYHfgL/49gAWAAOB5wBEJEpVE7K4zqe49WOjVfWAiNwMrFXV4z6D4y+q+rqI7AP64aYhVhKRlqr6hYiUAOqq6oYs2h8FTBSRHj6IC8WtK/vA79/jt/0ZNx0U4EDAc9mU1fVE5ICIRKvqClzQlWYZ0AtYLCJ1cSOdm4AmQfoXC6wEflLVjVncgzH5onXr1mnTgU+xaNGiU7a1bNmSzZs3Z9tmTEyMFVk1xhhjihAL2vLHk8A4IFFEiuHWSXUGugO3ikgK8BPwjKr+IiLLRWQ98F9cIpI0U3FT+hL9OVOA9JT+6hJxjMRNu9wHBAZl/8AFSom43+unuMyWp1DVRF8q4DMRUWAXLjgDiASeE5ETQApwj6oeE5E/A/8SkXK+/XFAVkHbJFzij6/8faQAY1V1n4hMwa1L+wn4KuCcWODlgEQkWV2vLzDF928psN+f/xIwyU+7TMUlLjnqBkFPuf+fReRrYHYW/TfGGGOMMeasIVn9C7AxZyMRCVXVZP95GFBVVQflcFrmNkrh1u418Wv2shQeHq6bNmWe9WqKgj59+jB37lwqV67M+vXrAUhISKB///4cOXKEkJAQXnrpJQ4dOsT27dsZM2YMqkqZMmWYNGkSjRs3Blwx7ClTpqCq3Hnnndx3332FeVumEMTFxdnIqMmWvSMmJ/aOFA0iEq+qzYLts0Qk5lxzfVpxc+Aq4Km8nCwi7YGvgQk5BWymaOvduzfz58/PsO2hhx7iscceIyEhgSeeeIKHHnoIgMsvv5ylS5eybt06RowYwV133QXA+vXrmTJlCitXrmTt2rXMnTuX7777rsDvxRhjjDHnNgvazmMicocPcAJ/JorIcBHZICKJflv0abY/MUj7vzuxh4gkiUjFYPtUdaaqRqlqQ1W9XlV356HdUsD9wGHgThEZ/Xv7as5fbdq0oUKFChm2iQi//fYbAPv37+eSSy4BoFWrVvzhD38AoEWLFmzbtg1wSUWio6MpVaoUISEhXH311bz33nsFeBfGGGOMOR/YmrbzmKq+iivunU5EWuKKVjfxa74q4mq+nU77A35/Lwvc8+qKbl8ALBKRTqr638LulDk3jBs3jmuvvZYHHniAEydO8Pnnn7Nly5YMx0ybNo1OnToB0LBhQ4YPH87evXspWbIk8+bNo1mzoLMejDHGGGOyZEFb0VMV2KOqRwFUdQ+AiDTFBXOhwB5cIo+dInIncBcusPsOuFVVD4nIX4DHgOPAflVt41P6T8IVAk8FBvsAqTfQBSgFhAHvq+pDeem0iDQHxuNKBBwG7lDVTX70LBZX120TcAkwQFVXZW5DVQ8BS/znYyKymhzqzR1OOU7NYR/lpavmPJA0+vqg2ydNmsQLL7xAt27dmDVrFn379uWRRx5J379kyRKmTZvGZ599BkD9+vUZOnQoHTt2pHTp0kRFRVG8ePECuQdjjDHGnD8sEUkR41Ptf4YLoD7BFej+HJeJ8UZV3S0iPYBrVbWPiFysqnv9uU8BP6vqBJ+l8f9UdbuIlPeZIYcAEf68ergyBHVxqfkfBa4AjuKCq9aqujWLPibharntCdhWFjjkSyG0x2W17CYiDwB1VPVuEWmIy6jZIljQluka5YHVQPsg9eTuwgWqVKxYqemj46bk6tma80fkpeUA+Omnn3j44Yd59VU3YN25c2c+/PBDRARVpXPnzsycOZPQ0FD+97//8eijjzJ69Gguu+yyoO1OmTKFSpUqcdNNNxXYvZjCl5ycTGhoaGF3w5zF7B0xObF3pGho27ZtlolIbKStiFHVZD+qdhXQFhe0PYUbqVroU+QXB3b6Uxr6YK08bhTuY799ORArIrOAtEU6rYEJ/jrfiMgPuKANYFFa4g8R2QjUAIIGbVkoB7wmInUABUoEXHO8v+Z6X/IgWyISArwJ/CtzwObbmQxMBpc9cmCvG/PQTXM+SUpKonTp0ukZuy677DJEhJiYGBYtWkS9evUIDQ2lVq1a9OvXj7fffptWrVplaGPXrl1UrlyZH3/8kfj4eL788kvKlz/dOvHmXGRZ30xO7B0xObF3xFjQVgSp6nEgDojzI2YDgA2q2jLI4bHATaq61k9zjPFt9PcJTK4H4n0gmJ2jAZ+Pk/d370lgiap2FZGavv+nazLwraqO+x1tmPNcz549iYuLY8+ePVSrVo3HH3+cKVOmMGjQIFJTU7nooouYPHkyBw4c4IknnmDv3r38/e9/ByAkJIRVq9xgb7du3di7dy8lSpRg4sSJFrAZY4wxJs8saCtiRCQcOKGq3/pNUbgU+B1FpKWqfiEiJYC6qroBKAPs9Nt6Adt9O2GqugJYISKdgMuAZf6YxSJSF6iOmwrZJB+6Xi7t2kDvgO3LcUXMl4hIA1xx8Cz5UcNynCwmbkxQb775ZtDt8fHxGb7HxcUxdepUpk6dGvT4ZcuW5XvfjDHGGFO0WNBW9IQCE/yarlRccpG7cKNP/xKRcrj3YhywARgBrAB2+/+W8e0856cqCrAIWAt8A0zyo3epuGQmR/2Uy7xKFJET/vMs4Fnc9MhHgMDMIC/57Rv99TcAQeuviUg1YLg/brXv14uqGvyvbWOMMcYYY84CFrQVMaoaD7QKsmsP0CbI8ZNwGSEzb785SBtHgFPqtKlqLG6aZdr3zjn0sWYWu+oGfE5L2XcEuEVVj4hIGC65yg9ZtLsNF2QaY4wxxhhzzrCgzZzrSuGmRpbABWR/V9Vjhdwnc47p06cPc+fOpXLlyqxfvx6AHj16sGnTJgD27dtH+fLlSUhIICkpifr16xMeHg5A9erV0xeHx8TEsHPnTkqWLAnAggULqFy5csHfkDHGGGPOKxa0mUIjIiuACzNtvlVV1+W2DVU9gKsLl+9tm6Kjd+/e3Hvvvdx2223p22bOnJn+eciQIZQrVy79e1hYGAkJCYBb0xbojTfesALaxhhjjMlXFrQVQSIyHPgbLovjCeBun1SkQKlqdLDtweq05WPbTwO3AX9QVSt4YgBo06YNSUlJQfepKrNmzWLx4sUF2yljjDHGGM+CtiJGRFoCnYEmPklIReCCQu5WQfoQeBH4NqcDAQ6nHKfmsI9yPtCcs5JGX5/t/mXLllGlShXq1KmTvm3Lli1cccUVlC1blm7dumWonXPHHXdQvHhxunXrxiOPPMJpJuIxxhhjjElnQVvRUxXYo6pHAdJGs3ydtX/iskvuwWV+3Ckid+KyS16AyzR5q6oeEpG/AI/hRuv2q2obEbkIl7SkGS575GBVXeLru3XBrT8LA95X1Yfy0mkRaY4ron0RcBi4Q1U3iUgpXJKThrjyApcAA1R1VbB2VPVL315217rL3zMVK1bi0cjUvHTVnGPSpjf+9NNPHDx48JTpji+88ALNmzdP337s2DH+85//UK5cOTZt2sQjjzxCWFgYpUuXZsCAAVSqVIlDhw7x2GOPcejQIa699tqCvSFz1klOTj7lvTImkL0jJif2jhhR1cLugylAIhIKfIYLoD4BZgKfA0uBG1V1t4j0AK5V1T4icrGq7vXnPgX8rKoTfFr//1PV7SJSXlX3icgQIMKfVw9YgMv4+FfgUeAKXJHtTUBrVd2aRR+TyDQ9UkTKAodUNVVE2gP3qGo3EXkAqKOqd4tIQyABaJFV0BbQXnJupkeGh4drWjIKc35LSkqic+fO6YlIAFJTU7n00kuJj4+nWrVqQc+Liopi6tSpp6xji42NZdWqVbz44otntN/m7BcXF5dhNNaYzOwdMTmxd6RoEJF4VQ26MN5G2ooYVU32o2pXAW1xQdtTuJGqhX4Eqjiw05/S0Adr5XGjcB/77cuBWBGZBbznt7UGJvjrfCMiP3AyTf8iVd0P4Guq1QCCBm1ZKIerx1YHUKBEwDXH+2uuF5HEPLRpTLY++eQT6tWrlyFg2717NxUqVKB48eJ8//33bN++nVq1apGamsq+ffuoWLEiKSkpzJ07l/bt2xdi740xxhhzvrCgrQhS1eNAHBDnR8wGABtUtWWQw2OBm1R1rZ/mGOPb6C8i0cD1QLwPBLNzNODzcfL+7j0JLFHVriJS0/ffmHzRs2dP4uLi2LNnD9WqVePxxx+nb9++vPXWW/Ts2TPDsZ9++imPPvooJUqUoFixYtx///1UqFCBgwcPcu2115KSksLx48dp3749d955ZyHdkTHGGGPOJxa0FTEiEg6cUNW0RBxRwNdARxFpqapf+JpndVV1A1AG2Om39QK2+3bCfMbJFSLSCbgMWOaPWSwidYHquKmQTfKh6+XSrg30Dti+HOiOq9XWAIjMh2uZIubNN98Muj02NvaUbd26daNbt27p39PWGJQuXZr4+Pgz0T1jjDHGFHHFCrsDpsCF4qYZbvRTCRvg1pv9GRgjImtx68Ja+eNHACtwwdE3Ae08JyLrRGQ9bk3cWuAloJgfvZuJS2YSOMKWF4kiss3//BN4FhglImvI+I8NLwGV/JTLp4ANwP6sGhWRZ0VkG1DKtz3yNPtnjDHGGGNMgbCRtiJGVeM5GZAF2gO0CXL8JFxGyMzbbw7SxhHgjiDHxuKmWaZ975xDH2tmsatuwOdHAq55i6oeEZEwXHKVH7Jp+yEgT5krTcHq06cPc+fOpXLlyulJQUaMGMGcOXMoVqwYlStXJjY2lksuuYQ5c+YwYsQIihUrRkhICOPGjaN169YA/Pjjj/Tr14+tW7ciIsybN4+aNWsW4p0ZY4wxxpweG2kz57pSwGd+hPB94O+qeqyQ+2R+h969ezN//vwM2x588EESExNJSEigc+fOPPHEEwC0a9eOtWvXkpCQwCuvvEK/fv3Sz7ntttt48MEH+frrr1m5ciWVK1cu0PswxhhjjMkvNtJm8p2IDAf+hks4cgK4269/y3zcCqA2sAuXqKQB0E5Vl2U67mJgkf/6R9/ubv+9ebDUqL7tCzNtvlVV153ufZmC0aZNG5KSkjJsK1u2bPrngwcPptfZCw0NDbp948aNpKam0qFDh1OOM8YYY4w511jQZvKViLQEOgNNVPWoiFTEFeY+hapGZzo3CZcUJfNxe3EJU/Br0JJV9fns+pG5bXPuGz58ONOnT6dcuXIsWbIkffv777/Pww8/zK5du/joo48A2Lx5M+XLl+fmm29my5YttG/fntGjR1O8ePHC6r4xxhhjzGmzoM3kt6rAnrQEJGkFskWkHfA87p37Clcc+6iIxAEP5FQMOzMRKQMk4rJcpvji22tx694W+s9X++v1UdWVIlIaV0euIa7O20hVnZPddQ6nHKfmsI/y0jXzOySNvj7LfU8//TRPP/00o0aN4sUXX+Txxx8HoGvXrnTt2pVPP/2UESNG8Mknn5CamsqyZctYs2YN1atXp0ePHsTGxtK3b9+CuhVjjDHGmHxjQZvJbwuAR0VkMy4pyExc9slY3NTHzSIyHbgHGHe6F1HVAz7gux6YDfwVeM8HcAClVDVKRNoAr+ACteHAYlXtIyLlgZUi8omqHgxsW0TuAu4CqFixEo9Gpp5uN00epaXP/+mnnzh48GD690C1atVi2LBhtG3b9pR9GzduZM6cOezatYuaNWvy448/8uOPPxIeHs6HH35IWFhYvvc5OTk5aD+NSWPviMmJvSMmJ/aOGAvaTL5S1WRfaPsqoC0uaBsFbFHVzf6w13AFvU87aPOm4jJBzsZlrQysZPym78+nIlLWB2kdgS4i8oA/5iJcLbkMUzJVdTIwGSA8PFwH9rrxd3bT5FVSUhKlS5cmJiYGgG+//ZY6deoAMGHCBJo2bUpMTAzfffcdYWFhiAirV69GROjSpQsnTpzg3//+NxEREVSqVInXXnuNDh06pLeXn+Li4s5Iu+b8Ye+IyYm9IyYn9o4YC9pMvlPV40AcEOdrtg04Q9dZLiI1RSQGKK6q6wN3Zz4cEKCbqm46E/0x+aNnz57ExcWxZ88eqlWrxuOPP868efPYtGkTxYoVo0aNGrz88ssAvPvuu0yfPp0SJUpQsmRJZs6ciYhQvHhxnn/+edq1a4eq0rRpU+68884crmyMMcYYc3ayoM3kKxEJB06o6rd+UxTwP6CjiNRW1e+AW4Gl+XTJ6cB/gCczbe8BLBGR1sB+Vd0vIh8DA0VkoKqqiFyhqmvyqR8mn7z55punbMtqLdrQoUMZOnRo0H0dOnQgMTExX/tmjDHGGFMYLGgz+S0UmOCnI6YC3+HWh70JvC0iaYlIXs6n670BPOXbD3RERNbgEo708duexE3JTBSRYsAWXKZLY4wxxhhjzloWtJl8parxQKsguxYBVwQ5Pibgc81ctD8y06bWwDuqui/T9tdV9b5M5x4G7s7pGsYYY4wxxpxNihV2B4w5XSIyARjNqVMjzTmsT58+VK5cmYYNG6ZvGzFiBI0aNSIqKoqOHTuyY8cOAN544w0aNWpEZGQkrVq1Yu3atennjB8/noYNG/L/27vz+Kqqc//jn69EZJBBRC1DNXJVECIGRakWMYrggBURr1S9SkALbZXqr8XeOFFEfxWH3moB9YpacSgOiNCqPxCFKFJkkgRBBGmJFRUEFCRpEALP74+9Ek8OJzlJCBngeb9eeeWctfdea+3D6mke117P6tKlCw89tLc5b5xzzjnnao8Hba5OkXS4pJwEP4fHn2tmI8zsuJislMXlGZXd983VHZmZmcyYMaNU2S233MKyZcvIycnh4osvZsyYMQAce+yxvPPOO3z44YfceeedDBs2DIDly5czceJEFi5cSG5uLq+99hpr1qyp8XtxzjnnnKsOHrTtRyTdLmmFpGUh0OlRzrlPSOocXudJap3gnNgAar2kz2PeN9xHt3ESsM7M0uN+Nu9txZL6SFoi6cPw+9xq6K+rZr169aJVq1alypo3b17yuqCggLAXH2eeeSaHHXYYAD/60Y9Yt24dACtXrqRHjx40adKElJQUzj77bKZOnVpDd+Ccc845V718Tdt+QtIZREk1TjGz70IQVmZgZWbXJ6szBErpof7RQL6ZPVg9Pa4Vm4CfmNkXktKAmUC78i4o3LmL1KzXa6RzB7q8sf3KPX777bfzzDPP0KJFC+bMmbPH8SeffJILL7wQgLS0NG6//XY2b95M48aNeeONN+jevfs+6bdzzjnn3L7mQdv+ow2wycy+AzCzTQCSegMPEv1bLwJ+EYK6bGBkZR8jlNQMWAacYGY7JTUHcoETgFnh9dmhvaFmtlBSU2AckEaUzXG0mU2vZLujgJ8AjYG/A8ND2v7TgCeB3aH9C80sLVEdcen9VwCNJR1S/JnFtDWMKOMlrVsfwaiTiirTVVdF2dnZJa/Xr19PQUFBqbI+ffrQp08fnn/+eUaOHMmQIUNKji1dupRx48bxpz/9qeSa/v37c8YZZ9C4cWNSU1P58ssvS9VXXfLz8/dJvW7/4WPEJeNjxCXjY8R50Lb/eBMYJWk18BbwIrAAeBrobWarJT0D/IIo7X2VmNm2EPD1A6YBPwWmhgAOoImZpUvqBTxFFKjdDsw2s6FhK4CFkt4ys4JKND3ezMYASHqWaFbxb8CfgZ+Z2XxJYytR30Dgg/iALdzj48DjAB07drQRV/evRLWuOuTl5dG0aVMyMjL2ONahQwcuuugiJk2aBMCyZcsYP348s2bN4oQTTig5LyMjgwceeACA2267jfbt2yesb29lZ2fvk3rd/sPHiEvGx4hLxseI8zVt+wkzywdOJZoh2kgUtA0H1sYk6pgE9KqG5p4Aiqc5hhAFTsUmh/68CzQPQVpfIEtSDpANNAKOrmSb50haIOlD4FygS6i7mZnND+f8pSIVSeoC3Ien/683Pvnkk5LX06dPp1OnTgD861//4rLLLuPZZ58tFbABfPXVVyXnTJ06lauuuqrmOuycc845V418pm0/Yma7iIKi7BDc3LCP2pknKVVSBtDAzJbHHo4/HRAw0MxWVaU9SY2AR4DuZvZZWF/XqIp1tQdeBa41s39UpQ63b1155ZVkZ2ezadMm2rdvz1133cUbb7zBqlWrOOiggzjmmGN47LFob/YxY8awefNmfvnLXwKQkpLC4sXRE78DBw5k8+bNHHzwwUyYMIGWLVvW2j0555xzzu0ND9r2E5I6ArvNrHhKIh34B9BX0nFmtga4Bninmpp8hmhmK36PtEHAHEk9ga1mtlXSTGCEpBFhHVq3uPVlyRQHaJskHQpcTthQW9I2ST3MbAHRo5plCjNzrwNZZjavEu27GjR58uQ9yq677rqE5z7xxBM88cQTCY/NnTu3WvvlnHPOOVdb/PHI/cehwCRJH0laBnQGsogeX3w5zLztBh6rpvaeBw4jPA4ZY7ukpaGd4r+07yZKQLJM0gqSb4bdW9K64h/gRGAisJwo4+OimHOvAyaGRy+bAlvLqfdG4DiitX/FWxccmexGnXPOOeecq00+07afMLM4arMPAAAgAElEQVQlwJkJDr0NdEtwfkbM69QK1D86rqgnYbYrrvw5M7s57tpCKrh+zMyyiTJExpsP3JGgfIWZdQWQlAWUmQ3TzO4B7qlIP9zeGzp0KK+99hpHHnkky5dHT9C+/PLLjB49mpUrV7Jw4cKSNPwLFy4s2RjbzBg9ejQDBgzgs88+49prr2XDhg1IYtiwYdx00021dk/OOeecc7XBZ9pcpUkaB4wl+YxZTegXZsyWA2fhQVmdkZmZyYwZM0qVpaWlMXXqVHr16rVH+eLFi8nJyWHGjBkMHz6coqIiUlJS+MMf/sBHH33E+++/z4QJE/joo49q8jacc84552qdz7Q5ACQdTjQrF6932GS7hJmNiLnuB0RbCJwGbAHGSLo5JmNlWe2dT5TBMdZaMxuQpG8/AHYRZcgEON3MXqxq3W7f6dWrF3l5eaXKTjzxxITnNmnSpOT19u3bCdtH0KZNG9q0aQNAs2bNOPHEE/n888/p3Lnzvum0c84551wd5EGbAyAEZumVuUbRX9avApPM7Keh7GTgKGB1eJ9iZnvsTm1mM4nWp1WqbyFzZL6ZPVjO+RWu29UdCxYsYOjQoXz66ac8++yzpKSU/nrKy8tj6dKl9OjRo5Z66JxzzjlXOzxoc3vjHGCnmZUkNzGzXEkZkuYC3wCdJHUFHgW6A0XAr81sjqRMYADQAmhHtB7uroo0LKkZsAw4IWzs3RzIBU4AZoXXZxON8aFmtlBSU2Ac0YbfBwOjzWx6ee0U7txFatbrFfw4XLG8sf0qfU2PHj1YsWIFK1euZPDgwVx44YU0ahQlDs3Pz2fgwIE89NBDNG/evLq765xzzjlXp3nQ5vZGGrCkjGOnAGlmtlbSbwAzs5MkdQLelFS8E/LpoZ5/A4skvW5mZSYTKWZm2yRlA/2AaUTp/qeGAA6giZmlS+oFPBXauB2YbWZDQ/r/hZLeMrOC2LolDSPapJzWrY9g1El7TBS6JLKzswFYv349BQUFJe+LbdmyhSVLlpCfn5/w+qKiIiZNmkTHjh0pKiri1ltvpUePHrRq1WqPumpbfn5+neuTq1t8jLhkfIy4ZHyMOA/a3L6y0MzWhtc9iWa4MLOPJX1KNCMGMKt4zZykqeHcpEFb8ATwW6KgbQjws5hjk0N770pqHoK0vsAlkkaGcxoBRwMrYys1s8eBxwE6duxoI67uX8HuuHh5eXk0bdqUjIyMUuUtW7bk1FNPLckeuXbtWn74wx+SkpLCp59+yvr16xk4cCCHH344gwcP5sc//jEPPfRQLdxBctnZ2Xvcn3OxfIy4ZHyMuGR8jDjPHun2xgrg1DKOFZRRHs+SvC/7wmiD7FRJGUADM1uepF4BA80sPfwcbWYrcfvElVdeyRlnnMGqVato3749Tz75JK+++irt27dn/vz59OvXj/PPPx+A9957j5NPPpn09HQGDBjAI488QuvWrZk3bx7PPvsss2fPJj09nfT0dN54441avjPnnHPOuZrlM21ub8wGfi9pWJidIqxfOyvuvLnA1cDs8Fjk0cAqokco+0hqBRQClwJDK9mHZ4C/sOf2A4OAOZJ6AlvNbKukmcAISSPMzCR1M7OllWzPVdDkyfH7rkcGDNgziec111zDNddcs0d5z549MatwHO+cc845t1/ymTZXZRb9NT0AOE/SPyStAO4F1sed+ghwkKQPgReBTDP7LhxbCLxClFTklYqsZ4vzPHAY4XHIGNslLQUeA64LZXcTJSBZFvpaF/aZc84555xzrlw+0+b2ipl9AVyR4NDEmHO2E605S2SdmV1aifZGxxX1BKaY2Za48ufM7Oa4awuB4RVtyznnnHPOubrAZ9pcvSVpHDAWnzGrE4YOHcqRRx5JWlpaSdnXX39Nnz59OP744+nTpw/ffPMNAA888EDJGrW0tDQaNGjA119/zfbt2zn99NM5+eST6dKlC7/73e9q63acc8455+oMD9pcrTGzp83sxtgySYdLyknwc3iC60eY2XFmtjquPKMKj1m6vZSZmcmMGTNKlY0dO5bevXvzySef0Lt3b8aOHQvALbfcQk5ODjk5Odx7772cffbZtGrVikMOOYTZs2eTm5tLTk4OM2bM4P3336+N23HOOeecqzM8aNuPSLpd0gpJy0Kg06Occ5+Q1Dm8zpPUOsE5sQHUekmfx7xvuI9u4ySiRybT4342723F4X7mSMqXNL4a+upi9OrVi1atWpUqmz59OoMHDwZg8ODBTJs2bY/rJk+ezJVXXgmAJA499FAAdu7cyc6dOwn77jnnnHPOHbB8Tdt+QtIZwMXAKWb2XQjCygyszOz6ZHWGQCk91D8ayDezB6unx7ViO3An0UbbaUnOBaBw5y5Ss17fp53aH+SN7ZewfMOGDbRp0waAH/zgB2zYsKHU8X//+9/MmDGD8eO/j6F37drFqaeeypo1a7jhhhvo0aPM//bgnHPOOXdA8KBt/9EG2FScldHMNgFI6g08SPRvvQj4RQjqsoGRlX2MUFIzokyPJ5jZTknNgVyizbJnhddnh/aGmtlCSU2JNtdOI8reONrMpley3VHAT4DGwN+B4SFt/2nAk8Du0P6FZpYwIDOzAuA9ScclaWsYMAygdesjGHVSUWW6ekDKzs4GYP369RQUFJS8LyoqKnkNUUAW+3727Nl06tSJZcuWlarvoYceIj8/nzvvvJNOnTpx7LHH7uM7qLr8/PxS9+RcPB8jLhkfIy4ZHyPOg7b9x5vAKEmrgbeIUusvAJ4GepvZaknPAL8AHqpqI2a2LQR8/YBpwE+BqSGAA2hiZumSegFPEQVqtwOzzWyopJbAQklvhSCqosab2RgASc8SzSr+Dfgz8DMzmy9pbFXvK+4eHwceB+jYsaONuLp/dVR7QMjLy6Np06ZkZGQA0K5dOzp27EibNm348ssvadu2bckxgIcffpgbb7yxVFmsDz74gM2bNzNkSFnJR2tfdnZ2mf13DnyMuOR8jLhkfIw4X9O2nzCzfOBUohmijURB23BgbUyijklAr2po7gm+T+E/hChwKjY59OddoHkI0voCWZJygGygEdEG25VxjqQFYa+3c4Euoe5mZjY/nPOXqtyM23cuueQSJk2aBMCkSZPo3//7AHjr1q288847pco2btzIli3R7g2FhYXMmjWLTp061WynnXPOOefqGJ9p24+Y2S6ioCg7BDc37KN25klKlZQBNDCz5bGH408HBAw0s1VVaU9SI6INurub2WdhfV2jqtTl9p0rr7yS7OxsNm3aRPv27bnrrrvIysriiiuu4Mknn+SYY47hpZdeKjn/1VdfpW/fvjRt2rSk7Msvv2Tw4MHs2rWL3bt3c8UVV3DxxRfXxu0455xzztUZHrTtJyR1BHab2SehKB34B9BX0nFmtga4Bninmpp8hmhmK36PtEHAHEk9ga1mtlXSTGCEpBFhHVo3M1taibaKA7RNkg4FLidsqC1pm6QeZraA6FFNV0smT56csPztt99OWJ6ZmUlmZmapsq5du7J0aWWGhnPOOefc/s+Dtv3HocC48MhgEbCG6FHJycDLkooTkTxWTe09D9wT6o+1XdJSooQjQ0PZ3UTr6JZJOghYS7QmrSy9Ja2Lef+fwERgObCe6D6KXQdMlLSbKCDdWl6nJeUBzYGGki4F+prZR+Vd45xzzjnnXG3yoG0/YWZLgDMTHHob6Jbg/IyY16kVqH90XFFPwmxXXPlzZnZz3LWFROvrkjKzbKIMkfHmA3ckKF9hZl0BJGUB5WbDrMi91lepqak0a9aMBg0akJKSwuLFi8nNzeXnP/85+fn5pKam8vzzz9O8eXN27tzJ9ddfzwcffEBRURHXXnstt956a23fgnPOOeecS8ATkbhKkzQOGMuej0bWhn5hs+/lwFlEs38HrDlz5pCTk8PixVHsev311zN27Fg+/PBDBgwYwAMPPADAyy+/zHfffceHH37IkiVL+N///V/y8vJqsefOOeecc64s9SJok7Sr+A9zSS9LalLJ61tK+mXM+7aSpiS55glJnROUZ0oan+iaJPWNljQyriwvbIJdLSSNlPRx+KwWSbo2yfmZktqG14eH6+J/Do+/zsxGmNlxMVkpi8szKrrvm6TzE7T1qqQMSa9V9J7N7EUzSzezNDPrZ2Yby6n7cElzJOVX5d+wPlq9ejW9ekUJQ/v06cMrr7wCgCQKCgooKiqisLCQhg0b0rx589rsqnPOOeecK0O9CNqAwuI/zIEdwM8reX1LoCRoM7MvzOzy8i4ws+vr01onST8H+gCnm1k60Jsoa2N5MoG2AGa2OXzG8T+b97JfCR/BNbOZCdoasDdtVaDu7cCdwMgkVdRLkujbty+nnnoqjz/+OABdunRh+vRoH/OXX36Zzz77DIDLL7+cpk2b0qZNG44++mhGjhxJq1ataq3vzjnnnHOubPVxTdtcoKukpsA4os2bDwZGm9l0SV2I9g1rSBSUDiR6jO8/wj5hs4AJwGtmliapAXAfcAGwG5hoZuPCBtIjzWyxpCHArcAWIBf4DkDSEUSJPYr3HLvZzOZV9obCvbwEtAcaAHeb2YuSTgX+hyjJyCYg08y+LKOa24AMM/sWIPyeFOofBfyEaK3Y34nWlw0EugPPSyoEzgA6J2pP0mnAk+HzmQVcGD67RsCjoZ4i4NdmNkdSJnBZqKeBpE+JNuCeFvrzPPCSmU2vxGe0xz2ETJQJ+5aojrCZ93uSjqtou4U7d5Ga9XpFT68VeWP7AfDee+/Rrl07vvrqK/r06UOnTp146qmn+NWvfsXdd9/NJZdcQsOGDQFYuHAhDRo04IsvvuCbb77hrLPO4rzzzqNDhw61eSvOOeeccy6BehW0hVmbC4EZwO3AbDMbGjImLpT0FtEs3MNm9rykhkRBUBaQFmagkJQaU+0wIBVIN7MiSaWmGyS1Ae4i2rh6KzAHKM5J/jDwRzN7T9LRwEzgxCrc2gXAF2bWL7TZQtLBREFp//DI3yDg//J9RsbYPjYn2mT6n2XUP97MxoRznwUuNrMpkm7k+8C0vPb+DPzMzOZLGhtT7w2AmdlJkjoBb0o6IRw7BehqZl9LOhv4P8A0SS2IEqYMruRntMc9AH8rp29VJmkY0bigdesjGHVSUXVUu89kZ2eXvP7kk2jHh27dujF58mQGDRrEbbfdBsBnn33GkUceSXZ2Ng899BCdO3dm3rzovzF06NCBSZMmcc4559R4/+u7/Pz8Uv8GzsXzMeKS8THikvEx4upL0NY4zJJBNNP2JNFsyyUx68QaEc14zQdul9SeaHbnE6ncpwTPAx4zsyIAM/s67ngPINvMNgJIehE4IebazjH1N5d0qJnlJ2gnftPp2PIPgT9Iuo9oBnCupDSiWcRZof4GQFmzbMmcI+m3QBOgFbCCKOCJ1TFReyEgbmZm88N5f+H7dP09iQI9zOzjMKNW/NnMKv4szewdSY+EmcmBwCvFn/fe3IOkueX0rcrM7HHgcYCOHTvaiKv7722V+1xBQQG7d++mWbNmFBQUcNtttzFq1Cg6d+7MkUceye7du8nMzOSWW24hIyODBQsW8PHHH5ORkUFBQQGffvop9913H127dq3tW6l3srOzycjIqO1uuDrMx4hLxseIS8bHiKsvQVth8SxZMUWRxUAzWxV37kpJC4B+wBuShgNlzUDtrYOAH5nZ9gqcuxloE1fWDNhiZpslnQJcBNwj6W3gVaJ09mckq9jMvg3JNTrEz7aFRxgfAbqb2WeSRvP9ZtWlTk3UXgjaqqIg7v0zwH8RbYA9pDIVVeIeDlgbNmxgwIBoSWBRURFXXXUVF1xwAQ8//DATJkwA4LLLLmPIkOijv+GGGxgyZAhdunTBzBgyZIgHbM4555xzdVR9CdoSmQmMkDQirG3qZmZLJXUA/mlmfwqPLHYlWofWrIx6ZgHDJc0pfjwybrZtAfBwyKL4LdFGz7nh2JvACOABAEnpZpZDYu8SrR8ba2bbJF0G5JrZrpDB8Wsze07SFuB6opT6R0g6Izz6dzBwgpmtKKP+e4EJkgaFIO5QonVlfw3HN4Wyy4HizJnbYj6XVWW1J2mbpB5mtoAo6Co2F7gamB0eizw61HNKgv49DSwE1lchwUtxgFbqHsxsSzl9O6B06NCB3NzcPcpvuukmbrrppj3KDz30UF5++eWa6JpzzjnnnNtL9Tlouxt4CFgm6SBgLdGjcVcA10jaCawHfh/WVc1TtJfX/yNKRFLsCaJH+paFayYCJengQyKO0USPXW4BYoOyXxEFSsuIPst3KSOzpZktC2nm35NkwFdEwRnAScADknYDO4FfmNkOSZcDfwrrwFLC/ZYVtD1KlPhjUbiPncAfQmAzEVgePo9FMdc8DTwWk4ikrPauAyaG/r1DtLYPotmvRyV9SJSIJNPMvkv0OKqZbZC0EphWRv9j9Za0Lub9fxL9uyS6h7L6lpCkPKA50FDSpUDf+pQl1DnnnHPOHXhkVtZSK+cisev0JGUBbcxsz+mb8utoQrR27xQzKzewqum+ladjx462alX8E7jOfc/XGbhkfIy4ZHyMuGR8jBwYJC0xs+6JjtXnmTZXc/pJupVovHxKtL9bhUk6jyh5zB+rM2Crjr7tD1JTU2nWrBkNGjQgJSWFxYsXM2jQIIqDzS1bttCyZUtycnKYNWsWWVlZ7Nixg4YNG/LAAw9w7rnn1vIdOOecc8658njQVs0U7ekWP9Mzz8xuqKb6JwA/jit+2Mz+XB31J2JmLwIv7sX1bwHHxJZJOp9of7xYayu7wXaivlVX3fXJnDlzaN26dcn7F1/8/iP5zW9+Q4sWLQBo3bo1f/vb32jbti3Lly/n/PPP5/PPP6/x/jrnnHPOuYrzoK2aheBpXwZQNwBI+gHRmrPTgBsl/SfR5t6rq6utkHzl7fD2B8AuYGN4f7qZ7ahq3WY2kyiZTHybGUR7x1U5dX85dfchSvDSENgB3GJms6vaTn1gZrz00kvMnh3dZrdu3UqOdenShcLCQr777jsOOeSQ2uqic84555xLwoO2eihsd/AqMMnMfhrKTgaOAlaH9ylV2AutFDPbDBRvSD4ayDezB/emzlq2CfiJmX0R9sGbCbQr74LCnbtIzXq9RjpXFXlj+yGJvn37Ionhw4czbNiwkuNz587lqKOO4vjjj9/j2ldeeYVTTjnFAzbnnHPOuTrOg7b66Rxgp5k9VlxgZrmSMsKG098AnSR1Jcoq2Z0ou+OvzWyOpExgANCCKGh5zszuqkjDkpoBy4i2A9gpqTnRFggnEG2fkAucTTS2hprZQklNiTbhTgMOBkab2fTK3LCkUcBPgMZEG6sPD1s9nEa0Xm53aP9CM0tLVIeZLY15u4Jo0/ZDzOy7uLaGAcMAWrc+glEn7VXsu09lZ2dz//33c8QRR/DNN98wcuRICgsLOfnkkwH44x//yOmnn052dnap69auXcsdd9zB/fffv8cxVzn5+fn+Gbpy+RhxyfgYccn4GHEetNVPacCSMo6dAqSZ2VpJvwHMzE6S1Al4M+ynBnB6qOffRNsEvG5mi5M1HPaYyybavHwa0d5oU0MAB9DEzNIl9QKeCm3cDsw2s6Fhs+6Fkt4ys/gNuMsz3szGAEh6lmh7h78RPYr6s7C33NhK1DcQ+CA+YAv3+DjwOETZI0dc3b8S1dau3Nxcdu7cSUZGBkVFRQwaNIglS5bQvn37knPWrVvHsGHDeOmll/jxj+OXR7rK8oxeLhkfIy4ZHyMuGR8j7qDa7oCrdgvNbG143RN4DsDMPibKrlgctM0ys81mVghMDedW1BPAkPB6CKXX8E0O7b0LNA9BWl8gS1IOkE20WfbRlbyvcyQtCHvCnQt0CXU3M7P54Zy/VKQiSV2IEpUMr2Qf6pyCggK2bdtW8vrNN98kLS2aaHzrrbfo1KlTqYBty5Yt9OvXj7Fjx3rA5pxzzjlXT/hMW/20gmgj7EQqOnsVv0FfhTfsM7N5klJD0pAGZrY8Sb0CBppZlTY8k9SIaCPv7mb2WVhf16iKdbUnWg94rZn9oyp11CUbNmxgwIAoKWZRURFXXXUVF1xwAQAvvPACV155Zanzx48fz5o1axgzZgxjxowB4M033+TII4+s2Y4755xzzrkK86CtfpoN/F7SsPAoH2H92llx580FrgZmh8cijwZWET1C2UdSK6AQuBQYWsk+PEM0s3V3XPkgYI6knsBWM9sqaSYwQtKIsA6tW9z6smSKA7RNkg4lClinmNkWSdsk9TCzBUSPapYpzMy9DmSZ2bxKtF9ndejQgdzc3ITHnn766T3K7rjjDu6444593CvnnHPOOVed/PHIesjMjCiRyHmS/iFpBXAvsD7u1EeAg8IjhS8CmTFruBYCrxAlFXmlIuvZ4jwPHEZ4HDLGdklLgceA60LZ3UQJSJaFvsYHevF6S1pX/AOcCEwElhNlfFwUc+51wMTw6GVToLzNu28EjgNGScoJPz7F5Jxzzjnn6jSfaaunzOwL4IoEhybGnLOd79eexVtnZpdWor3RcUU9CbNdceXPmdnNcdcWUsH1Y2aWTZQhMt58INEU0Qoz6wogKQsoM/g0s3uAeyrSj/okNTWVZs2a0aBBA1JSUli8OPoIxo0bx4QJE2jQoAH9+vXj/vvvZ/PmzVx++eUsWrSIzMxMxo8fX8u9d84555xzyXjQ5ipN0jjgQuCi2u4L0E/SrURj+VMgs3a7UzvmzJlD69atS72fPn06ubm5HHLIIXz11VcANGrUiLvvvpvly5ezfPnysqpzzjnnnHN1iAdt9ZSkHwAPAacBW4ANwM1mtjrZtWb2NPB0XH2HA2/Hndog/N4F/CD83hjKOpvZjrh6MyrR//OJMjjGWgs8DIw0s4srUo+ZvUj06GdF6r4emEL0mT1tZjdWtL/1zaOPPkpWVlbJxtnFiUaaNm1Kz549WbNmTW12zznnnHPOVYIHbfWQog3RXgUmmdlPQ9nJwFHA6vA+xcwqvCu0mW0G0stpczSQb2YP7kXXY9ubSbQ+Lb6djH1Yd1PgTqK94xJuwF0fSaJv375IYvjw4QwbNozVq1czd+5cbr/9dho1asSDDz7IaaedVttddc4555xzVeBBW/10DrDTzB4rLjCzXEkZkuYC3wCdQkbJR4HuQBHwazObIymTKJFJC6Ad0Tq0uyrSsKRmRMlLTggbajcHcon2f5sVXp9NNLaGmtnCECyNIwqUDgZGm9n0ytywpFHAT4jWu/0dGB4yUZ4GPAnsDu1faGYJA7Kwmfd7ko6raLuFO3eRmvV6ZbpaY/LG9gPgvffeo127dnz11Vf06dOHTp06UVRUxNdff83777/PokWLuOKKK/jnP/9J2ADdOeecc87VIx601U9pwJIyjp0CpJnZWkm/IUo2eZKkTsCbIfU/wOmhnn8DiyS9XpEMkma2TVI20A+YRpRmf2oI4ACamFm6pF7AU6GN24HZZjY0pN1fKOmtEERV1HgzGwMg6VngYuBvRBt7/8zM5ksaW4n6yiRpGDAMoHXrIxh1UoUnLGtUdnZ2yetPPvkEgG7dujF58mSaNGlChw4deOeddwDYsWMH06dPp2XLlgB8/PHHfP7556XqcFWTn5/vn6Mrl48Rl4yPEZeMjxHnQdv+Z6GZrQ2vexLNcGFmH0v6lGhGDGBWeCQSSVPDuRVN+/8E8FuioG0I8LOYY5NDe+9Kah6CtL7AJZJGhnMaEe0Zt7IS93WOpN8CTYBWwIowq9jMzOaHc/5CFMztlbD33eMAHTt2tBFX99/bKveZgoICdu/eTbNmzSgoKOC2225j1KhRnHzyyXzxxRdkZGSwevVqDjroIPr3718y05aXl0d+fj4ZGRm1ewP7gezsbP8cXbl8jLhkfIy4ZHyMOA/a6qcVRBtMJ1LR2StL8r7sC83mSUoN688amFlsGsJE9QoYaGarKtpGLEmNiPac625mn4X1dY3Kv+rAsGHDBgYMGABAUVERV111FRdccAE7duxg6NChpKWl0bBhQyZNmlQSsKWmpvLtt9+yY8cOpk2bxptvvknnzp1r8zacc84551w5PGirn2YDv5c0LMwKEdavnRV33lzgamB2eCzyaGAV0SOUfSS1AgqBS4GhlezDM0QzW/EbZQ8C5kjqCWw1s62SZgIjJI0I69C6mdnSSrRVHKBtknQoUcA6xcy2SNomqYeZLSB6VPOA0qFDB3Jzc/cob9iwIc8991zCa/Ly8vZxr5xzzjnnXHU6qLY74CrPzIwokch5kv4haQVwL7A+7tRHgIMkfUiUFj/TzL4LxxYCrxAlFXmlIuvZ4jwPHEZ4HDLGdklLgceA60LZ3UQJSJaFvsYHevF6S1pX/AOcSLRp+HKirJCLYs69DpgoKQdoCmwtr2JJecD/AJmhfp9ics4555xzdZrPtNVTZvYFcEWCQxNjztlOtOYskXVmdmkl2hsdV9STMNsVV/6cmd0cd20hMLyC7WQTZYiMNx+4I0H5CjPrCiApiyTr8swstSL9cM4555xzrq7wmTZXaZLGAWNJPmNWE/pJypG0nOjx0Htqu0P7yq5du+jWrRsXXxzlWjnrrLNIT08nPT2dtm3bcumlUQyenZ1NixYtSo6NGTOmNrvtnHPOOef2ks+0HYDM7Gng6dgySYcDbyc4vXdxlsmY60eUUW9GRfsg6XzgvrjitWY2oKJ1hDZfJHr0s9rrrmsefvhhTjzxRL799lsA5s6dW3Js4MCB9O//fZbLs846i9dee63G++icc84556pfvZhpk7SreDZF0suSmlTy+paSfhnzvq2kKUmueSLReidJmZLGV6b9cN3omJT3xWV5klpXtq5y2hgp6ePwWS2SdG2S8zMltQUws81mlp7gZ3N5dVSVmc1M0NaAsEH4XkUb5dTdR9ISSR+G3+dW1/3sa+vWreP111/n+uuv3+PYt99+y+zZs0tm2pxzzjnn3P6lvsy0FVkYUtgAAA+5SURBVJpZOoCk54GfEyWTqKiWwC+JEnMUrwcrK2U+4Zw9/zquwyT9HOgDnG5m30pqTpSspDyZRMk9vtiH/Uoxs7qyO/Um4Cdm9oWkNKKkJu3Ku6Bw5y5Ss16vkc6VJW9sP26++Wbuv/9+tm3btsfxadOm0bt3b5o3b15SNn/+fE4++WTatm3Lgw8+SJcuXWqyy84555xzrhrVl6At1lygq6SmRBtHpxFlJhxtZtMldQH+DDQkmkkcSLT26j9ChsFZwATgNTNLk9SA6FG6C4DdwEQzGycpGxhpZoslDQFuBbYAucB3AJKOIMqSeHTo281mNq+yNxTu5SWgPdAAuNvMXpR0KlFweihRwJFpZl+WUc1tQIaZfQsQfk8K9Y8CfkKU4OPvRElBBgLdgeclFQJnAJ0TtSfpNODJ8PnMAi4Mn10j4NFQTxHwazObIykTuCzU0yBs6j3VzKaF/jwPvGRm0yvxGe1xD2H7gIR9S1RH3DYDK4DGkg6JyahZ3NYwYBhA69ZHMOqk2o057733Xnbu3Mm2bdvIyclh8+bNZGdnlxyfMGECF110UUlZQUEBzz33HI0bN+b999/n/PPPLzP9v9t7+fn5pf49nIvnY8Ql42PEJeNjxGFmdf4HyA+/U4DpwC+A3wP/FcpbAquJUr6PA64O5Q2J/shPBZbH1FfyPtQ1BUgJ71uF39lEwUgb4F/AEaG+ecD4cM5fgJ7h9dHAynLuYTRREBhblge0JgqgJsaUtyAKRP8OHBHKBgFPlVF3c+CbctpuFfP6WaLZppJ7DK/LbI9oNu6M8HpszGf3m5hzOoXPqRHRDN66mM/ybGBazL2tLf68E/Q1gyigrug9JOxbBcbU5cBbyc474YQTrLZlZWVZu3bt7JhjjrGjjjrKGjdubFdffbWZmW3cuNFatWplhYWFZV5/zDHH2MaNG2uquwecOXPm1HYXXB3nY8Ql42PEJeNj5MAALLYy/iatF2vaiGZEcojSuf+LaGalL5AVyrOJgoWjiVLD3ybpv4FjLEo3X57zgP+18AifmX0dd7wHkG1mG81sB6WTXpwHjA99+CvQPGz+nIiVU/4h0WbX90k6y8y2Ah2JZhFnhfrvIJqJq4pzJC0I+7WdCyR6Vi5he5JaAs3MbH447y8x1/QEngMws4+BT4ETwrFZxZ+lmb0DHB9mJq8k2heustNXe9xDkr6VKczG3kcFtyGobffeey/r1q0jLy+PF154gXPPPbdk5mzKlClcfPHFNGrUqOT89evXFwemLFy4kN27d3P44YfXSt+dc84559zeqy+PR5asaSsmScBAM1sVd+5KSQuAfsAbkoYD/9xH/ToI+JFF+6Els5lo1i5WM2CLmW2WdApwEXCPpLeBV4n2IDsjWcUWrWHLl9TBzErda3iE8RGiGbXPJI0mCnDjKVF7ITCqioK4988A/wX8lLL3jkuoEvdQkbraE32215rZP6pSR13ywgsvkJWVVapsypQpPProo6SkpNC4cWNeeOEFov+5OOecc865+qi+zLQlMhMYEYI3JHULvzsA/zSzPxE9StkV2EYUICUyCxguKSVc3yru+ALgbEmHSzoY+M+YY28CJenvJaVTtneBSyQ1C+deBuSa2a6QwfHfZvYc8ABwCrAKOELSGeH8g8MMUVnuBSaEBCRIOjRkjywObjaFWcDYBCyxn0vC9izaPHubpB7hvJ/GXD8XuDqcfwLRTGd8EF3saeBmADP7qJz7SCThPSTp2x5CAPo6kGVVWHtYF2RkZJRK5Z+dnc0FF1xQ6pwbb7yRFStWkJuby/vvv8+ZZ55Z0910zjnnnHPVqD4HbXcTrcNaJmkF32/0fAWwPDzilwY8Y1Ha+nlhy4AH4up5guiRy2WScoGrYg9alPhjNNFjl/OAlTGHfwV0l7RM0kdEWS0TMrNlwHjgvdC3nwPFGSpPAhaG8t8B94RHMS8H7gv9ygHK++v7UWAOsEjRRtNzgd0hsJlItPZrJrAo5pqngcdCuw3Kae86YGI4rymwNZQ/AhwUHll8kShxSamkHjH3v4Hos/tzOfdQrLekdcU/wInl3ENZfUvkRuA4YFTYFiFH0pEV6I9zzjnnnHO1RsVrX5wri6RDzSw/vM4C2pjZTZWsownR2r1Twpq9OtO38nTs2NFWrSpr8tC5aLYzIyOjtrvh6jAfIy4ZHyMuGR8jBwZJS8yse6Jj9XmmzdWcfmFWajlwFnBPZS6WdB7RLNu46gzYqqNvzjnnnHPO1XX1JRFJvRH2dIuf6ZlnZjdUU/0TgB/HFT9sZhV57LBKzOxFSmfNrOz1bwHHxJZJOp8og2OstWaWbEPwpH2rrrqdc84555yrCzxoq2YheNqXAVS1BH+1zcxmEq1Pq1d1O+ecc845V9P88UjnnHPOOeecq8M8EYlz5ZC0jbK3MXAOoDWwqbY74eo0HyMuGR8jLhkfIweGY8zsiEQH/PFI58q3qqwsPs4BSFrsY8SVx8eIS8bHiEvGx4jzxyOdc84555xzrg7zoM0555xzzjnn6jAP2pwr3+O13QFX5/kYccn4GHHJ+BhxyfgYOcB5IhLnnHPOOeecq8N8ps0555xzzjnn6jAP2pwrg6QLJK2StEZSVm33x9UMST+UNEfSR5JWSLoplLeSNEvSJ+H3YaFckv4UxskySafE1DU4nP+JpMG1dU9u35DUQNJSSa+F98dKWhDGwouSGobyQ8L7NeF4akwdt4byVZLOr507cfuCpJaSpkj6WNJKSWf494iLJen/hP+fWS5psqRG/j3iyuJBm3MJSGoATAAuBDoDV0rqXLu9cjWkCPiNmXUGfgTcEP7ts4C3zex44O3wHqIxcnz4GQY8ClGQB/wO6AGcDvyu+A80t9+4CVgZ8/4+4I9mdhzwDXBdKL8O+CaU/zGcRxhXPwW6ABcAj4TvHrd/eBiYYWadgJOJxop/jzgAJLUDfgV0N7M0oAHR94F/j7iEPGhzLrHTgTVm9k8z2wG8APSv5T65GmBmX5rZB+H1NqI/tNoR/ftPCqdNAi4Nr/sDz1jkfaClpDbA+cAsM/vazL4BZhH9H6rbD0hqD/QDngjvBZwLTAmnxI+R4rEzBegdzu8PvGBm35nZWmAN0XePq+cktQB6AU8CmNkOM9uCf4+40lKAxpJSgCbAl/j3iCuDB23OJdYO+Czm/bpQ5g4g4fGTbsAC4Cgz+zIcWg8cFV6XNVZ8DO3fHgJ+C+wO7w8HtphZUXgf++9dMhbC8a3hfB8j+69jgY3An8MjtE9Iaop/j7jAzD4HHgT+RRSsbQWW4N8jrgwetDnnXAKSDgVeAW42s29jj1mUdtdT7x6gJF0MfGVmS2q7L67OSgFOAR41s25AAd8/Cgn498iBLjzm2p8owG8LNMVnUV05PGhzLrHPgR/GvG8fytwBQNLBRAHb82Y2NRRvCI8rEX5/FcrLGis+hvZfPwYukZRH9Oj0uUTrl1qGx5yg9L93yVgIx1sAm/Exsj9bB6wzswXh/RSiIM6/R1yx84C1ZrbRzHYCU4m+W/x7xCXkQZtziS0Cjg9ZnBoSLfL9ay33ydWAsEbgSWClmf1PzKG/AsWZ2wYD02PKrw3Z334EbA2PP80E+ko6LPwX1b6hzNVzZnarmbU3s1Si74bZZnY1MAe4PJwWP0aKx87l4XwL5T8NWeGOJUpCsbCGbsPtQ2a2HvhMUsdQ1Bv4CP8ecd/7F/AjSU3C/+8UjxH/HnEJpSQ/xbkDj5kVSbqR6P8cGwBPmdmKWu6Wqxk/Bq4BPpSUE8puA8YCL0m6DvgUuCIcewO4iGjx97+BIQBm9rWku4n+AwDAGDP7umZuwdWS/wZekHQPsJSQhCL8flbSGuBrokAPM1sh6SWiP9SKgBvMbFfNd9vtIyOA58N/+Psn0XfDQfj3iAPMbIGkKcAHRP/7Xwo8DryOf4+4BBQF6c4555xzzjnn6iJ/PNI555xzzjnn6jAP2pxzzjnnnHOuDvOgzTnnnHPOOefqMA/anHPOOeecc64O86DNOeecc8455+owD9qcc865OkbSLkk5MT+pVajjUkmdq793IKltSFdeYySlS7qoJtt0zrm6wvdpc8455+qeQjNL38s6LgVeI9q/qUIkpZhZUbLzzOwLvt8AeJ+TlAKkA92J9jRzzrkDis+0Oeecc/WApFMlvSNpiaSZktqE8p9JWiQpV9IrkppIOhO4BHggzNT9h6RsSd3DNa0l5YXXmZL+Kmk28LakppKekrRQ0lJJ/RP0JVXS8pjrp0maJSlP0o2Sfh2ufV9Sq3BetqSHQ3+WSzo9lLcK1y8L53cN5aMlPStpHvAsMAYYFK4fJOl0SfNDO3+X1DGmP1MlzZD0iaT7Y/p9gaQPwmf1dihLer/OOVfbfKbNOeecq3saS8oJr9cCVwDjgP5mtlHSIOD/AkOBqWY2EUDSPcB1ZjZO0l+B18xsSjhWXnunAF3N7GtJvwdmm9lQSS2BhZLeMrOCcq5PA7oBjYA1wH+bWTdJfwSuBR4K5zUxs3RJvYCnwnV3AUvN7FJJ5wLPEM2qAXQGeppZoaRMoLuZ3RjupzlwlpkVSToP+D0wMFyXHvrzHbBK0jhgOzAR6GVma4uDSeD2Ktyvc87VKA/anHPOubqn1OORktKIApxZIfhqAHwZDqeFYK0lcCgwswrtzTKzr8PrvsAlkkaG942Ao4GV5Vw/x8y2AdskbQX+Fso/BLrGnDcZwMzeldQ8BEk9CcGWmc2WdHgIyAD+amaFZbTZApgk6XjAgINjjr1tZlsBJH0EHAMcBrxrZmtDW3tzv845V6M8aHPOOefqPgErzOyMBMeeBi41s9wwG5VRRh1FfL8solHcsdhZJQEDzWxVJfr3Xczr3THvd1P6bw2Luy7+fbzyZrvuJgoWB4RELdll9GcX5f+9U5X7dc65GuVr2pxzzrm6bxVwhKQzACQdLKlLONYM+FLSwcDVMddsC8eK5QGnhtflJRGZCYxQmNKT1G3vu19iUKizJ7A1zIbNJfRbUgawycy+TXBt/P20AD4PrzMr0Pb7QC9Jx4a2ih+P3Jf365xz1cKDNuecc66OM7MdRIHWfZJygRzgzHD4TmABMA/4OOayF4BbQnKN/wAeBH4haSnQupzm7iZ61HCZpBXhfXXZHtp/DLgulI0GTpW0DBgLDC7j2jlA5+JEJMD9wL2hvqRPDpnZRmAYMDV8hi+GQ/vyfp1zrlrILNmTCc4555xze0dSNjDSzBbXdl+cc66+8Zk255xzzjnnnKvDfKbNOeecc8455+own2lzzjnnnHPOuTrMgzbnnHPOOeecq8M8aHPOOeecc865OsyDNuecc84555yrwzxoc84555xzzrk6zIM255xzzjnnnKvD/j97YKincNFcMAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YApmvWRsBoJk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "1c265567-0067-4985-a174-f83b99c1ea5c"
      },
      "source": [
        "submission"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Crop_Damage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>F00000002</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>F00000007</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>F00000011</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>F00000013</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>F00000014</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148159</th>\n",
              "      <td>F00155937</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148162</th>\n",
              "      <td>F00155940</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148163</th>\n",
              "      <td>F00155941</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148165</th>\n",
              "      <td>F00155943</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148166</th>\n",
              "      <td>F00155944</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>59310 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               ID  Crop_Damage\n",
              "1       F00000002            0\n",
              "6       F00000007            1\n",
              "10      F00000011            1\n",
              "12      F00000013            1\n",
              "13      F00000014            1\n",
              "...           ...          ...\n",
              "148159  F00155937            0\n",
              "148162  F00155940            0\n",
              "148163  F00155941            0\n",
              "148165  F00155943            0\n",
              "148166  F00155944            0\n",
              "\n",
              "[59310 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_28Wf5_ghW8S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "a725d0eb-7e4d-4984-cb39-ae014e680f0a"
      },
      "source": [
        "submission.to_csv(\"Submit.csv\", index=False)\n",
        "from google.colab import files\n",
        "files.download('Submit.csv') "
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_11cf9066-b4c6-47f7-8340-0d1232f1d85e\", \"Submit.csv\", 711735)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}