ensemble_v1_0.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<text style=\"color:green;font-size: 250%\">Попытка ID\t5759214</text><br>\n",
        "<text style=\"color:green;font-size: 120%\">Решение ансамблем из LightGBM, Catbost и нейросети.</text>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YM5ks-N2rLa6"
      },
      "source": [
        "## Задача и данные\n",
        "\n",
        "**Прогнозирование оттока клиентов телекоммуникационной компании**\n",
        "\n",
        "На основе данных о клиентах телекоммуникационной компании предсказать: уйдет ли этот клиент в течение следующего месяца.\n",
        "\n",
        "**Целевая переменная**. \n",
        "Churn (1 - клиент расторгнет договор в течение следующего месяца, 0 - останется клиентом).\n",
        "\n",
        "**Метрика** [F2](https://dyakonov.org/2019/05/31/%d1%84%d1%83%d0%bd%d0%ba%d1%86%d0%b8%d0%be%d0%bd%d0%b0%d0%bb%d1%8b-%d0%ba%d0%b0%d1%87%d0%b5%d1%81%d1%82%d0%b2%d0%b0-%d0%b2-%d0%b7%d0%b0%d0%b4%d0%b0%d1%87%d0%b5-%d0%b1%d0%b8%d0%bd%d0%b0%d1%80%d0%bd/#more-6567) \n",
        "\n",
        "**Данные**\n",
        "\n",
        "train.csv - набор данных для обучения   \n",
        "test.csv - набор данных для предсказания  \n",
        "sample_submission.csv - пример сабмита для отправки в проверяющую систему "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SNgzYcGpTLwM"
      },
      "source": [
        "## Описание полей"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UebqlsM9WbZQ"
      },
      "source": [
        "В датасете собраны данные о пользователях телекоммуникационной компании:\n",
        "\n",
        "*   State - штат\n",
        "*   Account length - как долго этот клиент с компанией (количество дней)\n",
        "*   Area code - код географической зоны\n",
        "*   International plan - подключен ли спец тариф для международных звонков\n",
        "*   Voice mail plan - подключен сервис голосовых сообщений\n",
        "*   Number vmail messages - количество голосовых сообщений\n",
        "*   Total day minutes - продолжительность разговоров в дневное время (минуты) \n",
        "*   Total day calls - количество звонков в дневное время\n",
        "*   Total day charge - расходы на звонки в дневное время\n",
        "*   Total eve minutes - продолжительность разговоров в вечернее время\n",
        "*   Total eve calls - количество звонков в вечернее время\n",
        "*   Total eve charge - расходы на звонки в вечернее время\n",
        "*   Total night minutes - продолжительность разговоров в ночное время\n",
        "*   Total night calls - количество звонков в дневное время\n",
        "*   Total night charge - расходы на звонки в вечернее время\n",
        "*   Total intl minutes - продолжительность международных звонков\n",
        "*   Total intl calls - количество международных звонков\n",
        "*   Total intl charge - расходы на межденародные звонки \n",
        "*   Customer service calls - количество обращений в службу поддержки\n",
        "*   Churn - текущий статус клиента (ушел/остался)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Решение"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Перечень установленных пакетов"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "!pip freeze > requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Package                       Version\n",
            "----------------------------- ---------\n",
            "absl-py                       1.0.0\n",
            "astunparse                    1.6.3\n",
            "backcall                      0.2.0\n",
            "backports.functools-lru-cache 1.6.4\n",
            "cachetools                    4.2.4\n",
            "catboost                      1.0.3\n",
            "certifi                       2021.10.8\n",
            "charset-normalizer            2.0.7\n",
            "colorama                      0.4.4\n",
            "cycler                        0.11.0\n",
            "debugpy                       1.5.1\n",
            "decorator                     5.1.0\n",
            "entrypoints                   0.3\n",
            "flatbuffers                   2.0\n",
            "gast                          0.4.0\n",
            "google-auth                   2.3.3\n",
            "google-auth-oauthlib          0.4.6\n",
            "google-pasta                  0.2.0\n",
            "graphviz                      0.18\n",
            "grpcio                        1.41.1\n",
            "h5py                          3.5.0\n",
            "idna                          3.3\n",
            "ipykernel                     6.5.0\n",
            "ipython                       7.29.0\n",
            "jedi                          0.18.0\n",
            "joblib                        1.1.0\n",
            "jupyter-client                7.0.6\n",
            "jupyter-core                  4.9.1\n",
            "keras                         2.7.0\n",
            "Keras-Preprocessing           1.1.2\n",
            "kiwisolver                    1.3.2\n",
            "libclang                      12.0.0\n",
            "lightgbm                      3.3.1\n",
            "Markdown                      3.3.4\n",
            "matplotlib                    3.4.3\n",
            "matplotlib-inline             0.1.3\n",
            "nest-asyncio                  1.5.1\n",
            "numpy                         1.21.4\n",
            "oauthlib                      3.1.1\n",
            "opt-einsum                    3.3.0\n",
            "pandas                        1.3.4\n",
            "parso                         0.8.2\n",
            "pickleshare                   0.7.5\n",
            "Pillow                        8.4.0\n",
            "pip                           21.3.1\n",
            "plotly                        5.3.1\n",
            "prompt-toolkit                3.0.22\n",
            "protobuf                      3.19.1\n",
            "pyasn1                        0.4.8\n",
            "pyasn1-modules                0.2.8\n",
            "Pygments                      2.10.0\n",
            "pyparsing                     3.0.5\n",
            "python-dateutil               2.8.2\n",
            "pytz                          2021.3\n",
            "pywin32                       302\n",
            "pyzmq                         22.3.0\n",
            "requests                      2.26.0\n",
            "requests-oauthlib             1.3.0\n",
            "rsa                           4.7.2\n",
            "scikit-learn                  1.0.1\n",
            "scipy                         1.7.2\n",
            "seaborn                       0.11.2\n",
            "setuptools                    58.5.3\n",
            "six                           1.16.0\n",
            "tenacity                      8.0.1\n",
            "tensorboard                   2.7.0\n",
            "tensorboard-data-server       0.6.1\n",
            "tensorboard-plugin-wit        1.8.0\n",
            "tensorflow                    2.7.0\n",
            "tensorflow-estimator          2.7.0\n",
            "tensorflow-io-gcs-filesystem  0.22.0\n",
            "termcolor                     1.1.0\n",
            "threadpoolctl                 3.0.0\n",
            "tornado                       6.1\n",
            "tqdm                          4.62.3\n",
            "traitlets                     5.1.1\n",
            "typing-extensions             3.10.0.2\n",
            "urllib3                       1.26.7\n",
            "wcwidth                       0.2.5\n",
            "Werkzeug                      2.0.2\n",
            "wheel                         0.37.0\n",
            "wrapt                         1.13.3\n",
            "xgboost                       1.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip list"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Удалим линейнозависимые признаки"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "train = pd.read_csv('train.csv')\n",
        "test = pd.read_csv('test.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "columns_to_del = ['State', 'Total day minutes', 'Total eve minutes', 'Total night minutes', 'Total intl minutes']\n",
        "\n",
        "train = train.drop(columns_to_del, axis = 1)\n",
        "\n",
        "# А также заменить Yes/No на 0/1 в признаках International plan и Voice mail plan\n",
        "\n",
        "train['International plan'] = train['International plan'].replace({\"Yes\":1,\"No\":0}).astype('int64')\n",
        "train['Voice mail plan'] = train['Voice mail plan'].replace({\"Yes\":1,\"No\":0}).astype('int64')\n",
        "\n",
        "# Теперь точно так же обработаем test датасет\n",
        "test = test.drop(columns_to_del, axis = 1)\n",
        "\n",
        "# А также заменить Yes/No на 0/1 в признаках International plan и Voice mail plan\n",
        "\n",
        "test['International plan'] = test['International plan'].replace({\"Yes\":1,\"No\":0}).astype('int64')\n",
        "test['Voice mail plan'] = test['Voice mail plan'].replace({\"Yes\":1,\"No\":0}).astype('int64')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Разделим train датасет на данные для обучениz и валидации\n",
        "X_train, X_test, y_train, y_test = train_test_split(train.drop(['Churn'],axis=1),\n",
        "                                                    train['Churn'], test_size=0.2, \n",
        "                                                    random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.model_selection import GridSearchCV, learning_curve, validation_curve\n",
        "from xgboost import XGBClassifier\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "categorical_features = ['Area code', 'International plan', 'Voice mail plan']\n",
        "numeric_features = X_train.columns.drop(categorical_features)\n",
        "\n",
        "#Scaler потребуется нам для масштабирования числовых признаков (их стандартизации)\n",
        "numeric_transformer = StandardScaler()\n",
        "categorical_transformer = OneHotEncoder(handle_unknown=\"ignore\")\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        (\"num\", numeric_transformer, numeric_features),\n",
        "        (\"cat\", categorical_transformer, categorical_features),\n",
        "    ]\n",
        ")\n",
        "#для простоты обучения моделей организуем конвейр\n",
        "def pipe_lr(clf):\n",
        "    return Pipeline(\n",
        "        steps=[(\"scl\", preprocessor), (\"clf\", clf)]\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "В предоставленном базовом решении лучший результат показала модель бустинга XGBClassifier.\n",
        "Продолжим ее исследовать"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "pipe_lr_xgb = pipe_lr(\n",
        "                XGBClassifier(\n",
        "                        n_estimator=1000,\n",
        "                        eval_metric='logloss',\n",
        "                        verbosity = 0,\n",
        "                        random_state = 42,\n",
        "                    ))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Построеним кривых обучения"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "train_sizes, train_scores, test_scores =\\\n",
        "                learning_curve(estimator=pipe_lr_xgb,\n",
        "                               X=X_train,\n",
        "                               y=y_train,\n",
        "                               train_sizes=np.linspace(0.1, 1.0, 5),\n",
        "                               cv=10,\n",
        "                               n_jobs=1)\n",
        "\n",
        "train_mean = np.mean(train_scores, axis=1)\n",
        "train_std = np.std(train_scores, axis=1)\n",
        "test_mean = np.mean(test_scores, axis=1)\n",
        "test_std = np.std(test_scores, axis=1)\n",
        "\n",
        "plt.plot(train_sizes, train_mean,\n",
        "         color='blue', marker='o',\n",
        "         markersize=5, label='training accuracy')\n",
        "\n",
        "plt.fill_between(train_sizes,\n",
        "                 train_mean + train_std,\n",
        "                 train_mean - train_std,\n",
        "                 alpha=0.15, color='blue')\n",
        "\n",
        "plt.plot(train_sizes, test_mean,\n",
        "         color='green', linestyle='--',\n",
        "         marker='s', markersize=5,\n",
        "         label='validation accuracy')\n",
        "\n",
        "plt.fill_between(train_sizes,\n",
        "                 test_mean + test_std,\n",
        "                 test_mean - test_std,\n",
        "                 alpha=0.15, color='green')\n",
        "\n",
        "plt.grid()\n",
        "plt.xlabel('Number of training samples')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylim([0.8, 1.0])\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "По графику функции кривой обучения видно, что наша модель переобучена."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Теперь построим проверочную кривую обучения"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [],
      "source": [
        "pipe_lr_xgb = pipe_lr(\n",
        "                XGBClassifier(\n",
        "                        eval_metric='logloss',\n",
        "                        verbosity = 0,\n",
        "                    ))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "param_range = [10, 25, 50, 100, 500, 1000]\n",
        "train_scores, test_scores = validation_curve(\n",
        "                estimator=pipe_lr_xgb, \n",
        "                X=X_train, \n",
        "                y=y_train, \n",
        "                param_name='clf__n_estimators', \n",
        "                param_range=param_range,\n",
        "                cv=10)\n",
        "\n",
        "train_mean = np.mean(train_scores, axis=1)\n",
        "train_std = np.std(train_scores, axis=1)\n",
        "test_mean = np.mean(test_scores, axis=1)\n",
        "test_std = np.std(test_scores, axis=1)\n",
        "\n",
        "plt.plot(param_range, train_mean, \n",
        "         color='blue', marker='o', \n",
        "         markersize=5, label='training accuracy')\n",
        "\n",
        "plt.fill_between(param_range, train_mean + train_std,\n",
        "                 train_mean - train_std, alpha=0.15,\n",
        "                 color='blue')\n",
        "\n",
        "plt.plot(param_range, test_mean, \n",
        "         color='green', linestyle='--', \n",
        "         marker='s', markersize=5, \n",
        "         label='validation accuracy')\n",
        "\n",
        "plt.fill_between(param_range, \n",
        "                 test_mean + test_std,\n",
        "                 test_mean - test_std, \n",
        "                 alpha=0.15, color='green')\n",
        "\n",
        "plt.grid()\n",
        "plt.xscale('log')\n",
        "plt.legend(loc='lower right')\n",
        "plt.xlabel('Parameter n_estimators')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0.8, 1.0])\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Из графика кривой обучения можно увидеть, что достаточно 50 деревьев для предсказательной модели вместо 1000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [],
      "source": [
        "pipe_lr_xgb = pipe_lr(\n",
        "                XGBClassifier(\n",
        "                        eval_metric='logloss',\n",
        "                        verbosity = 0,\n",
        "                        random_state=42\n",
        "                    ))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Поищем лучшие параметры для XGBClassifier жадным алгоритмом поиска"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.metrics import fbeta_score, make_scorer\n",
        "ftwo_scorer = make_scorer(fbeta_score, beta=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.8037806573406412\n",
            "{'clf__max_depth': 4, 'clf__min_samples_leaf': 1, 'clf__n_estimators': 80}\n"
          ]
        }
      ],
      "source": [
        "param_grid = {'clf__max_depth': range(4,11),\n",
        "              'clf__n_estimators':[40, 60, 80, 100, 200],\n",
        "            #   'clf__max_features': [i for i in range(1, 18)], \n",
        "              'clf__min_samples_leaf': range(1,6)}\n",
        "\n",
        "gs = GridSearchCV(estimator=pipe_lr_xgb, \n",
        "                  param_grid=param_grid, \n",
        "                  scoring=ftwo_scorer, \n",
        "                  cv=4,\n",
        "                  n_jobs=-1)\n",
        "gs = gs.fit(X_train, y_train)\n",
        "print(gs.best_score_)\n",
        "print(gs.best_params_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Итоговая модель XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7804232804232804"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pipe_lr_xgb = pipe_lr(XGBClassifier(**gs.best_params_))\n",
        "pipe_lr_xgb.fit(X_train, y_train)\n",
        "\n",
        "y_pred = pipe_lr_xgb.predict(X_test)\n",
        "fbeta_score(y_test, y_pred, beta=2)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Прирост метрики Fbeta был незначительный. Посмотрим другие модели бустинга, такие как LightGBM, Catboost"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Модель LightGBM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [],
      "source": [
        "import lightgbm as lgb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 4 folds for each of 12 candidates, totalling 48 fits\n",
            "0.7973625252139378\n",
            "{'clf__min_child_samples': 2, 'clf__n_estimators': 50}\n"
          ]
        }
      ],
      "source": [
        "lgb_params = {\n",
        "    'clf__n_estimators': [10, 50, 100],\n",
        "    'clf__min_child_samples': range(1,5)\n",
        "}\n",
        "pipe_lr_lgbmr = pipe_lr(lgb.LGBMClassifier(random_state=42))\n",
        "gs = GridSearchCV(estimator=pipe_lr_lgbmr, \n",
        "                  param_grid=lgb_params, \n",
        "                  scoring=ftwo_scorer, \n",
        "                  cv=4,\n",
        "                  n_jobs=-1, \n",
        "                  verbose=True)\n",
        "gs = gs.fit(X_train, y_train)\n",
        "print(gs.best_score_)\n",
        "print(gs.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7579787234042552"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pipe_lr_lgbmr = pipe_lr(lgb.LGBMRegressor(**gs.best_params_))\n",
        "pipe_lr_lgbmr.fit(X_train, y_train)\n",
        "\n",
        "y_pred = pipe_lr_lgbmr.predict(X_test) > 0.5\n",
        "fbeta_score(y_test, y_pred, beta=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Предсказательная способность LightGBM оказалась ниже, чем у ранее исследованной модели XGBoost."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Модель Catbost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {},
      "outputs": [],
      "source": [
        "from catboost import Pool, CatBoostClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {},
      "outputs": [],
      "source": [
        "model = CatBoostClassifier(iterations=10,\n",
        "                random_seed=42, depth=6, loss_function=\"Logloss\",\n",
        "                bootstrap_type=\"MVS\", custom_metric='F1')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0:\tlearn: 0.6922643\ttest: 0.6922946\tbest: 0.6922946 (0)\ttotal: 1.02ms\tremaining: 101ms\n",
            "1:\tlearn: 0.6914255\ttest: 0.6914448\tbest: 0.6914448 (1)\ttotal: 2.18ms\tremaining: 107ms\n",
            "2:\tlearn: 0.6906934\ttest: 0.6907219\tbest: 0.6907219 (2)\ttotal: 3.14ms\tremaining: 102ms\n",
            "3:\tlearn: 0.6899548\ttest: 0.6899926\tbest: 0.6899926 (3)\ttotal: 4.13ms\tremaining: 99.1ms\n",
            "4:\tlearn: 0.6892063\ttest: 0.6892645\tbest: 0.6892645 (4)\ttotal: 5.08ms\tremaining: 96.6ms\n",
            "5:\tlearn: 0.6884913\ttest: 0.6885410\tbest: 0.6885410 (5)\ttotal: 6.21ms\tremaining: 97.2ms\n",
            "6:\tlearn: 0.6876493\ttest: 0.6877171\tbest: 0.6877171 (6)\ttotal: 7.2ms\tremaining: 95.6ms\n",
            "7:\tlearn: 0.6869309\ttest: 0.6870070\tbest: 0.6870070 (7)\ttotal: 8.45ms\tremaining: 97.2ms\n",
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            "\n",
            "bestTest = 0.6259183992\n",
            "bestIteration = 99\n",
            "\n",
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            "57:\tlearn: 0.2182523\ttest: 0.2152792\tbest: 0.2152792 (57)\ttotal: 64.3ms\tremaining: 46.5ms\n",
            "58:\tlearn: 0.2176416\ttest: 0.2150584\tbest: 0.2150584 (58)\ttotal: 65.2ms\tremaining: 45.3ms\n",
            "59:\tlearn: 0.2168082\ttest: 0.2148661\tbest: 0.2148661 (59)\ttotal: 66.2ms\tremaining: 44.2ms\n",
            "60:\tlearn: 0.2152634\ttest: 0.2131293\tbest: 0.2131293 (60)\ttotal: 67.3ms\tremaining: 43ms\n",
            "61:\tlearn: 0.2138971\ttest: 0.2114002\tbest: 0.2114002 (61)\ttotal: 68.3ms\tremaining: 41.8ms\n",
            "62:\tlearn: 0.2132533\ttest: 0.2109522\tbest: 0.2109522 (62)\ttotal: 69.3ms\tremaining: 40.7ms\n",
            "63:\tlearn: 0.2121497\ttest: 0.2093384\tbest: 0.2093384 (63)\ttotal: 70.2ms\tremaining: 39.5ms\n",
            "64:\tlearn: 0.2119254\ttest: 0.2089809\tbest: 0.2089809 (64)\ttotal: 71.2ms\tremaining: 38.4ms\n",
            "65:\tlearn: 0.2115740\ttest: 0.2088449\tbest: 0.2088449 (65)\ttotal: 72.4ms\tremaining: 37.3ms\n",
            "66:\tlearn: 0.2103324\ttest: 0.2065299\tbest: 0.2065299 (66)\ttotal: 73.6ms\tremaining: 36.2ms\n",
            "67:\tlearn: 0.2097163\ttest: 0.2057654\tbest: 0.2057654 (67)\ttotal: 74.9ms\tremaining: 35.2ms\n",
            "68:\tlearn: 0.2088865\ttest: 0.2053749\tbest: 0.2053749 (68)\ttotal: 76ms\tremaining: 34.2ms\n",
            "69:\tlearn: 0.2085441\ttest: 0.2048827\tbest: 0.2048827 (69)\ttotal: 77.2ms\tremaining: 33.1ms\n",
            "70:\tlearn: 0.2075745\ttest: 0.2030893\tbest: 0.2030893 (70)\ttotal: 78.3ms\tremaining: 32ms\n",
            "71:\tlearn: 0.2073554\ttest: 0.2029889\tbest: 0.2029889 (71)\ttotal: 79.3ms\tremaining: 30.8ms\n",
            "72:\tlearn: 0.2051097\ttest: 0.2005996\tbest: 0.2005996 (72)\ttotal: 80.4ms\tremaining: 29.7ms\n",
            "73:\tlearn: 0.2044436\ttest: 0.2002806\tbest: 0.2002806 (73)\ttotal: 81.3ms\tremaining: 28.6ms\n",
            "74:\tlearn: 0.2041524\ttest: 0.2001158\tbest: 0.2001158 (74)\ttotal: 82.4ms\tremaining: 27.5ms\n",
            "75:\tlearn: 0.2027688\ttest: 0.1988120\tbest: 0.1988120 (75)\ttotal: 83.4ms\tremaining: 26.3ms\n",
            "76:\tlearn: 0.2019399\ttest: 0.1977265\tbest: 0.1977265 (76)\ttotal: 84.4ms\tremaining: 25.2ms\n",
            "77:\tlearn: 0.2013041\ttest: 0.1975315\tbest: 0.1975315 (77)\ttotal: 85.6ms\tremaining: 24.1ms\n",
            "78:\tlearn: 0.2005272\ttest: 0.1958763\tbest: 0.1958763 (78)\ttotal: 86.8ms\tremaining: 23.1ms\n",
            "79:\tlearn: 0.1998222\ttest: 0.1955400\tbest: 0.1955400 (79)\ttotal: 87.9ms\tremaining: 22ms\n",
            "80:\tlearn: 0.1996385\ttest: 0.1956149\tbest: 0.1955400 (79)\ttotal: 89ms\tremaining: 20.9ms\n",
            "81:\tlearn: 0.1988497\ttest: 0.1944921\tbest: 0.1944921 (81)\ttotal: 90.1ms\tremaining: 19.8ms\n",
            "82:\tlearn: 0.1982765\ttest: 0.1944959\tbest: 0.1944921 (81)\ttotal: 91.3ms\tremaining: 18.7ms\n",
            "83:\tlearn: 0.1975180\ttest: 0.1931328\tbest: 0.1931328 (83)\ttotal: 92.3ms\tremaining: 17.6ms\n",
            "84:\tlearn: 0.1968926\ttest: 0.1930449\tbest: 0.1930449 (84)\ttotal: 93.4ms\tremaining: 16.5ms\n",
            "85:\tlearn: 0.1960043\ttest: 0.1929247\tbest: 0.1929247 (85)\ttotal: 94.5ms\tremaining: 15.4ms\n",
            "86:\tlearn: 0.1953487\ttest: 0.1928049\tbest: 0.1928049 (86)\ttotal: 95.6ms\tremaining: 14.3ms\n",
            "87:\tlearn: 0.1951430\ttest: 0.1926833\tbest: 0.1926833 (87)\ttotal: 96.8ms\tremaining: 13.2ms\n",
            "88:\tlearn: 0.1948598\ttest: 0.1925858\tbest: 0.1925858 (88)\ttotal: 98ms\tremaining: 12.1ms\n",
            "89:\tlearn: 0.1939271\ttest: 0.1917132\tbest: 0.1917132 (89)\ttotal: 99ms\tremaining: 11ms\n",
            "90:\tlearn: 0.1927859\ttest: 0.1908244\tbest: 0.1908244 (90)\ttotal: 99.9ms\tremaining: 9.88ms\n",
            "91:\tlearn: 0.1924118\ttest: 0.1906788\tbest: 0.1906788 (91)\ttotal: 101ms\tremaining: 8.82ms\n",
            "92:\tlearn: 0.1915834\ttest: 0.1892498\tbest: 0.1892498 (92)\ttotal: 103ms\tremaining: 7.72ms\n",
            "93:\tlearn: 0.1910872\ttest: 0.1892031\tbest: 0.1892031 (93)\ttotal: 104ms\tremaining: 6.63ms\n",
            "94:\tlearn: 0.1904455\ttest: 0.1891931\tbest: 0.1891931 (94)\ttotal: 105ms\tremaining: 5.53ms\n",
            "95:\tlearn: 0.1888244\ttest: 0.1874994\tbest: 0.1874994 (95)\ttotal: 106ms\tremaining: 4.42ms\n",
            "96:\tlearn: 0.1885081\ttest: 0.1873297\tbest: 0.1873297 (96)\ttotal: 107ms\tremaining: 3.32ms\n",
            "97:\tlearn: 0.1878664\ttest: 0.1870568\tbest: 0.1870568 (97)\ttotal: 108ms\tremaining: 2.21ms\n",
            "98:\tlearn: 0.1869147\ttest: 0.1862784\tbest: 0.1862784 (98)\ttotal: 109ms\tremaining: 1.1ms\n",
            "99:\tlearn: 0.1867657\ttest: 0.1860265\tbest: 0.1860265 (99)\ttotal: 110ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1860265253\n",
            "bestIteration = 99\n",
            "\n",
            "1:\tloss: 0.1860265\tbest: 0.1860265 (1)\ttotal: 659ms\tremaining: 19.1s\n",
            "0:\tlearn: 0.5415238\ttest: 0.5469725\tbest: 0.5469725 (0)\ttotal: 886us\tremaining: 87.7ms\n",
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            "2:\tlearn: 0.4156663\ttest: 0.4181476\tbest: 0.4181476 (2)\ttotal: 2.96ms\tremaining: 95.7ms\n",
            "3:\tlearn: 0.3799023\ttest: 0.3858239\tbest: 0.3858239 (3)\ttotal: 3.88ms\tremaining: 93.1ms\n",
            "4:\tlearn: 0.3492300\ttest: 0.3537907\tbest: 0.3537907 (4)\ttotal: 4.91ms\tremaining: 93.2ms\n",
            "5:\tlearn: 0.3339287\ttest: 0.3413714\tbest: 0.3413714 (5)\ttotal: 5.84ms\tremaining: 91.5ms\n",
            "6:\tlearn: 0.3182803\ttest: 0.3231083\tbest: 0.3231083 (6)\ttotal: 7.1ms\tremaining: 94.3ms\n",
            "7:\tlearn: 0.3107480\ttest: 0.3145319\tbest: 0.3145319 (7)\ttotal: 8.25ms\tremaining: 94.9ms\n",
            "8:\tlearn: 0.2980046\ttest: 0.2977209\tbest: 0.2977209 (8)\ttotal: 9.54ms\tremaining: 96.5ms\n",
            "9:\tlearn: 0.2920198\ttest: 0.2936672\tbest: 0.2936672 (9)\ttotal: 10.7ms\tremaining: 96.1ms\n",
            "10:\tlearn: 0.2840935\ttest: 0.2833396\tbest: 0.2833396 (10)\ttotal: 11.8ms\tremaining: 95.4ms\n",
            "11:\tlearn: 0.2691230\ttest: 0.2675195\tbest: 0.2675195 (11)\ttotal: 13.1ms\tremaining: 95.8ms\n",
            "12:\tlearn: 0.2642524\ttest: 0.2611164\tbest: 0.2611164 (12)\ttotal: 14.2ms\tremaining: 94.9ms\n",
            "13:\tlearn: 0.2599733\ttest: 0.2573630\tbest: 0.2573630 (13)\ttotal: 15.5ms\tremaining: 95ms\n",
            "14:\tlearn: 0.2557836\ttest: 0.2564400\tbest: 0.2564400 (14)\ttotal: 16.6ms\tremaining: 94.2ms\n",
            "15:\tlearn: 0.2511711\ttest: 0.2513239\tbest: 0.2513239 (15)\ttotal: 17.8ms\tremaining: 93.5ms\n",
            "16:\tlearn: 0.2456971\ttest: 0.2435273\tbest: 0.2435273 (16)\ttotal: 19ms\tremaining: 92.7ms\n",
            "17:\tlearn: 0.2417676\ttest: 0.2362956\tbest: 0.2362956 (17)\ttotal: 20.6ms\tremaining: 93.7ms\n",
            "18:\tlearn: 0.2383761\ttest: 0.2339071\tbest: 0.2339071 (18)\ttotal: 23.1ms\tremaining: 98.3ms\n",
            "19:\tlearn: 0.2371364\ttest: 0.2332299\tbest: 0.2332299 (19)\ttotal: 24.4ms\tremaining: 97.5ms\n",
            "20:\tlearn: 0.2298938\ttest: 0.2262571\tbest: 0.2262571 (20)\ttotal: 26ms\tremaining: 98ms\n",
            "21:\tlearn: 0.2270269\ttest: 0.2206623\tbest: 0.2206623 (21)\ttotal: 27.2ms\tremaining: 96.4ms\n",
            "22:\tlearn: 0.2224100\ttest: 0.2158512\tbest: 0.2158512 (22)\ttotal: 28.3ms\tremaining: 94.7ms\n",
            "23:\tlearn: 0.2212736\ttest: 0.2136448\tbest: 0.2136448 (23)\ttotal: 29.6ms\tremaining: 93.8ms\n",
            "24:\tlearn: 0.2183704\ttest: 0.2100218\tbest: 0.2100218 (24)\ttotal: 30.7ms\tremaining: 92.1ms\n",
            "25:\tlearn: 0.2154253\ttest: 0.2066902\tbest: 0.2066902 (25)\ttotal: 31.8ms\tremaining: 90.5ms\n",
            "26:\tlearn: 0.2148990\ttest: 0.2072474\tbest: 0.2066902 (25)\ttotal: 33ms\tremaining: 89.3ms\n",
            "27:\tlearn: 0.2096504\ttest: 0.2022986\tbest: 0.2022986 (27)\ttotal: 34.6ms\tremaining: 88.8ms\n",
            "28:\tlearn: 0.2075910\ttest: 0.2005693\tbest: 0.2005693 (28)\ttotal: 35.6ms\tremaining: 87.2ms\n",
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            "52:\tlearn: 0.1789577\ttest: 0.1808079\tbest: 0.1808079 (52)\ttotal: 65.6ms\tremaining: 58.2ms\n",
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            "54:\tlearn: 0.1757448\ttest: 0.1757686\tbest: 0.1757686 (54)\ttotal: 67.6ms\tremaining: 55.3ms\n",
            "55:\tlearn: 0.1751829\ttest: 0.1755300\tbest: 0.1755300 (55)\ttotal: 68.6ms\tremaining: 53.9ms\n",
            "56:\tlearn: 0.1746629\ttest: 0.1755079\tbest: 0.1755079 (56)\ttotal: 69.7ms\tremaining: 52.6ms\n",
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            "61:\tlearn: 0.1717644\ttest: 0.1748145\tbest: 0.1740531 (59)\ttotal: 75.3ms\tremaining: 46.1ms\n",
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            "63:\tlearn: 0.1699046\ttest: 0.1731587\tbest: 0.1731587 (63)\ttotal: 77.3ms\tremaining: 43.5ms\n",
            "64:\tlearn: 0.1656959\ttest: 0.1691860\tbest: 0.1691860 (64)\ttotal: 78.3ms\tremaining: 42.1ms\n",
            "65:\tlearn: 0.1649541\ttest: 0.1695026\tbest: 0.1691860 (64)\ttotal: 79.2ms\tremaining: 40.8ms\n",
            "66:\tlearn: 0.1639779\ttest: 0.1691600\tbest: 0.1691600 (66)\ttotal: 80.2ms\tremaining: 39.5ms\n",
            "67:\tlearn: 0.1634088\ttest: 0.1684369\tbest: 0.1684369 (67)\ttotal: 81.4ms\tremaining: 38.3ms\n",
            "68:\tlearn: 0.1631999\ttest: 0.1684563\tbest: 0.1684369 (67)\ttotal: 82.4ms\tremaining: 37ms\n",
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            "75:\tlearn: 0.1584882\ttest: 0.1668084\tbest: 0.1667353 (74)\ttotal: 90.4ms\tremaining: 28.6ms\n",
            "76:\tlearn: 0.1581960\ttest: 0.1669977\tbest: 0.1667353 (74)\ttotal: 91.5ms\tremaining: 27.3ms\n",
            "77:\tlearn: 0.1575303\ttest: 0.1674836\tbest: 0.1667353 (74)\ttotal: 92.5ms\tremaining: 26.1ms\n",
            "78:\tlearn: 0.1571178\ttest: 0.1672133\tbest: 0.1667353 (74)\ttotal: 93.4ms\tremaining: 24.8ms\n",
            "79:\tlearn: 0.1565056\ttest: 0.1673241\tbest: 0.1667353 (74)\ttotal: 94.3ms\tremaining: 23.6ms\n",
            "80:\tlearn: 0.1560518\ttest: 0.1670701\tbest: 0.1667353 (74)\ttotal: 95.3ms\tremaining: 22.4ms\n",
            "81:\tlearn: 0.1554990\ttest: 0.1670116\tbest: 0.1667353 (74)\ttotal: 96.2ms\tremaining: 21.1ms\n",
            "82:\tlearn: 0.1551862\ttest: 0.1671405\tbest: 0.1667353 (74)\ttotal: 97.3ms\tremaining: 19.9ms\n",
            "83:\tlearn: 0.1549920\ttest: 0.1672386\tbest: 0.1667353 (74)\ttotal: 98.3ms\tremaining: 18.7ms\n",
            "84:\tlearn: 0.1545614\ttest: 0.1677659\tbest: 0.1667353 (74)\ttotal: 99.4ms\tremaining: 17.5ms\n",
            "85:\tlearn: 0.1538478\ttest: 0.1677914\tbest: 0.1667353 (74)\ttotal: 101ms\tremaining: 16.4ms\n",
            "86:\tlearn: 0.1533117\ttest: 0.1679079\tbest: 0.1667353 (74)\ttotal: 102ms\tremaining: 15.2ms\n",
            "87:\tlearn: 0.1529555\ttest: 0.1674705\tbest: 0.1667353 (74)\ttotal: 103ms\tremaining: 14ms\n",
            "88:\tlearn: 0.1528417\ttest: 0.1675585\tbest: 0.1667353 (74)\ttotal: 104ms\tremaining: 12.8ms\n",
            "89:\tlearn: 0.1517561\ttest: 0.1655498\tbest: 0.1655498 (89)\ttotal: 105ms\tremaining: 11.7ms\n",
            "90:\tlearn: 0.1503304\ttest: 0.1644669\tbest: 0.1644669 (90)\ttotal: 106ms\tremaining: 10.5ms\n",
            "91:\tlearn: 0.1501888\ttest: 0.1642817\tbest: 0.1642817 (91)\ttotal: 107ms\tremaining: 9.3ms\n",
            "92:\tlearn: 0.1494220\ttest: 0.1650355\tbest: 0.1642817 (91)\ttotal: 108ms\tremaining: 8.14ms\n",
            "93:\tlearn: 0.1491433\ttest: 0.1646677\tbest: 0.1642817 (91)\ttotal: 109ms\tremaining: 6.99ms\n",
            "94:\tlearn: 0.1487726\ttest: 0.1647620\tbest: 0.1642817 (91)\ttotal: 111ms\tremaining: 5.83ms\n",
            "95:\tlearn: 0.1481015\ttest: 0.1646289\tbest: 0.1642817 (91)\ttotal: 112ms\tremaining: 4.66ms\n",
            "96:\tlearn: 0.1477854\ttest: 0.1640863\tbest: 0.1640863 (96)\ttotal: 113ms\tremaining: 3.5ms\n",
            "97:\tlearn: 0.1474357\ttest: 0.1640702\tbest: 0.1640702 (97)\ttotal: 114ms\tremaining: 2.33ms\n",
            "98:\tlearn: 0.1467352\ttest: 0.1631790\tbest: 0.1631790 (98)\ttotal: 116ms\tremaining: 1.17ms\n",
            "99:\tlearn: 0.1464302\ttest: 0.1631565\tbest: 0.1631565 (99)\ttotal: 117ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1631565409\n",
            "bestIteration = 99\n",
            "\n",
            "2:\tloss: 0.1631565\tbest: 0.1631565 (2)\ttotal: 787ms\tremaining: 14.9s\n",
            "0:\tlearn: 0.4847052\ttest: 0.4924048\tbest: 0.4924048 (0)\ttotal: 1.59ms\tremaining: 157ms\n",
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            "3:\tlearn: 0.3411990\ttest: 0.3392176\tbest: 0.3392176 (3)\ttotal: 5.4ms\tremaining: 130ms\n",
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            "\n",
            "bestTest = 0.1588480615\n",
            "bestIteration = 90\n",
            "\n",
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            "83:\tlearn: 0.1113455\ttest: 0.1542199\tbest: 0.1506604 (70)\ttotal: 97.7ms\tremaining: 18.6ms\n",
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            "96:\tlearn: 0.1016961\ttest: 0.1542656\tbest: 0.1506604 (70)\ttotal: 112ms\tremaining: 3.45ms\n",
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            "99:\tlearn: 0.0996972\ttest: 0.1532627\tbest: 0.1506604 (70)\ttotal: 115ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1506603619\n",
            "bestIteration = 70\n",
            "\n",
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            "89:\tlearn: 0.6316415\ttest: 0.6324527\tbest: 0.6324527 (89)\ttotal: 95.1ms\tremaining: 10.6ms\n",
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            "92:\tlearn: 0.6296863\ttest: 0.6305767\tbest: 0.6305767 (92)\ttotal: 98ms\tremaining: 7.38ms\n",
            "93:\tlearn: 0.6290799\ttest: 0.6299906\tbest: 0.6299906 (93)\ttotal: 98.9ms\tremaining: 6.32ms\n",
            "94:\tlearn: 0.6284542\ttest: 0.6293726\tbest: 0.6293726 (94)\ttotal: 99.9ms\tremaining: 5.26ms\n",
            "95:\tlearn: 0.6278285\ttest: 0.6287420\tbest: 0.6287420 (95)\ttotal: 101ms\tremaining: 4.21ms\n",
            "96:\tlearn: 0.6271380\ttest: 0.6280424\tbest: 0.6280424 (96)\ttotal: 102ms\tremaining: 3.16ms\n",
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            "99:\tlearn: 0.6252533\ttest: 0.6261963\tbest: 0.6261963 (99)\ttotal: 105ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.6261962671\n",
            "bestIteration = 99\n",
            "\n",
            "5:\tloss: 0.6261963\tbest: 0.1506604 (4)\ttotal: 1.17s\tremaining: 10.5s\n",
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            "\n",
            "bestTest = 0.1835881152\n",
            "bestIteration = 99\n",
            "\n",
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            "59:\tlearn: 0.1745173\ttest: 0.1681368\tbest: 0.1677528 (58)\ttotal: 64.3ms\tremaining: 42.9ms\n",
            "60:\tlearn: 0.1734001\ttest: 0.1675108\tbest: 0.1675108 (60)\ttotal: 65.5ms\tremaining: 41.9ms\n",
            "61:\tlearn: 0.1731225\ttest: 0.1674943\tbest: 0.1674943 (61)\ttotal: 66.5ms\tremaining: 40.8ms\n",
            "62:\tlearn: 0.1724663\ttest: 0.1674842\tbest: 0.1674842 (62)\ttotal: 67.6ms\tremaining: 39.7ms\n",
            "63:\tlearn: 0.1719036\ttest: 0.1673784\tbest: 0.1673784 (63)\ttotal: 68.6ms\tremaining: 38.6ms\n",
            "64:\tlearn: 0.1718040\ttest: 0.1676961\tbest: 0.1673784 (63)\ttotal: 69.8ms\tremaining: 37.6ms\n",
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            "67:\tlearn: 0.1698943\ttest: 0.1680285\tbest: 0.1673784 (63)\ttotal: 72.9ms\tremaining: 34.3ms\n",
            "68:\tlearn: 0.1688894\ttest: 0.1676462\tbest: 0.1673784 (63)\ttotal: 73.9ms\tremaining: 33.2ms\n",
            "69:\tlearn: 0.1684808\ttest: 0.1672412\tbest: 0.1672412 (69)\ttotal: 75ms\tremaining: 32.2ms\n",
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            "71:\tlearn: 0.1672514\ttest: 0.1674680\tbest: 0.1672412 (69)\ttotal: 77.3ms\tremaining: 30.1ms\n",
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            "73:\tlearn: 0.1652468\ttest: 0.1665224\tbest: 0.1665224 (73)\ttotal: 79.4ms\tremaining: 27.9ms\n",
            "74:\tlearn: 0.1646282\ttest: 0.1657655\tbest: 0.1657655 (74)\ttotal: 80.5ms\tremaining: 26.8ms\n",
            "75:\tlearn: 0.1639441\ttest: 0.1651584\tbest: 0.1651584 (75)\ttotal: 81.5ms\tremaining: 25.7ms\n",
            "76:\tlearn: 0.1636817\ttest: 0.1651040\tbest: 0.1651040 (76)\ttotal: 82.6ms\tremaining: 24.7ms\n",
            "77:\tlearn: 0.1633935\ttest: 0.1653001\tbest: 0.1651040 (76)\ttotal: 83.7ms\tremaining: 23.6ms\n",
            "78:\tlearn: 0.1628120\ttest: 0.1649572\tbest: 0.1649572 (78)\ttotal: 84.8ms\tremaining: 22.5ms\n",
            "79:\tlearn: 0.1623189\ttest: 0.1649169\tbest: 0.1649169 (79)\ttotal: 86.1ms\tremaining: 21.5ms\n",
            "80:\tlearn: 0.1617217\ttest: 0.1649464\tbest: 0.1649169 (79)\ttotal: 87.2ms\tremaining: 20.5ms\n",
            "81:\tlearn: 0.1615597\ttest: 0.1647769\tbest: 0.1647769 (81)\ttotal: 88.4ms\tremaining: 19.4ms\n",
            "82:\tlearn: 0.1603770\ttest: 0.1648626\tbest: 0.1647769 (81)\ttotal: 89.4ms\tremaining: 18.3ms\n",
            "83:\tlearn: 0.1601091\ttest: 0.1648494\tbest: 0.1647769 (81)\ttotal: 90.4ms\tremaining: 17.2ms\n",
            "84:\tlearn: 0.1595909\ttest: 0.1647086\tbest: 0.1647086 (84)\ttotal: 91.3ms\tremaining: 16.1ms\n",
            "85:\tlearn: 0.1589679\ttest: 0.1643789\tbest: 0.1643789 (85)\ttotal: 92.9ms\tremaining: 15.1ms\n",
            "86:\tlearn: 0.1583382\ttest: 0.1636609\tbest: 0.1636609 (86)\ttotal: 94ms\tremaining: 14ms\n",
            "87:\tlearn: 0.1582186\ttest: 0.1636580\tbest: 0.1636580 (87)\ttotal: 95.1ms\tremaining: 13ms\n",
            "88:\tlearn: 0.1578648\ttest: 0.1637078\tbest: 0.1636580 (87)\ttotal: 96.5ms\tremaining: 11.9ms\n",
            "89:\tlearn: 0.1576466\ttest: 0.1638850\tbest: 0.1636580 (87)\ttotal: 97.7ms\tremaining: 10.9ms\n",
            "90:\tlearn: 0.1571406\ttest: 0.1644496\tbest: 0.1636580 (87)\ttotal: 98.7ms\tremaining: 9.77ms\n",
            "91:\tlearn: 0.1570455\ttest: 0.1645210\tbest: 0.1636580 (87)\ttotal: 99.8ms\tremaining: 8.68ms\n",
            "92:\tlearn: 0.1565406\ttest: 0.1650138\tbest: 0.1636580 (87)\ttotal: 101ms\tremaining: 7.59ms\n",
            "93:\tlearn: 0.1555795\ttest: 0.1660950\tbest: 0.1636580 (87)\ttotal: 102ms\tremaining: 6.5ms\n",
            "94:\tlearn: 0.1550087\ttest: 0.1664383\tbest: 0.1636580 (87)\ttotal: 103ms\tremaining: 5.41ms\n",
            "95:\tlearn: 0.1546409\ttest: 0.1663955\tbest: 0.1636580 (87)\ttotal: 104ms\tremaining: 4.33ms\n",
            "96:\tlearn: 0.1539519\ttest: 0.1663827\tbest: 0.1636580 (87)\ttotal: 105ms\tremaining: 3.25ms\n",
            "97:\tlearn: 0.1535841\ttest: 0.1669292\tbest: 0.1636580 (87)\ttotal: 106ms\tremaining: 2.16ms\n",
            "98:\tlearn: 0.1530328\ttest: 0.1669279\tbest: 0.1636580 (87)\ttotal: 107ms\tremaining: 1.08ms\n",
            "99:\tlearn: 0.1526763\ttest: 0.1669992\tbest: 0.1636580 (87)\ttotal: 108ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1636580458\n",
            "bestIteration = 87\n",
            "\n",
            "7:\tloss: 0.1636580\tbest: 0.1506604 (4)\ttotal: 1.4s\tremaining: 9.1s\n",
            "0:\tlearn: 0.4847058\ttest: 0.4924054\tbest: 0.4924054 (0)\ttotal: 918us\tremaining: 90.9ms\n",
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            "3:\tlearn: 0.3392298\ttest: 0.3405705\tbest: 0.3405705 (3)\ttotal: 4.09ms\tremaining: 98.3ms\n",
            "4:\tlearn: 0.3239945\ttest: 0.3291594\tbest: 0.3291594 (4)\ttotal: 5.21ms\tremaining: 99ms\n",
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            "10:\tlearn: 0.2487524\ttest: 0.2440313\tbest: 0.2440313 (10)\ttotal: 11.4ms\tremaining: 92.1ms\n",
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            "12:\tlearn: 0.2425008\ttest: 0.2361669\tbest: 0.2361669 (12)\ttotal: 13.5ms\tremaining: 90.3ms\n",
            "13:\tlearn: 0.2365006\ttest: 0.2286578\tbest: 0.2286578 (13)\ttotal: 14.8ms\tremaining: 90.7ms\n",
            "14:\tlearn: 0.2317616\ttest: 0.2221945\tbest: 0.2221945 (14)\ttotal: 15.8ms\tremaining: 89.4ms\n",
            "15:\tlearn: 0.2243099\ttest: 0.2124738\tbest: 0.2124738 (15)\ttotal: 16.8ms\tremaining: 88.3ms\n",
            "16:\tlearn: 0.2196408\ttest: 0.2086893\tbest: 0.2086893 (16)\ttotal: 17.8ms\tremaining: 87ms\n",
            "17:\tlearn: 0.2185767\ttest: 0.2067727\tbest: 0.2067727 (17)\ttotal: 19ms\tremaining: 86.7ms\n",
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            "22:\tlearn: 0.2116975\ttest: 0.2024761\tbest: 0.2024761 (22)\ttotal: 24.2ms\tremaining: 81.1ms\n",
            "23:\tlearn: 0.2096412\ttest: 0.2027335\tbest: 0.2024761 (22)\ttotal: 25.3ms\tremaining: 80ms\n",
            "24:\tlearn: 0.2053394\ttest: 0.1961148\tbest: 0.1961148 (24)\ttotal: 26.3ms\tremaining: 78.8ms\n",
            "25:\tlearn: 0.2035183\ttest: 0.1957901\tbest: 0.1957901 (25)\ttotal: 27.3ms\tremaining: 77.7ms\n",
            "26:\tlearn: 0.2029802\ttest: 0.1964819\tbest: 0.1957901 (25)\ttotal: 28.5ms\tremaining: 76.9ms\n",
            "27:\tlearn: 0.2000829\ttest: 0.1957854\tbest: 0.1957854 (27)\ttotal: 29.5ms\tremaining: 76ms\n",
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            "36:\tlearn: 0.1896663\ttest: 0.1914570\tbest: 0.1899177 (33)\ttotal: 39.2ms\tremaining: 66.8ms\n",
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            "38:\tlearn: 0.1811751\ttest: 0.1834897\tbest: 0.1834897 (38)\ttotal: 41.4ms\tremaining: 64.7ms\n",
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            "43:\tlearn: 0.1717095\ttest: 0.1718148\tbest: 0.1718148 (43)\ttotal: 46.4ms\tremaining: 59.1ms\n",
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            "45:\tlearn: 0.1709537\ttest: 0.1710637\tbest: 0.1710637 (45)\ttotal: 48.5ms\tremaining: 56.9ms\n",
            "46:\tlearn: 0.1704602\ttest: 0.1712984\tbest: 0.1710637 (45)\ttotal: 49.5ms\tremaining: 55.8ms\n",
            "47:\tlearn: 0.1693258\ttest: 0.1720306\tbest: 0.1710637 (45)\ttotal: 50.5ms\tremaining: 54.7ms\n",
            "48:\tlearn: 0.1665658\ttest: 0.1696554\tbest: 0.1696554 (48)\ttotal: 51.6ms\tremaining: 53.7ms\n",
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            "52:\tlearn: 0.1621260\ttest: 0.1663951\tbest: 0.1663951 (52)\ttotal: 55.6ms\tremaining: 49.3ms\n",
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            "54:\tlearn: 0.1600060\ttest: 0.1649464\tbest: 0.1649464 (54)\ttotal: 57.7ms\tremaining: 47.2ms\n",
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            "61:\tlearn: 0.1553962\ttest: 0.1667229\tbest: 0.1649464 (54)\ttotal: 64.9ms\tremaining: 39.8ms\n",
            "62:\tlearn: 0.1537030\ttest: 0.1659060\tbest: 0.1649464 (54)\ttotal: 65.9ms\tremaining: 38.7ms\n",
            "63:\tlearn: 0.1522140\ttest: 0.1631442\tbest: 0.1631442 (63)\ttotal: 67ms\tremaining: 37.7ms\n",
            "64:\tlearn: 0.1510796\ttest: 0.1622598\tbest: 0.1622598 (64)\ttotal: 68ms\tremaining: 36.6ms\n",
            "65:\tlearn: 0.1509953\ttest: 0.1627297\tbest: 0.1622598 (64)\ttotal: 69.1ms\tremaining: 35.6ms\n",
            "66:\tlearn: 0.1503261\ttest: 0.1625006\tbest: 0.1622598 (64)\ttotal: 70.1ms\tremaining: 34.5ms\n",
            "67:\tlearn: 0.1493544\ttest: 0.1626401\tbest: 0.1622598 (64)\ttotal: 71.1ms\tremaining: 33.5ms\n",
            "68:\tlearn: 0.1484693\ttest: 0.1635694\tbest: 0.1622598 (64)\ttotal: 72.1ms\tremaining: 32.4ms\n",
            "69:\tlearn: 0.1483847\ttest: 0.1640137\tbest: 0.1622598 (64)\ttotal: 73.2ms\tremaining: 31.4ms\n",
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            "75:\tlearn: 0.1429756\ttest: 0.1642146\tbest: 0.1622598 (64)\ttotal: 78.8ms\tremaining: 24.9ms\n",
            "76:\tlearn: 0.1421932\ttest: 0.1643381\tbest: 0.1622598 (64)\ttotal: 79.7ms\tremaining: 23.8ms\n",
            "77:\tlearn: 0.1414557\ttest: 0.1639098\tbest: 0.1622598 (64)\ttotal: 80.7ms\tremaining: 22.8ms\n",
            "78:\tlearn: 0.1409241\ttest: 0.1631339\tbest: 0.1622598 (64)\ttotal: 81.8ms\tremaining: 21.7ms\n",
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            "80:\tlearn: 0.1397174\ttest: 0.1624542\tbest: 0.1622598 (64)\ttotal: 83.8ms\tremaining: 19.7ms\n",
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            "82:\tlearn: 0.1390312\ttest: 0.1625300\tbest: 0.1622598 (64)\ttotal: 85.7ms\tremaining: 17.6ms\n",
            "83:\tlearn: 0.1380609\ttest: 0.1628972\tbest: 0.1622598 (64)\ttotal: 86.7ms\tremaining: 16.5ms\n",
            "84:\tlearn: 0.1376725\ttest: 0.1627215\tbest: 0.1622598 (64)\ttotal: 87.8ms\tremaining: 15.5ms\n",
            "85:\tlearn: 0.1368722\ttest: 0.1630285\tbest: 0.1622598 (64)\ttotal: 88.8ms\tremaining: 14.5ms\n",
            "86:\tlearn: 0.1365640\ttest: 0.1639197\tbest: 0.1622598 (64)\ttotal: 89.8ms\tremaining: 13.4ms\n",
            "87:\tlearn: 0.1352517\ttest: 0.1626252\tbest: 0.1622598 (64)\ttotal: 90.7ms\tremaining: 12.4ms\n",
            "88:\tlearn: 0.1345214\ttest: 0.1636247\tbest: 0.1622598 (64)\ttotal: 91.8ms\tremaining: 11.3ms\n",
            "89:\tlearn: 0.1344940\ttest: 0.1638703\tbest: 0.1622598 (64)\ttotal: 92.8ms\tremaining: 10.3ms\n",
            "90:\tlearn: 0.1337933\ttest: 0.1641321\tbest: 0.1622598 (64)\ttotal: 93.9ms\tremaining: 9.28ms\n",
            "91:\tlearn: 0.1330595\ttest: 0.1628691\tbest: 0.1622598 (64)\ttotal: 94.9ms\tremaining: 8.25ms\n",
            "92:\tlearn: 0.1323023\ttest: 0.1622845\tbest: 0.1622598 (64)\ttotal: 95.9ms\tremaining: 7.22ms\n",
            "93:\tlearn: 0.1317167\ttest: 0.1624470\tbest: 0.1622598 (64)\ttotal: 97ms\tremaining: 6.19ms\n",
            "94:\tlearn: 0.1311928\ttest: 0.1638122\tbest: 0.1622598 (64)\ttotal: 98.1ms\tremaining: 5.16ms\n",
            "95:\tlearn: 0.1310863\ttest: 0.1638412\tbest: 0.1622598 (64)\ttotal: 99ms\tremaining: 4.13ms\n",
            "96:\tlearn: 0.1309330\ttest: 0.1641722\tbest: 0.1622598 (64)\ttotal: 100ms\tremaining: 3.1ms\n",
            "97:\tlearn: 0.1304639\ttest: 0.1640166\tbest: 0.1622598 (64)\ttotal: 101ms\tremaining: 2.06ms\n",
            "98:\tlearn: 0.1296100\ttest: 0.1639500\tbest: 0.1622598 (64)\ttotal: 102ms\tremaining: 1.03ms\n",
            "99:\tlearn: 0.1281871\ttest: 0.1629716\tbest: 0.1622598 (64)\ttotal: 103ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1622597523\n",
            "bestIteration = 64\n",
            "\n",
            "8:\tloss: 0.1622598\tbest: 0.1506604 (4)\ttotal: 1.51s\tremaining: 8.56s\n",
            "0:\tlearn: 0.4391882\ttest: 0.4488596\tbest: 0.4488596 (0)\ttotal: 901us\tremaining: 89.2ms\n",
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            "\n",
            "bestTest = 0.1598235849\n",
            "bestIteration = 70\n",
            "\n",
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            "94:\tlearn: 0.6291173\ttest: 0.6299333\tbest: 0.6299333 (94)\ttotal: 95.3ms\tremaining: 5.02ms\n",
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            "96:\tlearn: 0.6277899\ttest: 0.6286560\tbest: 0.6286560 (96)\ttotal: 97.3ms\tremaining: 3.01ms\n",
            "97:\tlearn: 0.6271878\ttest: 0.6280719\tbest: 0.6280719 (97)\ttotal: 98.4ms\tremaining: 2.01ms\n",
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            "99:\tlearn: 0.6260191\ttest: 0.6269181\tbest: 0.6269181 (99)\ttotal: 100ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.6269181309\n",
            "bestIteration = 99\n",
            "\n",
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            "95:\tlearn: 0.1910684\ttest: 0.1883835\tbest: 0.1883835 (95)\ttotal: 104ms\tremaining: 4.34ms\n",
            "96:\tlearn: 0.1899438\ttest: 0.1874686\tbest: 0.1874686 (96)\ttotal: 105ms\tremaining: 3.25ms\n",
            "97:\tlearn: 0.1897694\ttest: 0.1872952\tbest: 0.1872952 (97)\ttotal: 106ms\tremaining: 2.17ms\n",
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            "99:\tlearn: 0.1891691\ttest: 0.1869206\tbest: 0.1869206 (99)\ttotal: 108ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1869205704\n",
            "bestIteration = 99\n",
            "\n",
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            "\n",
            "bestTest = 0.1710373482\n",
            "bestIteration = 93\n",
            "\n",
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            "50:\tlearn: 0.1643410\ttest: 0.1709185\tbest: 0.1699616 (48)\ttotal: 53.9ms\tremaining: 51.8ms\n",
            "51:\tlearn: 0.1642602\ttest: 0.1712544\tbest: 0.1699616 (48)\ttotal: 54.9ms\tremaining: 50.7ms\n",
            "52:\tlearn: 0.1632876\ttest: 0.1708131\tbest: 0.1699616 (48)\ttotal: 56ms\tremaining: 49.7ms\n",
            "53:\tlearn: 0.1626033\ttest: 0.1697567\tbest: 0.1697567 (53)\ttotal: 57.1ms\tremaining: 48.6ms\n",
            "54:\tlearn: 0.1613914\ttest: 0.1699427\tbest: 0.1697567 (53)\ttotal: 58.2ms\tremaining: 47.6ms\n",
            "55:\tlearn: 0.1608159\ttest: 0.1696318\tbest: 0.1696318 (55)\ttotal: 59.1ms\tremaining: 46.5ms\n",
            "56:\tlearn: 0.1595805\ttest: 0.1667803\tbest: 0.1667803 (56)\ttotal: 60.1ms\tremaining: 45.4ms\n",
            "57:\tlearn: 0.1575158\ttest: 0.1657470\tbest: 0.1657470 (57)\ttotal: 61.2ms\tremaining: 44.3ms\n",
            "58:\tlearn: 0.1570224\ttest: 0.1654730\tbest: 0.1654730 (58)\ttotal: 62.3ms\tremaining: 43.3ms\n",
            "59:\tlearn: 0.1563725\ttest: 0.1658925\tbest: 0.1654730 (58)\ttotal: 63.4ms\tremaining: 42.3ms\n",
            "60:\tlearn: 0.1560201\ttest: 0.1664457\tbest: 0.1654730 (58)\ttotal: 64.6ms\tremaining: 41.3ms\n",
            "61:\tlearn: 0.1549337\ttest: 0.1670769\tbest: 0.1654730 (58)\ttotal: 65.5ms\tremaining: 40.2ms\n",
            "62:\tlearn: 0.1542261\ttest: 0.1668528\tbest: 0.1654730 (58)\ttotal: 66.6ms\tremaining: 39.1ms\n",
            "63:\tlearn: 0.1537576\ttest: 0.1668730\tbest: 0.1654730 (58)\ttotal: 67.7ms\tremaining: 38.1ms\n",
            "64:\tlearn: 0.1534991\ttest: 0.1669738\tbest: 0.1654730 (58)\ttotal: 68.7ms\tremaining: 37ms\n",
            "65:\tlearn: 0.1528424\ttest: 0.1676158\tbest: 0.1654730 (58)\ttotal: 69.9ms\tremaining: 36ms\n",
            "66:\tlearn: 0.1510619\ttest: 0.1666227\tbest: 0.1654730 (58)\ttotal: 70.9ms\tremaining: 34.9ms\n",
            "67:\tlearn: 0.1504318\ttest: 0.1663889\tbest: 0.1654730 (58)\ttotal: 72ms\tremaining: 33.9ms\n",
            "68:\tlearn: 0.1494978\ttest: 0.1665365\tbest: 0.1654730 (58)\ttotal: 72.9ms\tremaining: 32.8ms\n",
            "69:\tlearn: 0.1491217\ttest: 0.1667699\tbest: 0.1654730 (58)\ttotal: 73.9ms\tremaining: 31.7ms\n",
            "70:\tlearn: 0.1485194\ttest: 0.1662047\tbest: 0.1654730 (58)\ttotal: 75ms\tremaining: 30.6ms\n",
            "71:\tlearn: 0.1479655\ttest: 0.1660245\tbest: 0.1654730 (58)\ttotal: 76ms\tremaining: 29.6ms\n",
            "72:\tlearn: 0.1471506\ttest: 0.1656915\tbest: 0.1654730 (58)\ttotal: 77.1ms\tremaining: 28.5ms\n",
            "73:\tlearn: 0.1467980\ttest: 0.1658070\tbest: 0.1654730 (58)\ttotal: 78.2ms\tremaining: 27.5ms\n",
            "74:\tlearn: 0.1462340\ttest: 0.1658123\tbest: 0.1654730 (58)\ttotal: 79.3ms\tremaining: 26.4ms\n",
            "75:\tlearn: 0.1461308\ttest: 0.1659746\tbest: 0.1654730 (58)\ttotal: 80.3ms\tremaining: 25.4ms\n",
            "76:\tlearn: 0.1458535\ttest: 0.1662924\tbest: 0.1654730 (58)\ttotal: 81.3ms\tremaining: 24.3ms\n",
            "77:\tlearn: 0.1457344\ttest: 0.1661966\tbest: 0.1654730 (58)\ttotal: 82.3ms\tremaining: 23.2ms\n",
            "78:\tlearn: 0.1451613\ttest: 0.1663885\tbest: 0.1654730 (58)\ttotal: 83.3ms\tremaining: 22.1ms\n",
            "79:\tlearn: 0.1448969\ttest: 0.1661650\tbest: 0.1654730 (58)\ttotal: 84.4ms\tremaining: 21.1ms\n",
            "80:\tlearn: 0.1445957\ttest: 0.1661751\tbest: 0.1654730 (58)\ttotal: 85.5ms\tremaining: 20ms\n",
            "81:\tlearn: 0.1438813\ttest: 0.1663240\tbest: 0.1654730 (58)\ttotal: 86.5ms\tremaining: 19ms\n",
            "82:\tlearn: 0.1438160\ttest: 0.1663156\tbest: 0.1654730 (58)\ttotal: 87.5ms\tremaining: 17.9ms\n",
            "83:\tlearn: 0.1431360\ttest: 0.1666717\tbest: 0.1654730 (58)\ttotal: 88.8ms\tremaining: 16.9ms\n",
            "84:\tlearn: 0.1424433\ttest: 0.1672632\tbest: 0.1654730 (58)\ttotal: 89.8ms\tremaining: 15.9ms\n",
            "85:\tlearn: 0.1417844\ttest: 0.1680298\tbest: 0.1654730 (58)\ttotal: 90.9ms\tremaining: 14.8ms\n",
            "86:\tlearn: 0.1410532\ttest: 0.1686638\tbest: 0.1654730 (58)\ttotal: 92ms\tremaining: 13.8ms\n",
            "87:\tlearn: 0.1407847\ttest: 0.1691047\tbest: 0.1654730 (58)\ttotal: 93.1ms\tremaining: 12.7ms\n",
            "88:\tlearn: 0.1405733\ttest: 0.1691356\tbest: 0.1654730 (58)\ttotal: 94.2ms\tremaining: 11.6ms\n",
            "89:\tlearn: 0.1392324\ttest: 0.1681748\tbest: 0.1654730 (58)\ttotal: 95.2ms\tremaining: 10.6ms\n",
            "90:\tlearn: 0.1373318\ttest: 0.1689519\tbest: 0.1654730 (58)\ttotal: 96.2ms\tremaining: 9.52ms\n",
            "91:\tlearn: 0.1359002\ttest: 0.1664606\tbest: 0.1654730 (58)\ttotal: 97.4ms\tremaining: 8.47ms\n",
            "92:\tlearn: 0.1343666\ttest: 0.1645974\tbest: 0.1645974 (92)\ttotal: 98.5ms\tremaining: 7.41ms\n",
            "93:\tlearn: 0.1334304\ttest: 0.1649894\tbest: 0.1645974 (92)\ttotal: 99.5ms\tremaining: 6.35ms\n",
            "94:\tlearn: 0.1329855\ttest: 0.1654761\tbest: 0.1645974 (92)\ttotal: 101ms\tremaining: 5.3ms\n",
            "95:\tlearn: 0.1321699\ttest: 0.1654815\tbest: 0.1645974 (92)\ttotal: 102ms\tremaining: 4.24ms\n",
            "96:\tlearn: 0.1320431\ttest: 0.1655365\tbest: 0.1645974 (92)\ttotal: 103ms\tremaining: 3.18ms\n",
            "97:\tlearn: 0.1316266\ttest: 0.1659092\tbest: 0.1645974 (92)\ttotal: 104ms\tremaining: 2.12ms\n",
            "98:\tlearn: 0.1308605\ttest: 0.1648086\tbest: 0.1645974 (92)\ttotal: 105ms\tremaining: 1.06ms\n",
            "99:\tlearn: 0.1304129\ttest: 0.1650630\tbest: 0.1645974 (92)\ttotal: 106ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1645973856\n",
            "bestIteration = 92\n",
            "\n",
            "13:\tloss: 0.1645974\tbest: 0.1506604 (4)\ttotal: 2.08s\tremaining: 6.85s\n",
            "0:\tlearn: 0.4391884\ttest: 0.4488598\tbest: 0.4488598 (0)\ttotal: 1.25ms\tremaining: 123ms\n",
            "1:\tlearn: 0.3535112\ttest: 0.3578671\tbest: 0.3578671 (1)\ttotal: 2.31ms\tremaining: 113ms\n",
            "2:\tlearn: 0.3224467\ttest: 0.3296216\tbest: 0.3296216 (2)\ttotal: 3.24ms\tremaining: 105ms\n",
            "3:\tlearn: 0.2941363\ttest: 0.3000548\tbest: 0.3000548 (3)\ttotal: 4.75ms\tremaining: 114ms\n",
            "4:\tlearn: 0.2759066\ttest: 0.2797781\tbest: 0.2797781 (4)\ttotal: 5.89ms\tremaining: 112ms\n",
            "5:\tlearn: 0.2553580\ttest: 0.2540785\tbest: 0.2540785 (5)\ttotal: 7.06ms\tremaining: 111ms\n",
            "6:\tlearn: 0.2465402\ttest: 0.2423945\tbest: 0.2423945 (6)\ttotal: 8.13ms\tremaining: 108ms\n",
            "7:\tlearn: 0.2428700\ttest: 0.2414986\tbest: 0.2414986 (7)\ttotal: 9.16ms\tremaining: 105ms\n",
            "8:\tlearn: 0.2394120\ttest: 0.2397433\tbest: 0.2397433 (8)\ttotal: 10.4ms\tremaining: 105ms\n",
            "9:\tlearn: 0.2349546\ttest: 0.2376049\tbest: 0.2376049 (9)\ttotal: 11.5ms\tremaining: 104ms\n",
            "10:\tlearn: 0.2280379\ttest: 0.2334626\tbest: 0.2334626 (10)\ttotal: 12.6ms\tremaining: 102ms\n",
            "11:\tlearn: 0.2256048\ttest: 0.2314940\tbest: 0.2314940 (11)\ttotal: 14.1ms\tremaining: 104ms\n",
            "12:\tlearn: 0.2249055\ttest: 0.2314919\tbest: 0.2314919 (12)\ttotal: 15.6ms\tremaining: 104ms\n",
            "13:\tlearn: 0.2199306\ttest: 0.2236823\tbest: 0.2236823 (13)\ttotal: 16.7ms\tremaining: 102ms\n",
            "14:\tlearn: 0.2139236\ttest: 0.2171068\tbest: 0.2171068 (14)\ttotal: 17.8ms\tremaining: 101ms\n",
            "15:\tlearn: 0.2075890\ttest: 0.2067763\tbest: 0.2067763 (15)\ttotal: 18.9ms\tremaining: 99.1ms\n",
            "16:\tlearn: 0.2057061\ttest: 0.2055415\tbest: 0.2055415 (16)\ttotal: 19.9ms\tremaining: 97.1ms\n",
            "17:\tlearn: 0.1972836\ttest: 0.1962159\tbest: 0.1962159 (17)\ttotal: 20.8ms\tremaining: 94.9ms\n",
            "18:\tlearn: 0.1965742\ttest: 0.1962437\tbest: 0.1962159 (17)\ttotal: 22ms\tremaining: 93.7ms\n",
            "19:\tlearn: 0.1940973\ttest: 0.1958760\tbest: 0.1958760 (19)\ttotal: 23ms\tremaining: 92.1ms\n",
            "20:\tlearn: 0.1926308\ttest: 0.1933920\tbest: 0.1933920 (20)\ttotal: 24.1ms\tremaining: 90.6ms\n",
            "21:\tlearn: 0.1846785\ttest: 0.1861542\tbest: 0.1861542 (21)\ttotal: 25.1ms\tremaining: 89ms\n",
            "22:\tlearn: 0.1833496\ttest: 0.1851509\tbest: 0.1851509 (22)\ttotal: 26.2ms\tremaining: 87.6ms\n",
            "23:\tlearn: 0.1809758\ttest: 0.1816184\tbest: 0.1816184 (23)\ttotal: 27.4ms\tremaining: 86.8ms\n",
            "24:\tlearn: 0.1764087\ttest: 0.1770758\tbest: 0.1770758 (24)\ttotal: 28.6ms\tremaining: 85.8ms\n",
            "25:\tlearn: 0.1748305\ttest: 0.1764234\tbest: 0.1764234 (25)\ttotal: 30ms\tremaining: 85.4ms\n",
            "26:\tlearn: 0.1744899\ttest: 0.1777092\tbest: 0.1764234 (25)\ttotal: 31.3ms\tremaining: 84.6ms\n",
            "27:\tlearn: 0.1740933\ttest: 0.1776705\tbest: 0.1764234 (25)\ttotal: 32.5ms\tremaining: 83.6ms\n",
            "28:\tlearn: 0.1726436\ttest: 0.1762479\tbest: 0.1762479 (28)\ttotal: 33.6ms\tremaining: 82.2ms\n",
            "29:\tlearn: 0.1715913\ttest: 0.1762357\tbest: 0.1762357 (29)\ttotal: 34.6ms\tremaining: 80.8ms\n",
            "30:\tlearn: 0.1693303\ttest: 0.1713985\tbest: 0.1713985 (30)\ttotal: 35.7ms\tremaining: 79.4ms\n",
            "31:\tlearn: 0.1684112\ttest: 0.1714920\tbest: 0.1713985 (30)\ttotal: 36.8ms\tremaining: 78.3ms\n",
            "32:\tlearn: 0.1682570\ttest: 0.1724582\tbest: 0.1713985 (30)\ttotal: 37.9ms\tremaining: 76.9ms\n",
            "33:\tlearn: 0.1670332\ttest: 0.1717801\tbest: 0.1713985 (30)\ttotal: 39ms\tremaining: 75.6ms\n",
            "34:\tlearn: 0.1659541\ttest: 0.1703832\tbest: 0.1703832 (34)\ttotal: 40ms\tremaining: 74.3ms\n",
            "35:\tlearn: 0.1648043\ttest: 0.1702952\tbest: 0.1702952 (35)\ttotal: 41.3ms\tremaining: 73.4ms\n",
            "36:\tlearn: 0.1646884\ttest: 0.1710543\tbest: 0.1702952 (35)\ttotal: 42.3ms\tremaining: 72ms\n",
            "37:\tlearn: 0.1638972\ttest: 0.1699964\tbest: 0.1699964 (37)\ttotal: 43.5ms\tremaining: 71ms\n",
            "38:\tlearn: 0.1635993\ttest: 0.1700173\tbest: 0.1699964 (37)\ttotal: 44.9ms\tremaining: 70.2ms\n",
            "39:\tlearn: 0.1626328\ttest: 0.1706959\tbest: 0.1699964 (37)\ttotal: 46.1ms\tremaining: 69.2ms\n",
            "40:\tlearn: 0.1614970\ttest: 0.1725623\tbest: 0.1699964 (37)\ttotal: 47.2ms\tremaining: 68ms\n",
            "41:\tlearn: 0.1604586\ttest: 0.1723844\tbest: 0.1699964 (37)\ttotal: 48.5ms\tremaining: 66.9ms\n",
            "42:\tlearn: 0.1599581\ttest: 0.1719879\tbest: 0.1699964 (37)\ttotal: 49.6ms\tremaining: 65.7ms\n",
            "43:\tlearn: 0.1590739\ttest: 0.1729182\tbest: 0.1699964 (37)\ttotal: 50.7ms\tremaining: 64.6ms\n",
            "44:\tlearn: 0.1587235\ttest: 0.1729020\tbest: 0.1699964 (37)\ttotal: 51.9ms\tremaining: 63.4ms\n",
            "45:\tlearn: 0.1572951\ttest: 0.1721222\tbest: 0.1699964 (37)\ttotal: 52.9ms\tremaining: 62.1ms\n",
            "46:\tlearn: 0.1571839\ttest: 0.1722738\tbest: 0.1699964 (37)\ttotal: 53.9ms\tremaining: 60.8ms\n",
            "47:\tlearn: 0.1556111\ttest: 0.1710696\tbest: 0.1699964 (37)\ttotal: 54.9ms\tremaining: 59.4ms\n",
            "48:\tlearn: 0.1547168\ttest: 0.1709040\tbest: 0.1699964 (37)\ttotal: 56ms\tremaining: 58.3ms\n",
            "49:\tlearn: 0.1537490\ttest: 0.1696146\tbest: 0.1696146 (49)\ttotal: 57.1ms\tremaining: 57.1ms\n",
            "50:\tlearn: 0.1520824\ttest: 0.1684756\tbest: 0.1684756 (50)\ttotal: 58.5ms\tremaining: 56.2ms\n",
            "51:\tlearn: 0.1514726\ttest: 0.1690170\tbest: 0.1684756 (50)\ttotal: 60ms\tremaining: 55.4ms\n",
            "52:\tlearn: 0.1507186\ttest: 0.1698503\tbest: 0.1684756 (50)\ttotal: 61.5ms\tremaining: 54.6ms\n",
            "53:\tlearn: 0.1506727\ttest: 0.1702016\tbest: 0.1684756 (50)\ttotal: 62.9ms\tremaining: 53.6ms\n",
            "54:\tlearn: 0.1497225\ttest: 0.1706112\tbest: 0.1684756 (50)\ttotal: 64.1ms\tremaining: 52.5ms\n",
            "55:\tlearn: 0.1494372\ttest: 0.1706115\tbest: 0.1684756 (50)\ttotal: 65.2ms\tremaining: 51.2ms\n",
            "56:\tlearn: 0.1476246\ttest: 0.1690261\tbest: 0.1684756 (50)\ttotal: 66.2ms\tremaining: 49.9ms\n",
            "57:\tlearn: 0.1457595\ttest: 0.1648584\tbest: 0.1648584 (57)\ttotal: 67.3ms\tremaining: 48.7ms\n",
            "58:\tlearn: 0.1449479\ttest: 0.1639571\tbest: 0.1639571 (58)\ttotal: 68.3ms\tremaining: 47.5ms\n",
            "59:\tlearn: 0.1437719\ttest: 0.1637441\tbest: 0.1637441 (59)\ttotal: 69.4ms\tremaining: 46.2ms\n",
            "60:\tlearn: 0.1435942\ttest: 0.1637142\tbest: 0.1637142 (60)\ttotal: 70.4ms\tremaining: 45ms\n",
            "61:\tlearn: 0.1427813\ttest: 0.1623380\tbest: 0.1623380 (61)\ttotal: 71.5ms\tremaining: 43.8ms\n",
            "62:\tlearn: 0.1422701\ttest: 0.1623857\tbest: 0.1623380 (61)\ttotal: 72.7ms\tremaining: 42.7ms\n",
            "63:\tlearn: 0.1403152\ttest: 0.1626670\tbest: 0.1623380 (61)\ttotal: 74.2ms\tremaining: 41.7ms\n",
            "64:\tlearn: 0.1383136\ttest: 0.1604251\tbest: 0.1604251 (64)\ttotal: 75.3ms\tremaining: 40.5ms\n",
            "65:\tlearn: 0.1375531\ttest: 0.1609564\tbest: 0.1604251 (64)\ttotal: 76.5ms\tremaining: 39.4ms\n",
            "66:\tlearn: 0.1370400\ttest: 0.1597189\tbest: 0.1597189 (66)\ttotal: 77.7ms\tremaining: 38.3ms\n",
            "67:\tlearn: 0.1369818\ttest: 0.1594919\tbest: 0.1594919 (67)\ttotal: 78.9ms\tremaining: 37.1ms\n",
            "68:\tlearn: 0.1359927\ttest: 0.1590997\tbest: 0.1590997 (68)\ttotal: 80ms\tremaining: 35.9ms\n",
            "69:\tlearn: 0.1354018\ttest: 0.1591001\tbest: 0.1590997 (68)\ttotal: 81.1ms\tremaining: 34.7ms\n",
            "70:\tlearn: 0.1345936\ttest: 0.1593133\tbest: 0.1590997 (68)\ttotal: 82.1ms\tremaining: 33.5ms\n",
            "71:\tlearn: 0.1337189\ttest: 0.1595157\tbest: 0.1590997 (68)\ttotal: 83ms\tremaining: 32.3ms\n",
            "72:\tlearn: 0.1335399\ttest: 0.1595495\tbest: 0.1590997 (68)\ttotal: 84.1ms\tremaining: 31.1ms\n",
            "73:\tlearn: 0.1310536\ttest: 0.1573287\tbest: 0.1573287 (73)\ttotal: 85.2ms\tremaining: 29.9ms\n",
            "74:\tlearn: 0.1300498\ttest: 0.1572211\tbest: 0.1572211 (74)\ttotal: 86.5ms\tremaining: 28.8ms\n",
            "75:\tlearn: 0.1283019\ttest: 0.1562812\tbest: 0.1562812 (75)\ttotal: 87.9ms\tremaining: 27.8ms\n",
            "76:\tlearn: 0.1268207\ttest: 0.1562490\tbest: 0.1562490 (76)\ttotal: 89.5ms\tremaining: 26.7ms\n",
            "77:\tlearn: 0.1254581\ttest: 0.1544296\tbest: 0.1544296 (77)\ttotal: 90.6ms\tremaining: 25.6ms\n",
            "78:\tlearn: 0.1241630\ttest: 0.1551092\tbest: 0.1544296 (77)\ttotal: 91.6ms\tremaining: 24.3ms\n",
            "79:\tlearn: 0.1241076\ttest: 0.1551258\tbest: 0.1544296 (77)\ttotal: 92.9ms\tremaining: 23.2ms\n",
            "80:\tlearn: 0.1233158\ttest: 0.1551393\tbest: 0.1544296 (77)\ttotal: 93.9ms\tremaining: 22ms\n",
            "81:\tlearn: 0.1221472\ttest: 0.1525543\tbest: 0.1525543 (81)\ttotal: 95.1ms\tremaining: 20.9ms\n",
            "82:\tlearn: 0.1211650\ttest: 0.1514962\tbest: 0.1514962 (82)\ttotal: 96.1ms\tremaining: 19.7ms\n",
            "83:\tlearn: 0.1206223\ttest: 0.1515125\tbest: 0.1514962 (82)\ttotal: 97ms\tremaining: 18.5ms\n",
            "84:\tlearn: 0.1199008\ttest: 0.1527868\tbest: 0.1514962 (82)\ttotal: 98ms\tremaining: 17.3ms\n",
            "85:\tlearn: 0.1193525\ttest: 0.1528023\tbest: 0.1514962 (82)\ttotal: 99.1ms\tremaining: 16.1ms\n",
            "86:\tlearn: 0.1181490\ttest: 0.1522445\tbest: 0.1514962 (82)\ttotal: 100ms\tremaining: 15ms\n",
            "87:\tlearn: 0.1175569\ttest: 0.1534247\tbest: 0.1514962 (82)\ttotal: 101ms\tremaining: 13.8ms\n",
            "88:\tlearn: 0.1167652\ttest: 0.1529141\tbest: 0.1514962 (82)\ttotal: 102ms\tremaining: 12.6ms\n",
            "89:\tlearn: 0.1158646\ttest: 0.1533035\tbest: 0.1514962 (82)\ttotal: 103ms\tremaining: 11.5ms\n",
            "90:\tlearn: 0.1156289\ttest: 0.1537243\tbest: 0.1514962 (82)\ttotal: 105ms\tremaining: 10.3ms\n",
            "91:\tlearn: 0.1147132\ttest: 0.1514085\tbest: 0.1514085 (91)\ttotal: 106ms\tremaining: 9.19ms\n",
            "92:\tlearn: 0.1138578\ttest: 0.1525587\tbest: 0.1514085 (91)\ttotal: 107ms\tremaining: 8.04ms\n",
            "93:\tlearn: 0.1136886\ttest: 0.1525961\tbest: 0.1514085 (91)\ttotal: 108ms\tremaining: 6.91ms\n",
            "94:\tlearn: 0.1129988\ttest: 0.1528666\tbest: 0.1514085 (91)\ttotal: 110ms\tremaining: 5.76ms\n",
            "95:\tlearn: 0.1127951\ttest: 0.1519660\tbest: 0.1514085 (91)\ttotal: 111ms\tremaining: 4.62ms\n",
            "96:\tlearn: 0.1125371\ttest: 0.1527311\tbest: 0.1514085 (91)\ttotal: 112ms\tremaining: 3.47ms\n",
            "97:\tlearn: 0.1124817\ttest: 0.1530817\tbest: 0.1514085 (91)\ttotal: 113ms\tremaining: 2.31ms\n",
            "98:\tlearn: 0.1119257\ttest: 0.1537040\tbest: 0.1514085 (91)\ttotal: 114ms\tremaining: 1.16ms\n",
            "99:\tlearn: 0.1114058\ttest: 0.1537329\tbest: 0.1514085 (91)\ttotal: 115ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1514085422\n",
            "bestIteration = 91\n",
            "\n",
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            "95:\tlearn: 0.6280472\ttest: 0.6290080\tbest: 0.6290080 (95)\ttotal: 114ms\tremaining: 4.76ms\n",
            "96:\tlearn: 0.6273578\ttest: 0.6283061\tbest: 0.6283061 (96)\ttotal: 116ms\tremaining: 3.6ms\n",
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            "99:\tlearn: 0.6255817\ttest: 0.6265346\tbest: 0.6265346 (99)\ttotal: 120ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.6265346209\n",
            "bestIteration = 99\n",
            "\n",
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            "81:\tlearn: 0.2036437\ttest: 0.1968060\tbest: 0.1968060 (81)\ttotal: 96ms\tremaining: 21.1ms\n",
            "82:\tlearn: 0.2033405\ttest: 0.1961716\tbest: 0.1961716 (82)\ttotal: 97.1ms\tremaining: 19.9ms\n",
            "83:\tlearn: 0.2022737\ttest: 0.1949152\tbest: 0.1949152 (83)\ttotal: 98.5ms\tremaining: 18.8ms\n",
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            "87:\tlearn: 0.2001678\ttest: 0.1931599\tbest: 0.1931599 (87)\ttotal: 104ms\tremaining: 14.2ms\n",
            "88:\tlearn: 0.1997379\ttest: 0.1928205\tbest: 0.1928205 (88)\ttotal: 105ms\tremaining: 13ms\n",
            "89:\tlearn: 0.1996351\ttest: 0.1928376\tbest: 0.1928205 (88)\ttotal: 106ms\tremaining: 11.8ms\n",
            "90:\tlearn: 0.1984642\ttest: 0.1913406\tbest: 0.1913406 (90)\ttotal: 107ms\tremaining: 10.6ms\n",
            "91:\tlearn: 0.1975281\ttest: 0.1898313\tbest: 0.1898313 (91)\ttotal: 108ms\tremaining: 9.4ms\n",
            "92:\tlearn: 0.1964621\ttest: 0.1888408\tbest: 0.1888408 (92)\ttotal: 109ms\tremaining: 8.22ms\n",
            "93:\tlearn: 0.1957190\ttest: 0.1880519\tbest: 0.1880519 (93)\ttotal: 110ms\tremaining: 7.03ms\n",
            "94:\tlearn: 0.1955665\ttest: 0.1880463\tbest: 0.1880463 (94)\ttotal: 111ms\tremaining: 5.85ms\n",
            "95:\tlearn: 0.1944666\ttest: 0.1871345\tbest: 0.1871345 (95)\ttotal: 112ms\tremaining: 4.68ms\n",
            "96:\tlearn: 0.1936573\ttest: 0.1863068\tbest: 0.1863068 (96)\ttotal: 113ms\tremaining: 3.51ms\n",
            "97:\tlearn: 0.1933562\ttest: 0.1859468\tbest: 0.1859468 (97)\ttotal: 115ms\tremaining: 2.35ms\n",
            "98:\tlearn: 0.1931911\ttest: 0.1858239\tbest: 0.1858239 (98)\ttotal: 116ms\tremaining: 1.17ms\n",
            "99:\tlearn: 0.1923961\ttest: 0.1857144\tbest: 0.1857144 (99)\ttotal: 118ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1857143795\n",
            "bestIteration = 99\n",
            "\n",
            "16:\tloss: 0.1857144\tbest: 0.1506604 (4)\ttotal: 2.47s\tremaining: 6.25s\n",
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            "14:\tlearn: 0.2692882\ttest: 0.2667352\tbest: 0.2667352 (14)\ttotal: 16.1ms\tremaining: 91.1ms\n",
            "15:\tlearn: 0.2626473\ttest: 0.2600903\tbest: 0.2600903 (15)\ttotal: 17.1ms\tremaining: 89.9ms\n",
            "16:\tlearn: 0.2583501\ttest: 0.2540644\tbest: 0.2540644 (16)\ttotal: 18.1ms\tremaining: 88.5ms\n",
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            "23:\tlearn: 0.2323292\ttest: 0.2266348\tbest: 0.2266348 (23)\ttotal: 25.7ms\tremaining: 81.3ms\n",
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            "63:\tlearn: 0.1775437\ttest: 0.1733888\tbest: 0.1733888 (63)\ttotal: 70.9ms\tremaining: 39.9ms\n",
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            "67:\tlearn: 0.1748435\ttest: 0.1723563\tbest: 0.1723563 (67)\ttotal: 76.1ms\tremaining: 35.8ms\n",
            "68:\tlearn: 0.1744295\ttest: 0.1722204\tbest: 0.1722204 (68)\ttotal: 77.1ms\tremaining: 34.7ms\n",
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            "75:\tlearn: 0.1691247\ttest: 0.1696383\tbest: 0.1695603 (74)\ttotal: 84.5ms\tremaining: 26.7ms\n",
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            "77:\tlearn: 0.1660740\ttest: 0.1673768\tbest: 0.1673768 (77)\ttotal: 86.5ms\tremaining: 24.4ms\n",
            "78:\tlearn: 0.1653828\ttest: 0.1679144\tbest: 0.1673768 (77)\ttotal: 87.5ms\tremaining: 23.3ms\n",
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            "80:\tlearn: 0.1638877\ttest: 0.1674482\tbest: 0.1673768 (77)\ttotal: 89.6ms\tremaining: 21ms\n",
            "81:\tlearn: 0.1636374\ttest: 0.1676403\tbest: 0.1673768 (77)\ttotal: 90.6ms\tremaining: 19.9ms\n",
            "82:\tlearn: 0.1631597\ttest: 0.1677055\tbest: 0.1673768 (77)\ttotal: 91.7ms\tremaining: 18.8ms\n",
            "83:\tlearn: 0.1623526\ttest: 0.1665041\tbest: 0.1665041 (83)\ttotal: 92.7ms\tremaining: 17.6ms\n",
            "84:\tlearn: 0.1622812\ttest: 0.1666442\tbest: 0.1665041 (83)\ttotal: 93.9ms\tremaining: 16.6ms\n",
            "85:\tlearn: 0.1621483\ttest: 0.1668133\tbest: 0.1665041 (83)\ttotal: 94.9ms\tremaining: 15.5ms\n",
            "86:\tlearn: 0.1612324\ttest: 0.1661218\tbest: 0.1661218 (86)\ttotal: 95.9ms\tremaining: 14.3ms\n",
            "87:\tlearn: 0.1610293\ttest: 0.1660868\tbest: 0.1660868 (87)\ttotal: 97ms\tremaining: 13.2ms\n",
            "88:\tlearn: 0.1606120\ttest: 0.1659240\tbest: 0.1659240 (88)\ttotal: 97.9ms\tremaining: 12.1ms\n",
            "89:\tlearn: 0.1600704\ttest: 0.1653843\tbest: 0.1653843 (89)\ttotal: 99ms\tremaining: 11ms\n",
            "90:\tlearn: 0.1600136\ttest: 0.1654304\tbest: 0.1653843 (89)\ttotal: 100ms\tremaining: 9.89ms\n",
            "91:\tlearn: 0.1595141\ttest: 0.1646153\tbest: 0.1646153 (91)\ttotal: 101ms\tremaining: 8.79ms\n",
            "92:\tlearn: 0.1590569\ttest: 0.1641450\tbest: 0.1641450 (92)\ttotal: 102ms\tremaining: 7.69ms\n",
            "93:\tlearn: 0.1578456\ttest: 0.1641825\tbest: 0.1641450 (92)\ttotal: 103ms\tremaining: 6.59ms\n",
            "94:\tlearn: 0.1572067\ttest: 0.1642137\tbest: 0.1641450 (92)\ttotal: 104ms\tremaining: 5.49ms\n",
            "95:\tlearn: 0.1566899\ttest: 0.1645691\tbest: 0.1641450 (92)\ttotal: 105ms\tremaining: 4.39ms\n",
            "96:\tlearn: 0.1566252\ttest: 0.1647133\tbest: 0.1641450 (92)\ttotal: 106ms\tremaining: 3.29ms\n",
            "97:\tlearn: 0.1564327\ttest: 0.1645661\tbest: 0.1641450 (92)\ttotal: 107ms\tremaining: 2.19ms\n",
            "98:\tlearn: 0.1559207\ttest: 0.1636978\tbest: 0.1636978 (98)\ttotal: 108ms\tremaining: 1.09ms\n",
            "99:\tlearn: 0.1555227\ttest: 0.1631876\tbest: 0.1631876 (99)\ttotal: 109ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1631876211\n",
            "bestIteration = 99\n",
            "\n",
            "17:\tloss: 0.1631876\tbest: 0.1506604 (4)\ttotal: 2.59s\tremaining: 6.04s\n",
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            "\n",
            "bestTest = 0.1588855248\n",
            "bestIteration = 98\n",
            "\n",
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            "80:\tlearn: 0.1263699\ttest: 0.1541444\tbest: 0.1529676 (79)\ttotal: 83.4ms\tremaining: 19.6ms\n",
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            "83:\tlearn: 0.1253162\ttest: 0.1537259\tbest: 0.1529676 (79)\ttotal: 87ms\tremaining: 16.6ms\n",
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            "87:\tlearn: 0.1239338\ttest: 0.1546797\tbest: 0.1529676 (79)\ttotal: 92.2ms\tremaining: 12.6ms\n",
            "88:\tlearn: 0.1229598\ttest: 0.1541534\tbest: 0.1529676 (79)\ttotal: 93.3ms\tremaining: 11.5ms\n",
            "89:\tlearn: 0.1227315\ttest: 0.1543019\tbest: 0.1529676 (79)\ttotal: 94.5ms\tremaining: 10.5ms\n",
            "90:\tlearn: 0.1206440\ttest: 0.1512148\tbest: 0.1512148 (90)\ttotal: 95.6ms\tremaining: 9.46ms\n",
            "91:\tlearn: 0.1206005\ttest: 0.1514469\tbest: 0.1512148 (90)\ttotal: 96.8ms\tremaining: 8.41ms\n",
            "92:\tlearn: 0.1200575\ttest: 0.1517480\tbest: 0.1512148 (90)\ttotal: 97.9ms\tremaining: 7.37ms\n",
            "93:\tlearn: 0.1194191\ttest: 0.1526101\tbest: 0.1512148 (90)\ttotal: 99ms\tremaining: 6.32ms\n",
            "94:\tlearn: 0.1187213\ttest: 0.1531989\tbest: 0.1512148 (90)\ttotal: 100ms\tremaining: 5.27ms\n",
            "95:\tlearn: 0.1182268\ttest: 0.1531961\tbest: 0.1512148 (90)\ttotal: 101ms\tremaining: 4.22ms\n",
            "96:\tlearn: 0.1175223\ttest: 0.1523502\tbest: 0.1512148 (90)\ttotal: 102ms\tremaining: 3.16ms\n",
            "97:\tlearn: 0.1159486\ttest: 0.1524422\tbest: 0.1512148 (90)\ttotal: 104ms\tremaining: 2.12ms\n",
            "98:\tlearn: 0.1151012\ttest: 0.1523884\tbest: 0.1512148 (90)\ttotal: 106ms\tremaining: 1.07ms\n",
            "99:\tlearn: 0.1138446\ttest: 0.1537696\tbest: 0.1512148 (90)\ttotal: 107ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1512148446\n",
            "bestIteration = 90\n",
            "\n",
            "19:\tloss: 0.1512148\tbest: 0.1506604 (4)\ttotal: 2.82s\tremaining: 5.63s\n",
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            "23:\tlearn: 0.6743150\ttest: 0.6745877\tbest: 0.6745877 (23)\ttotal: 29.8ms\tremaining: 94.5ms\n",
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            "25:\tlearn: 0.6728684\ttest: 0.6731730\tbest: 0.6731730 (25)\ttotal: 32.1ms\tremaining: 91.5ms\n",
            "26:\tlearn: 0.6718499\ttest: 0.6721596\tbest: 0.6721596 (26)\ttotal: 33.6ms\tremaining: 90.8ms\n",
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            "66:\tlearn: 0.6423909\ttest: 0.6431570\tbest: 0.6431570 (66)\ttotal: 80.5ms\tremaining: 39.7ms\n",
            "67:\tlearn: 0.6416960\ttest: 0.6424626\tbest: 0.6424626 (67)\ttotal: 81.7ms\tremaining: 38.4ms\n",
            "68:\tlearn: 0.6410525\ttest: 0.6418327\tbest: 0.6418327 (68)\ttotal: 82.8ms\tremaining: 37.2ms\n",
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            "81:\tlearn: 0.6321392\ttest: 0.6329775\tbest: 0.6329775 (81)\ttotal: 97.4ms\tremaining: 21.4ms\n",
            "82:\tlearn: 0.6314807\ttest: 0.6323117\tbest: 0.6323117 (82)\ttotal: 98.5ms\tremaining: 20.2ms\n",
            "83:\tlearn: 0.6309624\ttest: 0.6318012\tbest: 0.6318012 (83)\ttotal: 99.8ms\tremaining: 19ms\n",
            "84:\tlearn: 0.6302579\ttest: 0.6310930\tbest: 0.6310930 (84)\ttotal: 101ms\tremaining: 17.8ms\n",
            "85:\tlearn: 0.6295366\ttest: 0.6304037\tbest: 0.6304037 (85)\ttotal: 102ms\tremaining: 16.6ms\n",
            "86:\tlearn: 0.6289750\ttest: 0.6298375\tbest: 0.6298375 (86)\ttotal: 103ms\tremaining: 15.4ms\n",
            "87:\tlearn: 0.6281945\ttest: 0.6290695\tbest: 0.6290695 (87)\ttotal: 104ms\tremaining: 14.2ms\n",
            "88:\tlearn: 0.6275850\ttest: 0.6284803\tbest: 0.6284803 (88)\ttotal: 105ms\tremaining: 13ms\n",
            "89:\tlearn: 0.6269705\ttest: 0.6278802\tbest: 0.6278802 (89)\ttotal: 106ms\tremaining: 11.8ms\n",
            "90:\tlearn: 0.6262758\ttest: 0.6271995\tbest: 0.6271995 (90)\ttotal: 108ms\tremaining: 10.7ms\n",
            "91:\tlearn: 0.6255397\ttest: 0.6264602\tbest: 0.6264602 (91)\ttotal: 109ms\tremaining: 9.47ms\n",
            "92:\tlearn: 0.6249472\ttest: 0.6258787\tbest: 0.6258787 (92)\ttotal: 110ms\tremaining: 8.28ms\n",
            "93:\tlearn: 0.6243380\ttest: 0.6252812\tbest: 0.6252812 (93)\ttotal: 111ms\tremaining: 7.11ms\n",
            "94:\tlearn: 0.6236464\ttest: 0.6245852\tbest: 0.6245852 (94)\ttotal: 113ms\tremaining: 5.92ms\n",
            "95:\tlearn: 0.6229160\ttest: 0.6238696\tbest: 0.6238696 (95)\ttotal: 114ms\tremaining: 4.74ms\n",
            "96:\tlearn: 0.6221899\ttest: 0.6231497\tbest: 0.6231497 (96)\ttotal: 115ms\tremaining: 3.55ms\n",
            "97:\tlearn: 0.6216145\ttest: 0.6225796\tbest: 0.6225796 (97)\ttotal: 116ms\tremaining: 2.37ms\n",
            "98:\tlearn: 0.6210254\ttest: 0.6220024\tbest: 0.6220024 (98)\ttotal: 117ms\tremaining: 1.18ms\n",
            "99:\tlearn: 0.6202890\ttest: 0.6212747\tbest: 0.6212747 (99)\ttotal: 118ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.6212746706\n",
            "bestIteration = 99\n",
            "\n",
            "20:\tloss: 0.6212747\tbest: 0.1506604 (4)\ttotal: 2.94s\tremaining: 5.47s\n",
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            "\n",
            "bestTest = 0.1505850427\n",
            "bestIteration = 99\n",
            "\n",
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            "52:\tlearn: 0.1426138\ttest: 0.1483963\tbest: 0.1483963 (52)\ttotal: 62.6ms\tremaining: 55.6ms\n",
            "53:\tlearn: 0.1419112\ttest: 0.1483429\tbest: 0.1483429 (53)\ttotal: 63.8ms\tremaining: 54.4ms\n",
            "54:\tlearn: 0.1403056\ttest: 0.1470449\tbest: 0.1470449 (54)\ttotal: 65ms\tremaining: 53.2ms\n",
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            "56:\tlearn: 0.1379936\ttest: 0.1459210\tbest: 0.1459210 (56)\ttotal: 67.4ms\tremaining: 50.9ms\n",
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            "58:\tlearn: 0.1365822\ttest: 0.1446506\tbest: 0.1446367 (57)\ttotal: 70ms\tremaining: 48.6ms\n",
            "59:\tlearn: 0.1362095\ttest: 0.1446925\tbest: 0.1446367 (57)\ttotal: 71ms\tremaining: 47.4ms\n",
            "60:\tlearn: 0.1355092\ttest: 0.1455340\tbest: 0.1446367 (57)\ttotal: 72.2ms\tremaining: 46.2ms\n",
            "61:\tlearn: 0.1350899\ttest: 0.1456523\tbest: 0.1446367 (57)\ttotal: 73.5ms\tremaining: 45ms\n",
            "62:\tlearn: 0.1342870\ttest: 0.1459071\tbest: 0.1446367 (57)\ttotal: 74.6ms\tremaining: 43.8ms\n",
            "63:\tlearn: 0.1337177\ttest: 0.1458551\tbest: 0.1446367 (57)\ttotal: 75.9ms\tremaining: 42.7ms\n",
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            "66:\tlearn: 0.1300194\ttest: 0.1444297\tbest: 0.1444297 (66)\ttotal: 79.6ms\tremaining: 39.2ms\n",
            "67:\tlearn: 0.1291147\ttest: 0.1435712\tbest: 0.1435712 (67)\ttotal: 80.8ms\tremaining: 38ms\n",
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            "72:\tlearn: 0.1241917\ttest: 0.1439399\tbest: 0.1434274 (69)\ttotal: 86.9ms\tremaining: 32.1ms\n",
            "73:\tlearn: 0.1236775\ttest: 0.1439608\tbest: 0.1434274 (69)\ttotal: 88ms\tremaining: 30.9ms\n",
            "74:\tlearn: 0.1231130\ttest: 0.1446014\tbest: 0.1434274 (69)\ttotal: 89.2ms\tremaining: 29.7ms\n",
            "75:\tlearn: 0.1218670\ttest: 0.1446941\tbest: 0.1434274 (69)\ttotal: 90.4ms\tremaining: 28.6ms\n",
            "76:\tlearn: 0.1204625\ttest: 0.1428164\tbest: 0.1428164 (76)\ttotal: 91.6ms\tremaining: 27.4ms\n",
            "77:\tlearn: 0.1197120\ttest: 0.1422551\tbest: 0.1422551 (77)\ttotal: 93.1ms\tremaining: 26.2ms\n",
            "78:\tlearn: 0.1171146\ttest: 0.1393590\tbest: 0.1393590 (78)\ttotal: 94.3ms\tremaining: 25.1ms\n",
            "79:\tlearn: 0.1169083\ttest: 0.1392081\tbest: 0.1392081 (79)\ttotal: 95.6ms\tremaining: 23.9ms\n",
            "80:\tlearn: 0.1164443\ttest: 0.1391648\tbest: 0.1391648 (80)\ttotal: 96.8ms\tremaining: 22.7ms\n",
            "81:\tlearn: 0.1156379\ttest: 0.1388101\tbest: 0.1388101 (81)\ttotal: 98ms\tremaining: 21.5ms\n",
            "82:\tlearn: 0.1148217\ttest: 0.1393781\tbest: 0.1388101 (81)\ttotal: 99.1ms\tremaining: 20.3ms\n",
            "83:\tlearn: 0.1142709\ttest: 0.1392137\tbest: 0.1388101 (81)\ttotal: 100ms\tremaining: 19.1ms\n",
            "84:\tlearn: 0.1131701\ttest: 0.1379908\tbest: 0.1379908 (84)\ttotal: 101ms\tremaining: 17.9ms\n",
            "85:\tlearn: 0.1115774\ttest: 0.1365405\tbest: 0.1365405 (85)\ttotal: 103ms\tremaining: 16.7ms\n",
            "86:\tlearn: 0.1109278\ttest: 0.1366277\tbest: 0.1365405 (85)\ttotal: 104ms\tremaining: 15.5ms\n",
            "87:\tlearn: 0.1097351\ttest: 0.1364645\tbest: 0.1364645 (87)\ttotal: 105ms\tremaining: 14.3ms\n",
            "88:\tlearn: 0.1096140\ttest: 0.1364896\tbest: 0.1364645 (87)\ttotal: 106ms\tremaining: 13.1ms\n",
            "89:\tlearn: 0.1086665\ttest: 0.1368033\tbest: 0.1364645 (87)\ttotal: 107ms\tremaining: 11.9ms\n",
            "90:\tlearn: 0.1077280\ttest: 0.1373259\tbest: 0.1364645 (87)\ttotal: 109ms\tremaining: 10.7ms\n",
            "91:\tlearn: 0.1071925\ttest: 0.1367246\tbest: 0.1364645 (87)\ttotal: 110ms\tremaining: 9.57ms\n",
            "92:\tlearn: 0.1065603\ttest: 0.1362799\tbest: 0.1362799 (92)\ttotal: 111ms\tremaining: 8.38ms\n",
            "93:\tlearn: 0.1050508\ttest: 0.1364808\tbest: 0.1362799 (92)\ttotal: 112ms\tremaining: 7.17ms\n",
            "94:\tlearn: 0.1038124\ttest: 0.1373798\tbest: 0.1362799 (92)\ttotal: 114ms\tremaining: 5.98ms\n",
            "95:\tlearn: 0.1035380\ttest: 0.1372514\tbest: 0.1362799 (92)\ttotal: 115ms\tremaining: 4.78ms\n",
            "96:\tlearn: 0.1026949\ttest: 0.1368991\tbest: 0.1362799 (92)\ttotal: 116ms\tremaining: 3.59ms\n",
            "97:\tlearn: 0.1023893\ttest: 0.1370213\tbest: 0.1362799 (92)\ttotal: 117ms\tremaining: 2.39ms\n",
            "98:\tlearn: 0.1021898\ttest: 0.1369054\tbest: 0.1362799 (92)\ttotal: 118ms\tremaining: 1.2ms\n",
            "99:\tlearn: 0.1012880\ttest: 0.1380123\tbest: 0.1362799 (92)\ttotal: 120ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1362798796\n",
            "bestIteration = 92\n",
            "\n",
            "22:\tloss: 0.1362799\tbest: 0.1362799 (22)\ttotal: 3.21s\tremaining: 5.16s\n",
            "0:\tlearn: 0.4704439\ttest: 0.4761317\tbest: 0.4761317 (0)\ttotal: 1.12ms\tremaining: 111ms\n",
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            "3:\tlearn: 0.3106530\ttest: 0.3167400\tbest: 0.3167400 (3)\ttotal: 5.62ms\tremaining: 135ms\n",
            "4:\tlearn: 0.2740363\ttest: 0.2747702\tbest: 0.2747702 (4)\ttotal: 6.74ms\tremaining: 128ms\n",
            "5:\tlearn: 0.2582454\ttest: 0.2695202\tbest: 0.2695202 (5)\ttotal: 7.92ms\tremaining: 124ms\n",
            "6:\tlearn: 0.2452706\ttest: 0.2545407\tbest: 0.2545407 (6)\ttotal: 9.2ms\tremaining: 122ms\n",
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            "9:\tlearn: 0.2056111\ttest: 0.2048231\tbest: 0.2048231 (9)\ttotal: 12.9ms\tremaining: 116ms\n",
            "10:\tlearn: 0.1970682\ttest: 0.1930113\tbest: 0.1930113 (10)\ttotal: 14.1ms\tremaining: 114ms\n",
            "11:\tlearn: 0.1933543\ttest: 0.1870204\tbest: 0.1870204 (11)\ttotal: 15.2ms\tremaining: 112ms\n",
            "12:\tlearn: 0.1865496\ttest: 0.1803335\tbest: 0.1803335 (12)\ttotal: 16.3ms\tremaining: 109ms\n",
            "13:\tlearn: 0.1826782\ttest: 0.1764823\tbest: 0.1764823 (13)\ttotal: 17.5ms\tremaining: 107ms\n",
            "14:\tlearn: 0.1776732\ttest: 0.1712790\tbest: 0.1712790 (14)\ttotal: 18.6ms\tremaining: 106ms\n",
            "15:\tlearn: 0.1746563\ttest: 0.1700923\tbest: 0.1700923 (15)\ttotal: 19.9ms\tremaining: 104ms\n",
            "16:\tlearn: 0.1739015\ttest: 0.1700343\tbest: 0.1700343 (16)\ttotal: 21ms\tremaining: 103ms\n",
            "17:\tlearn: 0.1727625\ttest: 0.1687540\tbest: 0.1687540 (17)\ttotal: 22.3ms\tremaining: 101ms\n",
            "18:\tlearn: 0.1662563\ttest: 0.1708791\tbest: 0.1687540 (17)\ttotal: 23.5ms\tremaining: 100ms\n",
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            "21:\tlearn: 0.1578524\ttest: 0.1654410\tbest: 0.1651344 (20)\ttotal: 27.2ms\tremaining: 96.4ms\n",
            "22:\tlearn: 0.1568148\ttest: 0.1645880\tbest: 0.1645880 (22)\ttotal: 28.4ms\tremaining: 95.1ms\n",
            "23:\tlearn: 0.1561282\ttest: 0.1642154\tbest: 0.1642154 (23)\ttotal: 29.6ms\tremaining: 93.6ms\n",
            "24:\tlearn: 0.1527443\ttest: 0.1587236\tbest: 0.1587236 (24)\ttotal: 30.8ms\tremaining: 92.4ms\n",
            "25:\tlearn: 0.1500077\ttest: 0.1579126\tbest: 0.1579126 (25)\ttotal: 32ms\tremaining: 91ms\n",
            "26:\tlearn: 0.1490325\ttest: 0.1571694\tbest: 0.1571694 (26)\ttotal: 33.1ms\tremaining: 89.6ms\n",
            "27:\tlearn: 0.1472649\ttest: 0.1560959\tbest: 0.1560959 (27)\ttotal: 34.4ms\tremaining: 88.4ms\n",
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            "53:\tlearn: 0.1102886\ttest: 0.1501907\tbest: 0.1501907 (53)\ttotal: 66.8ms\tremaining: 56.9ms\n",
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            "55:\tlearn: 0.1071594\ttest: 0.1489678\tbest: 0.1489678 (55)\ttotal: 69.2ms\tremaining: 54.3ms\n",
            "56:\tlearn: 0.1055090\ttest: 0.1493028\tbest: 0.1489678 (55)\ttotal: 70.6ms\tremaining: 53.3ms\n",
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            "61:\tlearn: 0.1017086\ttest: 0.1471934\tbest: 0.1471130 (60)\ttotal: 78.6ms\tremaining: 48.2ms\n",
            "62:\tlearn: 0.1007741\ttest: 0.1474902\tbest: 0.1471130 (60)\ttotal: 80.3ms\tremaining: 47.2ms\n",
            "63:\tlearn: 0.0989976\ttest: 0.1470391\tbest: 0.1470391 (63)\ttotal: 82.2ms\tremaining: 46.2ms\n",
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            "65:\tlearn: 0.0970899\ttest: 0.1448393\tbest: 0.1448393 (65)\ttotal: 86.2ms\tremaining: 44.4ms\n",
            "66:\tlearn: 0.0959154\ttest: 0.1446145\tbest: 0.1446145 (66)\ttotal: 88ms\tremaining: 43.3ms\n",
            "67:\tlearn: 0.0945969\ttest: 0.1439537\tbest: 0.1439537 (67)\ttotal: 89.4ms\tremaining: 42.1ms\n",
            "68:\tlearn: 0.0939982\ttest: 0.1436421\tbest: 0.1436421 (68)\ttotal: 90.9ms\tremaining: 40.8ms\n",
            "69:\tlearn: 0.0935342\ttest: 0.1438708\tbest: 0.1436421 (68)\ttotal: 92.1ms\tremaining: 39.5ms\n",
            "70:\tlearn: 0.0925161\ttest: 0.1441508\tbest: 0.1436421 (68)\ttotal: 93.2ms\tremaining: 38.1ms\n",
            "71:\tlearn: 0.0919626\ttest: 0.1445398\tbest: 0.1436421 (68)\ttotal: 94.6ms\tremaining: 36.8ms\n",
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            "73:\tlearn: 0.0904679\ttest: 0.1452694\tbest: 0.1436421 (68)\ttotal: 97.7ms\tremaining: 34.3ms\n",
            "74:\tlearn: 0.0893271\ttest: 0.1472405\tbest: 0.1436421 (68)\ttotal: 99.4ms\tremaining: 33.1ms\n",
            "75:\tlearn: 0.0879054\ttest: 0.1487444\tbest: 0.1436421 (68)\ttotal: 101ms\tremaining: 31.9ms\n",
            "76:\tlearn: 0.0860112\ttest: 0.1445521\tbest: 0.1436421 (68)\ttotal: 102ms\tremaining: 30.5ms\n",
            "77:\tlearn: 0.0852102\ttest: 0.1433203\tbest: 0.1433203 (77)\ttotal: 104ms\tremaining: 29.2ms\n",
            "78:\tlearn: 0.0841468\ttest: 0.1431631\tbest: 0.1431631 (78)\ttotal: 105ms\tremaining: 27.8ms\n",
            "79:\tlearn: 0.0837331\ttest: 0.1431232\tbest: 0.1431232 (79)\ttotal: 106ms\tremaining: 26.5ms\n",
            "80:\tlearn: 0.0830245\ttest: 0.1424706\tbest: 0.1424706 (80)\ttotal: 107ms\tremaining: 25.2ms\n",
            "81:\tlearn: 0.0823302\ttest: 0.1430147\tbest: 0.1424706 (80)\ttotal: 108ms\tremaining: 23.8ms\n",
            "82:\tlearn: 0.0819691\ttest: 0.1431657\tbest: 0.1424706 (80)\ttotal: 110ms\tremaining: 22.4ms\n",
            "83:\tlearn: 0.0805906\ttest: 0.1439348\tbest: 0.1424706 (80)\ttotal: 111ms\tremaining: 21.1ms\n",
            "84:\tlearn: 0.0797938\ttest: 0.1437592\tbest: 0.1424706 (80)\ttotal: 112ms\tremaining: 19.8ms\n",
            "85:\tlearn: 0.0791924\ttest: 0.1436811\tbest: 0.1424706 (80)\ttotal: 113ms\tremaining: 18.5ms\n",
            "86:\tlearn: 0.0785376\ttest: 0.1438492\tbest: 0.1424706 (80)\ttotal: 115ms\tremaining: 17.1ms\n",
            "87:\tlearn: 0.0777444\ttest: 0.1439718\tbest: 0.1424706 (80)\ttotal: 116ms\tremaining: 15.8ms\n",
            "88:\tlearn: 0.0771755\ttest: 0.1425833\tbest: 0.1424706 (80)\ttotal: 117ms\tremaining: 14.4ms\n",
            "89:\tlearn: 0.0759613\ttest: 0.1403701\tbest: 0.1403701 (89)\ttotal: 118ms\tremaining: 13.1ms\n",
            "90:\tlearn: 0.0752471\ttest: 0.1409260\tbest: 0.1403701 (89)\ttotal: 119ms\tremaining: 11.8ms\n",
            "91:\tlearn: 0.0743575\ttest: 0.1420406\tbest: 0.1403701 (89)\ttotal: 120ms\tremaining: 10.5ms\n",
            "92:\tlearn: 0.0739198\ttest: 0.1422464\tbest: 0.1403701 (89)\ttotal: 121ms\tremaining: 9.13ms\n",
            "93:\tlearn: 0.0731674\ttest: 0.1432366\tbest: 0.1403701 (89)\ttotal: 122ms\tremaining: 7.82ms\n",
            "94:\tlearn: 0.0716058\ttest: 0.1407789\tbest: 0.1403701 (89)\ttotal: 124ms\tremaining: 6.51ms\n",
            "95:\tlearn: 0.0700959\ttest: 0.1367651\tbest: 0.1367651 (95)\ttotal: 125ms\tremaining: 5.2ms\n",
            "96:\tlearn: 0.0693013\ttest: 0.1359738\tbest: 0.1359738 (96)\ttotal: 126ms\tremaining: 3.9ms\n",
            "97:\tlearn: 0.0686937\ttest: 0.1353408\tbest: 0.1353408 (97)\ttotal: 127ms\tremaining: 2.6ms\n",
            "98:\tlearn: 0.0679081\ttest: 0.1357004\tbest: 0.1353408 (97)\ttotal: 128ms\tremaining: 1.3ms\n",
            "99:\tlearn: 0.0678654\ttest: 0.1359272\tbest: 0.1353408 (97)\ttotal: 129ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1353408206\n",
            "bestIteration = 97\n",
            "\n",
            "23:\tloss: 0.1353408\tbest: 0.1353408 (23)\ttotal: 3.35s\tremaining: 5.02s\n",
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            "\n",
            "bestTest = 0.1411955795\n",
            "bestIteration = 99\n",
            "\n",
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            "82:\tlearn: 0.6309535\ttest: 0.6319338\tbest: 0.6319338 (82)\ttotal: 105ms\tremaining: 21.6ms\n",
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            "90:\tlearn: 0.6256021\ttest: 0.6266267\tbest: 0.6266267 (90)\ttotal: 115ms\tremaining: 11.3ms\n",
            "91:\tlearn: 0.6249356\ttest: 0.6259636\tbest: 0.6259636 (91)\ttotal: 116ms\tremaining: 10.1ms\n",
            "92:\tlearn: 0.6241734\ttest: 0.6252202\tbest: 0.6252202 (92)\ttotal: 117ms\tremaining: 8.81ms\n",
            "93:\tlearn: 0.6235781\ttest: 0.6246490\tbest: 0.6246490 (93)\ttotal: 118ms\tremaining: 7.55ms\n",
            "94:\tlearn: 0.6229076\ttest: 0.6239848\tbest: 0.6239848 (94)\ttotal: 119ms\tremaining: 6.29ms\n",
            "95:\tlearn: 0.6223349\ttest: 0.6234196\tbest: 0.6234196 (95)\ttotal: 121ms\tremaining: 5.03ms\n",
            "96:\tlearn: 0.6217520\ttest: 0.6228186\tbest: 0.6228186 (96)\ttotal: 122ms\tremaining: 3.77ms\n",
            "97:\tlearn: 0.6211265\ttest: 0.6221979\tbest: 0.6221979 (97)\ttotal: 123ms\tremaining: 2.51ms\n",
            "98:\tlearn: 0.6203765\ttest: 0.6214584\tbest: 0.6214584 (98)\ttotal: 124ms\tremaining: 1.25ms\n",
            "99:\tlearn: 0.6196844\ttest: 0.6207809\tbest: 0.6207809 (99)\ttotal: 125ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.6207808977\n",
            "bestIteration = 99\n",
            "\n",
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            "67:\tlearn: 0.1663429\ttest: 0.1675820\tbest: 0.1675820 (67)\ttotal: 86.1ms\tremaining: 40.5ms\n",
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            "83:\tlearn: 0.1536879\ttest: 0.1576497\tbest: 0.1573692 (82)\ttotal: 106ms\tremaining: 20.2ms\n",
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            "87:\tlearn: 0.1506323\ttest: 0.1551924\tbest: 0.1551924 (87)\ttotal: 112ms\tremaining: 15.3ms\n",
            "88:\tlearn: 0.1499302\ttest: 0.1546584\tbest: 0.1546584 (88)\ttotal: 114ms\tremaining: 14ms\n",
            "89:\tlearn: 0.1495320\ttest: 0.1549002\tbest: 0.1546584 (88)\ttotal: 115ms\tremaining: 12.8ms\n",
            "90:\tlearn: 0.1489034\ttest: 0.1544575\tbest: 0.1544575 (90)\ttotal: 116ms\tremaining: 11.5ms\n",
            "91:\tlearn: 0.1481615\ttest: 0.1547117\tbest: 0.1544575 (90)\ttotal: 117ms\tremaining: 10.2ms\n",
            "92:\tlearn: 0.1474948\ttest: 0.1545286\tbest: 0.1544575 (90)\ttotal: 119ms\tremaining: 8.93ms\n",
            "93:\tlearn: 0.1469291\ttest: 0.1543455\tbest: 0.1543455 (93)\ttotal: 120ms\tremaining: 7.65ms\n",
            "94:\tlearn: 0.1468772\ttest: 0.1544513\tbest: 0.1543455 (93)\ttotal: 121ms\tremaining: 6.37ms\n",
            "95:\tlearn: 0.1464505\ttest: 0.1543389\tbest: 0.1543389 (95)\ttotal: 122ms\tremaining: 5.09ms\n",
            "96:\tlearn: 0.1463271\ttest: 0.1543542\tbest: 0.1543389 (95)\ttotal: 123ms\tremaining: 3.81ms\n",
            "97:\tlearn: 0.1458281\ttest: 0.1542094\tbest: 0.1542094 (97)\ttotal: 125ms\tremaining: 2.54ms\n",
            "98:\tlearn: 0.1451571\ttest: 0.1534618\tbest: 0.1534618 (98)\ttotal: 126ms\tremaining: 1.27ms\n",
            "99:\tlearn: 0.1448801\ttest: 0.1533173\tbest: 0.1533173 (99)\ttotal: 127ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1533172507\n",
            "bestIteration = 99\n",
            "\n",
            "26:\tloss: 0.1533173\tbest: 0.1353408 (23)\ttotal: 3.75s\tremaining: 4.59s\n",
            "0:\tlearn: 0.5313338\ttest: 0.5353990\tbest: 0.5353990 (0)\ttotal: 1.08ms\tremaining: 108ms\n",
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            "99:\tlearn: 0.1015649\ttest: 0.1394094\tbest: 0.1367207 (88)\ttotal: 124ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1367206542\n",
            "bestIteration = 88\n",
            "\n",
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            "54:\tlearn: 0.1180320\ttest: 0.1504435\tbest: 0.1485219 (40)\ttotal: 64.5ms\tremaining: 52.8ms\n",
            "55:\tlearn: 0.1172114\ttest: 0.1489265\tbest: 0.1485219 (40)\ttotal: 65.7ms\tremaining: 51.6ms\n",
            "56:\tlearn: 0.1161694\ttest: 0.1478607\tbest: 0.1478607 (56)\ttotal: 66.9ms\tremaining: 50.4ms\n",
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            "58:\tlearn: 0.1143633\ttest: 0.1458893\tbest: 0.1458295 (57)\ttotal: 69.2ms\tremaining: 48.1ms\n",
            "59:\tlearn: 0.1127520\ttest: 0.1461814\tbest: 0.1458295 (57)\ttotal: 70.6ms\tremaining: 47ms\n",
            "60:\tlearn: 0.1115386\ttest: 0.1463743\tbest: 0.1458295 (57)\ttotal: 71.9ms\tremaining: 46ms\n",
            "61:\tlearn: 0.1111870\ttest: 0.1463418\tbest: 0.1458295 (57)\ttotal: 73.6ms\tremaining: 45.1ms\n",
            "62:\tlearn: 0.1102334\ttest: 0.1463846\tbest: 0.1458295 (57)\ttotal: 74.8ms\tremaining: 43.9ms\n",
            "63:\tlearn: 0.1086506\ttest: 0.1473942\tbest: 0.1458295 (57)\ttotal: 76ms\tremaining: 42.7ms\n",
            "64:\tlearn: 0.1078862\ttest: 0.1471561\tbest: 0.1458295 (57)\ttotal: 77.2ms\tremaining: 41.6ms\n",
            "65:\tlearn: 0.1069428\ttest: 0.1467049\tbest: 0.1458295 (57)\ttotal: 78.5ms\tremaining: 40.4ms\n",
            "66:\tlearn: 0.1062319\ttest: 0.1467829\tbest: 0.1458295 (57)\ttotal: 79.8ms\tremaining: 39.3ms\n",
            "67:\tlearn: 0.1060507\ttest: 0.1473464\tbest: 0.1458295 (57)\ttotal: 81.1ms\tremaining: 38.1ms\n",
            "68:\tlearn: 0.1053784\ttest: 0.1475111\tbest: 0.1458295 (57)\ttotal: 82.3ms\tremaining: 37ms\n",
            "69:\tlearn: 0.1045074\ttest: 0.1473150\tbest: 0.1458295 (57)\ttotal: 83.5ms\tremaining: 35.8ms\n",
            "70:\tlearn: 0.1028795\ttest: 0.1474115\tbest: 0.1458295 (57)\ttotal: 84.6ms\tremaining: 34.6ms\n",
            "71:\tlearn: 0.1025661\ttest: 0.1474726\tbest: 0.1458295 (57)\ttotal: 85.8ms\tremaining: 33.4ms\n",
            "72:\tlearn: 0.1015504\ttest: 0.1487870\tbest: 0.1458295 (57)\ttotal: 87.2ms\tremaining: 32.2ms\n",
            "73:\tlearn: 0.0992848\ttest: 0.1490394\tbest: 0.1458295 (57)\ttotal: 88.6ms\tremaining: 31.1ms\n",
            "74:\tlearn: 0.0987277\ttest: 0.1484388\tbest: 0.1458295 (57)\ttotal: 89.7ms\tremaining: 29.9ms\n",
            "75:\tlearn: 0.0976538\ttest: 0.1486965\tbest: 0.1458295 (57)\ttotal: 90.9ms\tremaining: 28.7ms\n",
            "76:\tlearn: 0.0951329\ttest: 0.1490687\tbest: 0.1458295 (57)\ttotal: 92.1ms\tremaining: 27.5ms\n",
            "77:\tlearn: 0.0947097\ttest: 0.1487036\tbest: 0.1458295 (57)\ttotal: 93.3ms\tremaining: 26.3ms\n",
            "78:\tlearn: 0.0935138\ttest: 0.1481627\tbest: 0.1458295 (57)\ttotal: 94.5ms\tremaining: 25.1ms\n",
            "79:\tlearn: 0.0927431\ttest: 0.1465868\tbest: 0.1458295 (57)\ttotal: 95.7ms\tremaining: 23.9ms\n",
            "80:\tlearn: 0.0922481\ttest: 0.1463177\tbest: 0.1458295 (57)\ttotal: 96.8ms\tremaining: 22.7ms\n",
            "81:\tlearn: 0.0915165\ttest: 0.1461258\tbest: 0.1458295 (57)\ttotal: 97.9ms\tremaining: 21.5ms\n",
            "82:\tlearn: 0.0907420\ttest: 0.1466899\tbest: 0.1458295 (57)\ttotal: 99.1ms\tremaining: 20.3ms\n",
            "83:\tlearn: 0.0906515\ttest: 0.1467722\tbest: 0.1458295 (57)\ttotal: 101ms\tremaining: 19.2ms\n",
            "84:\tlearn: 0.0897439\ttest: 0.1468640\tbest: 0.1458295 (57)\ttotal: 102ms\tremaining: 18ms\n",
            "85:\tlearn: 0.0894186\ttest: 0.1462214\tbest: 0.1458295 (57)\ttotal: 103ms\tremaining: 16.8ms\n",
            "86:\tlearn: 0.0885069\ttest: 0.1453959\tbest: 0.1453959 (86)\ttotal: 104ms\tremaining: 15.6ms\n",
            "87:\tlearn: 0.0870726\ttest: 0.1455123\tbest: 0.1453959 (86)\ttotal: 105ms\tremaining: 14.4ms\n",
            "88:\tlearn: 0.0851410\ttest: 0.1449988\tbest: 0.1449988 (88)\ttotal: 107ms\tremaining: 13.2ms\n",
            "89:\tlearn: 0.0837132\ttest: 0.1449027\tbest: 0.1449027 (89)\ttotal: 108ms\tremaining: 12ms\n",
            "90:\tlearn: 0.0824423\ttest: 0.1451197\tbest: 0.1449027 (89)\ttotal: 109ms\tremaining: 10.8ms\n",
            "91:\tlearn: 0.0817905\ttest: 0.1461367\tbest: 0.1449027 (89)\ttotal: 111ms\tremaining: 9.62ms\n",
            "92:\tlearn: 0.0813540\ttest: 0.1465652\tbest: 0.1449027 (89)\ttotal: 112ms\tremaining: 8.41ms\n",
            "93:\tlearn: 0.0804921\ttest: 0.1462779\tbest: 0.1449027 (89)\ttotal: 113ms\tremaining: 7.21ms\n",
            "94:\tlearn: 0.0798493\ttest: 0.1465780\tbest: 0.1449027 (89)\ttotal: 115ms\tremaining: 6.03ms\n",
            "95:\tlearn: 0.0788984\ttest: 0.1466542\tbest: 0.1449027 (89)\ttotal: 116ms\tremaining: 4.82ms\n",
            "96:\tlearn: 0.0778704\ttest: 0.1482638\tbest: 0.1449027 (89)\ttotal: 117ms\tremaining: 3.62ms\n",
            "97:\tlearn: 0.0772535\ttest: 0.1484055\tbest: 0.1449027 (89)\ttotal: 118ms\tremaining: 2.41ms\n",
            "98:\tlearn: 0.0766146\ttest: 0.1483293\tbest: 0.1449027 (89)\ttotal: 120ms\tremaining: 1.21ms\n",
            "99:\tlearn: 0.0756252\ttest: 0.1477288\tbest: 0.1449027 (89)\ttotal: 121ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1449027258\n",
            "bestIteration = 89\n",
            "\n",
            "28:\tloss: 0.1449027\tbest: 0.1353408 (23)\ttotal: 4.01s\tremaining: 4.29s\n",
            "0:\tlearn: 0.4232152\ttest: 0.4305716\tbest: 0.4305716 (0)\ttotal: 1.13ms\tremaining: 112ms\n",
            "1:\tlearn: 0.3264459\ttest: 0.3268264\tbest: 0.3268264 (1)\ttotal: 2.31ms\tremaining: 113ms\n",
            "2:\tlearn: 0.2920048\ttest: 0.2925335\tbest: 0.2925335 (2)\ttotal: 3.46ms\tremaining: 112ms\n",
            "3:\tlearn: 0.2656829\ttest: 0.2750346\tbest: 0.2750346 (3)\ttotal: 4.61ms\tremaining: 111ms\n",
            "4:\tlearn: 0.2462905\ttest: 0.2539553\tbest: 0.2539553 (4)\ttotal: 5.92ms\tremaining: 112ms\n",
            "5:\tlearn: 0.2305110\ttest: 0.2352183\tbest: 0.2352183 (5)\ttotal: 7.12ms\tremaining: 111ms\n",
            "6:\tlearn: 0.2118071\ttest: 0.2132201\tbest: 0.2132201 (6)\ttotal: 8.29ms\tremaining: 110ms\n",
            "7:\tlearn: 0.1945504\ttest: 0.1885178\tbest: 0.1885178 (7)\ttotal: 9.55ms\tremaining: 110ms\n",
            "8:\tlearn: 0.1893987\ttest: 0.1840574\tbest: 0.1840574 (8)\ttotal: 10.7ms\tremaining: 109ms\n",
            "9:\tlearn: 0.1839252\ttest: 0.1810107\tbest: 0.1810107 (9)\ttotal: 12.3ms\tremaining: 111ms\n",
            "10:\tlearn: 0.1779010\ttest: 0.1750341\tbest: 0.1750341 (10)\ttotal: 13.7ms\tremaining: 111ms\n",
            "11:\tlearn: 0.1753875\ttest: 0.1707107\tbest: 0.1707107 (11)\ttotal: 15.1ms\tremaining: 111ms\n",
            "12:\tlearn: 0.1691317\ttest: 0.1636638\tbest: 0.1636638 (12)\ttotal: 16.5ms\tremaining: 111ms\n",
            "13:\tlearn: 0.1671484\ttest: 0.1620980\tbest: 0.1620980 (13)\ttotal: 17.8ms\tremaining: 109ms\n",
            "14:\tlearn: 0.1668837\ttest: 0.1628193\tbest: 0.1620980 (13)\ttotal: 19ms\tremaining: 108ms\n",
            "15:\tlearn: 0.1637920\ttest: 0.1612521\tbest: 0.1612521 (15)\ttotal: 20.2ms\tremaining: 106ms\n",
            "16:\tlearn: 0.1601664\ttest: 0.1606652\tbest: 0.1606652 (16)\ttotal: 21.3ms\tremaining: 104ms\n",
            "17:\tlearn: 0.1584988\ttest: 0.1605376\tbest: 0.1605376 (17)\ttotal: 22.5ms\tremaining: 103ms\n",
            "18:\tlearn: 0.1572388\ttest: 0.1604952\tbest: 0.1604952 (18)\ttotal: 23.7ms\tremaining: 101ms\n",
            "19:\tlearn: 0.1547111\ttest: 0.1584452\tbest: 0.1584452 (19)\ttotal: 24.9ms\tremaining: 99.4ms\n",
            "20:\tlearn: 0.1496708\ttest: 0.1543983\tbest: 0.1543983 (20)\ttotal: 26ms\tremaining: 98ms\n",
            "21:\tlearn: 0.1471268\ttest: 0.1529182\tbest: 0.1529182 (21)\ttotal: 27.2ms\tremaining: 96.4ms\n",
            "22:\tlearn: 0.1446649\ttest: 0.1527768\tbest: 0.1527768 (22)\ttotal: 28.3ms\tremaining: 94.7ms\n",
            "23:\tlearn: 0.1414275\ttest: 0.1497895\tbest: 0.1497895 (23)\ttotal: 29.5ms\tremaining: 93.3ms\n",
            "24:\tlearn: 0.1377722\ttest: 0.1438031\tbest: 0.1438031 (24)\ttotal: 30.6ms\tremaining: 91.9ms\n",
            "25:\tlearn: 0.1355835\ttest: 0.1437084\tbest: 0.1437084 (25)\ttotal: 31.7ms\tremaining: 90.4ms\n",
            "26:\tlearn: 0.1341053\ttest: 0.1409679\tbest: 0.1409679 (26)\ttotal: 32.9ms\tremaining: 89ms\n",
            "27:\tlearn: 0.1333340\ttest: 0.1407988\tbest: 0.1407988 (27)\ttotal: 34.1ms\tremaining: 87.8ms\n",
            "28:\tlearn: 0.1331448\ttest: 0.1413178\tbest: 0.1407988 (27)\ttotal: 35.3ms\tremaining: 86.3ms\n",
            "29:\tlearn: 0.1312535\ttest: 0.1426270\tbest: 0.1407988 (27)\ttotal: 36.5ms\tremaining: 85.1ms\n",
            "30:\tlearn: 0.1291360\ttest: 0.1435360\tbest: 0.1407988 (27)\ttotal: 37.7ms\tremaining: 83.9ms\n",
            "31:\tlearn: 0.1264262\ttest: 0.1459707\tbest: 0.1407988 (27)\ttotal: 38.8ms\tremaining: 82.5ms\n",
            "32:\tlearn: 0.1255158\ttest: 0.1461587\tbest: 0.1407988 (27)\ttotal: 40ms\tremaining: 81.2ms\n",
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            "34:\tlearn: 0.1217242\ttest: 0.1455784\tbest: 0.1407988 (27)\ttotal: 42.4ms\tremaining: 78.7ms\n",
            "35:\tlearn: 0.1196402\ttest: 0.1421341\tbest: 0.1407988 (27)\ttotal: 43.6ms\tremaining: 77.4ms\n",
            "36:\tlearn: 0.1181264\ttest: 0.1427896\tbest: 0.1407988 (27)\ttotal: 44.7ms\tremaining: 76.1ms\n",
            "37:\tlearn: 0.1160006\ttest: 0.1440451\tbest: 0.1407988 (27)\ttotal: 45.9ms\tremaining: 74.8ms\n",
            "38:\tlearn: 0.1159412\ttest: 0.1442605\tbest: 0.1407988 (27)\ttotal: 47ms\tremaining: 73.5ms\n",
            "39:\tlearn: 0.1147227\ttest: 0.1452567\tbest: 0.1407988 (27)\ttotal: 48.2ms\tremaining: 72.2ms\n",
            "40:\tlearn: 0.1132106\ttest: 0.1469712\tbest: 0.1407988 (27)\ttotal: 49.3ms\tremaining: 71ms\n",
            "41:\tlearn: 0.1120551\ttest: 0.1443549\tbest: 0.1407988 (27)\ttotal: 50.5ms\tremaining: 69.7ms\n",
            "42:\tlearn: 0.1077514\ttest: 0.1452477\tbest: 0.1407988 (27)\ttotal: 51.7ms\tremaining: 68.5ms\n",
            "43:\tlearn: 0.1057686\ttest: 0.1419649\tbest: 0.1407988 (27)\ttotal: 53ms\tremaining: 67.4ms\n",
            "44:\tlearn: 0.1050521\ttest: 0.1418367\tbest: 0.1407988 (27)\ttotal: 54.1ms\tremaining: 66.1ms\n",
            "45:\tlearn: 0.1048647\ttest: 0.1416351\tbest: 0.1407988 (27)\ttotal: 55.3ms\tremaining: 64.9ms\n",
            "46:\tlearn: 0.1046974\ttest: 0.1420678\tbest: 0.1407988 (27)\ttotal: 56.4ms\tremaining: 63.6ms\n",
            "47:\tlearn: 0.1025273\ttest: 0.1432624\tbest: 0.1407988 (27)\ttotal: 57.7ms\tremaining: 62.5ms\n",
            "48:\tlearn: 0.1017190\ttest: 0.1428687\tbest: 0.1407988 (27)\ttotal: 58.8ms\tremaining: 61.2ms\n",
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            "50:\tlearn: 0.1004470\ttest: 0.1451030\tbest: 0.1407988 (27)\ttotal: 61.2ms\tremaining: 58.8ms\n",
            "51:\tlearn: 0.0988953\ttest: 0.1456548\tbest: 0.1407988 (27)\ttotal: 62.4ms\tremaining: 57.6ms\n",
            "52:\tlearn: 0.0983000\ttest: 0.1455042\tbest: 0.1407988 (27)\ttotal: 63.6ms\tremaining: 56.4ms\n",
            "53:\tlearn: 0.0973764\ttest: 0.1460427\tbest: 0.1407988 (27)\ttotal: 64.7ms\tremaining: 55.1ms\n",
            "54:\tlearn: 0.0960036\ttest: 0.1472389\tbest: 0.1407988 (27)\ttotal: 65.9ms\tremaining: 53.9ms\n",
            "55:\tlearn: 0.0943948\ttest: 0.1468648\tbest: 0.1407988 (27)\ttotal: 67.2ms\tremaining: 52.8ms\n",
            "56:\tlearn: 0.0930223\ttest: 0.1458061\tbest: 0.1407988 (27)\ttotal: 68.4ms\tremaining: 51.6ms\n",
            "57:\tlearn: 0.0911836\ttest: 0.1445482\tbest: 0.1407988 (27)\ttotal: 69.5ms\tremaining: 50.3ms\n",
            "58:\tlearn: 0.0908798\ttest: 0.1445520\tbest: 0.1407988 (27)\ttotal: 70.6ms\tremaining: 49.1ms\n",
            "59:\tlearn: 0.0891413\ttest: 0.1455906\tbest: 0.1407988 (27)\ttotal: 71.9ms\tremaining: 48ms\n",
            "60:\tlearn: 0.0883338\ttest: 0.1475557\tbest: 0.1407988 (27)\ttotal: 73.2ms\tremaining: 46.8ms\n",
            "61:\tlearn: 0.0870824\ttest: 0.1475813\tbest: 0.1407988 (27)\ttotal: 74.4ms\tremaining: 45.6ms\n",
            "62:\tlearn: 0.0857396\ttest: 0.1473027\tbest: 0.1407988 (27)\ttotal: 75.5ms\tremaining: 44.4ms\n",
            "63:\tlearn: 0.0851675\ttest: 0.1474561\tbest: 0.1407988 (27)\ttotal: 76.7ms\tremaining: 43.2ms\n",
            "64:\tlearn: 0.0839358\ttest: 0.1466755\tbest: 0.1407988 (27)\ttotal: 78ms\tremaining: 42ms\n",
            "65:\tlearn: 0.0832565\ttest: 0.1464177\tbest: 0.1407988 (27)\ttotal: 79.2ms\tremaining: 40.8ms\n",
            "66:\tlearn: 0.0819381\ttest: 0.1478116\tbest: 0.1407988 (27)\ttotal: 80.4ms\tremaining: 39.6ms\n",
            "67:\tlearn: 0.0812884\ttest: 0.1478095\tbest: 0.1407988 (27)\ttotal: 81.5ms\tremaining: 38.4ms\n",
            "68:\tlearn: 0.0807239\ttest: 0.1477411\tbest: 0.1407988 (27)\ttotal: 82.7ms\tremaining: 37.2ms\n",
            "69:\tlearn: 0.0795990\ttest: 0.1482851\tbest: 0.1407988 (27)\ttotal: 83.9ms\tremaining: 35.9ms\n",
            "70:\tlearn: 0.0780833\ttest: 0.1474397\tbest: 0.1407988 (27)\ttotal: 84.9ms\tremaining: 34.7ms\n",
            "71:\tlearn: 0.0764103\ttest: 0.1445113\tbest: 0.1407988 (27)\ttotal: 86.1ms\tremaining: 33.5ms\n",
            "72:\tlearn: 0.0760773\ttest: 0.1455171\tbest: 0.1407988 (27)\ttotal: 87.3ms\tremaining: 32.3ms\n",
            "73:\tlearn: 0.0744281\ttest: 0.1477941\tbest: 0.1407988 (27)\ttotal: 88.5ms\tremaining: 31.1ms\n",
            "74:\tlearn: 0.0733874\ttest: 0.1476256\tbest: 0.1407988 (27)\ttotal: 89.6ms\tremaining: 29.9ms\n",
            "75:\tlearn: 0.0726260\ttest: 0.1458524\tbest: 0.1407988 (27)\ttotal: 90.8ms\tremaining: 28.7ms\n",
            "76:\tlearn: 0.0724024\ttest: 0.1461689\tbest: 0.1407988 (27)\ttotal: 92ms\tremaining: 27.5ms\n",
            "77:\tlearn: 0.0705265\ttest: 0.1473400\tbest: 0.1407988 (27)\ttotal: 93.3ms\tremaining: 26.3ms\n",
            "78:\tlearn: 0.0692077\ttest: 0.1470076\tbest: 0.1407988 (27)\ttotal: 94.5ms\tremaining: 25.1ms\n",
            "79:\tlearn: 0.0684732\ttest: 0.1472575\tbest: 0.1407988 (27)\ttotal: 95.7ms\tremaining: 23.9ms\n",
            "80:\tlearn: 0.0672533\ttest: 0.1478047\tbest: 0.1407988 (27)\ttotal: 96.9ms\tremaining: 22.7ms\n",
            "81:\tlearn: 0.0657374\ttest: 0.1484449\tbest: 0.1407988 (27)\ttotal: 98.2ms\tremaining: 21.6ms\n",
            "82:\tlearn: 0.0649768\ttest: 0.1474437\tbest: 0.1407988 (27)\ttotal: 99.4ms\tremaining: 20.4ms\n",
            "83:\tlearn: 0.0639998\ttest: 0.1476354\tbest: 0.1407988 (27)\ttotal: 100ms\tremaining: 19.1ms\n",
            "84:\tlearn: 0.0621771\ttest: 0.1444481\tbest: 0.1407988 (27)\ttotal: 102ms\tremaining: 17.9ms\n",
            "85:\tlearn: 0.0618325\ttest: 0.1441320\tbest: 0.1407988 (27)\ttotal: 103ms\tremaining: 16.7ms\n",
            "86:\tlearn: 0.0610212\ttest: 0.1444246\tbest: 0.1407988 (27)\ttotal: 104ms\tremaining: 15.5ms\n",
            "87:\tlearn: 0.0601368\ttest: 0.1454547\tbest: 0.1407988 (27)\ttotal: 105ms\tremaining: 14.3ms\n",
            "88:\tlearn: 0.0587932\ttest: 0.1490008\tbest: 0.1407988 (27)\ttotal: 106ms\tremaining: 13.2ms\n",
            "89:\tlearn: 0.0575709\ttest: 0.1470146\tbest: 0.1407988 (27)\ttotal: 108ms\tremaining: 12ms\n",
            "90:\tlearn: 0.0568668\ttest: 0.1478827\tbest: 0.1407988 (27)\ttotal: 109ms\tremaining: 10.8ms\n",
            "91:\tlearn: 0.0564319\ttest: 0.1488959\tbest: 0.1407988 (27)\ttotal: 110ms\tremaining: 9.56ms\n",
            "92:\tlearn: 0.0558345\ttest: 0.1473300\tbest: 0.1407988 (27)\ttotal: 111ms\tremaining: 8.37ms\n",
            "93:\tlearn: 0.0555303\ttest: 0.1477546\tbest: 0.1407988 (27)\ttotal: 112ms\tremaining: 7.17ms\n",
            "94:\tlearn: 0.0545037\ttest: 0.1473599\tbest: 0.1407988 (27)\ttotal: 114ms\tremaining: 5.98ms\n",
            "95:\tlearn: 0.0537699\ttest: 0.1472164\tbest: 0.1407988 (27)\ttotal: 115ms\tremaining: 4.78ms\n",
            "96:\tlearn: 0.0537027\ttest: 0.1473228\tbest: 0.1407988 (27)\ttotal: 116ms\tremaining: 3.58ms\n",
            "97:\tlearn: 0.0535234\ttest: 0.1475154\tbest: 0.1407988 (27)\ttotal: 117ms\tremaining: 2.39ms\n",
            "98:\tlearn: 0.0528340\ttest: 0.1486147\tbest: 0.1407988 (27)\ttotal: 118ms\tremaining: 1.19ms\n",
            "99:\tlearn: 0.0518141\ttest: 0.1476044\tbest: 0.1407988 (27)\ttotal: 119ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1407988423\n",
            "bestIteration = 27\n",
            "\n",
            "29:\tloss: 0.1407988\tbest: 0.1353408 (23)\ttotal: 4.14s\tremaining: 4.14s\n",
            "0:\tlearn: 0.6922046\ttest: 0.6922272\tbest: 0.6922272 (0)\ttotal: 1.33ms\tremaining: 132ms\n",
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            "\n",
            "bestTest = 0.6214024666\n",
            "bestIteration = 99\n",
            "\n",
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            "55:\tlearn: 0.1759753\ttest: 0.1741902\tbest: 0.1741902 (55)\ttotal: 113ms\tremaining: 88.8ms\n",
            "56:\tlearn: 0.1757344\ttest: 0.1738447\tbest: 0.1738447 (56)\ttotal: 114ms\tremaining: 86.2ms\n",
            "57:\tlearn: 0.1745088\ttest: 0.1727447\tbest: 0.1727447 (57)\ttotal: 116ms\tremaining: 83.8ms\n",
            "58:\tlearn: 0.1742037\ttest: 0.1725678\tbest: 0.1725678 (58)\ttotal: 118ms\tremaining: 82ms\n",
            "59:\tlearn: 0.1734396\ttest: 0.1718521\tbest: 0.1718521 (59)\ttotal: 120ms\tremaining: 79.9ms\n",
            "60:\tlearn: 0.1726130\ttest: 0.1707467\tbest: 0.1707467 (60)\ttotal: 122ms\tremaining: 77.9ms\n",
            "61:\tlearn: 0.1720514\ttest: 0.1706462\tbest: 0.1706462 (61)\ttotal: 124ms\tremaining: 75.8ms\n",
            "62:\tlearn: 0.1715623\ttest: 0.1710178\tbest: 0.1706462 (61)\ttotal: 125ms\tremaining: 73.7ms\n",
            "63:\tlearn: 0.1706379\ttest: 0.1697155\tbest: 0.1697155 (63)\ttotal: 128ms\tremaining: 71.8ms\n",
            "64:\tlearn: 0.1704430\ttest: 0.1696796\tbest: 0.1696796 (64)\ttotal: 129ms\tremaining: 69.5ms\n",
            "65:\tlearn: 0.1698336\ttest: 0.1694641\tbest: 0.1694641 (65)\ttotal: 132ms\tremaining: 67.8ms\n",
            "66:\tlearn: 0.1691538\ttest: 0.1695684\tbest: 0.1694641 (65)\ttotal: 133ms\tremaining: 65.5ms\n",
            "67:\tlearn: 0.1685792\ttest: 0.1691124\tbest: 0.1691124 (67)\ttotal: 134ms\tremaining: 63.3ms\n",
            "68:\tlearn: 0.1683435\ttest: 0.1686145\tbest: 0.1686145 (68)\ttotal: 136ms\tremaining: 61.3ms\n",
            "69:\tlearn: 0.1680110\ttest: 0.1687207\tbest: 0.1686145 (68)\ttotal: 138ms\tremaining: 59.2ms\n",
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            "71:\tlearn: 0.1654406\ttest: 0.1657458\tbest: 0.1657458 (71)\ttotal: 142ms\tremaining: 55.1ms\n",
            "72:\tlearn: 0.1652794\ttest: 0.1656185\tbest: 0.1656185 (72)\ttotal: 143ms\tremaining: 52.9ms\n",
            "73:\tlearn: 0.1646886\ttest: 0.1655697\tbest: 0.1655697 (73)\ttotal: 145ms\tremaining: 50.9ms\n",
            "74:\tlearn: 0.1638501\ttest: 0.1653198\tbest: 0.1653198 (74)\ttotal: 146ms\tremaining: 48.8ms\n",
            "75:\tlearn: 0.1637058\ttest: 0.1652865\tbest: 0.1652865 (75)\ttotal: 148ms\tremaining: 46.7ms\n",
            "76:\tlearn: 0.1632591\ttest: 0.1652180\tbest: 0.1652180 (76)\ttotal: 149ms\tremaining: 44.5ms\n",
            "77:\tlearn: 0.1621879\ttest: 0.1640977\tbest: 0.1640977 (77)\ttotal: 150ms\tremaining: 42.4ms\n",
            "78:\tlearn: 0.1619463\ttest: 0.1638570\tbest: 0.1638570 (78)\ttotal: 152ms\tremaining: 40.4ms\n",
            "79:\tlearn: 0.1618527\ttest: 0.1638120\tbest: 0.1638120 (79)\ttotal: 153ms\tremaining: 38.4ms\n",
            "80:\tlearn: 0.1612254\ttest: 0.1632989\tbest: 0.1632989 (80)\ttotal: 155ms\tremaining: 36.4ms\n",
            "81:\tlearn: 0.1602742\ttest: 0.1623152\tbest: 0.1623152 (81)\ttotal: 157ms\tremaining: 34.4ms\n",
            "82:\tlearn: 0.1595834\ttest: 0.1625711\tbest: 0.1623152 (81)\ttotal: 158ms\tremaining: 32.4ms\n",
            "83:\tlearn: 0.1594735\ttest: 0.1625043\tbest: 0.1623152 (81)\ttotal: 160ms\tremaining: 30.4ms\n",
            "84:\tlearn: 0.1587789\ttest: 0.1617513\tbest: 0.1617513 (84)\ttotal: 162ms\tremaining: 28.5ms\n",
            "85:\tlearn: 0.1586931\ttest: 0.1618671\tbest: 0.1617513 (84)\ttotal: 163ms\tremaining: 26.6ms\n",
            "86:\tlearn: 0.1577326\ttest: 0.1609511\tbest: 0.1609511 (86)\ttotal: 164ms\tremaining: 24.6ms\n",
            "87:\tlearn: 0.1570913\ttest: 0.1609898\tbest: 0.1609511 (86)\ttotal: 173ms\tremaining: 23.5ms\n",
            "88:\tlearn: 0.1561546\ttest: 0.1596637\tbest: 0.1596637 (88)\ttotal: 174ms\tremaining: 21.5ms\n",
            "89:\tlearn: 0.1553975\ttest: 0.1590394\tbest: 0.1590394 (89)\ttotal: 175ms\tremaining: 19.4ms\n",
            "90:\tlearn: 0.1548609\ttest: 0.1593081\tbest: 0.1590394 (89)\ttotal: 176ms\tremaining: 17.4ms\n",
            "91:\tlearn: 0.1547827\ttest: 0.1593403\tbest: 0.1590394 (89)\ttotal: 177ms\tremaining: 15.4ms\n",
            "92:\tlearn: 0.1541993\ttest: 0.1592462\tbest: 0.1590394 (89)\ttotal: 179ms\tremaining: 13.4ms\n",
            "93:\tlearn: 0.1538783\ttest: 0.1591328\tbest: 0.1590394 (89)\ttotal: 180ms\tremaining: 11.5ms\n",
            "94:\tlearn: 0.1534655\ttest: 0.1583985\tbest: 0.1583985 (94)\ttotal: 181ms\tremaining: 9.52ms\n",
            "95:\tlearn: 0.1530503\ttest: 0.1585011\tbest: 0.1583985 (94)\ttotal: 182ms\tremaining: 7.59ms\n",
            "96:\tlearn: 0.1523413\ttest: 0.1584203\tbest: 0.1583985 (94)\ttotal: 184ms\tremaining: 5.68ms\n",
            "97:\tlearn: 0.1522194\ttest: 0.1584182\tbest: 0.1583985 (94)\ttotal: 185ms\tremaining: 3.78ms\n",
            "98:\tlearn: 0.1521157\ttest: 0.1585038\tbest: 0.1583985 (94)\ttotal: 187ms\tremaining: 1.89ms\n",
            "99:\tlearn: 0.1516396\ttest: 0.1582076\tbest: 0.1582076 (99)\ttotal: 189ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1582075697\n",
            "bestIteration = 99\n",
            "\n",
            "31:\tloss: 0.1582076\tbest: 0.1353408 (23)\ttotal: 4.48s\tremaining: 3.92s\n",
            "0:\tlearn: 0.5317924\ttest: 0.5359179\tbest: 0.5359179 (0)\ttotal: 1.32ms\tremaining: 131ms\n",
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            "2:\tlearn: 0.4007176\ttest: 0.4020759\tbest: 0.4020759 (2)\ttotal: 4.05ms\tremaining: 131ms\n",
            "3:\tlearn: 0.3640227\ttest: 0.3630823\tbest: 0.3630823 (3)\ttotal: 5.39ms\tremaining: 129ms\n",
            "4:\tlearn: 0.3349485\ttest: 0.3389314\tbest: 0.3389314 (4)\ttotal: 6.57ms\tremaining: 125ms\n",
            "5:\tlearn: 0.3085252\ttest: 0.3096978\tbest: 0.3096978 (5)\ttotal: 7.98ms\tremaining: 125ms\n",
            "6:\tlearn: 0.2895718\ttest: 0.2850078\tbest: 0.2850078 (6)\ttotal: 9.33ms\tremaining: 124ms\n",
            "7:\tlearn: 0.2787928\ttest: 0.2761022\tbest: 0.2761022 (7)\ttotal: 10.9ms\tremaining: 126ms\n",
            "8:\tlearn: 0.2682900\ttest: 0.2636699\tbest: 0.2636699 (8)\ttotal: 12.4ms\tremaining: 126ms\n",
            "9:\tlearn: 0.2597666\ttest: 0.2553333\tbest: 0.2553333 (9)\ttotal: 13.8ms\tremaining: 124ms\n",
            "10:\tlearn: 0.2526177\ttest: 0.2479351\tbest: 0.2479351 (10)\ttotal: 15ms\tremaining: 121ms\n",
            "11:\tlearn: 0.2468482\ttest: 0.2444720\tbest: 0.2444720 (11)\ttotal: 16.2ms\tremaining: 119ms\n",
            "12:\tlearn: 0.2337003\ttest: 0.2296111\tbest: 0.2296111 (12)\ttotal: 17.5ms\tremaining: 117ms\n",
            "13:\tlearn: 0.2295405\ttest: 0.2241151\tbest: 0.2241151 (13)\ttotal: 18.7ms\tremaining: 115ms\n",
            "14:\tlearn: 0.2187063\ttest: 0.2118467\tbest: 0.2118467 (14)\ttotal: 20ms\tremaining: 113ms\n",
            "15:\tlearn: 0.2149255\ttest: 0.2078706\tbest: 0.2078706 (15)\ttotal: 21.2ms\tremaining: 111ms\n",
            "16:\tlearn: 0.2138858\ttest: 0.2070876\tbest: 0.2070876 (16)\ttotal: 22.5ms\tremaining: 110ms\n",
            "17:\tlearn: 0.2108343\ttest: 0.2042146\tbest: 0.2042146 (17)\ttotal: 23.9ms\tremaining: 109ms\n",
            "18:\tlearn: 0.2101334\ttest: 0.2040321\tbest: 0.2040321 (18)\ttotal: 25.4ms\tremaining: 108ms\n",
            "19:\tlearn: 0.2045613\ttest: 0.1984978\tbest: 0.1984978 (19)\ttotal: 27.2ms\tremaining: 109ms\n",
            "20:\tlearn: 0.2007735\ttest: 0.1936079\tbest: 0.1936079 (20)\ttotal: 28.5ms\tremaining: 107ms\n",
            "21:\tlearn: 0.1992106\ttest: 0.1919134\tbest: 0.1919134 (21)\ttotal: 29.7ms\tremaining: 105ms\n",
            "22:\tlearn: 0.1960691\ttest: 0.1897751\tbest: 0.1897751 (22)\ttotal: 30.8ms\tremaining: 103ms\n",
            "23:\tlearn: 0.1918212\ttest: 0.1854602\tbest: 0.1854602 (23)\ttotal: 32.1ms\tremaining: 102ms\n",
            "24:\tlearn: 0.1904952\ttest: 0.1838683\tbest: 0.1838683 (24)\ttotal: 33.3ms\tremaining: 99.8ms\n",
            "25:\tlearn: 0.1881779\ttest: 0.1815496\tbest: 0.1815496 (25)\ttotal: 34.4ms\tremaining: 98ms\n",
            "26:\tlearn: 0.1845616\ttest: 0.1784054\tbest: 0.1784054 (26)\ttotal: 35.6ms\tremaining: 96.3ms\n",
            "27:\tlearn: 0.1833715\ttest: 0.1777018\tbest: 0.1777018 (27)\ttotal: 36.8ms\tremaining: 94.5ms\n",
            "28:\tlearn: 0.1815376\ttest: 0.1756337\tbest: 0.1756337 (28)\ttotal: 38.5ms\tremaining: 94.2ms\n",
            "29:\tlearn: 0.1812948\ttest: 0.1757898\tbest: 0.1756337 (28)\ttotal: 40.1ms\tremaining: 93.7ms\n",
            "30:\tlearn: 0.1774893\ttest: 0.1707490\tbest: 0.1707490 (30)\ttotal: 41.9ms\tremaining: 93.3ms\n",
            "31:\tlearn: 0.1766631\ttest: 0.1695778\tbest: 0.1695778 (31)\ttotal: 43.6ms\tremaining: 92.7ms\n",
            "32:\tlearn: 0.1736318\ttest: 0.1666285\tbest: 0.1666285 (32)\ttotal: 44.9ms\tremaining: 91.1ms\n",
            "33:\tlearn: 0.1718092\ttest: 0.1669515\tbest: 0.1666285 (32)\ttotal: 46.1ms\tremaining: 89.5ms\n",
            "34:\tlearn: 0.1711240\ttest: 0.1661459\tbest: 0.1661459 (34)\ttotal: 47.3ms\tremaining: 87.8ms\n",
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            "36:\tlearn: 0.1693206\ttest: 0.1654587\tbest: 0.1654587 (36)\ttotal: 49.7ms\tremaining: 84.6ms\n",
            "37:\tlearn: 0.1691700\ttest: 0.1655103\tbest: 0.1654587 (36)\ttotal: 50.8ms\tremaining: 82.9ms\n",
            "38:\tlearn: 0.1688164\ttest: 0.1657208\tbest: 0.1654587 (36)\ttotal: 52ms\tremaining: 81.3ms\n",
            "39:\tlearn: 0.1685667\ttest: 0.1657381\tbest: 0.1654587 (36)\ttotal: 53.2ms\tremaining: 79.8ms\n",
            "40:\tlearn: 0.1678943\ttest: 0.1659319\tbest: 0.1654587 (36)\ttotal: 54.6ms\tremaining: 78.6ms\n",
            "41:\tlearn: 0.1655991\ttest: 0.1641914\tbest: 0.1641914 (41)\ttotal: 56ms\tremaining: 77.4ms\n",
            "42:\tlearn: 0.1650842\ttest: 0.1641260\tbest: 0.1641260 (42)\ttotal: 57.9ms\tremaining: 76.8ms\n",
            "43:\tlearn: 0.1637628\ttest: 0.1639880\tbest: 0.1639880 (43)\ttotal: 59.4ms\tremaining: 75.6ms\n",
            "44:\tlearn: 0.1608998\ttest: 0.1603579\tbest: 0.1603579 (44)\ttotal: 60.7ms\tremaining: 74.1ms\n",
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            "46:\tlearn: 0.1573248\ttest: 0.1570333\tbest: 0.1570333 (46)\ttotal: 63.1ms\tremaining: 71.2ms\n",
            "47:\tlearn: 0.1554377\ttest: 0.1556913\tbest: 0.1556913 (47)\ttotal: 64.3ms\tremaining: 69.7ms\n",
            "48:\tlearn: 0.1553418\ttest: 0.1558396\tbest: 0.1556913 (47)\ttotal: 65.5ms\tremaining: 68.1ms\n",
            "49:\tlearn: 0.1537608\ttest: 0.1540175\tbest: 0.1540175 (49)\ttotal: 66.6ms\tremaining: 66.6ms\n",
            "50:\tlearn: 0.1524077\ttest: 0.1531169\tbest: 0.1531169 (50)\ttotal: 67.8ms\tremaining: 65.1ms\n",
            "51:\tlearn: 0.1517147\ttest: 0.1529178\tbest: 0.1529178 (51)\ttotal: 69.1ms\tremaining: 63.8ms\n",
            "52:\tlearn: 0.1515200\ttest: 0.1530688\tbest: 0.1529178 (51)\ttotal: 70.3ms\tremaining: 62.4ms\n",
            "53:\tlearn: 0.1495336\ttest: 0.1514244\tbest: 0.1514244 (53)\ttotal: 71.8ms\tremaining: 61.2ms\n",
            "54:\tlearn: 0.1482700\ttest: 0.1513989\tbest: 0.1513989 (54)\ttotal: 73.3ms\tremaining: 60ms\n",
            "55:\tlearn: 0.1474632\ttest: 0.1515060\tbest: 0.1513989 (54)\ttotal: 74.8ms\tremaining: 58.8ms\n",
            "56:\tlearn: 0.1461093\ttest: 0.1513303\tbest: 0.1513303 (56)\ttotal: 76ms\tremaining: 57.4ms\n",
            "57:\tlearn: 0.1450965\ttest: 0.1522232\tbest: 0.1513303 (56)\ttotal: 77.2ms\tremaining: 55.9ms\n",
            "58:\tlearn: 0.1449492\ttest: 0.1522070\tbest: 0.1513303 (56)\ttotal: 78.6ms\tremaining: 54.6ms\n",
            "59:\tlearn: 0.1447637\ttest: 0.1522419\tbest: 0.1513303 (56)\ttotal: 80.1ms\tremaining: 53.4ms\n",
            "60:\tlearn: 0.1431605\ttest: 0.1511871\tbest: 0.1511871 (60)\ttotal: 81.5ms\tremaining: 52.1ms\n",
            "61:\tlearn: 0.1423382\ttest: 0.1510730\tbest: 0.1510730 (61)\ttotal: 82.7ms\tremaining: 50.7ms\n",
            "62:\tlearn: 0.1417983\ttest: 0.1507797\tbest: 0.1507797 (62)\ttotal: 83.8ms\tremaining: 49.2ms\n",
            "63:\tlearn: 0.1407163\ttest: 0.1502990\tbest: 0.1502990 (63)\ttotal: 85.3ms\tremaining: 48ms\n",
            "64:\tlearn: 0.1394695\ttest: 0.1488721\tbest: 0.1488721 (64)\ttotal: 86.8ms\tremaining: 46.8ms\n",
            "65:\tlearn: 0.1383936\ttest: 0.1475075\tbest: 0.1475075 (65)\ttotal: 88.3ms\tremaining: 45.5ms\n",
            "66:\tlearn: 0.1372689\ttest: 0.1483091\tbest: 0.1475075 (65)\ttotal: 89.9ms\tremaining: 44.3ms\n",
            "67:\tlearn: 0.1358631\ttest: 0.1494389\tbest: 0.1475075 (65)\ttotal: 91.2ms\tremaining: 42.9ms\n",
            "68:\tlearn: 0.1347970\ttest: 0.1481271\tbest: 0.1475075 (65)\ttotal: 92.5ms\tremaining: 41.6ms\n",
            "69:\tlearn: 0.1341322\ttest: 0.1484331\tbest: 0.1475075 (65)\ttotal: 93.9ms\tremaining: 40.2ms\n",
            "70:\tlearn: 0.1306520\ttest: 0.1445209\tbest: 0.1445209 (70)\ttotal: 95.2ms\tremaining: 38.9ms\n",
            "71:\tlearn: 0.1300740\ttest: 0.1442573\tbest: 0.1442573 (71)\ttotal: 96.4ms\tremaining: 37.5ms\n",
            "72:\tlearn: 0.1283214\ttest: 0.1437254\tbest: 0.1437254 (72)\ttotal: 97.6ms\tremaining: 36.1ms\n",
            "73:\tlearn: 0.1260477\ttest: 0.1437783\tbest: 0.1437254 (72)\ttotal: 98.7ms\tremaining: 34.7ms\n",
            "74:\tlearn: 0.1251847\ttest: 0.1422023\tbest: 0.1422023 (74)\ttotal: 99.9ms\tremaining: 33.3ms\n",
            "75:\tlearn: 0.1245413\ttest: 0.1418061\tbest: 0.1418061 (75)\ttotal: 101ms\tremaining: 32ms\n",
            "76:\tlearn: 0.1231058\ttest: 0.1423437\tbest: 0.1418061 (75)\ttotal: 103ms\tremaining: 30.6ms\n",
            "77:\tlearn: 0.1230610\ttest: 0.1424780\tbest: 0.1418061 (75)\ttotal: 104ms\tremaining: 29.2ms\n",
            "78:\tlearn: 0.1222209\ttest: 0.1433338\tbest: 0.1418061 (75)\ttotal: 105ms\tremaining: 27.9ms\n",
            "79:\tlearn: 0.1208363\ttest: 0.1434519\tbest: 0.1418061 (75)\ttotal: 106ms\tremaining: 26.5ms\n",
            "80:\tlearn: 0.1203376\ttest: 0.1426506\tbest: 0.1418061 (75)\ttotal: 107ms\tremaining: 25.1ms\n",
            "81:\tlearn: 0.1186738\ttest: 0.1414713\tbest: 0.1414713 (81)\ttotal: 108ms\tremaining: 23.8ms\n",
            "82:\tlearn: 0.1183675\ttest: 0.1414838\tbest: 0.1414713 (81)\ttotal: 110ms\tremaining: 22.4ms\n",
            "83:\tlearn: 0.1175808\ttest: 0.1409629\tbest: 0.1409629 (83)\ttotal: 111ms\tremaining: 21.1ms\n",
            "84:\tlearn: 0.1167856\ttest: 0.1410181\tbest: 0.1409629 (83)\ttotal: 112ms\tremaining: 19.7ms\n",
            "85:\tlearn: 0.1166091\ttest: 0.1410083\tbest: 0.1409629 (83)\ttotal: 113ms\tremaining: 18.4ms\n",
            "86:\tlearn: 0.1160570\ttest: 0.1415707\tbest: 0.1409629 (83)\ttotal: 114ms\tremaining: 17ms\n",
            "87:\tlearn: 0.1158047\ttest: 0.1414347\tbest: 0.1409629 (83)\ttotal: 115ms\tremaining: 15.7ms\n",
            "88:\tlearn: 0.1148242\ttest: 0.1415447\tbest: 0.1409629 (83)\ttotal: 117ms\tremaining: 14.4ms\n",
            "89:\tlearn: 0.1132824\ttest: 0.1418139\tbest: 0.1409629 (83)\ttotal: 118ms\tremaining: 13.1ms\n",
            "90:\tlearn: 0.1131634\ttest: 0.1418830\tbest: 0.1409629 (83)\ttotal: 119ms\tremaining: 11.8ms\n",
            "91:\tlearn: 0.1126575\ttest: 0.1422029\tbest: 0.1409629 (83)\ttotal: 120ms\tremaining: 10.5ms\n",
            "92:\tlearn: 0.1118896\ttest: 0.1427362\tbest: 0.1409629 (83)\ttotal: 122ms\tremaining: 9.16ms\n",
            "93:\tlearn: 0.1111504\ttest: 0.1423933\tbest: 0.1409629 (83)\ttotal: 123ms\tremaining: 7.84ms\n",
            "94:\tlearn: 0.1111077\ttest: 0.1425322\tbest: 0.1409629 (83)\ttotal: 124ms\tremaining: 6.53ms\n",
            "95:\tlearn: 0.1106373\ttest: 0.1425836\tbest: 0.1409629 (83)\ttotal: 125ms\tremaining: 5.22ms\n",
            "96:\tlearn: 0.1099496\ttest: 0.1430793\tbest: 0.1409629 (83)\ttotal: 126ms\tremaining: 3.91ms\n",
            "97:\tlearn: 0.1098231\ttest: 0.1431263\tbest: 0.1409629 (83)\ttotal: 128ms\tremaining: 2.6ms\n",
            "98:\tlearn: 0.1091085\ttest: 0.1439548\tbest: 0.1409629 (83)\ttotal: 129ms\tremaining: 1.3ms\n",
            "99:\tlearn: 0.1085127\ttest: 0.1431096\tbest: 0.1409629 (83)\ttotal: 130ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1409628844\n",
            "bestIteration = 83\n",
            "\n",
            "32:\tloss: 0.1409629\tbest: 0.1353408 (23)\ttotal: 4.62s\tremaining: 3.78s\n",
            "0:\tlearn: 0.4716753\ttest: 0.4775514\tbest: 0.4775514 (0)\ttotal: 1.97ms\tremaining: 195ms\n",
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            "3:\tlearn: 0.3155099\ttest: 0.3218028\tbest: 0.3218028 (3)\ttotal: 5.51ms\tremaining: 132ms\n",
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            "\n",
            "bestTest = 0.1399227815\n",
            "bestIteration = 55\n",
            "\n",
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            "58:\tlearn: 0.0948234\ttest: 0.1435630\tbest: 0.1372165 (52)\ttotal: 73.1ms\tremaining: 50.8ms\n",
            "59:\tlearn: 0.0944685\ttest: 0.1436228\tbest: 0.1372165 (52)\ttotal: 74.2ms\tremaining: 49.5ms\n",
            "60:\tlearn: 0.0934474\ttest: 0.1452737\tbest: 0.1372165 (52)\ttotal: 75.3ms\tremaining: 48.2ms\n",
            "61:\tlearn: 0.0926280\ttest: 0.1444014\tbest: 0.1372165 (52)\ttotal: 76.5ms\tremaining: 46.9ms\n",
            "62:\tlearn: 0.0907358\ttest: 0.1456314\tbest: 0.1372165 (52)\ttotal: 77.7ms\tremaining: 45.6ms\n",
            "63:\tlearn: 0.0897680\ttest: 0.1457108\tbest: 0.1372165 (52)\ttotal: 79.1ms\tremaining: 44.5ms\n",
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            "65:\tlearn: 0.0884570\ttest: 0.1472976\tbest: 0.1372165 (52)\ttotal: 81.8ms\tremaining: 42.1ms\n",
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            "67:\tlearn: 0.0847432\ttest: 0.1468019\tbest: 0.1372165 (52)\ttotal: 84.1ms\tremaining: 39.6ms\n",
            "68:\tlearn: 0.0834546\ttest: 0.1451543\tbest: 0.1372165 (52)\ttotal: 85.3ms\tremaining: 38.3ms\n",
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            "72:\tlearn: 0.0801230\ttest: 0.1444454\tbest: 0.1372165 (52)\ttotal: 90ms\tremaining: 33.3ms\n",
            "73:\tlearn: 0.0787160\ttest: 0.1469473\tbest: 0.1372165 (52)\ttotal: 91.1ms\tremaining: 32ms\n",
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            "75:\tlearn: 0.0778659\ttest: 0.1483831\tbest: 0.1372165 (52)\ttotal: 93.5ms\tremaining: 29.5ms\n",
            "76:\tlearn: 0.0777042\ttest: 0.1479452\tbest: 0.1372165 (52)\ttotal: 94.7ms\tremaining: 28.3ms\n",
            "77:\tlearn: 0.0758079\ttest: 0.1444049\tbest: 0.1372165 (52)\ttotal: 96.1ms\tremaining: 27.1ms\n",
            "78:\tlearn: 0.0756642\ttest: 0.1443676\tbest: 0.1372165 (52)\ttotal: 97.2ms\tremaining: 25.9ms\n",
            "79:\tlearn: 0.0752139\ttest: 0.1442119\tbest: 0.1372165 (52)\ttotal: 98.4ms\tremaining: 24.6ms\n",
            "80:\tlearn: 0.0746034\ttest: 0.1436397\tbest: 0.1372165 (52)\ttotal: 99.8ms\tremaining: 23.4ms\n",
            "81:\tlearn: 0.0735630\ttest: 0.1432257\tbest: 0.1372165 (52)\ttotal: 101ms\tremaining: 22.3ms\n",
            "82:\tlearn: 0.0731360\ttest: 0.1434036\tbest: 0.1372165 (52)\ttotal: 103ms\tremaining: 21ms\n",
            "83:\tlearn: 0.0719141\ttest: 0.1442514\tbest: 0.1372165 (52)\ttotal: 104ms\tremaining: 19.8ms\n",
            "84:\tlearn: 0.0706509\ttest: 0.1442632\tbest: 0.1372165 (52)\ttotal: 105ms\tremaining: 18.6ms\n",
            "85:\tlearn: 0.0704748\ttest: 0.1442949\tbest: 0.1372165 (52)\ttotal: 107ms\tremaining: 17.4ms\n",
            "86:\tlearn: 0.0693633\ttest: 0.1462127\tbest: 0.1372165 (52)\ttotal: 108ms\tremaining: 16.1ms\n",
            "87:\tlearn: 0.0687293\ttest: 0.1445577\tbest: 0.1372165 (52)\ttotal: 109ms\tremaining: 14.9ms\n",
            "88:\tlearn: 0.0668484\ttest: 0.1445276\tbest: 0.1372165 (52)\ttotal: 110ms\tremaining: 13.6ms\n",
            "89:\tlearn: 0.0665865\ttest: 0.1450469\tbest: 0.1372165 (52)\ttotal: 111ms\tremaining: 12.4ms\n",
            "90:\tlearn: 0.0657746\ttest: 0.1462878\tbest: 0.1372165 (52)\ttotal: 113ms\tremaining: 11.1ms\n",
            "91:\tlearn: 0.0648088\ttest: 0.1451663\tbest: 0.1372165 (52)\ttotal: 114ms\tremaining: 9.88ms\n",
            "92:\tlearn: 0.0643950\ttest: 0.1458550\tbest: 0.1372165 (52)\ttotal: 115ms\tremaining: 8.64ms\n",
            "93:\tlearn: 0.0631890\ttest: 0.1463336\tbest: 0.1372165 (52)\ttotal: 116ms\tremaining: 7.4ms\n",
            "94:\tlearn: 0.0631025\ttest: 0.1465482\tbest: 0.1372165 (52)\ttotal: 117ms\tremaining: 6.16ms\n",
            "95:\tlearn: 0.0620813\ttest: 0.1451440\tbest: 0.1372165 (52)\ttotal: 118ms\tremaining: 4.93ms\n",
            "96:\tlearn: 0.0607352\ttest: 0.1451310\tbest: 0.1372165 (52)\ttotal: 119ms\tremaining: 3.69ms\n",
            "97:\tlearn: 0.0602766\ttest: 0.1446823\tbest: 0.1372165 (52)\ttotal: 121ms\tremaining: 2.46ms\n",
            "98:\tlearn: 0.0602281\ttest: 0.1449342\tbest: 0.1372165 (52)\ttotal: 122ms\tremaining: 1.23ms\n",
            "99:\tlearn: 0.0598515\ttest: 0.1446303\tbest: 0.1372165 (52)\ttotal: 123ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1372164772\n",
            "bestIteration = 52\n",
            "\n",
            "34:\tloss: 0.1372165\tbest: 0.1353408 (23)\ttotal: 4.89s\tremaining: 3.49s\n",
            "0:\tlearn: 0.6922074\ttest: 0.6922303\tbest: 0.6922303 (0)\ttotal: 1.22ms\tremaining: 121ms\n",
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            "2:\tlearn: 0.6908170\ttest: 0.6908540\tbest: 0.6908540 (2)\ttotal: 3.65ms\tremaining: 118ms\n",
            "3:\tlearn: 0.6900241\ttest: 0.6900893\tbest: 0.6900893 (3)\ttotal: 4.82ms\tremaining: 116ms\n",
            "4:\tlearn: 0.6892919\ttest: 0.6893756\tbest: 0.6893756 (4)\ttotal: 5.97ms\tremaining: 113ms\n",
            "5:\tlearn: 0.6885575\ttest: 0.6886499\tbest: 0.6886499 (5)\ttotal: 7.13ms\tremaining: 112ms\n",
            "6:\tlearn: 0.6878054\ttest: 0.6879197\tbest: 0.6879197 (6)\ttotal: 8.31ms\tremaining: 110ms\n",
            "7:\tlearn: 0.6869242\ttest: 0.6870685\tbest: 0.6870685 (7)\ttotal: 9.44ms\tremaining: 109ms\n",
            "8:\tlearn: 0.6861068\ttest: 0.6862428\tbest: 0.6862428 (8)\ttotal: 11ms\tremaining: 111ms\n",
            "9:\tlearn: 0.6854503\ttest: 0.6855767\tbest: 0.6855767 (9)\ttotal: 12.2ms\tremaining: 110ms\n",
            "10:\tlearn: 0.6845568\ttest: 0.6847152\tbest: 0.6847152 (10)\ttotal: 13.4ms\tremaining: 108ms\n",
            "11:\tlearn: 0.6836918\ttest: 0.6838668\tbest: 0.6838668 (11)\ttotal: 14.5ms\tremaining: 106ms\n",
            "12:\tlearn: 0.6828298\ttest: 0.6830415\tbest: 0.6830415 (12)\ttotal: 15.6ms\tremaining: 105ms\n",
            "13:\tlearn: 0.6820949\ttest: 0.6823324\tbest: 0.6823324 (13)\ttotal: 17ms\tremaining: 104ms\n",
            "14:\tlearn: 0.6813542\ttest: 0.6815976\tbest: 0.6815976 (14)\ttotal: 18.2ms\tremaining: 103ms\n",
            "15:\tlearn: 0.6805360\ttest: 0.6807973\tbest: 0.6807973 (15)\ttotal: 19.4ms\tremaining: 102ms\n",
            "16:\tlearn: 0.6795663\ttest: 0.6798339\tbest: 0.6798339 (16)\ttotal: 20.6ms\tremaining: 101ms\n",
            "17:\tlearn: 0.6787252\ttest: 0.6789875\tbest: 0.6789875 (17)\ttotal: 21.8ms\tremaining: 99.2ms\n",
            "18:\tlearn: 0.6778555\ttest: 0.6781521\tbest: 0.6781521 (18)\ttotal: 23.3ms\tremaining: 99.5ms\n",
            "19:\tlearn: 0.6770602\ttest: 0.6773708\tbest: 0.6773708 (19)\ttotal: 24.7ms\tremaining: 98.9ms\n",
            "20:\tlearn: 0.6764485\ttest: 0.6767804\tbest: 0.6767804 (20)\ttotal: 26ms\tremaining: 97.7ms\n",
            "21:\tlearn: 0.6755891\ttest: 0.6759012\tbest: 0.6759012 (21)\ttotal: 27.1ms\tremaining: 96.1ms\n",
            "22:\tlearn: 0.6747098\ttest: 0.6750505\tbest: 0.6750505 (22)\ttotal: 28.3ms\tremaining: 94.8ms\n",
            "23:\tlearn: 0.6739558\ttest: 0.6743230\tbest: 0.6743230 (23)\ttotal: 29.4ms\tremaining: 93.2ms\n",
            "24:\tlearn: 0.6731574\ttest: 0.6735094\tbest: 0.6735094 (24)\ttotal: 30.6ms\tremaining: 91.7ms\n",
            "25:\tlearn: 0.6724491\ttest: 0.6728022\tbest: 0.6728022 (25)\ttotal: 31.9ms\tremaining: 90.7ms\n",
            "26:\tlearn: 0.6717512\ttest: 0.6721062\tbest: 0.6721062 (26)\ttotal: 33.1ms\tremaining: 89.4ms\n",
            "27:\tlearn: 0.6709289\ttest: 0.6713038\tbest: 0.6713038 (27)\ttotal: 34.4ms\tremaining: 88.4ms\n",
            "28:\tlearn: 0.6701349\ttest: 0.6704952\tbest: 0.6704952 (28)\ttotal: 35.8ms\tremaining: 87.5ms\n",
            "29:\tlearn: 0.6694336\ttest: 0.6697866\tbest: 0.6697866 (29)\ttotal: 37.4ms\tremaining: 87.3ms\n",
            "30:\tlearn: 0.6686613\ttest: 0.6690161\tbest: 0.6690161 (30)\ttotal: 39ms\tremaining: 86.9ms\n",
            "31:\tlearn: 0.6680405\ttest: 0.6684114\tbest: 0.6684114 (31)\ttotal: 40.6ms\tremaining: 86.2ms\n",
            "32:\tlearn: 0.6673460\ttest: 0.6677248\tbest: 0.6677248 (32)\ttotal: 42ms\tremaining: 85.4ms\n",
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            "35:\tlearn: 0.6651189\ttest: 0.6655442\tbest: 0.6655442 (35)\ttotal: 45.5ms\tremaining: 80.9ms\n",
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            "37:\tlearn: 0.6638672\ttest: 0.6642864\tbest: 0.6642864 (37)\ttotal: 48ms\tremaining: 78.3ms\n",
            "38:\tlearn: 0.6630547\ttest: 0.6635066\tbest: 0.6635066 (38)\ttotal: 49.4ms\tremaining: 77.2ms\n",
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            "40:\tlearn: 0.6613735\ttest: 0.6618655\tbest: 0.6618655 (40)\ttotal: 52.8ms\tremaining: 75.9ms\n",
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            "42:\tlearn: 0.6598637\ttest: 0.6603620\tbest: 0.6603620 (42)\ttotal: 56.3ms\tremaining: 74.6ms\n",
            "43:\tlearn: 0.6592086\ttest: 0.6596961\tbest: 0.6596961 (43)\ttotal: 57.7ms\tremaining: 73.4ms\n",
            "44:\tlearn: 0.6584205\ttest: 0.6589324\tbest: 0.6589324 (44)\ttotal: 59.4ms\tremaining: 72.6ms\n",
            "45:\tlearn: 0.6578252\ttest: 0.6583393\tbest: 0.6583393 (45)\ttotal: 60.9ms\tremaining: 71.4ms\n",
            "46:\tlearn: 0.6570392\ttest: 0.6575377\tbest: 0.6575377 (46)\ttotal: 62.2ms\tremaining: 70.2ms\n",
            "47:\tlearn: 0.6562808\ttest: 0.6567669\tbest: 0.6567669 (47)\ttotal: 63.6ms\tremaining: 68.9ms\n",
            "48:\tlearn: 0.6554315\ttest: 0.6559313\tbest: 0.6559313 (48)\ttotal: 64.9ms\tremaining: 67.6ms\n",
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            "51:\tlearn: 0.6532765\ttest: 0.6538090\tbest: 0.6538090 (51)\ttotal: 69.4ms\tremaining: 64.1ms\n",
            "52:\tlearn: 0.6524866\ttest: 0.6530437\tbest: 0.6530437 (52)\ttotal: 71.1ms\tremaining: 63ms\n",
            "53:\tlearn: 0.6517038\ttest: 0.6522969\tbest: 0.6522969 (53)\ttotal: 72.3ms\tremaining: 61.6ms\n",
            "54:\tlearn: 0.6510597\ttest: 0.6516618\tbest: 0.6516618 (54)\ttotal: 73.4ms\tremaining: 60.1ms\n",
            "55:\tlearn: 0.6502958\ttest: 0.6508972\tbest: 0.6508972 (55)\ttotal: 74.7ms\tremaining: 58.7ms\n",
            "56:\tlearn: 0.6495226\ttest: 0.6501309\tbest: 0.6501309 (56)\ttotal: 75.9ms\tremaining: 57.2ms\n",
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            "58:\tlearn: 0.6480756\ttest: 0.6487004\tbest: 0.6487004 (58)\ttotal: 78.3ms\tremaining: 54.4ms\n",
            "59:\tlearn: 0.6472814\ttest: 0.6478859\tbest: 0.6478859 (59)\ttotal: 79.4ms\tremaining: 53ms\n",
            "60:\tlearn: 0.6466093\ttest: 0.6472174\tbest: 0.6472174 (60)\ttotal: 80.8ms\tremaining: 51.7ms\n",
            "61:\tlearn: 0.6458443\ttest: 0.6464514\tbest: 0.6464514 (61)\ttotal: 82.3ms\tremaining: 50.4ms\n",
            "62:\tlearn: 0.6451695\ttest: 0.6457930\tbest: 0.6457930 (62)\ttotal: 83.6ms\tremaining: 49.1ms\n",
            "63:\tlearn: 0.6445229\ttest: 0.6451482\tbest: 0.6451482 (63)\ttotal: 85ms\tremaining: 47.8ms\n",
            "64:\tlearn: 0.6437683\ttest: 0.6444178\tbest: 0.6444178 (64)\ttotal: 86.5ms\tremaining: 46.6ms\n",
            "65:\tlearn: 0.6432257\ttest: 0.6438778\tbest: 0.6438778 (65)\ttotal: 87.7ms\tremaining: 45.2ms\n",
            "66:\tlearn: 0.6424175\ttest: 0.6430882\tbest: 0.6430882 (66)\ttotal: 88.9ms\tremaining: 43.8ms\n",
            "67:\tlearn: 0.6417522\ttest: 0.6424242\tbest: 0.6424242 (67)\ttotal: 90ms\tremaining: 42.4ms\n",
            "68:\tlearn: 0.6411141\ttest: 0.6417680\tbest: 0.6417680 (68)\ttotal: 91.2ms\tremaining: 41ms\n",
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            "71:\tlearn: 0.6390687\ttest: 0.6397284\tbest: 0.6397284 (71)\ttotal: 94.6ms\tremaining: 36.8ms\n",
            "72:\tlearn: 0.6383922\ttest: 0.6390621\tbest: 0.6390621 (72)\ttotal: 95.8ms\tremaining: 35.4ms\n",
            "73:\tlearn: 0.6376551\ttest: 0.6383597\tbest: 0.6383597 (73)\ttotal: 97.1ms\tremaining: 34.1ms\n",
            "74:\tlearn: 0.6370169\ttest: 0.6377226\tbest: 0.6377226 (74)\ttotal: 98.5ms\tremaining: 32.8ms\n",
            "75:\tlearn: 0.6362642\ttest: 0.6369970\tbest: 0.6369970 (75)\ttotal: 100ms\tremaining: 31.6ms\n",
            "76:\tlearn: 0.6356318\ttest: 0.6363810\tbest: 0.6363810 (76)\ttotal: 101ms\tremaining: 30.3ms\n",
            "77:\tlearn: 0.6349009\ttest: 0.6356492\tbest: 0.6356492 (77)\ttotal: 103ms\tremaining: 29ms\n",
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            "79:\tlearn: 0.6334722\ttest: 0.6342299\tbest: 0.6342299 (79)\ttotal: 105ms\tremaining: 26.3ms\n",
            "80:\tlearn: 0.6328656\ttest: 0.6336352\tbest: 0.6336352 (80)\ttotal: 106ms\tremaining: 25ms\n",
            "81:\tlearn: 0.6321350\ttest: 0.6329146\tbest: 0.6329146 (81)\ttotal: 108ms\tremaining: 23.6ms\n",
            "82:\tlearn: 0.6315218\ttest: 0.6322843\tbest: 0.6322843 (82)\ttotal: 109ms\tremaining: 22.3ms\n",
            "83:\tlearn: 0.6309395\ttest: 0.6317204\tbest: 0.6317204 (83)\ttotal: 110ms\tremaining: 21ms\n",
            "84:\tlearn: 0.6302746\ttest: 0.6310780\tbest: 0.6310780 (84)\ttotal: 111ms\tremaining: 19.6ms\n",
            "85:\tlearn: 0.6295148\ttest: 0.6303418\tbest: 0.6303418 (85)\ttotal: 113ms\tremaining: 18.3ms\n",
            "86:\tlearn: 0.6287824\ttest: 0.6296394\tbest: 0.6296394 (86)\ttotal: 114ms\tremaining: 17.1ms\n",
            "87:\tlearn: 0.6280493\ttest: 0.6289245\tbest: 0.6289245 (87)\ttotal: 116ms\tremaining: 15.8ms\n",
            "88:\tlearn: 0.6274423\ttest: 0.6282997\tbest: 0.6282997 (88)\ttotal: 117ms\tremaining: 14.5ms\n",
            "89:\tlearn: 0.6267509\ttest: 0.6275965\tbest: 0.6275965 (89)\ttotal: 119ms\tremaining: 13.2ms\n",
            "90:\tlearn: 0.6260271\ttest: 0.6269054\tbest: 0.6269054 (90)\ttotal: 120ms\tremaining: 11.9ms\n",
            "91:\tlearn: 0.6253018\ttest: 0.6262087\tbest: 0.6262087 (91)\ttotal: 121ms\tremaining: 10.5ms\n",
            "92:\tlearn: 0.6247036\ttest: 0.6256278\tbest: 0.6256278 (92)\ttotal: 122ms\tremaining: 9.21ms\n",
            "93:\tlearn: 0.6239790\ttest: 0.6249263\tbest: 0.6249263 (93)\ttotal: 124ms\tremaining: 7.89ms\n",
            "94:\tlearn: 0.6233580\ttest: 0.6243299\tbest: 0.6243299 (94)\ttotal: 125ms\tremaining: 6.57ms\n",
            "95:\tlearn: 0.6226068\ttest: 0.6235889\tbest: 0.6235889 (95)\ttotal: 126ms\tremaining: 5.25ms\n",
            "96:\tlearn: 0.6219433\ttest: 0.6229197\tbest: 0.6229197 (96)\ttotal: 127ms\tremaining: 3.93ms\n",
            "97:\tlearn: 0.6213278\ttest: 0.6223236\tbest: 0.6223236 (97)\ttotal: 129ms\tremaining: 2.62ms\n",
            "98:\tlearn: 0.6206784\ttest: 0.6216769\tbest: 0.6216769 (98)\ttotal: 130ms\tremaining: 1.31ms\n",
            "99:\tlearn: 0.6200941\ttest: 0.6211049\tbest: 0.6211049 (99)\ttotal: 132ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.6211049134\n",
            "bestIteration = 99\n",
            "\n",
            "35:\tloss: 0.6211049\tbest: 0.1353408 (23)\ttotal: 5.03s\tremaining: 3.35s\n",
            "0:\tlearn: 0.6052959\ttest: 0.6074976\tbest: 0.6074976 (0)\ttotal: 1.18ms\tremaining: 117ms\n",
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            "\n",
            "bestTest = 0.1528423819\n",
            "bestIteration = 98\n",
            "\n",
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            "57:\tlearn: 0.1417218\ttest: 0.1523998\tbest: 0.1523998 (57)\ttotal: 74.2ms\tremaining: 53.8ms\n",
            "58:\tlearn: 0.1404301\ttest: 0.1527478\tbest: 0.1523998 (57)\ttotal: 75.4ms\tremaining: 52.4ms\n",
            "59:\tlearn: 0.1400434\ttest: 0.1519414\tbest: 0.1519414 (59)\ttotal: 76.5ms\tremaining: 51ms\n",
            "60:\tlearn: 0.1387132\ttest: 0.1532376\tbest: 0.1519414 (59)\ttotal: 77.7ms\tremaining: 49.7ms\n",
            "61:\tlearn: 0.1367213\ttest: 0.1508475\tbest: 0.1508475 (61)\ttotal: 78.9ms\tremaining: 48.3ms\n",
            "62:\tlearn: 0.1354983\ttest: 0.1501902\tbest: 0.1501902 (62)\ttotal: 80ms\tremaining: 47ms\n",
            "63:\tlearn: 0.1353107\ttest: 0.1502229\tbest: 0.1501902 (62)\ttotal: 81.7ms\tremaining: 46ms\n",
            "64:\tlearn: 0.1337775\ttest: 0.1483806\tbest: 0.1483806 (64)\ttotal: 83.1ms\tremaining: 44.8ms\n",
            "65:\tlearn: 0.1337235\ttest: 0.1485237\tbest: 0.1483806 (64)\ttotal: 84.7ms\tremaining: 43.6ms\n",
            "66:\tlearn: 0.1326777\ttest: 0.1480637\tbest: 0.1480637 (66)\ttotal: 86.2ms\tremaining: 42.5ms\n",
            "67:\tlearn: 0.1326335\ttest: 0.1482096\tbest: 0.1480637 (66)\ttotal: 87.6ms\tremaining: 41.2ms\n",
            "68:\tlearn: 0.1314034\ttest: 0.1477011\tbest: 0.1477011 (68)\ttotal: 88.8ms\tremaining: 39.9ms\n",
            "69:\tlearn: 0.1307109\ttest: 0.1478654\tbest: 0.1477011 (68)\ttotal: 90ms\tremaining: 38.6ms\n",
            "70:\tlearn: 0.1302380\ttest: 0.1470572\tbest: 0.1470572 (70)\ttotal: 91.1ms\tremaining: 37.2ms\n",
            "71:\tlearn: 0.1291833\ttest: 0.1465228\tbest: 0.1465228 (71)\ttotal: 92.3ms\tremaining: 35.9ms\n",
            "72:\tlearn: 0.1286325\ttest: 0.1464754\tbest: 0.1464754 (72)\ttotal: 93.5ms\tremaining: 34.6ms\n",
            "73:\tlearn: 0.1276149\ttest: 0.1468434\tbest: 0.1464754 (72)\ttotal: 94.7ms\tremaining: 33.3ms\n",
            "74:\tlearn: 0.1273384\ttest: 0.1469162\tbest: 0.1464754 (72)\ttotal: 96ms\tremaining: 32ms\n",
            "75:\tlearn: 0.1268348\ttest: 0.1472623\tbest: 0.1464754 (72)\ttotal: 97.3ms\tremaining: 30.7ms\n",
            "76:\tlearn: 0.1267287\ttest: 0.1474759\tbest: 0.1464754 (72)\ttotal: 98.6ms\tremaining: 29.5ms\n",
            "77:\tlearn: 0.1257201\ttest: 0.1465003\tbest: 0.1464754 (72)\ttotal: 100ms\tremaining: 28.2ms\n",
            "78:\tlearn: 0.1249733\ttest: 0.1457035\tbest: 0.1457035 (78)\ttotal: 102ms\tremaining: 27ms\n",
            "79:\tlearn: 0.1241153\ttest: 0.1465156\tbest: 0.1457035 (78)\ttotal: 103ms\tremaining: 25.8ms\n",
            "80:\tlearn: 0.1240297\ttest: 0.1465182\tbest: 0.1457035 (78)\ttotal: 104ms\tremaining: 24.5ms\n",
            "81:\tlearn: 0.1233008\ttest: 0.1454998\tbest: 0.1454998 (81)\ttotal: 106ms\tremaining: 23.2ms\n",
            "82:\tlearn: 0.1228676\ttest: 0.1461825\tbest: 0.1454998 (81)\ttotal: 107ms\tremaining: 21.9ms\n",
            "83:\tlearn: 0.1224502\ttest: 0.1454873\tbest: 0.1454873 (83)\ttotal: 108ms\tremaining: 20.6ms\n",
            "84:\tlearn: 0.1218240\ttest: 0.1455841\tbest: 0.1454873 (83)\ttotal: 109ms\tremaining: 19.3ms\n",
            "85:\tlearn: 0.1214719\ttest: 0.1450334\tbest: 0.1450334 (85)\ttotal: 111ms\tremaining: 18ms\n",
            "86:\tlearn: 0.1209451\ttest: 0.1450126\tbest: 0.1450126 (86)\ttotal: 112ms\tremaining: 16.7ms\n",
            "87:\tlearn: 0.1203439\ttest: 0.1447346\tbest: 0.1447346 (87)\ttotal: 113ms\tremaining: 15.5ms\n",
            "88:\tlearn: 0.1200526\ttest: 0.1443114\tbest: 0.1443114 (88)\ttotal: 115ms\tremaining: 14.2ms\n",
            "89:\tlearn: 0.1195906\ttest: 0.1444364\tbest: 0.1443114 (88)\ttotal: 117ms\tremaining: 13ms\n",
            "90:\tlearn: 0.1182417\ttest: 0.1442792\tbest: 0.1442792 (90)\ttotal: 118ms\tremaining: 11.7ms\n",
            "91:\tlearn: 0.1175709\ttest: 0.1442442\tbest: 0.1442442 (91)\ttotal: 119ms\tremaining: 10.4ms\n",
            "92:\tlearn: 0.1157522\ttest: 0.1446453\tbest: 0.1442442 (91)\ttotal: 121ms\tremaining: 9.08ms\n",
            "93:\tlearn: 0.1141369\ttest: 0.1433008\tbest: 0.1433008 (93)\ttotal: 122ms\tremaining: 7.77ms\n",
            "94:\tlearn: 0.1132490\ttest: 0.1421308\tbest: 0.1421308 (94)\ttotal: 123ms\tremaining: 6.47ms\n",
            "95:\tlearn: 0.1132117\ttest: 0.1422633\tbest: 0.1421308 (94)\ttotal: 124ms\tremaining: 5.18ms\n",
            "96:\tlearn: 0.1122027\ttest: 0.1407820\tbest: 0.1407820 (96)\ttotal: 125ms\tremaining: 3.88ms\n",
            "97:\tlearn: 0.1116538\ttest: 0.1410931\tbest: 0.1407820 (96)\ttotal: 127ms\tremaining: 2.58ms\n",
            "98:\tlearn: 0.1111616\ttest: 0.1411153\tbest: 0.1407820 (96)\ttotal: 128ms\tremaining: 1.3ms\n",
            "99:\tlearn: 0.1111464\ttest: 0.1412056\tbest: 0.1407820 (96)\ttotal: 130ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1407819914\n",
            "bestIteration = 96\n",
            "\n",
            "37:\tloss: 0.1407820\tbest: 0.1353408 (23)\ttotal: 5.33s\tremaining: 3.08s\n",
            "0:\tlearn: 0.4720561\ttest: 0.4779862\tbest: 0.4779862 (0)\ttotal: 1.36ms\tremaining: 135ms\n",
            "1:\tlearn: 0.3923732\ttest: 0.3944834\tbest: 0.3944834 (1)\ttotal: 2.62ms\tremaining: 128ms\n",
            "2:\tlearn: 0.3485526\ttest: 0.3567871\tbest: 0.3567871 (2)\ttotal: 3.81ms\tremaining: 123ms\n",
            "3:\tlearn: 0.3169789\ttest: 0.3233150\tbest: 0.3233150 (3)\ttotal: 4.96ms\tremaining: 119ms\n",
            "4:\tlearn: 0.2907706\ttest: 0.2936569\tbest: 0.2936569 (4)\ttotal: 6.07ms\tremaining: 115ms\n",
            "5:\tlearn: 0.2775863\ttest: 0.2846431\tbest: 0.2846431 (5)\ttotal: 7.28ms\tremaining: 114ms\n",
            "6:\tlearn: 0.2632930\ttest: 0.2687264\tbest: 0.2687264 (6)\ttotal: 8.53ms\tremaining: 113ms\n",
            "7:\tlearn: 0.2475749\ttest: 0.2474841\tbest: 0.2474841 (7)\ttotal: 9.67ms\tremaining: 111ms\n",
            "8:\tlearn: 0.2355412\ttest: 0.2365578\tbest: 0.2365578 (8)\ttotal: 10.9ms\tremaining: 110ms\n",
            "9:\tlearn: 0.2250719\ttest: 0.2292263\tbest: 0.2292263 (9)\ttotal: 12.3ms\tremaining: 111ms\n",
            "10:\tlearn: 0.2177291\ttest: 0.2195392\tbest: 0.2195392 (10)\ttotal: 13.7ms\tremaining: 111ms\n",
            "11:\tlearn: 0.2105181\ttest: 0.2143601\tbest: 0.2143601 (11)\ttotal: 15.4ms\tremaining: 113ms\n",
            "12:\tlearn: 0.2058188\ttest: 0.2090924\tbest: 0.2090924 (12)\ttotal: 16.9ms\tremaining: 113ms\n",
            "13:\tlearn: 0.1998968\ttest: 0.1996751\tbest: 0.1996751 (13)\ttotal: 18.2ms\tremaining: 112ms\n",
            "14:\tlearn: 0.1977700\ttest: 0.1967111\tbest: 0.1967111 (14)\ttotal: 19.8ms\tremaining: 112ms\n",
            "15:\tlearn: 0.1951691\ttest: 0.1941626\tbest: 0.1941626 (15)\ttotal: 21.1ms\tremaining: 111ms\n",
            "16:\tlearn: 0.1929351\ttest: 0.1938163\tbest: 0.1938163 (16)\ttotal: 22.3ms\tremaining: 109ms\n",
            "17:\tlearn: 0.1901382\ttest: 0.1905276\tbest: 0.1905276 (17)\ttotal: 23.6ms\tremaining: 107ms\n",
            "18:\tlearn: 0.1898008\ttest: 0.1897254\tbest: 0.1897254 (18)\ttotal: 24.8ms\tremaining: 106ms\n",
            "19:\tlearn: 0.1868699\ttest: 0.1863115\tbest: 0.1863115 (19)\ttotal: 26.1ms\tremaining: 105ms\n",
            "20:\tlearn: 0.1793814\ttest: 0.1771466\tbest: 0.1771466 (20)\ttotal: 27.7ms\tremaining: 104ms\n",
            "21:\tlearn: 0.1788655\ttest: 0.1769525\tbest: 0.1769525 (21)\ttotal: 29.1ms\tremaining: 103ms\n",
            "22:\tlearn: 0.1777995\ttest: 0.1753798\tbest: 0.1753798 (22)\ttotal: 30.8ms\tremaining: 103ms\n",
            "23:\tlearn: 0.1749041\ttest: 0.1728059\tbest: 0.1728059 (23)\ttotal: 32.1ms\tremaining: 102ms\n",
            "24:\tlearn: 0.1742471\ttest: 0.1725473\tbest: 0.1725473 (24)\ttotal: 33.4ms\tremaining: 100ms\n",
            "25:\tlearn: 0.1698022\ttest: 0.1683693\tbest: 0.1683693 (25)\ttotal: 34.7ms\tremaining: 98.6ms\n",
            "26:\tlearn: 0.1672256\ttest: 0.1648966\tbest: 0.1648966 (26)\ttotal: 35.9ms\tremaining: 97.1ms\n",
            "27:\tlearn: 0.1628122\ttest: 0.1604327\tbest: 0.1604327 (27)\ttotal: 37.4ms\tremaining: 96.1ms\n",
            "28:\tlearn: 0.1625611\ttest: 0.1607864\tbest: 0.1604327 (27)\ttotal: 38.6ms\tremaining: 94.4ms\n",
            "29:\tlearn: 0.1597247\ttest: 0.1598298\tbest: 0.1598298 (29)\ttotal: 39.8ms\tremaining: 92.9ms\n",
            "30:\tlearn: 0.1571183\ttest: 0.1587267\tbest: 0.1587267 (30)\ttotal: 41ms\tremaining: 91.2ms\n",
            "31:\tlearn: 0.1547039\ttest: 0.1544825\tbest: 0.1544825 (31)\ttotal: 42.3ms\tremaining: 89.8ms\n",
            "32:\tlearn: 0.1518774\ttest: 0.1519792\tbest: 0.1519792 (32)\ttotal: 43.6ms\tremaining: 88.6ms\n",
            "33:\tlearn: 0.1510331\ttest: 0.1517224\tbest: 0.1517224 (33)\ttotal: 45ms\tremaining: 87.3ms\n",
            "34:\tlearn: 0.1500748\ttest: 0.1511195\tbest: 0.1511195 (34)\ttotal: 46.4ms\tremaining: 86.2ms\n",
            "35:\tlearn: 0.1492452\ttest: 0.1513818\tbest: 0.1511195 (34)\ttotal: 47.8ms\tremaining: 84.9ms\n",
            "36:\tlearn: 0.1467889\ttest: 0.1522571\tbest: 0.1511195 (34)\ttotal: 48.9ms\tremaining: 83.3ms\n",
            "37:\tlearn: 0.1466137\ttest: 0.1526072\tbest: 0.1511195 (34)\ttotal: 50.1ms\tremaining: 81.8ms\n",
            "38:\tlearn: 0.1448705\ttest: 0.1541115\tbest: 0.1511195 (34)\ttotal: 51.3ms\tremaining: 80.2ms\n",
            "39:\tlearn: 0.1437114\ttest: 0.1544023\tbest: 0.1511195 (34)\ttotal: 52.4ms\tremaining: 78.7ms\n",
            "40:\tlearn: 0.1423310\ttest: 0.1538582\tbest: 0.1511195 (34)\ttotal: 53.6ms\tremaining: 77.1ms\n",
            "41:\tlearn: 0.1406106\ttest: 0.1522382\tbest: 0.1511195 (34)\ttotal: 54.8ms\tremaining: 75.6ms\n",
            "42:\tlearn: 0.1403633\ttest: 0.1522500\tbest: 0.1511195 (34)\ttotal: 55.9ms\tremaining: 74.2ms\n",
            "43:\tlearn: 0.1400336\ttest: 0.1522824\tbest: 0.1511195 (34)\ttotal: 66.4ms\tremaining: 84.5ms\n",
            "44:\tlearn: 0.1383415\ttest: 0.1535307\tbest: 0.1511195 (34)\ttotal: 67.6ms\tremaining: 82.6ms\n",
            "45:\tlearn: 0.1333678\ttest: 0.1480571\tbest: 0.1480571 (45)\ttotal: 68.8ms\tremaining: 80.8ms\n",
            "46:\tlearn: 0.1331775\ttest: 0.1480335\tbest: 0.1480335 (46)\ttotal: 70ms\tremaining: 78.9ms\n",
            "47:\tlearn: 0.1328240\ttest: 0.1478031\tbest: 0.1478031 (47)\ttotal: 71.2ms\tremaining: 77.1ms\n",
            "48:\tlearn: 0.1314532\ttest: 0.1478232\tbest: 0.1478031 (47)\ttotal: 72.3ms\tremaining: 75.3ms\n",
            "49:\tlearn: 0.1307586\ttest: 0.1476938\tbest: 0.1476938 (49)\ttotal: 73.5ms\tremaining: 73.5ms\n",
            "50:\tlearn: 0.1297254\ttest: 0.1475200\tbest: 0.1475200 (50)\ttotal: 75.1ms\tremaining: 72.1ms\n",
            "51:\tlearn: 0.1296871\ttest: 0.1477073\tbest: 0.1475200 (50)\ttotal: 76.7ms\tremaining: 70.8ms\n",
            "52:\tlearn: 0.1284365\ttest: 0.1489379\tbest: 0.1475200 (50)\ttotal: 78.5ms\tremaining: 69.6ms\n",
            "53:\tlearn: 0.1271272\ttest: 0.1486566\tbest: 0.1475200 (50)\ttotal: 79.8ms\tremaining: 67.9ms\n",
            "54:\tlearn: 0.1255500\ttest: 0.1489301\tbest: 0.1475200 (50)\ttotal: 81ms\tremaining: 66.2ms\n",
            "55:\tlearn: 0.1228544\ttest: 0.1467031\tbest: 0.1467031 (55)\ttotal: 82.4ms\tremaining: 64.7ms\n",
            "56:\tlearn: 0.1213778\ttest: 0.1455954\tbest: 0.1455954 (56)\ttotal: 83.5ms\tremaining: 63ms\n",
            "57:\tlearn: 0.1201080\ttest: 0.1440164\tbest: 0.1440164 (57)\ttotal: 84.7ms\tremaining: 61.3ms\n",
            "58:\tlearn: 0.1193523\ttest: 0.1444288\tbest: 0.1440164 (57)\ttotal: 85.9ms\tremaining: 59.7ms\n",
            "59:\tlearn: 0.1190766\ttest: 0.1444649\tbest: 0.1440164 (57)\ttotal: 87.1ms\tremaining: 58ms\n",
            "60:\tlearn: 0.1186210\ttest: 0.1444401\tbest: 0.1440164 (57)\ttotal: 88.2ms\tremaining: 56.4ms\n",
            "61:\tlearn: 0.1184983\ttest: 0.1446370\tbest: 0.1440164 (57)\ttotal: 89.6ms\tremaining: 54.9ms\n",
            "62:\tlearn: 0.1171245\ttest: 0.1446530\tbest: 0.1440164 (57)\ttotal: 91ms\tremaining: 53.4ms\n",
            "63:\tlearn: 0.1159136\ttest: 0.1443447\tbest: 0.1440164 (57)\ttotal: 92.3ms\tremaining: 51.9ms\n",
            "64:\tlearn: 0.1135127\ttest: 0.1442916\tbest: 0.1440164 (57)\ttotal: 93.8ms\tremaining: 50.5ms\n",
            "65:\tlearn: 0.1122086\ttest: 0.1441938\tbest: 0.1440164 (57)\ttotal: 95.3ms\tremaining: 49.1ms\n",
            "66:\tlearn: 0.1121675\ttest: 0.1444274\tbest: 0.1440164 (57)\ttotal: 96.5ms\tremaining: 47.5ms\n",
            "67:\tlearn: 0.1114952\ttest: 0.1444021\tbest: 0.1440164 (57)\ttotal: 97.6ms\tremaining: 45.9ms\n",
            "68:\tlearn: 0.1101007\ttest: 0.1446962\tbest: 0.1440164 (57)\ttotal: 98.8ms\tremaining: 44.4ms\n",
            "69:\tlearn: 0.1099860\ttest: 0.1444999\tbest: 0.1440164 (57)\ttotal: 99.9ms\tremaining: 42.8ms\n",
            "70:\tlearn: 0.1090411\ttest: 0.1455562\tbest: 0.1440164 (57)\ttotal: 101ms\tremaining: 41.3ms\n",
            "71:\tlearn: 0.1080674\ttest: 0.1469487\tbest: 0.1440164 (57)\ttotal: 102ms\tremaining: 39.8ms\n",
            "72:\tlearn: 0.1064178\ttest: 0.1472497\tbest: 0.1440164 (57)\ttotal: 103ms\tremaining: 38.2ms\n",
            "73:\tlearn: 0.1057058\ttest: 0.1471864\tbest: 0.1440164 (57)\ttotal: 105ms\tremaining: 36.7ms\n",
            "74:\tlearn: 0.1052993\ttest: 0.1470784\tbest: 0.1440164 (57)\ttotal: 106ms\tremaining: 35.3ms\n",
            "75:\tlearn: 0.1038918\ttest: 0.1460557\tbest: 0.1440164 (57)\ttotal: 107ms\tremaining: 33.9ms\n",
            "76:\tlearn: 0.1027877\ttest: 0.1454852\tbest: 0.1440164 (57)\ttotal: 109ms\tremaining: 32.5ms\n",
            "77:\tlearn: 0.1017704\ttest: 0.1463223\tbest: 0.1440164 (57)\ttotal: 110ms\tremaining: 31.1ms\n",
            "78:\tlearn: 0.1013593\ttest: 0.1468430\tbest: 0.1440164 (57)\ttotal: 112ms\tremaining: 29.7ms\n",
            "79:\tlearn: 0.1005023\ttest: 0.1486744\tbest: 0.1440164 (57)\ttotal: 113ms\tremaining: 28.2ms\n",
            "80:\tlearn: 0.0996992\ttest: 0.1479728\tbest: 0.1440164 (57)\ttotal: 115ms\tremaining: 26.9ms\n",
            "81:\tlearn: 0.0996778\ttest: 0.1481329\tbest: 0.1440164 (57)\ttotal: 116ms\tremaining: 25.5ms\n",
            "82:\tlearn: 0.0993026\ttest: 0.1481082\tbest: 0.1440164 (57)\ttotal: 117ms\tremaining: 24.1ms\n",
            "83:\tlearn: 0.0987502\ttest: 0.1486379\tbest: 0.1440164 (57)\ttotal: 119ms\tremaining: 22.6ms\n",
            "84:\tlearn: 0.0966777\ttest: 0.1491814\tbest: 0.1440164 (57)\ttotal: 121ms\tremaining: 21.3ms\n",
            "85:\tlearn: 0.0965763\ttest: 0.1491977\tbest: 0.1440164 (57)\ttotal: 122ms\tremaining: 19.9ms\n",
            "86:\tlearn: 0.0962000\ttest: 0.1493519\tbest: 0.1440164 (57)\ttotal: 125ms\tremaining: 18.6ms\n",
            "87:\tlearn: 0.0958993\ttest: 0.1497705\tbest: 0.1440164 (57)\ttotal: 126ms\tremaining: 17.2ms\n",
            "88:\tlearn: 0.0948425\ttest: 0.1494420\tbest: 0.1440164 (57)\ttotal: 128ms\tremaining: 15.8ms\n",
            "89:\tlearn: 0.0940790\ttest: 0.1503639\tbest: 0.1440164 (57)\ttotal: 129ms\tremaining: 14.3ms\n",
            "90:\tlearn: 0.0932985\ttest: 0.1504075\tbest: 0.1440164 (57)\ttotal: 130ms\tremaining: 12.9ms\n",
            "91:\tlearn: 0.0928647\ttest: 0.1508900\tbest: 0.1440164 (57)\ttotal: 131ms\tremaining: 11.4ms\n",
            "92:\tlearn: 0.0918176\ttest: 0.1503790\tbest: 0.1440164 (57)\ttotal: 133ms\tremaining: 9.98ms\n",
            "93:\tlearn: 0.0913968\ttest: 0.1493348\tbest: 0.1440164 (57)\ttotal: 134ms\tremaining: 8.54ms\n",
            "94:\tlearn: 0.0913426\ttest: 0.1496567\tbest: 0.1440164 (57)\ttotal: 135ms\tremaining: 7.1ms\n",
            "95:\tlearn: 0.0896686\ttest: 0.1482016\tbest: 0.1440164 (57)\ttotal: 137ms\tremaining: 5.7ms\n",
            "96:\tlearn: 0.0890704\ttest: 0.1479549\tbest: 0.1440164 (57)\ttotal: 138ms\tremaining: 4.28ms\n",
            "97:\tlearn: 0.0885639\ttest: 0.1476267\tbest: 0.1440164 (57)\ttotal: 140ms\tremaining: 2.85ms\n",
            "98:\tlearn: 0.0872206\ttest: 0.1486137\tbest: 0.1440164 (57)\ttotal: 141ms\tremaining: 1.43ms\n",
            "99:\tlearn: 0.0858533\ttest: 0.1488679\tbest: 0.1440164 (57)\ttotal: 142ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1440163738\n",
            "bestIteration = 57\n",
            "\n",
            "38:\tloss: 0.1440164\tbest: 0.1353408 (23)\ttotal: 5.48s\tremaining: 2.95s\n",
            "0:\tlearn: 0.4240427\ttest: 0.4315386\tbest: 0.4315386 (0)\ttotal: 1.12ms\tremaining: 111ms\n",
            "1:\tlearn: 0.3267292\ttest: 0.3289011\tbest: 0.3289011 (1)\ttotal: 2.28ms\tremaining: 112ms\n",
            "2:\tlearn: 0.2939533\ttest: 0.2961292\tbest: 0.2961292 (2)\ttotal: 3.46ms\tremaining: 112ms\n",
            "3:\tlearn: 0.2690182\ttest: 0.2798984\tbest: 0.2798984 (3)\ttotal: 4.61ms\tremaining: 111ms\n",
            "4:\tlearn: 0.2488122\ttest: 0.2557134\tbest: 0.2557134 (4)\ttotal: 5.83ms\tremaining: 111ms\n",
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            "6:\tlearn: 0.2120801\ttest: 0.2121573\tbest: 0.2121573 (6)\ttotal: 8.32ms\tremaining: 111ms\n",
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            "99:\tlearn: 0.0652573\ttest: 0.1414284\tbest: 0.1365469 (79)\ttotal: 151ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1365468678\n",
            "bestIteration = 79\n",
            "\n",
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            "59:\tlearn: 0.6430767\ttest: 0.6441745\tbest: 0.6441745 (59)\ttotal: 98.4ms\tremaining: 65.6ms\n",
            "60:\tlearn: 0.6423644\ttest: 0.6434727\tbest: 0.6434727 (60)\ttotal: 99.7ms\tremaining: 63.8ms\n",
            "61:\tlearn: 0.6414125\ttest: 0.6425181\tbest: 0.6425181 (61)\ttotal: 101ms\tremaining: 61.9ms\n",
            "62:\tlearn: 0.6406036\ttest: 0.6417592\tbest: 0.6417592 (62)\ttotal: 102ms\tremaining: 60.1ms\n",
            "63:\tlearn: 0.6398641\ttest: 0.6410136\tbest: 0.6410136 (63)\ttotal: 104ms\tremaining: 58.6ms\n",
            "64:\tlearn: 0.6388590\ttest: 0.6400300\tbest: 0.6400300 (64)\ttotal: 106ms\tremaining: 57.3ms\n",
            "65:\tlearn: 0.6380056\ttest: 0.6391943\tbest: 0.6391943 (65)\ttotal: 108ms\tremaining: 55.8ms\n",
            "66:\tlearn: 0.6372374\ttest: 0.6384442\tbest: 0.6384442 (66)\ttotal: 110ms\tremaining: 54.3ms\n",
            "67:\tlearn: 0.6365149\ttest: 0.6377658\tbest: 0.6377658 (67)\ttotal: 112ms\tremaining: 52.7ms\n",
            "68:\tlearn: 0.6357327\ttest: 0.6369743\tbest: 0.6369743 (68)\ttotal: 113ms\tremaining: 51ms\n",
            "69:\tlearn: 0.6351408\ttest: 0.6363815\tbest: 0.6363815 (69)\ttotal: 115ms\tremaining: 49.3ms\n",
            "70:\tlearn: 0.6345084\ttest: 0.6357593\tbest: 0.6357593 (70)\ttotal: 116ms\tremaining: 47.5ms\n",
            "71:\tlearn: 0.6335526\ttest: 0.6348101\tbest: 0.6348101 (71)\ttotal: 118ms\tremaining: 45.8ms\n",
            "72:\tlearn: 0.6327506\ttest: 0.6340383\tbest: 0.6340383 (72)\ttotal: 119ms\tremaining: 44.1ms\n",
            "73:\tlearn: 0.6317511\ttest: 0.6330503\tbest: 0.6330503 (73)\ttotal: 121ms\tremaining: 42.4ms\n",
            "74:\tlearn: 0.6310131\ttest: 0.6323451\tbest: 0.6323451 (74)\ttotal: 123ms\tremaining: 40.9ms\n",
            "75:\tlearn: 0.6300251\ttest: 0.6313757\tbest: 0.6313757 (75)\ttotal: 125ms\tremaining: 39.4ms\n",
            "76:\tlearn: 0.6293193\ttest: 0.6306616\tbest: 0.6306616 (76)\ttotal: 127ms\tremaining: 37.8ms\n",
            "77:\tlearn: 0.6286333\ttest: 0.6299796\tbest: 0.6299796 (77)\ttotal: 129ms\tremaining: 36.3ms\n",
            "78:\tlearn: 0.6278750\ttest: 0.6292623\tbest: 0.6292623 (78)\ttotal: 130ms\tremaining: 34.6ms\n",
            "79:\tlearn: 0.6270983\ttest: 0.6284836\tbest: 0.6284836 (79)\ttotal: 131ms\tremaining: 32.9ms\n",
            "80:\tlearn: 0.6264382\ttest: 0.6278316\tbest: 0.6278316 (80)\ttotal: 133ms\tremaining: 31.2ms\n",
            "81:\tlearn: 0.6256946\ttest: 0.6271118\tbest: 0.6271118 (81)\ttotal: 135ms\tremaining: 29.6ms\n",
            "82:\tlearn: 0.6250667\ttest: 0.6264808\tbest: 0.6264808 (82)\ttotal: 137ms\tremaining: 28ms\n",
            "83:\tlearn: 0.6244205\ttest: 0.6258577\tbest: 0.6258577 (83)\ttotal: 138ms\tremaining: 26.4ms\n",
            "84:\tlearn: 0.6237187\ttest: 0.6251625\tbest: 0.6251625 (84)\ttotal: 140ms\tremaining: 24.7ms\n",
            "85:\tlearn: 0.6230213\ttest: 0.6244602\tbest: 0.6244602 (85)\ttotal: 142ms\tremaining: 23.1ms\n",
            "86:\tlearn: 0.6222808\ttest: 0.6237506\tbest: 0.6237506 (86)\ttotal: 145ms\tremaining: 21.7ms\n",
            "87:\tlearn: 0.6216452\ttest: 0.6231136\tbest: 0.6231136 (87)\ttotal: 147ms\tremaining: 20ms\n",
            "88:\tlearn: 0.6210041\ttest: 0.6224785\tbest: 0.6224785 (88)\ttotal: 148ms\tremaining: 18.3ms\n",
            "89:\tlearn: 0.6203391\ttest: 0.6218393\tbest: 0.6218393 (89)\ttotal: 149ms\tremaining: 16.6ms\n",
            "90:\tlearn: 0.6196038\ttest: 0.6211005\tbest: 0.6211005 (90)\ttotal: 151ms\tremaining: 14.9ms\n",
            "91:\tlearn: 0.6188957\ttest: 0.6204058\tbest: 0.6204058 (91)\ttotal: 153ms\tremaining: 13.3ms\n",
            "92:\tlearn: 0.6180078\ttest: 0.6195136\tbest: 0.6195136 (92)\ttotal: 155ms\tremaining: 11.7ms\n",
            "93:\tlearn: 0.6173739\ttest: 0.6188999\tbest: 0.6188999 (93)\ttotal: 157ms\tremaining: 9.99ms\n",
            "94:\tlearn: 0.6167483\ttest: 0.6182681\tbest: 0.6182681 (94)\ttotal: 158ms\tremaining: 8.32ms\n",
            "95:\tlearn: 0.6161142\ttest: 0.6176360\tbest: 0.6176360 (95)\ttotal: 159ms\tremaining: 6.64ms\n",
            "96:\tlearn: 0.6153441\ttest: 0.6168654\tbest: 0.6168654 (96)\ttotal: 161ms\tremaining: 4.97ms\n",
            "97:\tlearn: 0.6147181\ttest: 0.6162362\tbest: 0.6162362 (97)\ttotal: 162ms\tremaining: 3.31ms\n",
            "98:\tlearn: 0.6141083\ttest: 0.6156005\tbest: 0.6156005 (98)\ttotal: 163ms\tremaining: 1.65ms\n",
            "99:\tlearn: 0.6131282\ttest: 0.6146348\tbest: 0.6146348 (99)\ttotal: 165ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.6146348196\n",
            "bestIteration = 99\n",
            "\n",
            "40:\tloss: 0.6146348\tbest: 0.1353408 (23)\ttotal: 5.82s\tremaining: 2.7s\n",
            "0:\tlearn: 0.6044396\ttest: 0.6068979\tbest: 0.6068979 (0)\ttotal: 33.8ms\tremaining: 3.34s\n",
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            "4:\tlearn: 0.4048866\ttest: 0.4085057\tbest: 0.4085057 (4)\ttotal: 40.6ms\tremaining: 771ms\n",
            "5:\tlearn: 0.3829154\ttest: 0.3882775\tbest: 0.3882775 (5)\ttotal: 42.5ms\tremaining: 666ms\n",
            "6:\tlearn: 0.3585348\ttest: 0.3646270\tbest: 0.3646270 (6)\ttotal: 44.1ms\tremaining: 586ms\n",
            "7:\tlearn: 0.3389477\ttest: 0.3435665\tbest: 0.3435665 (7)\ttotal: 45.8ms\tremaining: 527ms\n",
            "8:\tlearn: 0.3250853\ttest: 0.3303513\tbest: 0.3303513 (8)\ttotal: 47.4ms\tremaining: 480ms\n",
            "9:\tlearn: 0.3068226\ttest: 0.3103388\tbest: 0.3103388 (9)\ttotal: 49ms\tremaining: 441ms\n",
            "10:\tlearn: 0.2969274\ttest: 0.3005358\tbest: 0.3005358 (10)\ttotal: 50.8ms\tremaining: 411ms\n",
            "11:\tlearn: 0.2828834\ttest: 0.2838816\tbest: 0.2838816 (11)\ttotal: 52.7ms\tremaining: 386ms\n",
            "12:\tlearn: 0.2718741\ttest: 0.2719818\tbest: 0.2719818 (12)\ttotal: 54.2ms\tremaining: 363ms\n",
            "13:\tlearn: 0.2626738\ttest: 0.2627009\tbest: 0.2627009 (13)\ttotal: 55.9ms\tremaining: 343ms\n",
            "14:\tlearn: 0.2551301\ttest: 0.2568266\tbest: 0.2568266 (14)\ttotal: 57.2ms\tremaining: 324ms\n",
            "15:\tlearn: 0.2505396\ttest: 0.2530219\tbest: 0.2530219 (15)\ttotal: 58.6ms\tremaining: 308ms\n",
            "16:\tlearn: 0.2452372\ttest: 0.2472489\tbest: 0.2472489 (16)\ttotal: 59.9ms\tremaining: 292ms\n",
            "17:\tlearn: 0.2403623\ttest: 0.2453974\tbest: 0.2453974 (17)\ttotal: 61.2ms\tremaining: 279ms\n",
            "18:\tlearn: 0.2351282\ttest: 0.2386370\tbest: 0.2386370 (18)\ttotal: 62.9ms\tremaining: 268ms\n",
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            "20:\tlearn: 0.2262879\ttest: 0.2280477\tbest: 0.2280477 (20)\ttotal: 66.7ms\tremaining: 251ms\n",
            "21:\tlearn: 0.2212800\ttest: 0.2237102\tbest: 0.2237102 (21)\ttotal: 68.2ms\tremaining: 242ms\n",
            "22:\tlearn: 0.2190298\ttest: 0.2226301\tbest: 0.2226301 (22)\ttotal: 69.8ms\tremaining: 234ms\n",
            "23:\tlearn: 0.2173625\ttest: 0.2210547\tbest: 0.2210547 (23)\ttotal: 71.5ms\tremaining: 226ms\n",
            "24:\tlearn: 0.2134218\ttest: 0.2160102\tbest: 0.2160102 (24)\ttotal: 72.9ms\tremaining: 219ms\n",
            "25:\tlearn: 0.2094429\ttest: 0.2111256\tbest: 0.2111256 (25)\ttotal: 75.4ms\tremaining: 215ms\n",
            "26:\tlearn: 0.2059703\ttest: 0.2074165\tbest: 0.2074165 (26)\ttotal: 77.1ms\tremaining: 209ms\n",
            "27:\tlearn: 0.2042317\ttest: 0.2053605\tbest: 0.2053605 (27)\ttotal: 78.7ms\tremaining: 202ms\n",
            "28:\tlearn: 0.2009021\ttest: 0.2014435\tbest: 0.2014435 (28)\ttotal: 80.3ms\tremaining: 197ms\n",
            "29:\tlearn: 0.1951871\ttest: 0.1948679\tbest: 0.1948679 (29)\ttotal: 82.3ms\tremaining: 192ms\n",
            "30:\tlearn: 0.1924387\ttest: 0.1913797\tbest: 0.1913797 (30)\ttotal: 84.3ms\tremaining: 188ms\n",
            "31:\tlearn: 0.1897554\ttest: 0.1882185\tbest: 0.1882185 (31)\ttotal: 86.2ms\tremaining: 183ms\n",
            "32:\tlearn: 0.1874837\ttest: 0.1856312\tbest: 0.1856312 (32)\ttotal: 87.7ms\tremaining: 178ms\n",
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            "35:\tlearn: 0.1817415\ttest: 0.1793959\tbest: 0.1793959 (35)\ttotal: 91.8ms\tremaining: 163ms\n",
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            "45:\tlearn: 0.1696212\ttest: 0.1709427\tbest: 0.1709427 (45)\ttotal: 113ms\tremaining: 132ms\n",
            "46:\tlearn: 0.1689229\ttest: 0.1712281\tbest: 0.1709427 (45)\ttotal: 115ms\tremaining: 129ms\n",
            "47:\tlearn: 0.1675414\ttest: 0.1701161\tbest: 0.1701161 (47)\ttotal: 117ms\tremaining: 127ms\n",
            "48:\tlearn: 0.1665207\ttest: 0.1695868\tbest: 0.1695868 (48)\ttotal: 119ms\tremaining: 124ms\n",
            "49:\tlearn: 0.1647734\ttest: 0.1696402\tbest: 0.1695868 (48)\ttotal: 121ms\tremaining: 121ms\n",
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            "56:\tlearn: 0.1549739\ttest: 0.1616930\tbest: 0.1616930 (56)\ttotal: 136ms\tremaining: 103ms\n",
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            "58:\tlearn: 0.1518775\ttest: 0.1601715\tbest: 0.1601715 (58)\ttotal: 139ms\tremaining: 96.7ms\n",
            "59:\tlearn: 0.1506162\ttest: 0.1592385\tbest: 0.1592385 (59)\ttotal: 141ms\tremaining: 93.7ms\n",
            "60:\tlearn: 0.1498686\ttest: 0.1590809\tbest: 0.1590809 (60)\ttotal: 142ms\tremaining: 90.8ms\n",
            "61:\tlearn: 0.1485815\ttest: 0.1580281\tbest: 0.1580281 (61)\ttotal: 143ms\tremaining: 87.9ms\n",
            "62:\tlearn: 0.1476417\ttest: 0.1582916\tbest: 0.1580281 (61)\ttotal: 145ms\tremaining: 85ms\n",
            "63:\tlearn: 0.1469224\ttest: 0.1581664\tbest: 0.1580281 (61)\ttotal: 146ms\tremaining: 82.2ms\n",
            "64:\tlearn: 0.1457863\ttest: 0.1581231\tbest: 0.1580281 (61)\ttotal: 148ms\tremaining: 79.4ms\n",
            "65:\tlearn: 0.1439195\ttest: 0.1559723\tbest: 0.1559723 (65)\ttotal: 149ms\tremaining: 76.8ms\n",
            "66:\tlearn: 0.1435866\ttest: 0.1553110\tbest: 0.1553110 (66)\ttotal: 150ms\tremaining: 74.1ms\n",
            "67:\tlearn: 0.1433240\ttest: 0.1551939\tbest: 0.1551939 (67)\ttotal: 152ms\tremaining: 71.4ms\n",
            "68:\tlearn: 0.1423366\ttest: 0.1538620\tbest: 0.1538620 (68)\ttotal: 153ms\tremaining: 68.8ms\n",
            "69:\tlearn: 0.1418042\ttest: 0.1539440\tbest: 0.1538620 (68)\ttotal: 155ms\tremaining: 66.3ms\n",
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            "72:\tlearn: 0.1395252\ttest: 0.1544035\tbest: 0.1538620 (68)\ttotal: 159ms\tremaining: 58.8ms\n",
            "73:\tlearn: 0.1388740\ttest: 0.1541728\tbest: 0.1538620 (68)\ttotal: 160ms\tremaining: 56.3ms\n",
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            "78:\tlearn: 0.1328951\ttest: 0.1527444\tbest: 0.1523265 (77)\ttotal: 167ms\tremaining: 44.5ms\n",
            "79:\tlearn: 0.1324952\ttest: 0.1530797\tbest: 0.1523265 (77)\ttotal: 169ms\tremaining: 42.2ms\n",
            "80:\tlearn: 0.1320608\ttest: 0.1531427\tbest: 0.1523265 (77)\ttotal: 170ms\tremaining: 39.9ms\n",
            "81:\tlearn: 0.1314419\ttest: 0.1534063\tbest: 0.1523265 (77)\ttotal: 171ms\tremaining: 37.6ms\n",
            "82:\tlearn: 0.1307909\ttest: 0.1535195\tbest: 0.1523265 (77)\ttotal: 173ms\tremaining: 35.4ms\n",
            "83:\tlearn: 0.1296572\ttest: 0.1529123\tbest: 0.1523265 (77)\ttotal: 174ms\tremaining: 33.2ms\n",
            "84:\tlearn: 0.1290169\ttest: 0.1528968\tbest: 0.1523265 (77)\ttotal: 175ms\tremaining: 31ms\n",
            "85:\tlearn: 0.1277318\ttest: 0.1507789\tbest: 0.1507789 (85)\ttotal: 177ms\tremaining: 28.8ms\n",
            "86:\tlearn: 0.1269486\ttest: 0.1499168\tbest: 0.1499168 (86)\ttotal: 178ms\tremaining: 26.6ms\n",
            "87:\tlearn: 0.1264428\ttest: 0.1500048\tbest: 0.1499168 (86)\ttotal: 179ms\tremaining: 24.5ms\n",
            "88:\tlearn: 0.1256110\ttest: 0.1498762\tbest: 0.1498762 (88)\ttotal: 181ms\tremaining: 22.3ms\n",
            "89:\tlearn: 0.1246268\ttest: 0.1502588\tbest: 0.1498762 (88)\ttotal: 182ms\tremaining: 20.2ms\n",
            "90:\tlearn: 0.1231735\ttest: 0.1488784\tbest: 0.1488784 (90)\ttotal: 183ms\tremaining: 18.1ms\n",
            "91:\tlearn: 0.1227287\ttest: 0.1487149\tbest: 0.1487149 (91)\ttotal: 185ms\tremaining: 16ms\n",
            "92:\tlearn: 0.1222464\ttest: 0.1488282\tbest: 0.1487149 (91)\ttotal: 186ms\tremaining: 14ms\n",
            "93:\tlearn: 0.1215117\ttest: 0.1479580\tbest: 0.1479580 (93)\ttotal: 187ms\tremaining: 12ms\n",
            "94:\tlearn: 0.1203968\ttest: 0.1470688\tbest: 0.1470688 (94)\ttotal: 189ms\tremaining: 9.94ms\n",
            "95:\tlearn: 0.1199491\ttest: 0.1469294\tbest: 0.1469294 (95)\ttotal: 190ms\tremaining: 7.92ms\n",
            "96:\tlearn: 0.1193168\ttest: 0.1454491\tbest: 0.1454491 (96)\ttotal: 192ms\tremaining: 5.92ms\n",
            "97:\tlearn: 0.1185643\ttest: 0.1458165\tbest: 0.1454491 (96)\ttotal: 193ms\tremaining: 3.94ms\n",
            "98:\tlearn: 0.1175710\ttest: 0.1459857\tbest: 0.1454491 (96)\ttotal: 194ms\tremaining: 1.96ms\n",
            "99:\tlearn: 0.1170198\ttest: 0.1460565\tbest: 0.1454491 (96)\ttotal: 195ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1454491254\n",
            "bestIteration = 96\n",
            "\n",
            "41:\tloss: 0.1454491\tbest: 0.1353408 (23)\ttotal: 6.03s\tremaining: 2.58s\n",
            "0:\tlearn: 0.5307982\ttest: 0.5354685\tbest: 0.5354685 (0)\ttotal: 1.38ms\tremaining: 137ms\n",
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            "9:\tlearn: 0.2302577\ttest: 0.2326196\tbest: 0.2326196 (9)\ttotal: 14.8ms\tremaining: 134ms\n",
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            "\n",
            "bestTest = 0.1446928776\n",
            "bestIteration = 56\n",
            "\n",
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            "65:\tlearn: 0.0655369\ttest: 0.1380952\tbest: 0.1380952 (65)\ttotal: 104ms\tremaining: 53.4ms\n",
            "66:\tlearn: 0.0646800\ttest: 0.1385685\tbest: 0.1380952 (65)\ttotal: 105ms\tremaining: 51.9ms\n",
            "67:\tlearn: 0.0635180\ttest: 0.1395167\tbest: 0.1380952 (65)\ttotal: 107ms\tremaining: 50.2ms\n",
            "68:\tlearn: 0.0621810\ttest: 0.1386062\tbest: 0.1380952 (65)\ttotal: 108ms\tremaining: 48.7ms\n",
            "69:\tlearn: 0.0618246\ttest: 0.1379554\tbest: 0.1379554 (69)\ttotal: 110ms\tremaining: 47.1ms\n",
            "70:\tlearn: 0.0602591\ttest: 0.1388237\tbest: 0.1379554 (69)\ttotal: 111ms\tremaining: 45.5ms\n",
            "71:\tlearn: 0.0599804\ttest: 0.1389831\tbest: 0.1379554 (69)\ttotal: 113ms\tremaining: 43.8ms\n",
            "72:\tlearn: 0.0592255\ttest: 0.1373266\tbest: 0.1373266 (72)\ttotal: 114ms\tremaining: 42.1ms\n",
            "73:\tlearn: 0.0580736\ttest: 0.1363545\tbest: 0.1363545 (73)\ttotal: 115ms\tremaining: 40.5ms\n",
            "74:\tlearn: 0.0567630\ttest: 0.1356320\tbest: 0.1356320 (74)\ttotal: 116ms\tremaining: 38.8ms\n",
            "75:\tlearn: 0.0548230\ttest: 0.1322667\tbest: 0.1322667 (75)\ttotal: 118ms\tremaining: 37.3ms\n",
            "76:\tlearn: 0.0541158\ttest: 0.1322891\tbest: 0.1322667 (75)\ttotal: 120ms\tremaining: 35.8ms\n",
            "77:\tlearn: 0.0531729\ttest: 0.1346524\tbest: 0.1322667 (75)\ttotal: 122ms\tremaining: 34.3ms\n",
            "78:\tlearn: 0.0520525\ttest: 0.1344217\tbest: 0.1322667 (75)\ttotal: 124ms\tremaining: 32.8ms\n",
            "79:\tlearn: 0.0504024\ttest: 0.1348996\tbest: 0.1322667 (75)\ttotal: 125ms\tremaining: 31.4ms\n",
            "80:\tlearn: 0.0496004\ttest: 0.1345117\tbest: 0.1322667 (75)\ttotal: 127ms\tremaining: 29.8ms\n",
            "81:\tlearn: 0.0494041\ttest: 0.1345895\tbest: 0.1322667 (75)\ttotal: 129ms\tremaining: 28.3ms\n",
            "82:\tlearn: 0.0486238\ttest: 0.1335336\tbest: 0.1322667 (75)\ttotal: 130ms\tremaining: 26.7ms\n",
            "83:\tlearn: 0.0478645\ttest: 0.1357128\tbest: 0.1322667 (75)\ttotal: 132ms\tremaining: 25.1ms\n",
            "84:\tlearn: 0.0468561\ttest: 0.1368359\tbest: 0.1322667 (75)\ttotal: 134ms\tremaining: 23.6ms\n",
            "85:\tlearn: 0.0456681\ttest: 0.1366431\tbest: 0.1322667 (75)\ttotal: 136ms\tremaining: 22.1ms\n",
            "86:\tlearn: 0.0449204\ttest: 0.1370328\tbest: 0.1322667 (75)\ttotal: 138ms\tremaining: 20.7ms\n",
            "87:\tlearn: 0.0443478\ttest: 0.1377340\tbest: 0.1322667 (75)\ttotal: 140ms\tremaining: 19.1ms\n",
            "88:\tlearn: 0.0438133\ttest: 0.1386457\tbest: 0.1322667 (75)\ttotal: 141ms\tremaining: 17.5ms\n",
            "89:\tlearn: 0.0429183\ttest: 0.1372228\tbest: 0.1322667 (75)\ttotal: 143ms\tremaining: 15.8ms\n",
            "90:\tlearn: 0.0424953\ttest: 0.1369418\tbest: 0.1322667 (75)\ttotal: 144ms\tremaining: 14.3ms\n",
            "91:\tlearn: 0.0414948\ttest: 0.1388911\tbest: 0.1322667 (75)\ttotal: 146ms\tremaining: 12.7ms\n",
            "92:\tlearn: 0.0407855\ttest: 0.1375051\tbest: 0.1322667 (75)\ttotal: 147ms\tremaining: 11.1ms\n",
            "93:\tlearn: 0.0401735\ttest: 0.1352831\tbest: 0.1322667 (75)\ttotal: 149ms\tremaining: 9.48ms\n",
            "94:\tlearn: 0.0401161\ttest: 0.1355187\tbest: 0.1322667 (75)\ttotal: 150ms\tremaining: 7.9ms\n",
            "95:\tlearn: 0.0391373\ttest: 0.1348174\tbest: 0.1322667 (75)\ttotal: 152ms\tremaining: 6.34ms\n",
            "96:\tlearn: 0.0385078\ttest: 0.1350341\tbest: 0.1322667 (75)\ttotal: 154ms\tremaining: 4.76ms\n",
            "97:\tlearn: 0.0378866\ttest: 0.1350197\tbest: 0.1322667 (75)\ttotal: 155ms\tremaining: 3.17ms\n",
            "98:\tlearn: 0.0372955\ttest: 0.1352229\tbest: 0.1322667 (75)\ttotal: 157ms\tremaining: 1.58ms\n",
            "99:\tlearn: 0.0364573\ttest: 0.1352145\tbest: 0.1322667 (75)\ttotal: 158ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1322667405\n",
            "bestIteration = 75\n",
            "\n",
            "43:\tloss: 0.1322667\tbest: 0.1322667 (43)\ttotal: 6.37s\tremaining: 2.31s\n",
            "0:\tlearn: 0.4224987\ttest: 0.4309635\tbest: 0.4309635 (0)\ttotal: 1.76ms\tremaining: 174ms\n",
            "1:\tlearn: 0.3313178\ttest: 0.3367034\tbest: 0.3367034 (1)\ttotal: 3.3ms\tremaining: 162ms\n",
            "2:\tlearn: 0.2938332\ttest: 0.3100236\tbest: 0.3100236 (2)\ttotal: 5ms\tremaining: 162ms\n",
            "3:\tlearn: 0.2489273\ttest: 0.2501012\tbest: 0.2501012 (3)\ttotal: 6.88ms\tremaining: 165ms\n",
            "4:\tlearn: 0.2250565\ttest: 0.2289583\tbest: 0.2289583 (4)\ttotal: 8.7ms\tremaining: 165ms\n",
            "5:\tlearn: 0.2061386\ttest: 0.2083328\tbest: 0.2083328 (5)\ttotal: 10.6ms\tremaining: 167ms\n",
            "6:\tlearn: 0.1960984\ttest: 0.1972307\tbest: 0.1972307 (6)\ttotal: 12.2ms\tremaining: 162ms\n",
            "7:\tlearn: 0.1930326\ttest: 0.1976984\tbest: 0.1972307 (6)\ttotal: 13.6ms\tremaining: 157ms\n",
            "8:\tlearn: 0.1863625\ttest: 0.1888512\tbest: 0.1888512 (8)\ttotal: 15ms\tremaining: 152ms\n",
            "9:\tlearn: 0.1716702\ttest: 0.1733889\tbest: 0.1733889 (9)\ttotal: 16.5ms\tremaining: 149ms\n",
            "10:\tlearn: 0.1655451\ttest: 0.1675580\tbest: 0.1675580 (10)\ttotal: 17.9ms\tremaining: 145ms\n",
            "11:\tlearn: 0.1605439\ttest: 0.1624994\tbest: 0.1624994 (11)\ttotal: 19.3ms\tremaining: 142ms\n",
            "12:\tlearn: 0.1559347\ttest: 0.1599053\tbest: 0.1599053 (12)\ttotal: 20.7ms\tremaining: 139ms\n",
            "13:\tlearn: 0.1522175\ttest: 0.1586228\tbest: 0.1586228 (13)\ttotal: 22.3ms\tremaining: 137ms\n",
            "14:\tlearn: 0.1505318\ttest: 0.1597541\tbest: 0.1586228 (13)\ttotal: 24.1ms\tremaining: 137ms\n",
            "15:\tlearn: 0.1470018\ttest: 0.1585103\tbest: 0.1585103 (15)\ttotal: 26.1ms\tremaining: 137ms\n",
            "16:\tlearn: 0.1447993\ttest: 0.1597191\tbest: 0.1585103 (15)\ttotal: 27.6ms\tremaining: 135ms\n",
            "17:\tlearn: 0.1421033\ttest: 0.1611155\tbest: 0.1585103 (15)\ttotal: 29ms\tremaining: 132ms\n",
            "18:\tlearn: 0.1388868\ttest: 0.1587717\tbest: 0.1585103 (15)\ttotal: 30.3ms\tremaining: 129ms\n",
            "19:\tlearn: 0.1371963\ttest: 0.1582776\tbest: 0.1582776 (19)\ttotal: 31.6ms\tremaining: 127ms\n",
            "20:\tlearn: 0.1337901\ttest: 0.1593746\tbest: 0.1582776 (19)\ttotal: 33ms\tremaining: 124ms\n",
            "21:\tlearn: 0.1293250\ttest: 0.1602541\tbest: 0.1582776 (19)\ttotal: 34.4ms\tremaining: 122ms\n",
            "22:\tlearn: 0.1287070\ttest: 0.1601560\tbest: 0.1582776 (19)\ttotal: 35.8ms\tremaining: 120ms\n",
            "23:\tlearn: 0.1253379\ttest: 0.1627854\tbest: 0.1582776 (19)\ttotal: 37.6ms\tremaining: 119ms\n",
            "24:\tlearn: 0.1205183\ttest: 0.1614400\tbest: 0.1582776 (19)\ttotal: 39.9ms\tremaining: 120ms\n",
            "25:\tlearn: 0.1176049\ttest: 0.1551466\tbest: 0.1551466 (25)\ttotal: 41.9ms\tremaining: 119ms\n",
            "26:\tlearn: 0.1141837\ttest: 0.1534478\tbest: 0.1534478 (26)\ttotal: 43.3ms\tremaining: 117ms\n",
            "27:\tlearn: 0.1120226\ttest: 0.1529837\tbest: 0.1529837 (27)\ttotal: 44.8ms\tremaining: 115ms\n",
            "28:\tlearn: 0.1095825\ttest: 0.1528898\tbest: 0.1528898 (28)\ttotal: 46.1ms\tremaining: 113ms\n",
            "29:\tlearn: 0.1058020\ttest: 0.1486200\tbest: 0.1486200 (29)\ttotal: 47.6ms\tremaining: 111ms\n",
            "30:\tlearn: 0.1043174\ttest: 0.1491825\tbest: 0.1486200 (29)\ttotal: 49.1ms\tremaining: 109ms\n",
            "31:\tlearn: 0.1039823\ttest: 0.1494923\tbest: 0.1486200 (29)\ttotal: 50.5ms\tremaining: 107ms\n",
            "32:\tlearn: 0.0981121\ttest: 0.1431427\tbest: 0.1431427 (32)\ttotal: 52.1ms\tremaining: 106ms\n",
            "33:\tlearn: 0.0951579\ttest: 0.1454632\tbest: 0.1431427 (32)\ttotal: 53.8ms\tremaining: 104ms\n",
            "34:\tlearn: 0.0918444\ttest: 0.1445612\tbest: 0.1431427 (32)\ttotal: 55.9ms\tremaining: 104ms\n",
            "35:\tlearn: 0.0889361\ttest: 0.1359625\tbest: 0.1359625 (35)\ttotal: 58ms\tremaining: 103ms\n",
            "36:\tlearn: 0.0861877\ttest: 0.1374350\tbest: 0.1359625 (35)\ttotal: 59.9ms\tremaining: 102ms\n",
            "37:\tlearn: 0.0823779\ttest: 0.1363611\tbest: 0.1359625 (35)\ttotal: 61.4ms\tremaining: 100ms\n",
            "38:\tlearn: 0.0794658\ttest: 0.1370959\tbest: 0.1359625 (35)\ttotal: 62.9ms\tremaining: 98.3ms\n",
            "39:\tlearn: 0.0785885\ttest: 0.1374679\tbest: 0.1359625 (35)\ttotal: 64.5ms\tremaining: 96.7ms\n",
            "40:\tlearn: 0.0775012\ttest: 0.1390480\tbest: 0.1359625 (35)\ttotal: 66.4ms\tremaining: 95.6ms\n",
            "41:\tlearn: 0.0761136\ttest: 0.1391242\tbest: 0.1359625 (35)\ttotal: 68.5ms\tremaining: 94.7ms\n",
            "42:\tlearn: 0.0746706\ttest: 0.1363017\tbest: 0.1359625 (35)\ttotal: 70.4ms\tremaining: 93.3ms\n",
            "43:\tlearn: 0.0728169\ttest: 0.1372296\tbest: 0.1359625 (35)\ttotal: 73.9ms\tremaining: 94.1ms\n",
            "44:\tlearn: 0.0717114\ttest: 0.1370091\tbest: 0.1359625 (35)\ttotal: 75.7ms\tremaining: 92.5ms\n",
            "45:\tlearn: 0.0696661\ttest: 0.1389531\tbest: 0.1359625 (35)\ttotal: 77.1ms\tremaining: 90.6ms\n",
            "46:\tlearn: 0.0666351\ttest: 0.1340410\tbest: 0.1340410 (46)\ttotal: 78.6ms\tremaining: 88.6ms\n",
            "47:\tlearn: 0.0653135\ttest: 0.1352007\tbest: 0.1340410 (46)\ttotal: 80ms\tremaining: 86.7ms\n",
            "48:\tlearn: 0.0633259\ttest: 0.1320105\tbest: 0.1320105 (48)\ttotal: 81.8ms\tremaining: 85.1ms\n",
            "49:\tlearn: 0.0619032\ttest: 0.1317733\tbest: 0.1317733 (49)\ttotal: 83.5ms\tremaining: 83.5ms\n",
            "50:\tlearn: 0.0605886\ttest: 0.1314620\tbest: 0.1314620 (50)\ttotal: 85.2ms\tremaining: 81.8ms\n",
            "51:\tlearn: 0.0582216\ttest: 0.1299284\tbest: 0.1299284 (51)\ttotal: 87.3ms\tremaining: 80.5ms\n",
            "52:\tlearn: 0.0569145\ttest: 0.1292429\tbest: 0.1292429 (52)\ttotal: 89.1ms\tremaining: 79ms\n",
            "53:\tlearn: 0.0547320\ttest: 0.1315483\tbest: 0.1292429 (52)\ttotal: 90.7ms\tremaining: 77.2ms\n",
            "54:\tlearn: 0.0539179\ttest: 0.1315394\tbest: 0.1292429 (52)\ttotal: 92.1ms\tremaining: 75.4ms\n",
            "55:\tlearn: 0.0528301\ttest: 0.1325509\tbest: 0.1292429 (52)\ttotal: 93.5ms\tremaining: 73.5ms\n",
            "56:\tlearn: 0.0517862\ttest: 0.1328053\tbest: 0.1292429 (52)\ttotal: 94.8ms\tremaining: 71.5ms\n",
            "57:\tlearn: 0.0505754\ttest: 0.1320127\tbest: 0.1292429 (52)\ttotal: 96.2ms\tremaining: 69.7ms\n",
            "58:\tlearn: 0.0501866\ttest: 0.1323685\tbest: 0.1292429 (52)\ttotal: 97.7ms\tremaining: 67.9ms\n",
            "59:\tlearn: 0.0494206\ttest: 0.1321867\tbest: 0.1292429 (52)\ttotal: 99.1ms\tremaining: 66ms\n",
            "60:\tlearn: 0.0482174\ttest: 0.1315526\tbest: 0.1292429 (52)\ttotal: 101ms\tremaining: 64.3ms\n",
            "61:\tlearn: 0.0475838\ttest: 0.1309436\tbest: 0.1292429 (52)\ttotal: 102ms\tremaining: 62.7ms\n",
            "62:\tlearn: 0.0467156\ttest: 0.1327371\tbest: 0.1292429 (52)\ttotal: 104ms\tremaining: 61.2ms\n",
            "63:\tlearn: 0.0455807\ttest: 0.1323025\tbest: 0.1292429 (52)\ttotal: 106ms\tremaining: 59.5ms\n",
            "64:\tlearn: 0.0442111\ttest: 0.1331767\tbest: 0.1292429 (52)\ttotal: 107ms\tremaining: 57.7ms\n",
            "65:\tlearn: 0.0428991\ttest: 0.1300873\tbest: 0.1292429 (52)\ttotal: 108ms\tremaining: 55.8ms\n",
            "66:\tlearn: 0.0419342\ttest: 0.1304836\tbest: 0.1292429 (52)\ttotal: 110ms\tremaining: 54ms\n",
            "67:\tlearn: 0.0412140\ttest: 0.1315217\tbest: 0.1292429 (52)\ttotal: 111ms\tremaining: 52.2ms\n",
            "68:\tlearn: 0.0404269\ttest: 0.1307216\tbest: 0.1292429 (52)\ttotal: 112ms\tremaining: 50.5ms\n",
            "69:\tlearn: 0.0396113\ttest: 0.1317714\tbest: 0.1292429 (52)\ttotal: 114ms\tremaining: 48.7ms\n",
            "70:\tlearn: 0.0387818\ttest: 0.1327080\tbest: 0.1292429 (52)\ttotal: 115ms\tremaining: 47.2ms\n",
            "71:\tlearn: 0.0373183\ttest: 0.1325027\tbest: 0.1292429 (52)\ttotal: 117ms\tremaining: 45.7ms\n",
            "72:\tlearn: 0.0365353\ttest: 0.1327852\tbest: 0.1292429 (52)\ttotal: 120ms\tremaining: 44.2ms\n",
            "73:\tlearn: 0.0360086\ttest: 0.1317932\tbest: 0.1292429 (52)\ttotal: 121ms\tremaining: 42.6ms\n",
            "74:\tlearn: 0.0349777\ttest: 0.1315300\tbest: 0.1292429 (52)\ttotal: 123ms\tremaining: 40.9ms\n",
            "75:\tlearn: 0.0346417\ttest: 0.1310468\tbest: 0.1292429 (52)\ttotal: 124ms\tremaining: 39.1ms\n",
            "76:\tlearn: 0.0339249\ttest: 0.1308163\tbest: 0.1292429 (52)\ttotal: 125ms\tremaining: 37.4ms\n",
            "77:\tlearn: 0.0330568\ttest: 0.1294868\tbest: 0.1292429 (52)\ttotal: 127ms\tremaining: 35.7ms\n",
            "78:\tlearn: 0.0325565\ttest: 0.1294246\tbest: 0.1292429 (52)\ttotal: 128ms\tremaining: 34ms\n",
            "79:\tlearn: 0.0314684\ttest: 0.1282474\tbest: 0.1282474 (79)\ttotal: 130ms\tremaining: 32.4ms\n",
            "80:\tlearn: 0.0307008\ttest: 0.1288337\tbest: 0.1282474 (79)\ttotal: 131ms\tremaining: 30.7ms\n",
            "81:\tlearn: 0.0301971\ttest: 0.1294475\tbest: 0.1282474 (79)\ttotal: 133ms\tremaining: 29.1ms\n",
            "82:\tlearn: 0.0299912\ttest: 0.1300048\tbest: 0.1282474 (79)\ttotal: 135ms\tremaining: 27.6ms\n",
            "83:\tlearn: 0.0289860\ttest: 0.1290051\tbest: 0.1282474 (79)\ttotal: 136ms\tremaining: 25.9ms\n",
            "84:\tlearn: 0.0282895\ttest: 0.1277177\tbest: 0.1277177 (84)\ttotal: 137ms\tremaining: 24.3ms\n",
            "85:\tlearn: 0.0274512\ttest: 0.1283137\tbest: 0.1277177 (84)\ttotal: 139ms\tremaining: 22.6ms\n",
            "86:\tlearn: 0.0267779\ttest: 0.1283023\tbest: 0.1277177 (84)\ttotal: 140ms\tremaining: 21ms\n",
            "87:\tlearn: 0.0264722\ttest: 0.1289144\tbest: 0.1277177 (84)\ttotal: 142ms\tremaining: 19.3ms\n",
            "88:\tlearn: 0.0263124\ttest: 0.1289246\tbest: 0.1277177 (84)\ttotal: 143ms\tremaining: 17.7ms\n",
            "89:\tlearn: 0.0257464\ttest: 0.1301191\tbest: 0.1277177 (84)\ttotal: 145ms\tremaining: 16.1ms\n",
            "90:\tlearn: 0.0250121\ttest: 0.1311322\tbest: 0.1277177 (84)\ttotal: 147ms\tremaining: 14.5ms\n",
            "91:\tlearn: 0.0239279\ttest: 0.1324541\tbest: 0.1277177 (84)\ttotal: 149ms\tremaining: 13ms\n",
            "92:\tlearn: 0.0235313\ttest: 0.1329250\tbest: 0.1277177 (84)\ttotal: 151ms\tremaining: 11.3ms\n",
            "93:\tlearn: 0.0232318\ttest: 0.1317623\tbest: 0.1277177 (84)\ttotal: 153ms\tremaining: 9.73ms\n",
            "94:\tlearn: 0.0230480\ttest: 0.1324026\tbest: 0.1277177 (84)\ttotal: 154ms\tremaining: 8.11ms\n",
            "95:\tlearn: 0.0228528\ttest: 0.1328908\tbest: 0.1277177 (84)\ttotal: 156ms\tremaining: 6.48ms\n",
            "96:\tlearn: 0.0221087\ttest: 0.1341096\tbest: 0.1277177 (84)\ttotal: 157ms\tremaining: 4.86ms\n",
            "97:\tlearn: 0.0217569\ttest: 0.1345456\tbest: 0.1277177 (84)\ttotal: 158ms\tremaining: 3.23ms\n",
            "98:\tlearn: 0.0214947\ttest: 0.1352380\tbest: 0.1277177 (84)\ttotal: 160ms\tremaining: 1.62ms\n",
            "99:\tlearn: 0.0211307\ttest: 0.1355050\tbest: 0.1277177 (84)\ttotal: 162ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1277177277\n",
            "bestIteration = 84\n",
            "\n",
            "44:\tloss: 0.1277177\tbest: 0.1277177 (44)\ttotal: 6.54s\tremaining: 2.18s\n",
            "0:\tlearn: 0.6922037\ttest: 0.6922299\tbest: 0.6922299 (0)\ttotal: 8.67ms\tremaining: 859ms\n",
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            "2:\tlearn: 0.6904613\ttest: 0.6905196\tbest: 0.6905196 (2)\ttotal: 11.6ms\tremaining: 376ms\n",
            "3:\tlearn: 0.6895388\ttest: 0.6896319\tbest: 0.6896319 (3)\ttotal: 13.2ms\tremaining: 316ms\n",
            "4:\tlearn: 0.6887064\ttest: 0.6888315\tbest: 0.6888315 (4)\ttotal: 14.9ms\tremaining: 283ms\n",
            "5:\tlearn: 0.6879451\ttest: 0.6880926\tbest: 0.6880926 (5)\ttotal: 16.7ms\tremaining: 261ms\n",
            "6:\tlearn: 0.6870099\ttest: 0.6871990\tbest: 0.6871990 (6)\ttotal: 18ms\tremaining: 239ms\n",
            "7:\tlearn: 0.6863853\ttest: 0.6865848\tbest: 0.6865848 (7)\ttotal: 19.3ms\tremaining: 222ms\n",
            "8:\tlearn: 0.6855042\ttest: 0.6857336\tbest: 0.6857336 (8)\ttotal: 20.6ms\tremaining: 208ms\n",
            "9:\tlearn: 0.6846452\ttest: 0.6848697\tbest: 0.6848697 (9)\ttotal: 21.9ms\tremaining: 197ms\n",
            "10:\tlearn: 0.6837670\ttest: 0.6840249\tbest: 0.6840249 (10)\ttotal: 23.2ms\tremaining: 188ms\n",
            "11:\tlearn: 0.6830302\ttest: 0.6833030\tbest: 0.6833030 (11)\ttotal: 24.5ms\tremaining: 180ms\n",
            "12:\tlearn: 0.6823208\ttest: 0.6826038\tbest: 0.6826038 (12)\ttotal: 25.8ms\tremaining: 172ms\n",
            "13:\tlearn: 0.6813783\ttest: 0.6817234\tbest: 0.6817234 (13)\ttotal: 27.1ms\tremaining: 167ms\n",
            "14:\tlearn: 0.6806672\ttest: 0.6810222\tbest: 0.6810222 (14)\ttotal: 28.5ms\tremaining: 162ms\n",
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            "\n",
            "bestTest = 0.6150895382\n",
            "bestIteration = 99\n",
            "\n",
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            "70:\tlearn: 0.1455824\ttest: 0.1572197\tbest: 0.1570595 (68)\ttotal: 138ms\tremaining: 56.2ms\n",
            "71:\tlearn: 0.1447261\ttest: 0.1568964\tbest: 0.1568964 (71)\ttotal: 139ms\tremaining: 54.1ms\n",
            "72:\tlearn: 0.1442432\ttest: 0.1566934\tbest: 0.1566934 (72)\ttotal: 141ms\tremaining: 52ms\n",
            "73:\tlearn: 0.1438884\ttest: 0.1562963\tbest: 0.1562963 (73)\ttotal: 142ms\tremaining: 50ms\n",
            "74:\tlearn: 0.1425229\ttest: 0.1545568\tbest: 0.1545568 (74)\ttotal: 144ms\tremaining: 47.9ms\n",
            "75:\tlearn: 0.1416006\ttest: 0.1536419\tbest: 0.1536419 (75)\ttotal: 145ms\tremaining: 45.8ms\n",
            "76:\tlearn: 0.1414568\ttest: 0.1536669\tbest: 0.1536419 (75)\ttotal: 146ms\tremaining: 43.7ms\n",
            "77:\tlearn: 0.1408456\ttest: 0.1526959\tbest: 0.1526959 (77)\ttotal: 148ms\tremaining: 41.6ms\n",
            "78:\tlearn: 0.1400175\ttest: 0.1530878\tbest: 0.1526959 (77)\ttotal: 149ms\tremaining: 39.6ms\n",
            "79:\tlearn: 0.1394195\ttest: 0.1524839\tbest: 0.1524839 (79)\ttotal: 150ms\tremaining: 37.6ms\n",
            "80:\tlearn: 0.1387632\ttest: 0.1520106\tbest: 0.1520106 (80)\ttotal: 152ms\tremaining: 35.6ms\n",
            "81:\tlearn: 0.1377339\ttest: 0.1518851\tbest: 0.1518851 (81)\ttotal: 153ms\tremaining: 33.6ms\n",
            "82:\tlearn: 0.1374595\ttest: 0.1519273\tbest: 0.1518851 (81)\ttotal: 155ms\tremaining: 31.7ms\n",
            "83:\tlearn: 0.1372518\ttest: 0.1518876\tbest: 0.1518851 (81)\ttotal: 156ms\tremaining: 29.7ms\n",
            "84:\tlearn: 0.1364173\ttest: 0.1517286\tbest: 0.1517286 (84)\ttotal: 158ms\tremaining: 27.9ms\n",
            "85:\tlearn: 0.1348697\ttest: 0.1497628\tbest: 0.1497628 (85)\ttotal: 160ms\tremaining: 26ms\n",
            "86:\tlearn: 0.1342141\ttest: 0.1496986\tbest: 0.1496986 (86)\ttotal: 161ms\tremaining: 24ms\n",
            "87:\tlearn: 0.1338033\ttest: 0.1498770\tbest: 0.1496986 (86)\ttotal: 162ms\tremaining: 22.1ms\n",
            "88:\tlearn: 0.1333661\ttest: 0.1496952\tbest: 0.1496952 (88)\ttotal: 163ms\tremaining: 20.2ms\n",
            "89:\tlearn: 0.1323278\ttest: 0.1491046\tbest: 0.1491046 (89)\ttotal: 165ms\tremaining: 18.3ms\n",
            "90:\tlearn: 0.1319362\ttest: 0.1490834\tbest: 0.1490834 (90)\ttotal: 167ms\tremaining: 16.5ms\n",
            "91:\tlearn: 0.1313730\ttest: 0.1490661\tbest: 0.1490661 (91)\ttotal: 168ms\tremaining: 14.6ms\n",
            "92:\tlearn: 0.1308155\ttest: 0.1488272\tbest: 0.1488272 (92)\ttotal: 169ms\tremaining: 12.7ms\n",
            "93:\tlearn: 0.1302507\ttest: 0.1484961\tbest: 0.1484961 (93)\ttotal: 171ms\tremaining: 10.9ms\n",
            "94:\tlearn: 0.1294782\ttest: 0.1485837\tbest: 0.1484961 (93)\ttotal: 172ms\tremaining: 9.07ms\n",
            "95:\tlearn: 0.1284625\ttest: 0.1493424\tbest: 0.1484961 (93)\ttotal: 174ms\tremaining: 7.25ms\n",
            "96:\tlearn: 0.1273159\ttest: 0.1477952\tbest: 0.1477952 (96)\ttotal: 176ms\tremaining: 5.43ms\n",
            "97:\tlearn: 0.1265194\ttest: 0.1476577\tbest: 0.1476577 (97)\ttotal: 177ms\tremaining: 3.61ms\n",
            "98:\tlearn: 0.1260028\ttest: 0.1477630\tbest: 0.1476577 (97)\ttotal: 178ms\tremaining: 1.8ms\n",
            "99:\tlearn: 0.1250707\ttest: 0.1470490\tbest: 0.1470490 (99)\ttotal: 180ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1470489828\n",
            "bestIteration = 99\n",
            "\n",
            "46:\tloss: 0.1470490\tbest: 0.1277177 (44)\ttotal: 6.9s\tremaining: 1.91s\n",
            "0:\tlearn: 0.5315985\ttest: 0.5363748\tbest: 0.5363748 (0)\ttotal: 1.35ms\tremaining: 134ms\n",
            "1:\tlearn: 0.4285740\ttest: 0.4356404\tbest: 0.4356404 (1)\ttotal: 2.87ms\tremaining: 141ms\n",
            "2:\tlearn: 0.3774117\ttest: 0.3865499\tbest: 0.3865499 (2)\ttotal: 4.51ms\tremaining: 146ms\n",
            "3:\tlearn: 0.3216210\ttest: 0.3273369\tbest: 0.3273369 (3)\ttotal: 6.06ms\tremaining: 145ms\n",
            "4:\tlearn: 0.2974528\ttest: 0.3004295\tbest: 0.3004295 (4)\ttotal: 7.65ms\tremaining: 145ms\n",
            "5:\tlearn: 0.2800869\ttest: 0.2867274\tbest: 0.2867274 (5)\ttotal: 9.21ms\tremaining: 144ms\n",
            "6:\tlearn: 0.2640933\ttest: 0.2691698\tbest: 0.2691698 (6)\ttotal: 10.7ms\tremaining: 143ms\n",
            "7:\tlearn: 0.2513408\ttest: 0.2530845\tbest: 0.2530845 (7)\ttotal: 12.1ms\tremaining: 139ms\n",
            "8:\tlearn: 0.2432430\ttest: 0.2469584\tbest: 0.2469584 (8)\ttotal: 13.4ms\tremaining: 136ms\n",
            "9:\tlearn: 0.2340284\ttest: 0.2363642\tbest: 0.2363642 (9)\ttotal: 14.8ms\tremaining: 133ms\n",
            "10:\tlearn: 0.2279742\ttest: 0.2334241\tbest: 0.2334241 (10)\ttotal: 16.2ms\tremaining: 131ms\n",
            "11:\tlearn: 0.2170292\ttest: 0.2192851\tbest: 0.2192851 (11)\ttotal: 17.5ms\tremaining: 128ms\n",
            "12:\tlearn: 0.2098240\ttest: 0.2130651\tbest: 0.2130651 (12)\ttotal: 19ms\tremaining: 127ms\n",
            "13:\tlearn: 0.2029528\ttest: 0.2055701\tbest: 0.2055701 (13)\ttotal: 20.6ms\tremaining: 126ms\n",
            "14:\tlearn: 0.1965832\ttest: 0.2015221\tbest: 0.2015221 (14)\ttotal: 22.1ms\tremaining: 125ms\n",
            "15:\tlearn: 0.1936148\ttest: 0.1987594\tbest: 0.1987594 (15)\ttotal: 23.8ms\tremaining: 125ms\n",
            "16:\tlearn: 0.1886457\ttest: 0.1930928\tbest: 0.1930928 (16)\ttotal: 25.4ms\tremaining: 124ms\n",
            "17:\tlearn: 0.1872121\ttest: 0.1930307\tbest: 0.1930307 (17)\ttotal: 26.8ms\tremaining: 122ms\n",
            "18:\tlearn: 0.1802772\ttest: 0.1835311\tbest: 0.1835311 (18)\ttotal: 28.4ms\tremaining: 121ms\n",
            "19:\tlearn: 0.1775970\ttest: 0.1803649\tbest: 0.1803649 (19)\ttotal: 30ms\tremaining: 120ms\n",
            "20:\tlearn: 0.1747750\ttest: 0.1778040\tbest: 0.1778040 (20)\ttotal: 31.8ms\tremaining: 119ms\n",
            "21:\tlearn: 0.1708637\ttest: 0.1721962\tbest: 0.1721962 (21)\ttotal: 33.8ms\tremaining: 120ms\n",
            "22:\tlearn: 0.1686921\ttest: 0.1716616\tbest: 0.1716616 (22)\ttotal: 35.3ms\tremaining: 118ms\n",
            "23:\tlearn: 0.1655761\ttest: 0.1685334\tbest: 0.1685334 (23)\ttotal: 37.5ms\tremaining: 119ms\n",
            "24:\tlearn: 0.1635089\ttest: 0.1660994\tbest: 0.1660994 (24)\ttotal: 39.5ms\tremaining: 118ms\n",
            "25:\tlearn: 0.1592319\ttest: 0.1620929\tbest: 0.1620929 (25)\ttotal: 41.5ms\tremaining: 118ms\n",
            "26:\tlearn: 0.1573699\ttest: 0.1619232\tbest: 0.1619232 (26)\ttotal: 43.2ms\tremaining: 117ms\n",
            "27:\tlearn: 0.1566749\ttest: 0.1610818\tbest: 0.1610818 (27)\ttotal: 45.2ms\tremaining: 116ms\n",
            "28:\tlearn: 0.1534815\ttest: 0.1584256\tbest: 0.1584256 (28)\ttotal: 46.8ms\tremaining: 115ms\n",
            "29:\tlearn: 0.1524373\ttest: 0.1582222\tbest: 0.1582222 (29)\ttotal: 48.6ms\tremaining: 113ms\n",
            "30:\tlearn: 0.1492557\ttest: 0.1546346\tbest: 0.1546346 (30)\ttotal: 50.4ms\tremaining: 112ms\n",
            "31:\tlearn: 0.1484748\ttest: 0.1545944\tbest: 0.1545944 (31)\ttotal: 52.5ms\tremaining: 112ms\n",
            "32:\tlearn: 0.1469929\ttest: 0.1533183\tbest: 0.1533183 (32)\ttotal: 54.3ms\tremaining: 110ms\n",
            "33:\tlearn: 0.1457736\ttest: 0.1528242\tbest: 0.1528242 (33)\ttotal: 56.1ms\tremaining: 109ms\n",
            "34:\tlearn: 0.1451622\ttest: 0.1527867\tbest: 0.1527867 (34)\ttotal: 57.7ms\tremaining: 107ms\n",
            "35:\tlearn: 0.1442540\ttest: 0.1521456\tbest: 0.1521456 (35)\ttotal: 59.1ms\tremaining: 105ms\n",
            "36:\tlearn: 0.1438353\ttest: 0.1519516\tbest: 0.1519516 (36)\ttotal: 60.6ms\tremaining: 103ms\n",
            "37:\tlearn: 0.1425416\ttest: 0.1517007\tbest: 0.1517007 (37)\ttotal: 62ms\tremaining: 101ms\n",
            "38:\tlearn: 0.1407370\ttest: 0.1501166\tbest: 0.1501166 (38)\ttotal: 63.4ms\tremaining: 99.1ms\n",
            "39:\tlearn: 0.1405057\ttest: 0.1502733\tbest: 0.1501166 (38)\ttotal: 64.8ms\tremaining: 97.1ms\n",
            "40:\tlearn: 0.1387719\ttest: 0.1506533\tbest: 0.1501166 (38)\ttotal: 66.2ms\tremaining: 95.3ms\n",
            "41:\tlearn: 0.1372147\ttest: 0.1500164\tbest: 0.1500164 (41)\ttotal: 67.8ms\tremaining: 93.7ms\n",
            "42:\tlearn: 0.1353638\ttest: 0.1489905\tbest: 0.1489905 (42)\ttotal: 69.8ms\tremaining: 92.5ms\n",
            "43:\tlearn: 0.1348360\ttest: 0.1488111\tbest: 0.1488111 (43)\ttotal: 71.6ms\tremaining: 91.2ms\n",
            "44:\tlearn: 0.1340406\ttest: 0.1487039\tbest: 0.1487039 (44)\ttotal: 73.1ms\tremaining: 89.3ms\n",
            "45:\tlearn: 0.1330489\ttest: 0.1475960\tbest: 0.1475960 (45)\ttotal: 74.4ms\tremaining: 87.3ms\n",
            "46:\tlearn: 0.1310387\ttest: 0.1488391\tbest: 0.1475960 (45)\ttotal: 75.6ms\tremaining: 85.3ms\n",
            "47:\tlearn: 0.1305711\ttest: 0.1488482\tbest: 0.1475960 (45)\ttotal: 77ms\tremaining: 83.4ms\n",
            "48:\tlearn: 0.1292567\ttest: 0.1486640\tbest: 0.1475960 (45)\ttotal: 78.4ms\tremaining: 81.6ms\n",
            "49:\tlearn: 0.1285677\ttest: 0.1469564\tbest: 0.1469564 (49)\ttotal: 79.9ms\tremaining: 79.9ms\n",
            "50:\tlearn: 0.1278314\ttest: 0.1471086\tbest: 0.1469564 (49)\ttotal: 81.6ms\tremaining: 78.4ms\n",
            "51:\tlearn: 0.1264288\ttest: 0.1467560\tbest: 0.1467560 (51)\ttotal: 83.1ms\tremaining: 76.7ms\n",
            "52:\tlearn: 0.1249785\ttest: 0.1460611\tbest: 0.1460611 (52)\ttotal: 84.6ms\tremaining: 75ms\n",
            "53:\tlearn: 0.1241704\ttest: 0.1451584\tbest: 0.1451584 (53)\ttotal: 85.9ms\tremaining: 73.2ms\n",
            "54:\tlearn: 0.1226075\ttest: 0.1462361\tbest: 0.1451584 (53)\ttotal: 87.2ms\tremaining: 71.4ms\n",
            "55:\tlearn: 0.1212160\ttest: 0.1449250\tbest: 0.1449250 (55)\ttotal: 88.5ms\tremaining: 69.6ms\n",
            "56:\tlearn: 0.1194139\ttest: 0.1433253\tbest: 0.1433253 (56)\ttotal: 89.9ms\tremaining: 67.8ms\n",
            "57:\tlearn: 0.1180541\ttest: 0.1439144\tbest: 0.1433253 (56)\ttotal: 91.2ms\tremaining: 66.1ms\n",
            "58:\tlearn: 0.1162149\ttest: 0.1433881\tbest: 0.1433253 (56)\ttotal: 92.6ms\tremaining: 64.4ms\n",
            "59:\tlearn: 0.1160727\ttest: 0.1435046\tbest: 0.1433253 (56)\ttotal: 93.9ms\tremaining: 62.6ms\n",
            "60:\tlearn: 0.1149878\ttest: 0.1442730\tbest: 0.1433253 (56)\ttotal: 95.3ms\tremaining: 60.9ms\n",
            "61:\tlearn: 0.1138891\ttest: 0.1437278\tbest: 0.1433253 (56)\ttotal: 96.7ms\tremaining: 59.3ms\n",
            "62:\tlearn: 0.1123869\ttest: 0.1435152\tbest: 0.1433253 (56)\ttotal: 98.3ms\tremaining: 57.7ms\n",
            "63:\tlearn: 0.1117363\ttest: 0.1437307\tbest: 0.1433253 (56)\ttotal: 99.7ms\tremaining: 56.1ms\n",
            "64:\tlearn: 0.1104374\ttest: 0.1445878\tbest: 0.1433253 (56)\ttotal: 101ms\tremaining: 54.5ms\n",
            "65:\tlearn: 0.1093462\ttest: 0.1452648\tbest: 0.1433253 (56)\ttotal: 103ms\tremaining: 53.1ms\n",
            "66:\tlearn: 0.1085445\ttest: 0.1458362\tbest: 0.1433253 (56)\ttotal: 105ms\tremaining: 51.6ms\n",
            "67:\tlearn: 0.1079002\ttest: 0.1457970\tbest: 0.1433253 (56)\ttotal: 106ms\tremaining: 50ms\n",
            "68:\tlearn: 0.1067387\ttest: 0.1461849\tbest: 0.1433253 (56)\ttotal: 108ms\tremaining: 48.4ms\n",
            "69:\tlearn: 0.1045031\ttest: 0.1424179\tbest: 0.1424179 (69)\ttotal: 109ms\tremaining: 46.8ms\n",
            "70:\tlearn: 0.1037690\ttest: 0.1429632\tbest: 0.1424179 (69)\ttotal: 110ms\tremaining: 45.1ms\n",
            "71:\tlearn: 0.1022440\ttest: 0.1428268\tbest: 0.1424179 (69)\ttotal: 112ms\tremaining: 43.5ms\n",
            "72:\tlearn: 0.1009528\ttest: 0.1433657\tbest: 0.1424179 (69)\ttotal: 113ms\tremaining: 41.8ms\n",
            "73:\tlearn: 0.0996844\ttest: 0.1432967\tbest: 0.1424179 (69)\ttotal: 115ms\tremaining: 40.2ms\n",
            "74:\tlearn: 0.0987446\ttest: 0.1436536\tbest: 0.1424179 (69)\ttotal: 116ms\tremaining: 38.6ms\n",
            "75:\tlearn: 0.0972620\ttest: 0.1436956\tbest: 0.1424179 (69)\ttotal: 117ms\tremaining: 37ms\n",
            "76:\tlearn: 0.0950025\ttest: 0.1413425\tbest: 0.1413425 (76)\ttotal: 119ms\tremaining: 35.5ms\n",
            "77:\tlearn: 0.0939598\ttest: 0.1407663\tbest: 0.1407663 (77)\ttotal: 120ms\tremaining: 33.9ms\n",
            "78:\tlearn: 0.0933554\ttest: 0.1404779\tbest: 0.1404779 (78)\ttotal: 122ms\tremaining: 32.4ms\n",
            "79:\tlearn: 0.0926818\ttest: 0.1408865\tbest: 0.1404779 (78)\ttotal: 123ms\tremaining: 30.8ms\n",
            "80:\tlearn: 0.0907667\ttest: 0.1410827\tbest: 0.1404779 (78)\ttotal: 124ms\tremaining: 29.2ms\n",
            "81:\tlearn: 0.0902805\ttest: 0.1409875\tbest: 0.1404779 (78)\ttotal: 126ms\tremaining: 27.6ms\n",
            "82:\tlearn: 0.0891276\ttest: 0.1406985\tbest: 0.1404779 (78)\ttotal: 127ms\tremaining: 26ms\n",
            "83:\tlearn: 0.0881425\ttest: 0.1407775\tbest: 0.1404779 (78)\ttotal: 129ms\tremaining: 24.5ms\n",
            "84:\tlearn: 0.0875310\ttest: 0.1408616\tbest: 0.1404779 (78)\ttotal: 130ms\tremaining: 23ms\n",
            "85:\tlearn: 0.0869323\ttest: 0.1407175\tbest: 0.1404779 (78)\ttotal: 132ms\tremaining: 21.5ms\n",
            "86:\tlearn: 0.0856135\ttest: 0.1417143\tbest: 0.1404779 (78)\ttotal: 134ms\tremaining: 20ms\n",
            "87:\tlearn: 0.0852388\ttest: 0.1408297\tbest: 0.1404779 (78)\ttotal: 136ms\tremaining: 18.5ms\n",
            "88:\tlearn: 0.0845990\ttest: 0.1404975\tbest: 0.1404779 (78)\ttotal: 137ms\tremaining: 16.9ms\n",
            "89:\tlearn: 0.0832869\ttest: 0.1408405\tbest: 0.1404779 (78)\ttotal: 138ms\tremaining: 15.4ms\n",
            "90:\tlearn: 0.0827741\ttest: 0.1410953\tbest: 0.1404779 (78)\ttotal: 140ms\tremaining: 13.8ms\n",
            "91:\tlearn: 0.0821284\ttest: 0.1415792\tbest: 0.1404779 (78)\ttotal: 141ms\tremaining: 12.3ms\n",
            "92:\tlearn: 0.0811144\ttest: 0.1416477\tbest: 0.1404779 (78)\ttotal: 142ms\tremaining: 10.7ms\n",
            "93:\tlearn: 0.0801754\ttest: 0.1413364\tbest: 0.1404779 (78)\ttotal: 144ms\tremaining: 9.17ms\n",
            "94:\tlearn: 0.0795594\ttest: 0.1417761\tbest: 0.1404779 (78)\ttotal: 145ms\tremaining: 7.64ms\n",
            "95:\tlearn: 0.0790433\ttest: 0.1422737\tbest: 0.1404779 (78)\ttotal: 147ms\tremaining: 6.11ms\n",
            "96:\tlearn: 0.0782835\ttest: 0.1419087\tbest: 0.1404779 (78)\ttotal: 148ms\tremaining: 4.59ms\n",
            "97:\tlearn: 0.0771299\ttest: 0.1424454\tbest: 0.1404779 (78)\ttotal: 150ms\tremaining: 3.06ms\n",
            "98:\tlearn: 0.0770756\ttest: 0.1425899\tbest: 0.1404779 (78)\ttotal: 152ms\tremaining: 1.53ms\n",
            "99:\tlearn: 0.0760319\ttest: 0.1427110\tbest: 0.1404779 (78)\ttotal: 153ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1404779193\n",
            "bestIteration = 78\n",
            "\n",
            "47:\tloss: 0.1404779\tbest: 0.1277177 (44)\ttotal: 7.07s\tremaining: 1.77s\n",
            "0:\tlearn: 0.4713888\ttest: 0.4781853\tbest: 0.4781853 (0)\ttotal: 2.51ms\tremaining: 249ms\n",
            "1:\tlearn: 0.3605852\ttest: 0.3693518\tbest: 0.3693518 (1)\ttotal: 4.36ms\tremaining: 214ms\n",
            "2:\tlearn: 0.3178465\ttest: 0.3254315\tbest: 0.3254315 (2)\ttotal: 6.12ms\tremaining: 198ms\n",
            "3:\tlearn: 0.2727046\ttest: 0.2787044\tbest: 0.2787044 (3)\ttotal: 7.57ms\tremaining: 182ms\n",
            "4:\tlearn: 0.2470587\ttest: 0.2478933\tbest: 0.2478933 (4)\ttotal: 9.13ms\tremaining: 174ms\n",
            "5:\tlearn: 0.2280242\ttest: 0.2278267\tbest: 0.2278267 (5)\ttotal: 10.6ms\tremaining: 166ms\n",
            "6:\tlearn: 0.2162527\ttest: 0.2191693\tbest: 0.2191693 (6)\ttotal: 11.9ms\tremaining: 159ms\n",
            "7:\tlearn: 0.2118544\ttest: 0.2153723\tbest: 0.2153723 (7)\ttotal: 13.4ms\tremaining: 154ms\n",
            "8:\tlearn: 0.2037320\ttest: 0.2075884\tbest: 0.2075884 (8)\ttotal: 14.7ms\tremaining: 149ms\n",
            "9:\tlearn: 0.1938479\ttest: 0.1942938\tbest: 0.1942938 (9)\ttotal: 16.1ms\tremaining: 145ms\n",
            "10:\tlearn: 0.1912912\ttest: 0.1940659\tbest: 0.1940659 (10)\ttotal: 17.7ms\tremaining: 143ms\n",
            "11:\tlearn: 0.1889268\ttest: 0.1925979\tbest: 0.1925979 (11)\ttotal: 19ms\tremaining: 140ms\n",
            "12:\tlearn: 0.1824636\ttest: 0.1866417\tbest: 0.1866417 (12)\ttotal: 20.5ms\tremaining: 137ms\n",
            "13:\tlearn: 0.1781144\ttest: 0.1819692\tbest: 0.1819692 (13)\ttotal: 21.9ms\tremaining: 135ms\n",
            "14:\tlearn: 0.1755219\ttest: 0.1821562\tbest: 0.1819692 (13)\ttotal: 23.4ms\tremaining: 132ms\n",
            "15:\tlearn: 0.1702176\ttest: 0.1782686\tbest: 0.1782686 (15)\ttotal: 24.8ms\tremaining: 130ms\n",
            "16:\tlearn: 0.1696048\ttest: 0.1777438\tbest: 0.1777438 (16)\ttotal: 26.2ms\tremaining: 128ms\n",
            "17:\tlearn: 0.1654146\ttest: 0.1758874\tbest: 0.1758874 (17)\ttotal: 27.5ms\tremaining: 125ms\n",
            "18:\tlearn: 0.1624754\ttest: 0.1700119\tbest: 0.1700119 (18)\ttotal: 28.9ms\tremaining: 123ms\n",
            "19:\tlearn: 0.1584780\ttest: 0.1656587\tbest: 0.1656587 (19)\ttotal: 30.2ms\tremaining: 121ms\n",
            "20:\tlearn: 0.1559811\ttest: 0.1652920\tbest: 0.1652920 (20)\ttotal: 31.5ms\tremaining: 119ms\n",
            "21:\tlearn: 0.1541120\ttest: 0.1637233\tbest: 0.1637233 (21)\ttotal: 32.9ms\tremaining: 116ms\n",
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            "92:\tlearn: 0.0510574\ttest: 0.1399883\tbest: 0.1399883 (92)\ttotal: 139ms\tremaining: 10.5ms\n",
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            "94:\tlearn: 0.0499568\ttest: 0.1408306\tbest: 0.1399883 (92)\ttotal: 142ms\tremaining: 7.47ms\n",
            "95:\tlearn: 0.0490480\ttest: 0.1421213\tbest: 0.1399883 (92)\ttotal: 143ms\tremaining: 5.97ms\n",
            "96:\tlearn: 0.0482807\ttest: 0.1423693\tbest: 0.1399883 (92)\ttotal: 145ms\tremaining: 4.47ms\n",
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            "99:\tlearn: 0.0460829\ttest: 0.1423974\tbest: 0.1399883 (92)\ttotal: 149ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1399882847\n",
            "bestIteration = 92\n",
            "\n",
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            "74:\tlearn: 0.0456508\ttest: 0.1657938\tbest: 0.1550068 (33)\ttotal: 100ms\tremaining: 33.4ms\n",
            "75:\tlearn: 0.0450365\ttest: 0.1651207\tbest: 0.1550068 (33)\ttotal: 102ms\tremaining: 32.1ms\n",
            "76:\tlearn: 0.0434381\ttest: 0.1646685\tbest: 0.1550068 (33)\ttotal: 103ms\tremaining: 30.7ms\n",
            "77:\tlearn: 0.0426143\ttest: 0.1648095\tbest: 0.1550068 (33)\ttotal: 104ms\tremaining: 29.4ms\n",
            "78:\tlearn: 0.0415444\ttest: 0.1634778\tbest: 0.1550068 (33)\ttotal: 106ms\tremaining: 28ms\n",
            "79:\tlearn: 0.0404521\ttest: 0.1659584\tbest: 0.1550068 (33)\ttotal: 107ms\tremaining: 26.7ms\n",
            "80:\tlearn: 0.0395464\ttest: 0.1670078\tbest: 0.1550068 (33)\ttotal: 108ms\tremaining: 25.4ms\n",
            "81:\tlearn: 0.0391404\ttest: 0.1677356\tbest: 0.1550068 (33)\ttotal: 110ms\tremaining: 24ms\n",
            "82:\tlearn: 0.0388781\ttest: 0.1677689\tbest: 0.1550068 (33)\ttotal: 111ms\tremaining: 22.7ms\n",
            "83:\tlearn: 0.0380878\ttest: 0.1691780\tbest: 0.1550068 (33)\ttotal: 112ms\tremaining: 21.3ms\n",
            "84:\tlearn: 0.0373853\ttest: 0.1696459\tbest: 0.1550068 (33)\ttotal: 113ms\tremaining: 20ms\n",
            "85:\tlearn: 0.0369690\ttest: 0.1701387\tbest: 0.1550068 (33)\ttotal: 115ms\tremaining: 18.7ms\n",
            "86:\tlearn: 0.0365229\ttest: 0.1701459\tbest: 0.1550068 (33)\ttotal: 116ms\tremaining: 17.4ms\n",
            "87:\tlearn: 0.0363678\ttest: 0.1695927\tbest: 0.1550068 (33)\ttotal: 118ms\tremaining: 16ms\n",
            "88:\tlearn: 0.0354936\ttest: 0.1716212\tbest: 0.1550068 (33)\ttotal: 119ms\tremaining: 14.7ms\n",
            "89:\tlearn: 0.0348830\ttest: 0.1744236\tbest: 0.1550068 (33)\ttotal: 120ms\tremaining: 13.4ms\n",
            "90:\tlearn: 0.0337756\ttest: 0.1722174\tbest: 0.1550068 (33)\ttotal: 122ms\tremaining: 12ms\n",
            "91:\tlearn: 0.0326337\ttest: 0.1743050\tbest: 0.1550068 (33)\ttotal: 123ms\tremaining: 10.7ms\n",
            "92:\tlearn: 0.0319112\ttest: 0.1741820\tbest: 0.1550068 (33)\ttotal: 125ms\tremaining: 9.39ms\n",
            "93:\tlearn: 0.0312629\ttest: 0.1719134\tbest: 0.1550068 (33)\ttotal: 126ms\tremaining: 8.05ms\n",
            "94:\tlearn: 0.0309204\ttest: 0.1728734\tbest: 0.1550068 (33)\ttotal: 127ms\tremaining: 6.71ms\n",
            "95:\tlearn: 0.0304852\ttest: 0.1723082\tbest: 0.1550068 (33)\ttotal: 129ms\tremaining: 5.36ms\n",
            "96:\tlearn: 0.0295540\ttest: 0.1734883\tbest: 0.1550068 (33)\ttotal: 130ms\tremaining: 4.03ms\n",
            "97:\tlearn: 0.0285691\ttest: 0.1719688\tbest: 0.1550068 (33)\ttotal: 131ms\tremaining: 2.68ms\n",
            "98:\tlearn: 0.0283376\ttest: 0.1719142\tbest: 0.1550068 (33)\ttotal: 133ms\tremaining: 1.34ms\n",
            "99:\tlearn: 0.0279557\ttest: 0.1714793\tbest: 0.1550068 (33)\ttotal: 134ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1550067577\n",
            "bestIteration = 33\n",
            "\n",
            "49:\tloss: 0.1550068\tbest: 0.1277177 (44)\ttotal: 7.37s\tremaining: 1.47s\n",
            "0:\tlearn: 0.6922077\ttest: 0.6922344\tbest: 0.6922344 (0)\ttotal: 1.43ms\tremaining: 142ms\n",
            "1:\tlearn: 0.6912537\ttest: 0.6913063\tbest: 0.6913063 (1)\ttotal: 2.73ms\tremaining: 134ms\n",
            "2:\tlearn: 0.6904679\ttest: 0.6905250\tbest: 0.6905250 (2)\ttotal: 4.07ms\tremaining: 132ms\n",
            "3:\tlearn: 0.6895474\ttest: 0.6896391\tbest: 0.6896391 (3)\ttotal: 5.35ms\tremaining: 128ms\n",
            "4:\tlearn: 0.6887153\ttest: 0.6888387\tbest: 0.6888387 (4)\ttotal: 6.61ms\tremaining: 126ms\n",
            "5:\tlearn: 0.6879541\ttest: 0.6880996\tbest: 0.6880996 (5)\ttotal: 7.91ms\tremaining: 124ms\n",
            "6:\tlearn: 0.6870214\ttest: 0.6872076\tbest: 0.6872076 (6)\ttotal: 9.29ms\tremaining: 123ms\n",
            "7:\tlearn: 0.6863970\ttest: 0.6865935\tbest: 0.6865935 (7)\ttotal: 10.7ms\tremaining: 123ms\n",
            "8:\tlearn: 0.6855176\ttest: 0.6857430\tbest: 0.6857430 (8)\ttotal: 12ms\tremaining: 121ms\n",
            "9:\tlearn: 0.6846587\ttest: 0.6848792\tbest: 0.6848792 (9)\ttotal: 13.2ms\tremaining: 119ms\n",
            "10:\tlearn: 0.6837810\ttest: 0.6840340\tbest: 0.6840340 (10)\ttotal: 14.5ms\tremaining: 117ms\n",
            "11:\tlearn: 0.6830458\ttest: 0.6833143\tbest: 0.6833143 (11)\ttotal: 15.8ms\tremaining: 116ms\n",
            "12:\tlearn: 0.6823370\ttest: 0.6826151\tbest: 0.6826151 (12)\ttotal: 17.1ms\tremaining: 114ms\n",
            "13:\tlearn: 0.6814032\ttest: 0.6817424\tbest: 0.6817424 (13)\ttotal: 18.4ms\tremaining: 113ms\n",
            "14:\tlearn: 0.6806931\ttest: 0.6810421\tbest: 0.6810421 (14)\ttotal: 19.7ms\tremaining: 111ms\n",
            "15:\tlearn: 0.6798020\ttest: 0.6801605\tbest: 0.6801605 (15)\ttotal: 21ms\tremaining: 110ms\n",
            "16:\tlearn: 0.6789251\ttest: 0.6792691\tbest: 0.6792691 (16)\ttotal: 22.3ms\tremaining: 109ms\n",
            "17:\tlearn: 0.6781844\ttest: 0.6785544\tbest: 0.6785544 (17)\ttotal: 23.7ms\tremaining: 108ms\n",
            "18:\tlearn: 0.6771543\ttest: 0.6775373\tbest: 0.6775373 (18)\ttotal: 25.2ms\tremaining: 108ms\n",
            "19:\tlearn: 0.6764137\ttest: 0.6768218\tbest: 0.6768218 (19)\ttotal: 26.9ms\tremaining: 108ms\n",
            "20:\tlearn: 0.6755595\ttest: 0.6759988\tbest: 0.6759988 (20)\ttotal: 28.3ms\tremaining: 106ms\n",
            "21:\tlearn: 0.6747564\ttest: 0.6752055\tbest: 0.6752055 (21)\ttotal: 29.6ms\tremaining: 105ms\n",
            "22:\tlearn: 0.6738203\ttest: 0.6742995\tbest: 0.6742995 (22)\ttotal: 30.9ms\tremaining: 103ms\n",
            "23:\tlearn: 0.6729687\ttest: 0.6734881\tbest: 0.6734881 (23)\ttotal: 32.2ms\tremaining: 102ms\n",
            "24:\tlearn: 0.6720610\ttest: 0.6726178\tbest: 0.6726178 (24)\ttotal: 33.9ms\tremaining: 102ms\n",
            "25:\tlearn: 0.6712479\ttest: 0.6718024\tbest: 0.6718024 (25)\ttotal: 35.3ms\tremaining: 100ms\n",
            "26:\tlearn: 0.6705321\ttest: 0.6710988\tbest: 0.6710988 (26)\ttotal: 37ms\tremaining: 100ms\n",
            "27:\tlearn: 0.6696592\ttest: 0.6702293\tbest: 0.6702293 (27)\ttotal: 38.6ms\tremaining: 99.1ms\n",
            "28:\tlearn: 0.6689508\ttest: 0.6695399\tbest: 0.6695399 (28)\ttotal: 39.9ms\tremaining: 97.7ms\n",
            "29:\tlearn: 0.6679619\ttest: 0.6685760\tbest: 0.6685760 (29)\ttotal: 41.3ms\tremaining: 96.3ms\n",
            "30:\tlearn: 0.6671319\ttest: 0.6677542\tbest: 0.6677542 (30)\ttotal: 42.7ms\tremaining: 95.1ms\n",
            "31:\tlearn: 0.6662729\ttest: 0.6669072\tbest: 0.6669072 (31)\ttotal: 44.2ms\tremaining: 94ms\n",
            "32:\tlearn: 0.6654481\ttest: 0.6660855\tbest: 0.6660855 (32)\ttotal: 45.7ms\tremaining: 92.8ms\n",
            "33:\tlearn: 0.6644410\ttest: 0.6650919\tbest: 0.6650919 (33)\ttotal: 47.1ms\tremaining: 91.4ms\n",
            "34:\tlearn: 0.6636195\ttest: 0.6642623\tbest: 0.6642623 (34)\ttotal: 48.4ms\tremaining: 89.9ms\n",
            "35:\tlearn: 0.6629246\ttest: 0.6635400\tbest: 0.6635400 (35)\ttotal: 49.8ms\tremaining: 88.5ms\n",
            "36:\tlearn: 0.6620826\ttest: 0.6626721\tbest: 0.6626721 (36)\ttotal: 51.1ms\tremaining: 87.1ms\n",
            "37:\tlearn: 0.6612196\ttest: 0.6618233\tbest: 0.6618233 (37)\ttotal: 52.4ms\tremaining: 85.6ms\n",
            "38:\tlearn: 0.6604015\ttest: 0.6610399\tbest: 0.6610399 (38)\ttotal: 53.9ms\tremaining: 84.2ms\n",
            "39:\tlearn: 0.6596599\ttest: 0.6602983\tbest: 0.6602983 (39)\ttotal: 55.2ms\tremaining: 82.9ms\n",
            "40:\tlearn: 0.6589564\ttest: 0.6595938\tbest: 0.6595938 (40)\ttotal: 56.6ms\tremaining: 81.4ms\n",
            "41:\tlearn: 0.6581427\ttest: 0.6588220\tbest: 0.6588220 (41)\ttotal: 57.9ms\tremaining: 79.9ms\n",
            "42:\tlearn: 0.6571792\ttest: 0.6578674\tbest: 0.6578674 (42)\ttotal: 59.2ms\tremaining: 78.5ms\n",
            "43:\tlearn: 0.6563783\ttest: 0.6570965\tbest: 0.6570965 (43)\ttotal: 60.6ms\tremaining: 77.1ms\n",
            "44:\tlearn: 0.6557249\ttest: 0.6564361\tbest: 0.6564361 (44)\ttotal: 61.9ms\tremaining: 75.6ms\n",
            "45:\tlearn: 0.6550099\ttest: 0.6557258\tbest: 0.6557258 (45)\ttotal: 63.4ms\tremaining: 74.4ms\n",
            "46:\tlearn: 0.6543517\ttest: 0.6550634\tbest: 0.6550634 (46)\ttotal: 64.7ms\tremaining: 73ms\n",
            "47:\tlearn: 0.6536135\ttest: 0.6543161\tbest: 0.6543161 (47)\ttotal: 66ms\tremaining: 71.5ms\n",
            "48:\tlearn: 0.6529388\ttest: 0.6536718\tbest: 0.6536718 (48)\ttotal: 67.3ms\tremaining: 70.1ms\n",
            "49:\tlearn: 0.6521690\ttest: 0.6529049\tbest: 0.6529049 (49)\ttotal: 68.6ms\tremaining: 68.6ms\n",
            "50:\tlearn: 0.6514616\ttest: 0.6522269\tbest: 0.6522269 (50)\ttotal: 69.9ms\tremaining: 67.1ms\n",
            "51:\tlearn: 0.6506811\ttest: 0.6514531\tbest: 0.6514531 (51)\ttotal: 71.2ms\tremaining: 65.7ms\n",
            "52:\tlearn: 0.6496109\ttest: 0.6504067\tbest: 0.6504067 (52)\ttotal: 72.6ms\tremaining: 64.4ms\n",
            "53:\tlearn: 0.6486609\ttest: 0.6494713\tbest: 0.6494713 (53)\ttotal: 73.9ms\tremaining: 63ms\n",
            "54:\tlearn: 0.6478345\ttest: 0.6486602\tbest: 0.6486602 (54)\ttotal: 75.2ms\tremaining: 61.5ms\n",
            "55:\tlearn: 0.6471853\ttest: 0.6480013\tbest: 0.6480013 (55)\ttotal: 76.4ms\tremaining: 60.1ms\n",
            "56:\tlearn: 0.6464215\ttest: 0.6472747\tbest: 0.6472747 (56)\ttotal: 77.7ms\tremaining: 58.6ms\n",
            "57:\tlearn: 0.6455507\ttest: 0.6464275\tbest: 0.6464275 (57)\ttotal: 79.2ms\tremaining: 57.4ms\n",
            "58:\tlearn: 0.6448562\ttest: 0.6457517\tbest: 0.6457517 (58)\ttotal: 80.9ms\tremaining: 56.2ms\n",
            "59:\tlearn: 0.6441549\ttest: 0.6450572\tbest: 0.6450572 (59)\ttotal: 82.5ms\tremaining: 55ms\n",
            "60:\tlearn: 0.6433803\ttest: 0.6442857\tbest: 0.6442857 (60)\ttotal: 84.1ms\tremaining: 53.7ms\n",
            "61:\tlearn: 0.6426498\ttest: 0.6435474\tbest: 0.6435474 (61)\ttotal: 85.6ms\tremaining: 52.5ms\n",
            "62:\tlearn: 0.6418612\ttest: 0.6427620\tbest: 0.6427620 (62)\ttotal: 87.2ms\tremaining: 51.2ms\n",
            "63:\tlearn: 0.6410787\ttest: 0.6419618\tbest: 0.6419618 (63)\ttotal: 88.7ms\tremaining: 49.9ms\n",
            "64:\tlearn: 0.6401564\ttest: 0.6410522\tbest: 0.6410522 (64)\ttotal: 90.6ms\tremaining: 48.8ms\n",
            "65:\tlearn: 0.6393878\ttest: 0.6403093\tbest: 0.6403093 (65)\ttotal: 92.4ms\tremaining: 47.6ms\n",
            "66:\tlearn: 0.6387292\ttest: 0.6396325\tbest: 0.6396325 (66)\ttotal: 94ms\tremaining: 46.3ms\n",
            "67:\tlearn: 0.6380823\ttest: 0.6389953\tbest: 0.6389953 (67)\ttotal: 95.5ms\tremaining: 44.9ms\n",
            "68:\tlearn: 0.6373011\ttest: 0.6382314\tbest: 0.6382314 (68)\ttotal: 97.5ms\tremaining: 43.8ms\n",
            "69:\tlearn: 0.6366380\ttest: 0.6376061\tbest: 0.6376061 (69)\ttotal: 99.5ms\tremaining: 42.6ms\n",
            "70:\tlearn: 0.6360071\ttest: 0.6369746\tbest: 0.6369746 (70)\ttotal: 101ms\tremaining: 41.3ms\n",
            "71:\tlearn: 0.6353215\ttest: 0.6362977\tbest: 0.6362977 (71)\ttotal: 103ms\tremaining: 40.1ms\n",
            "72:\tlearn: 0.6346697\ttest: 0.6356604\tbest: 0.6356604 (72)\ttotal: 104ms\tremaining: 38.6ms\n",
            "73:\tlearn: 0.6341338\ttest: 0.6351355\tbest: 0.6351355 (73)\ttotal: 106ms\tremaining: 37.2ms\n",
            "74:\tlearn: 0.6333636\ttest: 0.6343903\tbest: 0.6343903 (74)\ttotal: 107ms\tremaining: 35.7ms\n",
            "75:\tlearn: 0.6325722\ttest: 0.6335938\tbest: 0.6335938 (75)\ttotal: 108ms\tremaining: 34.2ms\n",
            "76:\tlearn: 0.6319500\ttest: 0.6329925\tbest: 0.6329925 (76)\ttotal: 110ms\tremaining: 32.8ms\n",
            "77:\tlearn: 0.6313321\ttest: 0.6323923\tbest: 0.6323923 (77)\ttotal: 111ms\tremaining: 31.3ms\n",
            "78:\tlearn: 0.6307040\ttest: 0.6317771\tbest: 0.6317771 (78)\ttotal: 112ms\tremaining: 29.9ms\n",
            "79:\tlearn: 0.6299681\ttest: 0.6310760\tbest: 0.6310760 (79)\ttotal: 114ms\tremaining: 28.5ms\n",
            "80:\tlearn: 0.6293456\ttest: 0.6304673\tbest: 0.6304673 (80)\ttotal: 115ms\tremaining: 27ms\n",
            "81:\tlearn: 0.6285582\ttest: 0.6297193\tbest: 0.6297193 (81)\ttotal: 116ms\tremaining: 25.6ms\n",
            "82:\tlearn: 0.6277597\ttest: 0.6289195\tbest: 0.6289195 (82)\ttotal: 118ms\tremaining: 24.1ms\n",
            "83:\tlearn: 0.6269922\ttest: 0.6281571\tbest: 0.6281571 (83)\ttotal: 119ms\tremaining: 22.7ms\n",
            "84:\tlearn: 0.6263560\ttest: 0.6275283\tbest: 0.6275283 (84)\ttotal: 120ms\tremaining: 21.2ms\n",
            "85:\tlearn: 0.6255949\ttest: 0.6268004\tbest: 0.6268004 (85)\ttotal: 122ms\tremaining: 19.8ms\n",
            "86:\tlearn: 0.6250313\ttest: 0.6262429\tbest: 0.6262429 (86)\ttotal: 123ms\tremaining: 18.3ms\n",
            "87:\tlearn: 0.6244025\ttest: 0.6256323\tbest: 0.6256323 (87)\ttotal: 124ms\tremaining: 16.9ms\n",
            "88:\tlearn: 0.6236453\ttest: 0.6248770\tbest: 0.6248770 (88)\ttotal: 125ms\tremaining: 15.5ms\n",
            "89:\tlearn: 0.6230144\ttest: 0.6242958\tbest: 0.6242958 (89)\ttotal: 127ms\tremaining: 14.1ms\n",
            "90:\tlearn: 0.6223079\ttest: 0.6236124\tbest: 0.6236124 (90)\ttotal: 128ms\tremaining: 12.6ms\n",
            "91:\tlearn: 0.6215896\ttest: 0.6229214\tbest: 0.6229214 (91)\ttotal: 129ms\tremaining: 11.2ms\n",
            "92:\tlearn: 0.6208326\ttest: 0.6221894\tbest: 0.6221894 (92)\ttotal: 130ms\tremaining: 9.81ms\n",
            "93:\tlearn: 0.6201011\ttest: 0.6214939\tbest: 0.6214939 (93)\ttotal: 132ms\tremaining: 8.4ms\n",
            "94:\tlearn: 0.6193349\ttest: 0.6207298\tbest: 0.6207298 (94)\ttotal: 133ms\tremaining: 6.99ms\n",
            "95:\tlearn: 0.6184407\ttest: 0.6198463\tbest: 0.6198463 (95)\ttotal: 134ms\tremaining: 5.6ms\n",
            "96:\tlearn: 0.6177543\ttest: 0.6191539\tbest: 0.6191539 (96)\ttotal: 136ms\tremaining: 4.19ms\n",
            "97:\tlearn: 0.6170316\ttest: 0.6184335\tbest: 0.6184335 (97)\ttotal: 137ms\tremaining: 2.79ms\n",
            "98:\tlearn: 0.6164059\ttest: 0.6178064\tbest: 0.6178064 (98)\ttotal: 138ms\tremaining: 1.4ms\n",
            "99:\tlearn: 0.6155483\ttest: 0.6169586\tbest: 0.6169586 (99)\ttotal: 140ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.6169586044\n",
            "bestIteration = 99\n",
            "\n",
            "50:\tloss: 0.6169586\tbest: 0.1277177 (44)\ttotal: 7.52s\tremaining: 1.33s\n",
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            "10:\tlearn: 0.3012929\ttest: 0.3045369\tbest: 0.3045369 (10)\ttotal: 18.6ms\tremaining: 151ms\n",
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            "15:\tlearn: 0.2554643\ttest: 0.2561606\tbest: 0.2561606 (15)\ttotal: 25.7ms\tremaining: 135ms\n",
            "16:\tlearn: 0.2500148\ttest: 0.2503421\tbest: 0.2503421 (16)\ttotal: 27.1ms\tremaining: 132ms\n",
            "17:\tlearn: 0.2447254\ttest: 0.2479429\tbest: 0.2479429 (17)\ttotal: 28.4ms\tremaining: 130ms\n",
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            "22:\tlearn: 0.2239680\ttest: 0.2259296\tbest: 0.2259296 (22)\ttotal: 35.5ms\tremaining: 119ms\n",
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            "\n",
            "bestTest = 0.1421829854\n",
            "bestIteration = 95\n",
            "\n",
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            "77:\tlearn: 0.0978749\ttest: 0.1415580\tbest: 0.1415580 (77)\ttotal: 126ms\tremaining: 35.6ms\n",
            "78:\tlearn: 0.0968595\ttest: 0.1418768\tbest: 0.1415580 (77)\ttotal: 128ms\tremaining: 33.9ms\n",
            "79:\tlearn: 0.0967947\ttest: 0.1418130\tbest: 0.1415580 (77)\ttotal: 129ms\tremaining: 32.2ms\n",
            "80:\tlearn: 0.0962875\ttest: 0.1406171\tbest: 0.1406171 (80)\ttotal: 131ms\tremaining: 30.7ms\n",
            "81:\tlearn: 0.0948225\ttest: 0.1397002\tbest: 0.1397002 (81)\ttotal: 133ms\tremaining: 29.2ms\n",
            "82:\tlearn: 0.0943589\ttest: 0.1397197\tbest: 0.1397002 (81)\ttotal: 135ms\tremaining: 27.7ms\n",
            "83:\tlearn: 0.0938315\ttest: 0.1397979\tbest: 0.1397002 (81)\ttotal: 137ms\tremaining: 26.1ms\n",
            "84:\tlearn: 0.0933027\ttest: 0.1403412\tbest: 0.1397002 (81)\ttotal: 138ms\tremaining: 24.4ms\n",
            "85:\tlearn: 0.0925205\ttest: 0.1411585\tbest: 0.1397002 (81)\ttotal: 140ms\tremaining: 22.8ms\n",
            "86:\tlearn: 0.0918393\ttest: 0.1416109\tbest: 0.1397002 (81)\ttotal: 141ms\tremaining: 21.1ms\n",
            "87:\tlearn: 0.0915954\ttest: 0.1414798\tbest: 0.1397002 (81)\ttotal: 143ms\tremaining: 19.5ms\n",
            "88:\tlearn: 0.0912854\ttest: 0.1416896\tbest: 0.1397002 (81)\ttotal: 145ms\tremaining: 17.9ms\n",
            "89:\tlearn: 0.0902118\ttest: 0.1433392\tbest: 0.1397002 (81)\ttotal: 147ms\tremaining: 16.3ms\n",
            "90:\tlearn: 0.0895152\ttest: 0.1424684\tbest: 0.1397002 (81)\ttotal: 149ms\tremaining: 14.7ms\n",
            "91:\tlearn: 0.0881001\ttest: 0.1435852\tbest: 0.1397002 (81)\ttotal: 151ms\tremaining: 13.2ms\n",
            "92:\tlearn: 0.0873452\ttest: 0.1440368\tbest: 0.1397002 (81)\ttotal: 153ms\tremaining: 11.5ms\n",
            "93:\tlearn: 0.0853509\ttest: 0.1410806\tbest: 0.1397002 (81)\ttotal: 155ms\tremaining: 9.91ms\n",
            "94:\tlearn: 0.0853178\ttest: 0.1411315\tbest: 0.1397002 (81)\ttotal: 157ms\tremaining: 8.27ms\n",
            "95:\tlearn: 0.0845705\ttest: 0.1406357\tbest: 0.1397002 (81)\ttotal: 159ms\tremaining: 6.63ms\n",
            "96:\tlearn: 0.0834408\ttest: 0.1406841\tbest: 0.1397002 (81)\ttotal: 161ms\tremaining: 4.98ms\n",
            "97:\tlearn: 0.0825638\ttest: 0.1403461\tbest: 0.1397002 (81)\ttotal: 163ms\tremaining: 3.33ms\n",
            "98:\tlearn: 0.0825267\ttest: 0.1404031\tbest: 0.1397002 (81)\ttotal: 165ms\tremaining: 1.66ms\n",
            "99:\tlearn: 0.0817124\ttest: 0.1406349\tbest: 0.1397002 (81)\ttotal: 167ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.139700188\n",
            "bestIteration = 81\n",
            "\n",
            "52:\tloss: 0.1397002\tbest: 0.1277177 (44)\ttotal: 7.86s\tremaining: 1.04s\n",
            "0:\tlearn: 0.4719709\ttest: 0.4788500\tbest: 0.4788500 (0)\ttotal: 3.76ms\tremaining: 373ms\n",
            "1:\tlearn: 0.3616999\ttest: 0.3703565\tbest: 0.3703565 (1)\ttotal: 5.45ms\tremaining: 267ms\n",
            "2:\tlearn: 0.3191626\ttest: 0.3265600\tbest: 0.3265600 (2)\ttotal: 7.3ms\tremaining: 236ms\n",
            "3:\tlearn: 0.2747880\ttest: 0.2804471\tbest: 0.2804471 (3)\ttotal: 9.3ms\tremaining: 223ms\n",
            "4:\tlearn: 0.2490232\ttest: 0.2494625\tbest: 0.2494625 (4)\ttotal: 11.4ms\tremaining: 217ms\n",
            "5:\tlearn: 0.2344107\ttest: 0.2360335\tbest: 0.2360335 (5)\ttotal: 13.5ms\tremaining: 212ms\n",
            "6:\tlearn: 0.2266300\ttest: 0.2239661\tbest: 0.2239661 (6)\ttotal: 15.3ms\tremaining: 203ms\n",
            "7:\tlearn: 0.2171340\ttest: 0.2114054\tbest: 0.2114054 (7)\ttotal: 17ms\tremaining: 196ms\n",
            "8:\tlearn: 0.2075911\ttest: 0.2054156\tbest: 0.2054156 (8)\ttotal: 18.5ms\tremaining: 187ms\n",
            "9:\tlearn: 0.1975256\ttest: 0.1937502\tbest: 0.1937502 (9)\ttotal: 19.8ms\tremaining: 179ms\n",
            "10:\tlearn: 0.1903249\ttest: 0.1894662\tbest: 0.1894662 (10)\ttotal: 21.4ms\tremaining: 173ms\n",
            "11:\tlearn: 0.1872444\ttest: 0.1856780\tbest: 0.1856780 (11)\ttotal: 22.9ms\tremaining: 168ms\n",
            "12:\tlearn: 0.1821059\ttest: 0.1854197\tbest: 0.1854197 (12)\ttotal: 24.3ms\tremaining: 163ms\n",
            "13:\tlearn: 0.1774280\ttest: 0.1778089\tbest: 0.1778089 (13)\ttotal: 25.8ms\tremaining: 158ms\n",
            "14:\tlearn: 0.1739513\ttest: 0.1749030\tbest: 0.1749030 (14)\ttotal: 27.8ms\tremaining: 157ms\n",
            "15:\tlearn: 0.1697807\ttest: 0.1714805\tbest: 0.1714805 (15)\ttotal: 29.4ms\tremaining: 154ms\n",
            "16:\tlearn: 0.1691220\ttest: 0.1715895\tbest: 0.1714805 (15)\ttotal: 30.8ms\tremaining: 151ms\n",
            "17:\tlearn: 0.1634261\ttest: 0.1659979\tbest: 0.1659979 (17)\ttotal: 32.2ms\tremaining: 146ms\n",
            "18:\tlearn: 0.1598831\ttest: 0.1654136\tbest: 0.1654136 (18)\ttotal: 33.5ms\tremaining: 143ms\n",
            "19:\tlearn: 0.1561152\ttest: 0.1610829\tbest: 0.1610829 (19)\ttotal: 34.8ms\tremaining: 139ms\n",
            "20:\tlearn: 0.1538597\ttest: 0.1606141\tbest: 0.1606141 (20)\ttotal: 36.1ms\tremaining: 136ms\n",
            "21:\tlearn: 0.1531001\ttest: 0.1607980\tbest: 0.1606141 (20)\ttotal: 37.4ms\tremaining: 133ms\n",
            "22:\tlearn: 0.1515786\ttest: 0.1607198\tbest: 0.1606141 (20)\ttotal: 38.8ms\tremaining: 130ms\n",
            "23:\tlearn: 0.1492914\ttest: 0.1601067\tbest: 0.1601067 (23)\ttotal: 40.2ms\tremaining: 127ms\n",
            "24:\tlearn: 0.1477042\ttest: 0.1597880\tbest: 0.1597880 (24)\ttotal: 42ms\tremaining: 126ms\n",
            "25:\tlearn: 0.1458751\ttest: 0.1596180\tbest: 0.1596180 (25)\ttotal: 43.9ms\tremaining: 125ms\n",
            "26:\tlearn: 0.1430355\ttest: 0.1596456\tbest: 0.1596180 (25)\ttotal: 45.7ms\tremaining: 124ms\n",
            "27:\tlearn: 0.1412405\ttest: 0.1610581\tbest: 0.1596180 (25)\ttotal: 47.1ms\tremaining: 121ms\n",
            "28:\tlearn: 0.1401769\ttest: 0.1608979\tbest: 0.1596180 (25)\ttotal: 48.5ms\tremaining: 119ms\n",
            "29:\tlearn: 0.1390930\ttest: 0.1605555\tbest: 0.1596180 (25)\ttotal: 49.9ms\tremaining: 116ms\n",
            "30:\tlearn: 0.1371306\ttest: 0.1582043\tbest: 0.1582043 (30)\ttotal: 51.2ms\tremaining: 114ms\n",
            "31:\tlearn: 0.1369012\ttest: 0.1583465\tbest: 0.1582043 (30)\ttotal: 52.5ms\tremaining: 112ms\n",
            "32:\tlearn: 0.1352627\ttest: 0.1596206\tbest: 0.1582043 (30)\ttotal: 54ms\tremaining: 110ms\n",
            "33:\tlearn: 0.1337768\ttest: 0.1592707\tbest: 0.1582043 (30)\ttotal: 55.5ms\tremaining: 108ms\n",
            "34:\tlearn: 0.1300236\ttest: 0.1572849\tbest: 0.1572849 (34)\ttotal: 56.9ms\tremaining: 106ms\n",
            "35:\tlearn: 0.1255199\ttest: 0.1550611\tbest: 0.1550611 (35)\ttotal: 58.4ms\tremaining: 104ms\n",
            "36:\tlearn: 0.1215314\ttest: 0.1522180\tbest: 0.1522180 (36)\ttotal: 59.7ms\tremaining: 102ms\n",
            "37:\tlearn: 0.1185046\ttest: 0.1540252\tbest: 0.1522180 (36)\ttotal: 61.1ms\tremaining: 99.7ms\n",
            "38:\tlearn: 0.1177052\ttest: 0.1538090\tbest: 0.1522180 (36)\ttotal: 62.4ms\tremaining: 97.6ms\n",
            "39:\tlearn: 0.1151204\ttest: 0.1566222\tbest: 0.1522180 (36)\ttotal: 63.7ms\tremaining: 95.6ms\n",
            "40:\tlearn: 0.1132077\ttest: 0.1569320\tbest: 0.1522180 (36)\ttotal: 65ms\tremaining: 93.6ms\n",
            "41:\tlearn: 0.1128580\ttest: 0.1569341\tbest: 0.1522180 (36)\ttotal: 66.3ms\tremaining: 91.6ms\n",
            "42:\tlearn: 0.1105051\ttest: 0.1577624\tbest: 0.1522180 (36)\ttotal: 67.7ms\tremaining: 89.7ms\n",
            "43:\tlearn: 0.1104091\ttest: 0.1579560\tbest: 0.1522180 (36)\ttotal: 69ms\tremaining: 87.9ms\n",
            "44:\tlearn: 0.1083999\ttest: 0.1528506\tbest: 0.1522180 (36)\ttotal: 70.4ms\tremaining: 86ms\n",
            "45:\tlearn: 0.1070530\ttest: 0.1524199\tbest: 0.1522180 (36)\ttotal: 71.7ms\tremaining: 84.2ms\n",
            "46:\tlearn: 0.1055239\ttest: 0.1539187\tbest: 0.1522180 (36)\ttotal: 73.2ms\tremaining: 82.5ms\n",
            "47:\tlearn: 0.1042353\ttest: 0.1539060\tbest: 0.1522180 (36)\ttotal: 74.5ms\tremaining: 80.7ms\n",
            "48:\tlearn: 0.1025650\ttest: 0.1507498\tbest: 0.1507498 (48)\ttotal: 75.9ms\tremaining: 79ms\n",
            "49:\tlearn: 0.1000889\ttest: 0.1495540\tbest: 0.1495540 (49)\ttotal: 77.2ms\tremaining: 77.2ms\n",
            "50:\tlearn: 0.0998917\ttest: 0.1494567\tbest: 0.1494567 (50)\ttotal: 78.5ms\tremaining: 75.4ms\n",
            "51:\tlearn: 0.0986675\ttest: 0.1506329\tbest: 0.1494567 (50)\ttotal: 79.8ms\tremaining: 73.7ms\n",
            "52:\tlearn: 0.0979385\ttest: 0.1503235\tbest: 0.1494567 (50)\ttotal: 81.1ms\tremaining: 72ms\n",
            "53:\tlearn: 0.0974935\ttest: 0.1493949\tbest: 0.1493949 (53)\ttotal: 82.4ms\tremaining: 70.2ms\n",
            "54:\tlearn: 0.0968116\ttest: 0.1502410\tbest: 0.1493949 (53)\ttotal: 83.9ms\tremaining: 68.7ms\n",
            "55:\tlearn: 0.0945518\ttest: 0.1479724\tbest: 0.1479724 (55)\ttotal: 85.3ms\tremaining: 67ms\n",
            "56:\tlearn: 0.0942652\ttest: 0.1479086\tbest: 0.1479086 (56)\ttotal: 86.6ms\tremaining: 65.3ms\n",
            "57:\tlearn: 0.0932769\ttest: 0.1482277\tbest: 0.1479086 (56)\ttotal: 88ms\tremaining: 63.7ms\n",
            "58:\tlearn: 0.0918001\ttest: 0.1486777\tbest: 0.1479086 (56)\ttotal: 89.6ms\tremaining: 62.3ms\n",
            "59:\tlearn: 0.0904506\ttest: 0.1452123\tbest: 0.1452123 (59)\ttotal: 91.2ms\tremaining: 60.8ms\n",
            "60:\tlearn: 0.0891753\ttest: 0.1460158\tbest: 0.1452123 (59)\ttotal: 92.6ms\tremaining: 59.2ms\n",
            "61:\tlearn: 0.0867591\ttest: 0.1458726\tbest: 0.1452123 (59)\ttotal: 93.9ms\tremaining: 57.5ms\n",
            "62:\tlearn: 0.0851726\ttest: 0.1438214\tbest: 0.1438214 (62)\ttotal: 95.2ms\tremaining: 55.9ms\n",
            "63:\tlearn: 0.0845750\ttest: 0.1430317\tbest: 0.1430317 (63)\ttotal: 96.6ms\tremaining: 54.3ms\n",
            "64:\tlearn: 0.0823587\ttest: 0.1433502\tbest: 0.1430317 (63)\ttotal: 98ms\tremaining: 52.8ms\n",
            "65:\tlearn: 0.0807288\ttest: 0.1436050\tbest: 0.1430317 (63)\ttotal: 99.3ms\tremaining: 51.2ms\n",
            "66:\tlearn: 0.0800393\ttest: 0.1430544\tbest: 0.1430317 (63)\ttotal: 101ms\tremaining: 49.6ms\n",
            "67:\tlearn: 0.0792534\ttest: 0.1431301\tbest: 0.1430317 (63)\ttotal: 102ms\tremaining: 48.1ms\n",
            "68:\tlearn: 0.0763335\ttest: 0.1407949\tbest: 0.1407949 (68)\ttotal: 105ms\tremaining: 47ms\n",
            "69:\tlearn: 0.0752866\ttest: 0.1418391\tbest: 0.1407949 (68)\ttotal: 107ms\tremaining: 45.7ms\n",
            "70:\tlearn: 0.0741959\ttest: 0.1414541\tbest: 0.1407949 (68)\ttotal: 109ms\tremaining: 44.3ms\n",
            "71:\tlearn: 0.0725984\ttest: 0.1428922\tbest: 0.1407949 (68)\ttotal: 110ms\tremaining: 42.9ms\n",
            "72:\tlearn: 0.0724263\ttest: 0.1429242\tbest: 0.1407949 (68)\ttotal: 112ms\tremaining: 41.3ms\n",
            "73:\tlearn: 0.0709621\ttest: 0.1416521\tbest: 0.1407949 (68)\ttotal: 113ms\tremaining: 39.8ms\n",
            "74:\tlearn: 0.0697417\ttest: 0.1411653\tbest: 0.1407949 (68)\ttotal: 115ms\tremaining: 38.3ms\n",
            "75:\tlearn: 0.0685594\ttest: 0.1410248\tbest: 0.1407949 (68)\ttotal: 117ms\tremaining: 36.8ms\n",
            "76:\tlearn: 0.0685204\ttest: 0.1411739\tbest: 0.1407949 (68)\ttotal: 118ms\tremaining: 35.2ms\n",
            "77:\tlearn: 0.0675423\ttest: 0.1427163\tbest: 0.1407949 (68)\ttotal: 119ms\tremaining: 33.7ms\n",
            "78:\tlearn: 0.0651787\ttest: 0.1402658\tbest: 0.1402658 (78)\ttotal: 121ms\tremaining: 32.1ms\n",
            "79:\tlearn: 0.0642731\ttest: 0.1408441\tbest: 0.1402658 (78)\ttotal: 122ms\tremaining: 30.5ms\n",
            "80:\tlearn: 0.0632095\ttest: 0.1402745\tbest: 0.1402658 (78)\ttotal: 123ms\tremaining: 28.9ms\n",
            "81:\tlearn: 0.0619989\ttest: 0.1403339\tbest: 0.1402658 (78)\ttotal: 125ms\tremaining: 27.3ms\n",
            "82:\tlearn: 0.0617836\ttest: 0.1402953\tbest: 0.1402658 (78)\ttotal: 126ms\tremaining: 25.8ms\n",
            "83:\tlearn: 0.0613416\ttest: 0.1401576\tbest: 0.1401576 (83)\ttotal: 127ms\tremaining: 24.2ms\n",
            "84:\tlearn: 0.0606282\ttest: 0.1390927\tbest: 0.1390927 (84)\ttotal: 129ms\tremaining: 22.7ms\n",
            "85:\tlearn: 0.0593766\ttest: 0.1368635\tbest: 0.1368635 (85)\ttotal: 130ms\tremaining: 21.1ms\n",
            "86:\tlearn: 0.0579494\ttest: 0.1368759\tbest: 0.1368635 (85)\ttotal: 131ms\tremaining: 19.6ms\n",
            "87:\tlearn: 0.0577423\ttest: 0.1368379\tbest: 0.1368379 (87)\ttotal: 133ms\tremaining: 18.1ms\n",
            "88:\tlearn: 0.0560305\ttest: 0.1378290\tbest: 0.1368379 (87)\ttotal: 135ms\tremaining: 16.6ms\n",
            "89:\tlearn: 0.0548235\ttest: 0.1367784\tbest: 0.1367784 (89)\ttotal: 136ms\tremaining: 15.1ms\n",
            "90:\tlearn: 0.0541315\ttest: 0.1376150\tbest: 0.1367784 (89)\ttotal: 137ms\tremaining: 13.6ms\n",
            "91:\tlearn: 0.0528085\ttest: 0.1376005\tbest: 0.1367784 (89)\ttotal: 139ms\tremaining: 12.1ms\n",
            "92:\tlearn: 0.0522692\ttest: 0.1377599\tbest: 0.1367784 (89)\ttotal: 141ms\tremaining: 10.6ms\n",
            "93:\tlearn: 0.0517335\ttest: 0.1369216\tbest: 0.1367784 (89)\ttotal: 142ms\tremaining: 9.09ms\n",
            "94:\tlearn: 0.0516673\ttest: 0.1370447\tbest: 0.1367784 (89)\ttotal: 144ms\tremaining: 7.58ms\n",
            "95:\tlearn: 0.0512043\ttest: 0.1381025\tbest: 0.1367784 (89)\ttotal: 145ms\tremaining: 6.06ms\n",
            "96:\tlearn: 0.0511882\ttest: 0.1382640\tbest: 0.1367784 (89)\ttotal: 147ms\tremaining: 4.54ms\n",
            "97:\tlearn: 0.0501603\ttest: 0.1373490\tbest: 0.1367784 (89)\ttotal: 148ms\tremaining: 3.02ms\n",
            "98:\tlearn: 0.0498728\ttest: 0.1368573\tbest: 0.1367784 (89)\ttotal: 149ms\tremaining: 1.51ms\n",
            "99:\tlearn: 0.0487456\ttest: 0.1366009\tbest: 0.1366009 (99)\ttotal: 151ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1366009314\n",
            "bestIteration = 99\n",
            "\n",
            "53:\tloss: 0.1366009\tbest: 0.1277177 (44)\ttotal: 8.02s\tremaining: 892ms\n",
            "0:\tlearn: 0.4238878\ttest: 0.4325796\tbest: 0.4325796 (0)\ttotal: 1.49ms\tremaining: 148ms\n",
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            "2:\tlearn: 0.2838448\ttest: 0.2860183\tbest: 0.2860183 (2)\ttotal: 4.19ms\tremaining: 136ms\n",
            "3:\tlearn: 0.2556753\ttest: 0.2679755\tbest: 0.2679755 (3)\ttotal: 5.53ms\tremaining: 133ms\n",
            "4:\tlearn: 0.2206197\ttest: 0.2244155\tbest: 0.2244155 (4)\ttotal: 6.99ms\tremaining: 133ms\n",
            "5:\tlearn: 0.2110257\ttest: 0.2125012\tbest: 0.2125012 (5)\ttotal: 8.34ms\tremaining: 131ms\n",
            "6:\tlearn: 0.2023234\ttest: 0.2033788\tbest: 0.2033788 (6)\ttotal: 10ms\tremaining: 133ms\n",
            "7:\tlearn: 0.1971626\ttest: 0.2004751\tbest: 0.2004751 (7)\ttotal: 11.4ms\tremaining: 131ms\n",
            "8:\tlearn: 0.1818746\ttest: 0.1842280\tbest: 0.1842280 (8)\ttotal: 12.8ms\tremaining: 130ms\n",
            "9:\tlearn: 0.1779973\ttest: 0.1786680\tbest: 0.1786680 (9)\ttotal: 14.2ms\tremaining: 128ms\n",
            "10:\tlearn: 0.1744835\ttest: 0.1766997\tbest: 0.1766997 (10)\ttotal: 15.5ms\tremaining: 126ms\n",
            "11:\tlearn: 0.1737033\ttest: 0.1761779\tbest: 0.1761779 (11)\ttotal: 16.8ms\tremaining: 124ms\n",
            "12:\tlearn: 0.1705050\ttest: 0.1772029\tbest: 0.1761779 (11)\ttotal: 18.2ms\tremaining: 122ms\n",
            "13:\tlearn: 0.1648666\ttest: 0.1706571\tbest: 0.1706571 (13)\ttotal: 19.5ms\tremaining: 120ms\n",
            "14:\tlearn: 0.1607490\ttest: 0.1688107\tbest: 0.1688107 (14)\ttotal: 20.9ms\tremaining: 119ms\n",
            "15:\tlearn: 0.1582386\ttest: 0.1698260\tbest: 0.1688107 (14)\ttotal: 22.4ms\tremaining: 117ms\n",
            "16:\tlearn: 0.1575066\ttest: 0.1693787\tbest: 0.1688107 (14)\ttotal: 23.7ms\tremaining: 116ms\n",
            "17:\tlearn: 0.1538065\ttest: 0.1660459\tbest: 0.1660459 (17)\ttotal: 25.1ms\tremaining: 114ms\n",
            "18:\tlearn: 0.1512508\ttest: 0.1666847\tbest: 0.1660459 (17)\ttotal: 26.5ms\tremaining: 113ms\n",
            "19:\tlearn: 0.1462734\ttest: 0.1624619\tbest: 0.1624619 (19)\ttotal: 27.8ms\tremaining: 111ms\n",
            "20:\tlearn: 0.1449394\ttest: 0.1624725\tbest: 0.1624619 (19)\ttotal: 29.1ms\tremaining: 110ms\n",
            "21:\tlearn: 0.1394167\ttest: 0.1568151\tbest: 0.1568151 (21)\ttotal: 30.4ms\tremaining: 108ms\n",
            "22:\tlearn: 0.1368670\ttest: 0.1589540\tbest: 0.1568151 (21)\ttotal: 31.7ms\tremaining: 106ms\n",
            "23:\tlearn: 0.1362953\ttest: 0.1595173\tbest: 0.1568151 (21)\ttotal: 33ms\tremaining: 105ms\n",
            "24:\tlearn: 0.1352080\ttest: 0.1595435\tbest: 0.1568151 (21)\ttotal: 34.3ms\tremaining: 103ms\n",
            "25:\tlearn: 0.1297290\ttest: 0.1572608\tbest: 0.1568151 (21)\ttotal: 35.7ms\tremaining: 102ms\n",
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            "\n",
            "bestTest = 0.1396684376\n",
            "bestIteration = 99\n",
            "\n",
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            "80:\tlearn: 0.6293630\ttest: 0.6304520\tbest: 0.6304520 (80)\ttotal: 131ms\tremaining: 30.6ms\n",
            "81:\tlearn: 0.6285814\ttest: 0.6297098\tbest: 0.6297098 (81)\ttotal: 132ms\tremaining: 29ms\n",
            "82:\tlearn: 0.6277832\ttest: 0.6289102\tbest: 0.6289102 (82)\ttotal: 134ms\tremaining: 27.4ms\n",
            "83:\tlearn: 0.6270184\ttest: 0.6281511\tbest: 0.6281511 (83)\ttotal: 135ms\tremaining: 25.8ms\n",
            "84:\tlearn: 0.6263836\ttest: 0.6275239\tbest: 0.6275239 (84)\ttotal: 137ms\tremaining: 24.1ms\n",
            "85:\tlearn: 0.6256225\ttest: 0.6267961\tbest: 0.6267961 (85)\ttotal: 138ms\tremaining: 22.5ms\n",
            "86:\tlearn: 0.6250602\ttest: 0.6262397\tbest: 0.6262397 (86)\ttotal: 140ms\tremaining: 20.9ms\n",
            "87:\tlearn: 0.6244314\ttest: 0.6256291\tbest: 0.6256291 (87)\ttotal: 141ms\tremaining: 19.2ms\n",
            "88:\tlearn: 0.6236753\ttest: 0.6248740\tbest: 0.6248740 (88)\ttotal: 142ms\tremaining: 17.6ms\n",
            "89:\tlearn: 0.6230498\ttest: 0.6242941\tbest: 0.6242941 (89)\ttotal: 144ms\tremaining: 16ms\n",
            "90:\tlearn: 0.6223444\ttest: 0.6236116\tbest: 0.6236116 (90)\ttotal: 145ms\tremaining: 14.4ms\n",
            "91:\tlearn: 0.6216261\ttest: 0.6229203\tbest: 0.6229203 (91)\ttotal: 146ms\tremaining: 12.7ms\n",
            "92:\tlearn: 0.6208733\ttest: 0.6221903\tbest: 0.6221903 (92)\ttotal: 148ms\tremaining: 11.1ms\n",
            "93:\tlearn: 0.6201424\ttest: 0.6214953\tbest: 0.6214953 (93)\ttotal: 149ms\tremaining: 9.53ms\n",
            "94:\tlearn: 0.6193765\ttest: 0.6207317\tbest: 0.6207317 (94)\ttotal: 151ms\tremaining: 7.92ms\n",
            "95:\tlearn: 0.6184828\ttest: 0.6198480\tbest: 0.6198480 (95)\ttotal: 152ms\tremaining: 6.33ms\n",
            "96:\tlearn: 0.6178021\ttest: 0.6191628\tbest: 0.6191628 (96)\ttotal: 153ms\tremaining: 4.74ms\n",
            "97:\tlearn: 0.6170797\ttest: 0.6184424\tbest: 0.6184424 (97)\ttotal: 155ms\tremaining: 3.15ms\n",
            "98:\tlearn: 0.6164541\ttest: 0.6178152\tbest: 0.6178152 (98)\ttotal: 156ms\tremaining: 1.57ms\n",
            "99:\tlearn: 0.6155966\ttest: 0.6169677\tbest: 0.6169677 (99)\ttotal: 157ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.6169676522\n",
            "bestIteration = 99\n",
            "\n",
            "55:\tloss: 0.6169677\tbest: 0.1277177 (44)\ttotal: 8.34s\tremaining: 596ms\n",
            "0:\tlearn: 0.6055450\ttest: 0.6081344\tbest: 0.6081344 (0)\ttotal: 1.85ms\tremaining: 183ms\n",
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            "2:\tlearn: 0.4907906\ttest: 0.4949166\tbest: 0.4949166 (2)\ttotal: 4.51ms\tremaining: 146ms\n",
            "3:\tlearn: 0.4518463\ttest: 0.4542084\tbest: 0.4542084 (3)\ttotal: 5.95ms\tremaining: 143ms\n",
            "4:\tlearn: 0.4069823\ttest: 0.4095823\tbest: 0.4095823 (4)\ttotal: 7.25ms\tremaining: 138ms\n",
            "5:\tlearn: 0.3851388\ttest: 0.3894264\tbest: 0.3894264 (5)\ttotal: 8.51ms\tremaining: 133ms\n",
            "6:\tlearn: 0.3618608\ttest: 0.3672407\tbest: 0.3672407 (6)\ttotal: 9.88ms\tremaining: 131ms\n",
            "7:\tlearn: 0.3454993\ttest: 0.3502627\tbest: 0.3502627 (7)\ttotal: 11.2ms\tremaining: 129ms\n",
            "8:\tlearn: 0.3321045\ttest: 0.3375678\tbest: 0.3375678 (8)\ttotal: 12.5ms\tremaining: 126ms\n",
            "9:\tlearn: 0.3150106\ttest: 0.3191565\tbest: 0.3191565 (9)\ttotal: 13.8ms\tremaining: 124ms\n",
            "10:\tlearn: 0.3053212\ttest: 0.3095670\tbest: 0.3095670 (10)\ttotal: 15.1ms\tremaining: 122ms\n",
            "11:\tlearn: 0.2911436\ttest: 0.2925035\tbest: 0.2925035 (11)\ttotal: 16.5ms\tremaining: 121ms\n",
            "12:\tlearn: 0.2805962\ttest: 0.2811536\tbest: 0.2811536 (12)\ttotal: 17.8ms\tremaining: 119ms\n",
            "13:\tlearn: 0.2715352\ttest: 0.2720341\tbest: 0.2720341 (13)\ttotal: 19.1ms\tremaining: 117ms\n",
            "14:\tlearn: 0.2623086\ttest: 0.2620637\tbest: 0.2620637 (14)\ttotal: 20.3ms\tremaining: 115ms\n",
            "15:\tlearn: 0.2545690\ttest: 0.2530926\tbest: 0.2530926 (15)\ttotal: 21.7ms\tremaining: 114ms\n",
            "16:\tlearn: 0.2467782\ttest: 0.2438409\tbest: 0.2438409 (16)\ttotal: 23.1ms\tremaining: 113ms\n",
            "17:\tlearn: 0.2419202\ttest: 0.2417384\tbest: 0.2417384 (17)\ttotal: 24.5ms\tremaining: 112ms\n",
            "18:\tlearn: 0.2375316\ttest: 0.2356679\tbest: 0.2356679 (18)\ttotal: 25.8ms\tremaining: 110ms\n",
            "19:\tlearn: 0.2312426\ttest: 0.2295863\tbest: 0.2295863 (19)\ttotal: 27.1ms\tremaining: 108ms\n",
            "20:\tlearn: 0.2285864\ttest: 0.2274520\tbest: 0.2274520 (20)\ttotal: 28.4ms\tremaining: 107ms\n",
            "21:\tlearn: 0.2255746\ttest: 0.2260637\tbest: 0.2260637 (21)\ttotal: 29.8ms\tremaining: 106ms\n",
            "22:\tlearn: 0.2231582\ttest: 0.2246729\tbest: 0.2246729 (22)\ttotal: 31.2ms\tremaining: 104ms\n",
            "23:\tlearn: 0.2208733\ttest: 0.2229753\tbest: 0.2229753 (23)\ttotal: 32.6ms\tremaining: 103ms\n",
            "24:\tlearn: 0.2162865\ttest: 0.2164798\tbest: 0.2164798 (24)\ttotal: 33.9ms\tremaining: 102ms\n",
            "25:\tlearn: 0.2122721\ttest: 0.2122756\tbest: 0.2122756 (25)\ttotal: 35.2ms\tremaining: 100ms\n",
            "26:\tlearn: 0.2091301\ttest: 0.2081867\tbest: 0.2081867 (26)\ttotal: 36.5ms\tremaining: 98.6ms\n",
            "27:\tlearn: 0.2069417\ttest: 0.2046491\tbest: 0.2046491 (27)\ttotal: 38ms\tremaining: 97.6ms\n",
            "28:\tlearn: 0.2017142\ttest: 0.1987499\tbest: 0.1987499 (28)\ttotal: 39.3ms\tremaining: 96.1ms\n",
            "29:\tlearn: 0.1988932\ttest: 0.1959535\tbest: 0.1959535 (29)\ttotal: 40.6ms\tremaining: 94.7ms\n",
            "30:\tlearn: 0.1957991\ttest: 0.1922695\tbest: 0.1922695 (30)\ttotal: 41.8ms\tremaining: 93.1ms\n",
            "31:\tlearn: 0.1941613\ttest: 0.1905082\tbest: 0.1905082 (31)\ttotal: 43.1ms\tremaining: 91.6ms\n",
            "32:\tlearn: 0.1917619\ttest: 0.1876667\tbest: 0.1876667 (32)\ttotal: 44.4ms\tremaining: 90.2ms\n",
            "33:\tlearn: 0.1894986\ttest: 0.1853850\tbest: 0.1853850 (33)\ttotal: 45.7ms\tremaining: 88.8ms\n",
            "34:\tlearn: 0.1874327\ttest: 0.1838701\tbest: 0.1838701 (34)\ttotal: 47.3ms\tremaining: 87.8ms\n",
            "35:\tlearn: 0.1852904\ttest: 0.1818716\tbest: 0.1818716 (35)\ttotal: 48.6ms\tremaining: 86.4ms\n",
            "36:\tlearn: 0.1832580\ttest: 0.1806709\tbest: 0.1806709 (36)\ttotal: 49.9ms\tremaining: 84.9ms\n",
            "37:\tlearn: 0.1811491\ttest: 0.1787318\tbest: 0.1787318 (37)\ttotal: 51.1ms\tremaining: 83.4ms\n",
            "38:\tlearn: 0.1788713\ttest: 0.1767614\tbest: 0.1767614 (38)\ttotal: 52.4ms\tremaining: 82ms\n",
            "39:\tlearn: 0.1779985\ttest: 0.1759064\tbest: 0.1759064 (39)\ttotal: 53.7ms\tremaining: 80.6ms\n",
            "40:\tlearn: 0.1748566\ttest: 0.1724227\tbest: 0.1724227 (40)\ttotal: 55.1ms\tremaining: 79.3ms\n",
            "41:\tlearn: 0.1740927\ttest: 0.1718084\tbest: 0.1718084 (41)\ttotal: 56.4ms\tremaining: 77.9ms\n",
            "42:\tlearn: 0.1729368\ttest: 0.1704168\tbest: 0.1704168 (42)\ttotal: 57.7ms\tremaining: 76.5ms\n",
            "43:\tlearn: 0.1727789\ttest: 0.1703114\tbest: 0.1703114 (43)\ttotal: 59ms\tremaining: 75.1ms\n",
            "44:\tlearn: 0.1720502\ttest: 0.1699316\tbest: 0.1699316 (44)\ttotal: 60.3ms\tremaining: 73.7ms\n",
            "45:\tlearn: 0.1714995\ttest: 0.1691169\tbest: 0.1691169 (45)\ttotal: 61.8ms\tremaining: 72.5ms\n",
            "46:\tlearn: 0.1704704\ttest: 0.1684882\tbest: 0.1684882 (46)\ttotal: 63.1ms\tremaining: 71.1ms\n",
            "47:\tlearn: 0.1686728\ttest: 0.1671181\tbest: 0.1671181 (47)\ttotal: 64.3ms\tremaining: 69.7ms\n",
            "48:\tlearn: 0.1679684\ttest: 0.1658793\tbest: 0.1658793 (48)\ttotal: 65.6ms\tremaining: 68.3ms\n",
            "49:\tlearn: 0.1674716\ttest: 0.1657594\tbest: 0.1657594 (49)\ttotal: 66.9ms\tremaining: 66.9ms\n",
            "50:\tlearn: 0.1672461\ttest: 0.1657158\tbest: 0.1657158 (50)\ttotal: 68.2ms\tremaining: 65.5ms\n",
            "51:\tlearn: 0.1666590\ttest: 0.1657066\tbest: 0.1657066 (51)\ttotal: 69.5ms\tremaining: 64.2ms\n",
            "52:\tlearn: 0.1654151\ttest: 0.1645087\tbest: 0.1645087 (52)\ttotal: 70.8ms\tremaining: 62.8ms\n",
            "53:\tlearn: 0.1645987\ttest: 0.1640147\tbest: 0.1640147 (53)\ttotal: 72.2ms\tremaining: 61.5ms\n",
            "54:\tlearn: 0.1636957\ttest: 0.1634566\tbest: 0.1634566 (54)\ttotal: 73.5ms\tremaining: 60.1ms\n",
            "55:\tlearn: 0.1613850\ttest: 0.1611691\tbest: 0.1611691 (55)\ttotal: 74.8ms\tremaining: 58.8ms\n",
            "56:\tlearn: 0.1603342\ttest: 0.1610263\tbest: 0.1610263 (56)\ttotal: 76.1ms\tremaining: 57.4ms\n",
            "57:\tlearn: 0.1593237\ttest: 0.1603733\tbest: 0.1603733 (57)\ttotal: 77.3ms\tremaining: 56ms\n",
            "58:\tlearn: 0.1582199\ttest: 0.1594289\tbest: 0.1594289 (58)\ttotal: 78.6ms\tremaining: 54.6ms\n",
            "59:\tlearn: 0.1570219\ttest: 0.1592663\tbest: 0.1592663 (59)\ttotal: 79.9ms\tremaining: 53.3ms\n",
            "60:\tlearn: 0.1567421\ttest: 0.1590061\tbest: 0.1590061 (60)\ttotal: 81.2ms\tremaining: 51.9ms\n",
            "61:\tlearn: 0.1558786\ttest: 0.1579221\tbest: 0.1579221 (61)\ttotal: 82.4ms\tremaining: 50.5ms\n",
            "62:\tlearn: 0.1547539\ttest: 0.1571159\tbest: 0.1571159 (62)\ttotal: 83.8ms\tremaining: 49.2ms\n",
            "63:\tlearn: 0.1530353\ttest: 0.1553242\tbest: 0.1553242 (63)\ttotal: 85.3ms\tremaining: 48ms\n",
            "64:\tlearn: 0.1522449\ttest: 0.1549321\tbest: 0.1549321 (64)\ttotal: 86.6ms\tremaining: 46.6ms\n",
            "65:\tlearn: 0.1508036\ttest: 0.1533513\tbest: 0.1533513 (65)\ttotal: 87.9ms\tremaining: 45.3ms\n",
            "66:\tlearn: 0.1497597\ttest: 0.1531370\tbest: 0.1531370 (66)\ttotal: 89.2ms\tremaining: 43.9ms\n",
            "67:\tlearn: 0.1492081\ttest: 0.1526594\tbest: 0.1526594 (67)\ttotal: 90.5ms\tremaining: 42.6ms\n",
            "68:\tlearn: 0.1483625\ttest: 0.1518533\tbest: 0.1518533 (68)\ttotal: 91.8ms\tremaining: 41.3ms\n",
            "69:\tlearn: 0.1480370\ttest: 0.1520483\tbest: 0.1518533 (68)\ttotal: 93.2ms\tremaining: 39.9ms\n",
            "70:\tlearn: 0.1479770\ttest: 0.1518570\tbest: 0.1518533 (68)\ttotal: 94.5ms\tremaining: 38.6ms\n",
            "71:\tlearn: 0.1473481\ttest: 0.1519098\tbest: 0.1518533 (68)\ttotal: 95.8ms\tremaining: 37.3ms\n",
            "72:\tlearn: 0.1464773\ttest: 0.1521686\tbest: 0.1518533 (68)\ttotal: 97.1ms\tremaining: 35.9ms\n",
            "73:\tlearn: 0.1457968\ttest: 0.1519174\tbest: 0.1518533 (68)\ttotal: 98.4ms\tremaining: 34.6ms\n",
            "74:\tlearn: 0.1446086\ttest: 0.1508218\tbest: 0.1508218 (74)\ttotal: 99.7ms\tremaining: 33.2ms\n",
            "75:\tlearn: 0.1443957\ttest: 0.1508008\tbest: 0.1508008 (75)\ttotal: 101ms\tremaining: 31.9ms\n",
            "76:\tlearn: 0.1437059\ttest: 0.1503434\tbest: 0.1503434 (76)\ttotal: 102ms\tremaining: 30.5ms\n",
            "77:\tlearn: 0.1430822\ttest: 0.1502432\tbest: 0.1502432 (77)\ttotal: 104ms\tremaining: 29.2ms\n",
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            "79:\tlearn: 0.1411849\ttest: 0.1489983\tbest: 0.1489983 (79)\ttotal: 107ms\tremaining: 26.7ms\n",
            "80:\tlearn: 0.1405738\ttest: 0.1490407\tbest: 0.1489983 (79)\ttotal: 108ms\tremaining: 25.3ms\n",
            "81:\tlearn: 0.1391319\ttest: 0.1485055\tbest: 0.1485055 (81)\ttotal: 110ms\tremaining: 24ms\n",
            "82:\tlearn: 0.1387093\ttest: 0.1482400\tbest: 0.1482400 (82)\ttotal: 111ms\tremaining: 22.7ms\n",
            "83:\tlearn: 0.1381282\ttest: 0.1479271\tbest: 0.1479271 (83)\ttotal: 112ms\tremaining: 21.4ms\n",
            "84:\tlearn: 0.1380635\ttest: 0.1478943\tbest: 0.1478943 (84)\ttotal: 114ms\tremaining: 20ms\n",
            "85:\tlearn: 0.1377450\ttest: 0.1478918\tbest: 0.1478918 (85)\ttotal: 115ms\tremaining: 18.7ms\n",
            "86:\tlearn: 0.1367425\ttest: 0.1470165\tbest: 0.1470165 (86)\ttotal: 116ms\tremaining: 17.4ms\n",
            "87:\tlearn: 0.1365035\ttest: 0.1464292\tbest: 0.1464292 (87)\ttotal: 118ms\tremaining: 16.1ms\n",
            "88:\tlearn: 0.1353321\ttest: 0.1449453\tbest: 0.1449453 (88)\ttotal: 119ms\tremaining: 14.7ms\n",
            "89:\tlearn: 0.1342844\ttest: 0.1451235\tbest: 0.1449453 (88)\ttotal: 120ms\tremaining: 13.4ms\n",
            "90:\tlearn: 0.1334481\ttest: 0.1456877\tbest: 0.1449453 (88)\ttotal: 122ms\tremaining: 12ms\n",
            "91:\tlearn: 0.1334033\ttest: 0.1457369\tbest: 0.1449453 (88)\ttotal: 123ms\tremaining: 10.7ms\n",
            "92:\tlearn: 0.1329327\ttest: 0.1448721\tbest: 0.1448721 (92)\ttotal: 124ms\tremaining: 9.37ms\n",
            "93:\tlearn: 0.1322071\ttest: 0.1442139\tbest: 0.1442139 (93)\ttotal: 126ms\tremaining: 8.03ms\n",
            "94:\tlearn: 0.1316648\ttest: 0.1445472\tbest: 0.1442139 (93)\ttotal: 127ms\tremaining: 6.69ms\n",
            "95:\tlearn: 0.1310530\ttest: 0.1440677\tbest: 0.1440677 (95)\ttotal: 128ms\tremaining: 5.35ms\n",
            "96:\tlearn: 0.1309767\ttest: 0.1441236\tbest: 0.1440677 (95)\ttotal: 130ms\tremaining: 4.01ms\n",
            "97:\tlearn: 0.1295723\ttest: 0.1430765\tbest: 0.1430765 (97)\ttotal: 131ms\tremaining: 2.67ms\n",
            "98:\tlearn: 0.1295059\ttest: 0.1431246\tbest: 0.1430765 (97)\ttotal: 132ms\tremaining: 1.33ms\n",
            "99:\tlearn: 0.1290079\ttest: 0.1428614\tbest: 0.1428614 (99)\ttotal: 134ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1428614495\n",
            "bestIteration = 99\n",
            "\n",
            "56:\tloss: 0.1428614\tbest: 0.1277177 (44)\ttotal: 8.48s\tremaining: 446ms\n",
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            "10:\tlearn: 0.2214277\ttest: 0.2219109\tbest: 0.2219109 (10)\ttotal: 14.6ms\tremaining: 118ms\n",
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            "15:\tlearn: 0.1878661\ttest: 0.1861972\tbest: 0.1861972 (15)\ttotal: 21.9ms\tremaining: 115ms\n",
            "16:\tlearn: 0.1817193\ttest: 0.1799190\tbest: 0.1799190 (16)\ttotal: 23.4ms\tremaining: 114ms\n",
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            "23:\tlearn: 0.1635300\ttest: 0.1658154\tbest: 0.1658154 (23)\ttotal: 33.4ms\tremaining: 106ms\n",
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            "25:\tlearn: 0.1590789\ttest: 0.1606985\tbest: 0.1606985 (25)\ttotal: 36.2ms\tremaining: 103ms\n",
            "26:\tlearn: 0.1577077\ttest: 0.1602164\tbest: 0.1602164 (26)\ttotal: 37.5ms\tremaining: 101ms\n",
            "27:\tlearn: 0.1571753\ttest: 0.1595944\tbest: 0.1595944 (27)\ttotal: 38.8ms\tremaining: 99.7ms\n",
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            "99:\tlearn: 0.0841170\ttest: 0.1352924\tbest: 0.1347015 (85)\ttotal: 142ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1347015475\n",
            "bestIteration = 85\n",
            "\n",
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            "80:\tlearn: 0.0651982\ttest: 0.1274045\tbest: 0.1274045 (80)\ttotal: 115ms\tremaining: 27ms\n",
            "81:\tlearn: 0.0647774\ttest: 0.1271220\tbest: 0.1271220 (81)\ttotal: 117ms\tremaining: 25.6ms\n",
            "82:\tlearn: 0.0642204\ttest: 0.1280033\tbest: 0.1271220 (81)\ttotal: 118ms\tremaining: 24.1ms\n",
            "83:\tlearn: 0.0618974\ttest: 0.1243442\tbest: 0.1243442 (83)\ttotal: 119ms\tremaining: 22.7ms\n",
            "84:\tlearn: 0.0618444\ttest: 0.1245791\tbest: 0.1243442 (83)\ttotal: 121ms\tremaining: 21.3ms\n",
            "85:\tlearn: 0.0613071\ttest: 0.1246743\tbest: 0.1243442 (83)\ttotal: 122ms\tremaining: 19.9ms\n",
            "86:\tlearn: 0.0605254\ttest: 0.1243169\tbest: 0.1243169 (86)\ttotal: 123ms\tremaining: 18.4ms\n",
            "87:\tlearn: 0.0599848\ttest: 0.1243089\tbest: 0.1243089 (87)\ttotal: 125ms\tremaining: 17ms\n",
            "88:\tlearn: 0.0595237\ttest: 0.1249710\tbest: 0.1243089 (87)\ttotal: 126ms\tremaining: 15.6ms\n",
            "89:\tlearn: 0.0589342\ttest: 0.1249988\tbest: 0.1243089 (87)\ttotal: 127ms\tremaining: 14.1ms\n",
            "90:\tlearn: 0.0582755\ttest: 0.1243884\tbest: 0.1243089 (87)\ttotal: 129ms\tremaining: 12.7ms\n",
            "91:\tlearn: 0.0571294\ttest: 0.1237425\tbest: 0.1237425 (91)\ttotal: 130ms\tremaining: 11.3ms\n",
            "92:\tlearn: 0.0562498\ttest: 0.1235266\tbest: 0.1235266 (92)\ttotal: 131ms\tremaining: 9.87ms\n",
            "93:\tlearn: 0.0559948\ttest: 0.1237876\tbest: 0.1235266 (92)\ttotal: 133ms\tremaining: 8.46ms\n",
            "94:\tlearn: 0.0559741\ttest: 0.1240172\tbest: 0.1235266 (92)\ttotal: 134ms\tremaining: 7.05ms\n",
            "95:\tlearn: 0.0556308\ttest: 0.1244312\tbest: 0.1235266 (92)\ttotal: 135ms\tremaining: 5.63ms\n",
            "96:\tlearn: 0.0548439\ttest: 0.1246959\tbest: 0.1235266 (92)\ttotal: 137ms\tremaining: 4.22ms\n",
            "97:\tlearn: 0.0542969\ttest: 0.1241152\tbest: 0.1235266 (92)\ttotal: 138ms\tremaining: 2.82ms\n",
            "98:\tlearn: 0.0542666\ttest: 0.1243241\tbest: 0.1235266 (92)\ttotal: 139ms\tremaining: 1.41ms\n",
            "99:\tlearn: 0.0530460\ttest: 0.1227211\tbest: 0.1227211 (99)\ttotal: 141ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1227211108\n",
            "bestIteration = 99\n",
            "\n",
            "58:\tloss: 0.1227211\tbest: 0.1227211 (58)\ttotal: 8.79s\tremaining: 149ms\n",
            "0:\tlearn: 0.4243446\ttest: 0.4331054\tbest: 0.4331054 (0)\ttotal: 1.34ms\tremaining: 133ms\n",
            "1:\tlearn: 0.3355590\ttest: 0.3410518\tbest: 0.3410518 (1)\ttotal: 2.68ms\tremaining: 131ms\n",
            "2:\tlearn: 0.2853866\ttest: 0.2874554\tbest: 0.2874554 (2)\ttotal: 3.96ms\tremaining: 128ms\n",
            "3:\tlearn: 0.2576295\ttest: 0.2696464\tbest: 0.2696464 (3)\ttotal: 5.27ms\tremaining: 126ms\n",
            "4:\tlearn: 0.2223831\ttest: 0.2258196\tbest: 0.2258196 (4)\ttotal: 6.59ms\tremaining: 125ms\n",
            "5:\tlearn: 0.2128922\ttest: 0.2140558\tbest: 0.2140558 (5)\ttotal: 7.87ms\tremaining: 123ms\n",
            "6:\tlearn: 0.2065122\ttest: 0.2075920\tbest: 0.2075920 (6)\ttotal: 9.22ms\tremaining: 123ms\n",
            "7:\tlearn: 0.1982950\ttest: 0.1990510\tbest: 0.1990510 (7)\ttotal: 10.5ms\tremaining: 121ms\n",
            "8:\tlearn: 0.1836708\ttest: 0.1836354\tbest: 0.1836354 (8)\ttotal: 11.9ms\tremaining: 120ms\n",
            "9:\tlearn: 0.1796290\ttest: 0.1778306\tbest: 0.1778306 (9)\ttotal: 13.3ms\tremaining: 119ms\n",
            "10:\tlearn: 0.1763851\ttest: 0.1762669\tbest: 0.1762669 (10)\ttotal: 14.8ms\tremaining: 120ms\n",
            "11:\tlearn: 0.1750551\ttest: 0.1755025\tbest: 0.1755025 (11)\ttotal: 16.2ms\tremaining: 119ms\n",
            "12:\tlearn: 0.1718406\ttest: 0.1763496\tbest: 0.1755025 (11)\ttotal: 17.5ms\tremaining: 117ms\n",
            "13:\tlearn: 0.1680206\ttest: 0.1750592\tbest: 0.1750592 (13)\ttotal: 18.9ms\tremaining: 116ms\n",
            "14:\tlearn: 0.1619382\ttest: 0.1699627\tbest: 0.1699627 (14)\ttotal: 20.4ms\tremaining: 115ms\n",
            "15:\tlearn: 0.1563136\ttest: 0.1643409\tbest: 0.1643409 (15)\ttotal: 21.7ms\tremaining: 114ms\n",
            "16:\tlearn: 0.1528059\ttest: 0.1617419\tbest: 0.1617419 (16)\ttotal: 23.2ms\tremaining: 113ms\n",
            "17:\tlearn: 0.1468562\ttest: 0.1583222\tbest: 0.1583222 (17)\ttotal: 24.5ms\tremaining: 112ms\n",
            "18:\tlearn: 0.1453561\ttest: 0.1557048\tbest: 0.1557048 (18)\ttotal: 26.1ms\tremaining: 111ms\n",
            "19:\tlearn: 0.1447989\ttest: 0.1544819\tbest: 0.1544819 (19)\ttotal: 27.4ms\tremaining: 110ms\n",
            "20:\tlearn: 0.1400787\ttest: 0.1558199\tbest: 0.1544819 (19)\ttotal: 28.8ms\tremaining: 108ms\n",
            "21:\tlearn: 0.1363087\ttest: 0.1523685\tbest: 0.1523685 (21)\ttotal: 30.2ms\tremaining: 107ms\n",
            "22:\tlearn: 0.1332795\ttest: 0.1516323\tbest: 0.1516323 (22)\ttotal: 31.5ms\tremaining: 106ms\n",
            "23:\tlearn: 0.1331702\ttest: 0.1517648\tbest: 0.1516323 (22)\ttotal: 32.8ms\tremaining: 104ms\n",
            "24:\tlearn: 0.1287593\ttest: 0.1457926\tbest: 0.1457926 (24)\ttotal: 34.2ms\tremaining: 103ms\n",
            "25:\tlearn: 0.1273605\ttest: 0.1456407\tbest: 0.1456407 (25)\ttotal: 35.6ms\tremaining: 101ms\n",
            "26:\tlearn: 0.1241346\ttest: 0.1459537\tbest: 0.1456407 (25)\ttotal: 37.5ms\tremaining: 101ms\n",
            "27:\tlearn: 0.1215988\ttest: 0.1473186\tbest: 0.1456407 (25)\ttotal: 39ms\tremaining: 100ms\n",
            "28:\tlearn: 0.1193893\ttest: 0.1480578\tbest: 0.1456407 (25)\ttotal: 40.3ms\tremaining: 98.7ms\n",
            "29:\tlearn: 0.1174064\ttest: 0.1482020\tbest: 0.1456407 (25)\ttotal: 41.8ms\tremaining: 97.5ms\n",
            "30:\tlearn: 0.1148985\ttest: 0.1467915\tbest: 0.1456407 (25)\ttotal: 43.3ms\tremaining: 96.3ms\n",
            "31:\tlearn: 0.1115858\ttest: 0.1456065\tbest: 0.1456065 (31)\ttotal: 44.7ms\tremaining: 94.9ms\n",
            "32:\tlearn: 0.1111809\ttest: 0.1458439\tbest: 0.1456065 (31)\ttotal: 46ms\tremaining: 93.4ms\n",
            "33:\tlearn: 0.1078093\ttest: 0.1471568\tbest: 0.1456065 (31)\ttotal: 47.4ms\tremaining: 92ms\n",
            "34:\tlearn: 0.1067555\ttest: 0.1466628\tbest: 0.1456065 (31)\ttotal: 48.9ms\tremaining: 90.8ms\n",
            "35:\tlearn: 0.1065528\ttest: 0.1464701\tbest: 0.1456065 (31)\ttotal: 50.2ms\tremaining: 89.3ms\n",
            "36:\tlearn: 0.1040882\ttest: 0.1470235\tbest: 0.1456065 (31)\ttotal: 51.6ms\tremaining: 87.8ms\n",
            "37:\tlearn: 0.1006906\ttest: 0.1436854\tbest: 0.1436854 (37)\ttotal: 52.9ms\tremaining: 86.3ms\n",
            "38:\tlearn: 0.0984166\ttest: 0.1427055\tbest: 0.1427055 (38)\ttotal: 54.2ms\tremaining: 84.8ms\n",
            "39:\tlearn: 0.0961095\ttest: 0.1426909\tbest: 0.1426909 (39)\ttotal: 55.5ms\tremaining: 83.2ms\n",
            "40:\tlearn: 0.0959771\ttest: 0.1427336\tbest: 0.1426909 (39)\ttotal: 56.8ms\tremaining: 81.7ms\n",
            "41:\tlearn: 0.0935317\ttest: 0.1409996\tbest: 0.1409996 (41)\ttotal: 58.1ms\tremaining: 80.2ms\n",
            "42:\tlearn: 0.0912947\ttest: 0.1437043\tbest: 0.1409996 (41)\ttotal: 59.4ms\tremaining: 78.8ms\n",
            "43:\tlearn: 0.0912394\ttest: 0.1438190\tbest: 0.1409996 (41)\ttotal: 60.7ms\tremaining: 77.2ms\n",
            "44:\tlearn: 0.0897844\ttest: 0.1441541\tbest: 0.1409996 (41)\ttotal: 62ms\tremaining: 75.8ms\n",
            "45:\tlearn: 0.0885607\ttest: 0.1440902\tbest: 0.1409996 (41)\ttotal: 63.4ms\tremaining: 74.5ms\n",
            "46:\tlearn: 0.0867363\ttest: 0.1437957\tbest: 0.1409996 (41)\ttotal: 64.8ms\tremaining: 73.1ms\n",
            "47:\tlearn: 0.0866194\ttest: 0.1436802\tbest: 0.1409996 (41)\ttotal: 66.1ms\tremaining: 71.6ms\n",
            "48:\tlearn: 0.0853835\ttest: 0.1412789\tbest: 0.1409996 (41)\ttotal: 67.5ms\tremaining: 70.3ms\n",
            "49:\tlearn: 0.0833471\ttest: 0.1401833\tbest: 0.1401833 (49)\ttotal: 68.9ms\tremaining: 68.9ms\n",
            "50:\tlearn: 0.0828136\ttest: 0.1401812\tbest: 0.1401812 (50)\ttotal: 70.1ms\tremaining: 67.4ms\n",
            "51:\tlearn: 0.0812909\ttest: 0.1395079\tbest: 0.1395079 (51)\ttotal: 71.6ms\tremaining: 66.1ms\n",
            "52:\tlearn: 0.0791403\ttest: 0.1353115\tbest: 0.1353115 (52)\ttotal: 73.2ms\tremaining: 64.9ms\n",
            "53:\tlearn: 0.0783451\ttest: 0.1358361\tbest: 0.1353115 (52)\ttotal: 74.5ms\tremaining: 63.4ms\n",
            "54:\tlearn: 0.0769199\ttest: 0.1356883\tbest: 0.1353115 (52)\ttotal: 75.8ms\tremaining: 62ms\n",
            "55:\tlearn: 0.0761053\ttest: 0.1347149\tbest: 0.1347149 (55)\ttotal: 77.1ms\tremaining: 60.6ms\n",
            "56:\tlearn: 0.0742115\ttest: 0.1350933\tbest: 0.1347149 (55)\ttotal: 78.4ms\tremaining: 59.2ms\n",
            "57:\tlearn: 0.0733956\ttest: 0.1339035\tbest: 0.1339035 (57)\ttotal: 79.9ms\tremaining: 57.8ms\n",
            "58:\tlearn: 0.0716426\ttest: 0.1354344\tbest: 0.1339035 (57)\ttotal: 81.2ms\tremaining: 56.4ms\n",
            "59:\tlearn: 0.0701384\ttest: 0.1380214\tbest: 0.1339035 (57)\ttotal: 82.6ms\tremaining: 55ms\n",
            "60:\tlearn: 0.0685509\ttest: 0.1362595\tbest: 0.1339035 (57)\ttotal: 83.9ms\tremaining: 53.6ms\n",
            "61:\tlearn: 0.0672736\ttest: 0.1372920\tbest: 0.1339035 (57)\ttotal: 85.2ms\tremaining: 52.2ms\n",
            "62:\tlearn: 0.0643516\ttest: 0.1328826\tbest: 0.1328826 (62)\ttotal: 86.9ms\tremaining: 51ms\n",
            "63:\tlearn: 0.0634098\ttest: 0.1344261\tbest: 0.1328826 (62)\ttotal: 88.3ms\tremaining: 49.7ms\n",
            "64:\tlearn: 0.0632767\ttest: 0.1343618\tbest: 0.1328826 (62)\ttotal: 89.7ms\tremaining: 48.3ms\n",
            "65:\tlearn: 0.0627268\ttest: 0.1357742\tbest: 0.1328826 (62)\ttotal: 91.1ms\tremaining: 47ms\n",
            "66:\tlearn: 0.0626961\ttest: 0.1358442\tbest: 0.1328826 (62)\ttotal: 92.5ms\tremaining: 45.5ms\n",
            "67:\tlearn: 0.0607388\ttest: 0.1372426\tbest: 0.1328826 (62)\ttotal: 93.8ms\tremaining: 44.2ms\n",
            "68:\tlearn: 0.0596589\ttest: 0.1378678\tbest: 0.1328826 (62)\ttotal: 95.3ms\tremaining: 42.8ms\n",
            "69:\tlearn: 0.0589347\ttest: 0.1386131\tbest: 0.1328826 (62)\ttotal: 96.6ms\tremaining: 41.4ms\n",
            "70:\tlearn: 0.0579585\ttest: 0.1369186\tbest: 0.1328826 (62)\ttotal: 97.9ms\tremaining: 40ms\n",
            "71:\tlearn: 0.0564578\ttest: 0.1387575\tbest: 0.1328826 (62)\ttotal: 99.2ms\tremaining: 38.6ms\n",
            "72:\tlearn: 0.0547340\ttest: 0.1387141\tbest: 0.1328826 (62)\ttotal: 101ms\tremaining: 37.2ms\n",
            "73:\tlearn: 0.0523902\ttest: 0.1378395\tbest: 0.1328826 (62)\ttotal: 102ms\tremaining: 35.9ms\n",
            "74:\tlearn: 0.0511061\ttest: 0.1375566\tbest: 0.1328826 (62)\ttotal: 104ms\tremaining: 34.6ms\n",
            "75:\tlearn: 0.0495449\ttest: 0.1371212\tbest: 0.1328826 (62)\ttotal: 106ms\tremaining: 33.3ms\n",
            "76:\tlearn: 0.0492447\ttest: 0.1371752\tbest: 0.1328826 (62)\ttotal: 107ms\tremaining: 32ms\n",
            "77:\tlearn: 0.0478428\ttest: 0.1357127\tbest: 0.1328826 (62)\ttotal: 109ms\tremaining: 30.6ms\n",
            "78:\tlearn: 0.0475817\ttest: 0.1357743\tbest: 0.1328826 (62)\ttotal: 110ms\tremaining: 29.2ms\n",
            "79:\tlearn: 0.0467235\ttest: 0.1383771\tbest: 0.1328826 (62)\ttotal: 112ms\tremaining: 27.9ms\n",
            "80:\tlearn: 0.0454960\ttest: 0.1404277\tbest: 0.1328826 (62)\ttotal: 113ms\tremaining: 26.5ms\n",
            "81:\tlearn: 0.0454752\ttest: 0.1405058\tbest: 0.1328826 (62)\ttotal: 114ms\tremaining: 25.1ms\n",
            "82:\tlearn: 0.0445521\ttest: 0.1410879\tbest: 0.1328826 (62)\ttotal: 116ms\tremaining: 23.7ms\n",
            "83:\tlearn: 0.0442161\ttest: 0.1416169\tbest: 0.1328826 (62)\ttotal: 117ms\tremaining: 22.3ms\n",
            "84:\tlearn: 0.0438617\ttest: 0.1425089\tbest: 0.1328826 (62)\ttotal: 118ms\tremaining: 20.9ms\n",
            "85:\tlearn: 0.0428596\ttest: 0.1398232\tbest: 0.1328826 (62)\ttotal: 120ms\tremaining: 19.5ms\n",
            "86:\tlearn: 0.0426439\ttest: 0.1401088\tbest: 0.1328826 (62)\ttotal: 122ms\tremaining: 18.3ms\n",
            "87:\tlearn: 0.0421109\ttest: 0.1401883\tbest: 0.1328826 (62)\ttotal: 124ms\tremaining: 16.9ms\n",
            "88:\tlearn: 0.0417291\ttest: 0.1400767\tbest: 0.1328826 (62)\ttotal: 126ms\tremaining: 15.6ms\n",
            "89:\tlearn: 0.0416356\ttest: 0.1403178\tbest: 0.1328826 (62)\ttotal: 128ms\tremaining: 14.2ms\n",
            "90:\tlearn: 0.0415310\ttest: 0.1404226\tbest: 0.1328826 (62)\ttotal: 130ms\tremaining: 12.8ms\n",
            "91:\tlearn: 0.0413584\ttest: 0.1407409\tbest: 0.1328826 (62)\ttotal: 131ms\tremaining: 11.4ms\n",
            "92:\tlearn: 0.0407102\ttest: 0.1412337\tbest: 0.1328826 (62)\ttotal: 132ms\tremaining: 9.97ms\n",
            "93:\tlearn: 0.0396713\ttest: 0.1419901\tbest: 0.1328826 (62)\ttotal: 134ms\tremaining: 8.54ms\n",
            "94:\tlearn: 0.0389451\ttest: 0.1417016\tbest: 0.1328826 (62)\ttotal: 135ms\tremaining: 7.11ms\n",
            "95:\tlearn: 0.0378512\ttest: 0.1392073\tbest: 0.1328826 (62)\ttotal: 137ms\tremaining: 5.69ms\n",
            "96:\tlearn: 0.0371433\ttest: 0.1395163\tbest: 0.1328826 (62)\ttotal: 138ms\tremaining: 4.28ms\n",
            "97:\tlearn: 0.0369038\ttest: 0.1395134\tbest: 0.1328826 (62)\ttotal: 140ms\tremaining: 2.86ms\n",
            "98:\tlearn: 0.0368839\ttest: 0.1395504\tbest: 0.1328826 (62)\ttotal: 142ms\tremaining: 1.43ms\n",
            "99:\tlearn: 0.0363218\ttest: 0.1384684\tbest: 0.1328826 (62)\ttotal: 144ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1328825903\n",
            "bestIteration = 62\n",
            "\n",
            "59:\tloss: 0.1328826\tbest: 0.1227211 (58)\ttotal: 8.94s\tremaining: 0us\n",
            "Estimating final quality...\n",
            "Training on fold [0/4]\n",
            "0:\tlearn: 0.5081773\ttest: 0.5200826\tbest: 0.5200826 (0)\ttotal: 16.7ms\tremaining: 1.65s\n",
            "1:\tlearn: 0.3607101\ttest: 0.3800712\tbest: 0.3800712 (1)\ttotal: 19.2ms\tremaining: 942ms\n",
            "2:\tlearn: 0.3257613\ttest: 0.3548735\tbest: 0.3548735 (2)\ttotal: 23.2ms\tremaining: 749ms\n",
            "3:\tlearn: 0.2814116\ttest: 0.3195007\tbest: 0.3195007 (3)\ttotal: 26.1ms\tremaining: 627ms\n",
            "4:\tlearn: 0.2624682\ttest: 0.3013324\tbest: 0.3013324 (4)\ttotal: 27.8ms\tremaining: 529ms\n",
            "5:\tlearn: 0.2415217\ttest: 0.2825863\tbest: 0.2825863 (5)\ttotal: 29.7ms\tremaining: 465ms\n",
            "6:\tlearn: 0.2307208\ttest: 0.2776169\tbest: 0.2776169 (6)\ttotal: 31.2ms\tremaining: 414ms\n",
            "7:\tlearn: 0.2154336\ttest: 0.2594358\tbest: 0.2594358 (7)\ttotal: 32.7ms\tremaining: 376ms\n",
            "8:\tlearn: 0.2078602\ttest: 0.2534483\tbest: 0.2534483 (8)\ttotal: 34.1ms\tremaining: 345ms\n",
            "9:\tlearn: 0.1973495\ttest: 0.2443073\tbest: 0.2443073 (9)\ttotal: 35.6ms\tremaining: 320ms\n",
            "10:\tlearn: 0.1885313\ttest: 0.2364049\tbest: 0.2364049 (10)\ttotal: 36.9ms\tremaining: 298ms\n",
            "11:\tlearn: 0.1848591\ttest: 0.2347611\tbest: 0.2347611 (11)\ttotal: 38.2ms\tremaining: 280ms\n",
            "12:\tlearn: 0.1811312\ttest: 0.2300198\tbest: 0.2300198 (12)\ttotal: 39.6ms\tremaining: 265ms\n",
            "13:\tlearn: 0.1765240\ttest: 0.2250658\tbest: 0.2250658 (13)\ttotal: 40.8ms\tremaining: 251ms\n",
            "14:\tlearn: 0.1698331\ttest: 0.2230201\tbest: 0.2230201 (14)\ttotal: 42.2ms\tremaining: 239ms\n",
            "15:\tlearn: 0.1652433\ttest: 0.2187759\tbest: 0.2187759 (15)\ttotal: 43.5ms\tremaining: 228ms\n",
            "16:\tlearn: 0.1627442\ttest: 0.2163039\tbest: 0.2163039 (16)\ttotal: 45.1ms\tremaining: 220ms\n",
            "17:\tlearn: 0.1591947\ttest: 0.2178412\tbest: 0.2163039 (16)\ttotal: 46.4ms\tremaining: 211ms\n",
            "18:\tlearn: 0.1558145\ttest: 0.2123250\tbest: 0.2123250 (18)\ttotal: 47.6ms\tremaining: 203ms\n",
            "19:\tlearn: 0.1478232\ttest: 0.2056119\tbest: 0.2056119 (19)\ttotal: 48.9ms\tremaining: 195ms\n",
            "20:\tlearn: 0.1457661\ttest: 0.2041779\tbest: 0.2041779 (20)\ttotal: 50.1ms\tremaining: 188ms\n",
            "21:\tlearn: 0.1446336\ttest: 0.2045008\tbest: 0.2041779 (20)\ttotal: 51.4ms\tremaining: 182ms\n",
            "22:\tlearn: 0.1417994\ttest: 0.1997527\tbest: 0.1997527 (22)\ttotal: 52.7ms\tremaining: 176ms\n",
            "23:\tlearn: 0.1400267\ttest: 0.1987266\tbest: 0.1987266 (23)\ttotal: 54ms\tremaining: 171ms\n",
            "24:\tlearn: 0.1366133\ttest: 0.1969038\tbest: 0.1969038 (24)\ttotal: 55.3ms\tremaining: 166ms\n",
            "25:\tlearn: 0.1346967\ttest: 0.1967472\tbest: 0.1967472 (25)\ttotal: 56.6ms\tremaining: 161ms\n",
            "26:\tlearn: 0.1328281\ttest: 0.1968678\tbest: 0.1967472 (25)\ttotal: 57.8ms\tremaining: 156ms\n",
            "27:\tlearn: 0.1325025\ttest: 0.1968576\tbest: 0.1967472 (25)\ttotal: 59.2ms\tremaining: 152ms\n",
            "28:\tlearn: 0.1314615\ttest: 0.1969595\tbest: 0.1967472 (25)\ttotal: 60.7ms\tremaining: 149ms\n",
            "29:\tlearn: 0.1302830\ttest: 0.1965924\tbest: 0.1965924 (29)\ttotal: 62ms\tremaining: 145ms\n",
            "30:\tlearn: 0.1286938\ttest: 0.1936751\tbest: 0.1936751 (30)\ttotal: 63.4ms\tremaining: 141ms\n",
            "31:\tlearn: 0.1261289\ttest: 0.1943136\tbest: 0.1936751 (30)\ttotal: 64.6ms\tremaining: 137ms\n",
            "32:\tlearn: 0.1217236\ttest: 0.1929611\tbest: 0.1929611 (32)\ttotal: 65.9ms\tremaining: 134ms\n",
            "33:\tlearn: 0.1195530\ttest: 0.1901812\tbest: 0.1901812 (33)\ttotal: 67.2ms\tremaining: 130ms\n",
            "34:\tlearn: 0.1191508\ttest: 0.1903403\tbest: 0.1901812 (33)\ttotal: 68.4ms\tremaining: 127ms\n",
            "35:\tlearn: 0.1189850\ttest: 0.1903779\tbest: 0.1901812 (33)\ttotal: 69.7ms\tremaining: 124ms\n",
            "36:\tlearn: 0.1161237\ttest: 0.1865635\tbest: 0.1865635 (36)\ttotal: 70.9ms\tremaining: 121ms\n",
            "37:\tlearn: 0.1127978\ttest: 0.1865462\tbest: 0.1865462 (37)\ttotal: 72.3ms\tremaining: 118ms\n",
            "38:\tlearn: 0.1114392\ttest: 0.1864404\tbest: 0.1864404 (38)\ttotal: 73.9ms\tremaining: 116ms\n",
            "39:\tlearn: 0.1099179\ttest: 0.1877667\tbest: 0.1864404 (38)\ttotal: 75.6ms\tremaining: 113ms\n",
            "40:\tlearn: 0.1081175\ttest: 0.1861835\tbest: 0.1861835 (40)\ttotal: 77.3ms\tremaining: 111ms\n",
            "41:\tlearn: 0.1056791\ttest: 0.1867136\tbest: 0.1861835 (40)\ttotal: 78.7ms\tremaining: 109ms\n",
            "42:\tlearn: 0.1017478\ttest: 0.1853724\tbest: 0.1853724 (42)\ttotal: 80ms\tremaining: 106ms\n",
            "43:\tlearn: 0.0989105\ttest: 0.1850963\tbest: 0.1850963 (43)\ttotal: 81.4ms\tremaining: 104ms\n",
            "44:\tlearn: 0.0973844\ttest: 0.1868138\tbest: 0.1850963 (43)\ttotal: 82.9ms\tremaining: 101ms\n",
            "45:\tlearn: 0.0965280\ttest: 0.1866588\tbest: 0.1850963 (43)\ttotal: 84.2ms\tremaining: 98.8ms\n",
            "46:\tlearn: 0.0953610\ttest: 0.1839500\tbest: 0.1839500 (46)\ttotal: 85.5ms\tremaining: 96.4ms\n",
            "47:\tlearn: 0.0931468\ttest: 0.1829521\tbest: 0.1829521 (47)\ttotal: 86.8ms\tremaining: 94ms\n",
            "48:\tlearn: 0.0928604\ttest: 0.1830240\tbest: 0.1829521 (47)\ttotal: 88.1ms\tremaining: 91.7ms\n",
            "49:\tlearn: 0.0926819\ttest: 0.1830335\tbest: 0.1829521 (47)\ttotal: 89.4ms\tremaining: 89.4ms\n",
            "50:\tlearn: 0.0920416\ttest: 0.1836824\tbest: 0.1829521 (47)\ttotal: 90.7ms\tremaining: 87.1ms\n",
            "51:\tlearn: 0.0906733\ttest: 0.1834939\tbest: 0.1829521 (47)\ttotal: 92.2ms\tremaining: 85.1ms\n",
            "52:\tlearn: 0.0903678\ttest: 0.1836389\tbest: 0.1829521 (47)\ttotal: 93.5ms\tremaining: 82.9ms\n",
            "53:\tlearn: 0.0887556\ttest: 0.1824890\tbest: 0.1824890 (53)\ttotal: 94.8ms\tremaining: 80.8ms\n",
            "54:\tlearn: 0.0868510\ttest: 0.1799503\tbest: 0.1799503 (54)\ttotal: 96.1ms\tremaining: 78.6ms\n",
            "55:\tlearn: 0.0855039\ttest: 0.1794600\tbest: 0.1794600 (55)\ttotal: 97.4ms\tremaining: 76.5ms\n",
            "56:\tlearn: 0.0845780\ttest: 0.1805981\tbest: 0.1794600 (55)\ttotal: 98.7ms\tremaining: 74.5ms\n",
            "57:\tlearn: 0.0832548\ttest: 0.1809358\tbest: 0.1794600 (55)\ttotal: 100ms\tremaining: 72.5ms\n",
            "58:\tlearn: 0.0824861\ttest: 0.1810326\tbest: 0.1794600 (55)\ttotal: 101ms\tremaining: 70.5ms\n",
            "59:\tlearn: 0.0807526\ttest: 0.1816779\tbest: 0.1794600 (55)\ttotal: 103ms\tremaining: 68.6ms\n",
            "60:\tlearn: 0.0791630\ttest: 0.1805678\tbest: 0.1794600 (55)\ttotal: 104ms\tremaining: 66.6ms\n",
            "61:\tlearn: 0.0781225\ttest: 0.1805777\tbest: 0.1794600 (55)\ttotal: 105ms\tremaining: 64.6ms\n",
            "62:\tlearn: 0.0764085\ttest: 0.1813188\tbest: 0.1794600 (55)\ttotal: 107ms\tremaining: 62.6ms\n",
            "63:\tlearn: 0.0750235\ttest: 0.1811370\tbest: 0.1794600 (55)\ttotal: 108ms\tremaining: 60.8ms\n",
            "64:\tlearn: 0.0739369\ttest: 0.1814279\tbest: 0.1794600 (55)\ttotal: 109ms\tremaining: 58.9ms\n",
            "65:\tlearn: 0.0724013\ttest: 0.1815665\tbest: 0.1794600 (55)\ttotal: 111ms\tremaining: 57.1ms\n",
            "66:\tlearn: 0.0713160\ttest: 0.1804580\tbest: 0.1794600 (55)\ttotal: 112ms\tremaining: 55.2ms\n",
            "67:\tlearn: 0.0712284\ttest: 0.1805088\tbest: 0.1794600 (55)\ttotal: 113ms\tremaining: 53.4ms\n",
            "68:\tlearn: 0.0703418\ttest: 0.1801968\tbest: 0.1794600 (55)\ttotal: 115ms\tremaining: 51.6ms\n",
            "69:\tlearn: 0.0694475\ttest: 0.1808611\tbest: 0.1794600 (55)\ttotal: 116ms\tremaining: 49.7ms\n",
            "70:\tlearn: 0.0680565\ttest: 0.1808110\tbest: 0.1794600 (55)\ttotal: 117ms\tremaining: 47.9ms\n",
            "71:\tlearn: 0.0676406\ttest: 0.1808513\tbest: 0.1794600 (55)\ttotal: 119ms\tremaining: 46.1ms\n",
            "72:\tlearn: 0.0671063\ttest: 0.1817770\tbest: 0.1794600 (55)\ttotal: 120ms\tremaining: 44.4ms\n",
            "73:\tlearn: 0.0658038\ttest: 0.1811401\tbest: 0.1794600 (55)\ttotal: 122ms\tremaining: 42.8ms\n",
            "74:\tlearn: 0.0657658\ttest: 0.1811069\tbest: 0.1794600 (55)\ttotal: 124ms\tremaining: 41.2ms\n",
            "75:\tlearn: 0.0657338\ttest: 0.1810827\tbest: 0.1794600 (55)\ttotal: 125ms\tremaining: 39.5ms\n",
            "76:\tlearn: 0.0640342\ttest: 0.1797528\tbest: 0.1794600 (55)\ttotal: 127ms\tremaining: 37.9ms\n",
            "77:\tlearn: 0.0625339\ttest: 0.1772852\tbest: 0.1772852 (77)\ttotal: 128ms\tremaining: 36.2ms\n",
            "78:\tlearn: 0.0620837\ttest: 0.1778624\tbest: 0.1772852 (77)\ttotal: 130ms\tremaining: 34.6ms\n",
            "79:\tlearn: 0.0608832\ttest: 0.1779867\tbest: 0.1772852 (77)\ttotal: 132ms\tremaining: 32.9ms\n",
            "80:\tlearn: 0.0602369\ttest: 0.1789072\tbest: 0.1772852 (77)\ttotal: 133ms\tremaining: 31.2ms\n",
            "81:\tlearn: 0.0595695\ttest: 0.1784236\tbest: 0.1772852 (77)\ttotal: 135ms\tremaining: 29.7ms\n",
            "82:\tlearn: 0.0591103\ttest: 0.1786160\tbest: 0.1772852 (77)\ttotal: 137ms\tremaining: 28ms\n",
            "83:\tlearn: 0.0585040\ttest: 0.1780455\tbest: 0.1772852 (77)\ttotal: 138ms\tremaining: 26.3ms\n",
            "84:\tlearn: 0.0573455\ttest: 0.1791408\tbest: 0.1772852 (77)\ttotal: 139ms\tremaining: 24.6ms\n",
            "85:\tlearn: 0.0561917\ttest: 0.1785300\tbest: 0.1772852 (77)\ttotal: 141ms\tremaining: 22.9ms\n",
            "86:\tlearn: 0.0561702\ttest: 0.1784979\tbest: 0.1772852 (77)\ttotal: 142ms\tremaining: 21.2ms\n",
            "87:\tlearn: 0.0550164\ttest: 0.1786507\tbest: 0.1772852 (77)\ttotal: 143ms\tremaining: 19.5ms\n",
            "88:\tlearn: 0.0534429\ttest: 0.1784187\tbest: 0.1772852 (77)\ttotal: 144ms\tremaining: 17.9ms\n",
            "89:\tlearn: 0.0532716\ttest: 0.1786012\tbest: 0.1772852 (77)\ttotal: 146ms\tremaining: 16.2ms\n",
            "90:\tlearn: 0.0532428\ttest: 0.1785372\tbest: 0.1772852 (77)\ttotal: 147ms\tremaining: 14.5ms\n",
            "91:\tlearn: 0.0522258\ttest: 0.1788187\tbest: 0.1772852 (77)\ttotal: 148ms\tremaining: 12.9ms\n",
            "92:\tlearn: 0.0508904\ttest: 0.1790482\tbest: 0.1772852 (77)\ttotal: 150ms\tremaining: 11.3ms\n",
            "93:\tlearn: 0.0505305\ttest: 0.1785432\tbest: 0.1772852 (77)\ttotal: 151ms\tremaining: 9.63ms\n",
            "94:\tlearn: 0.0501617\ttest: 0.1786100\tbest: 0.1772852 (77)\ttotal: 152ms\tremaining: 8.01ms\n",
            "95:\tlearn: 0.0501075\ttest: 0.1785849\tbest: 0.1772852 (77)\ttotal: 153ms\tremaining: 6.39ms\n",
            "96:\tlearn: 0.0489121\ttest: 0.1781514\tbest: 0.1772852 (77)\ttotal: 155ms\tremaining: 4.79ms\n",
            "97:\tlearn: 0.0480527\ttest: 0.1784764\tbest: 0.1772852 (77)\ttotal: 156ms\tremaining: 3.18ms\n",
            "98:\tlearn: 0.0472237\ttest: 0.1789830\tbest: 0.1772852 (77)\ttotal: 157ms\tremaining: 1.59ms\n",
            "99:\tlearn: 0.0470112\ttest: 0.1790625\tbest: 0.1772852 (77)\ttotal: 159ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1772851738\n",
            "bestIteration = 77\n",
            "\n",
            "Training on fold [1/4]\n",
            "0:\tlearn: 0.4809001\ttest: 0.4919205\tbest: 0.4919205 (0)\ttotal: 13.3ms\tremaining: 1.31s\n",
            "1:\tlearn: 0.3645013\ttest: 0.3806254\tbest: 0.3806254 (1)\ttotal: 14.6ms\tremaining: 716ms\n",
            "2:\tlearn: 0.3143050\ttest: 0.3304141\tbest: 0.3304141 (2)\ttotal: 15.9ms\tremaining: 514ms\n",
            "3:\tlearn: 0.2639552\ttest: 0.2872639\tbest: 0.2872639 (3)\ttotal: 17.2ms\tremaining: 413ms\n",
            "4:\tlearn: 0.2492795\ttest: 0.2782634\tbest: 0.2782634 (4)\ttotal: 18.4ms\tremaining: 350ms\n",
            "5:\tlearn: 0.2298021\ttest: 0.2587628\tbest: 0.2587628 (5)\ttotal: 19.8ms\tremaining: 310ms\n",
            "6:\tlearn: 0.2147257\ttest: 0.2464103\tbest: 0.2464103 (6)\ttotal: 21.3ms\tremaining: 283ms\n",
            "7:\tlearn: 0.2074589\ttest: 0.2431436\tbest: 0.2431436 (7)\ttotal: 22.8ms\tremaining: 262ms\n",
            "8:\tlearn: 0.1916945\ttest: 0.2322588\tbest: 0.2322588 (8)\ttotal: 24.5ms\tremaining: 247ms\n",
            "9:\tlearn: 0.1870986\ttest: 0.2291719\tbest: 0.2291719 (9)\ttotal: 26.3ms\tremaining: 237ms\n",
            "10:\tlearn: 0.1771200\ttest: 0.2265368\tbest: 0.2265368 (10)\ttotal: 27.9ms\tremaining: 226ms\n",
            "11:\tlearn: 0.1704948\ttest: 0.2228519\tbest: 0.2228519 (11)\ttotal: 29.5ms\tremaining: 216ms\n",
            "12:\tlearn: 0.1662210\ttest: 0.2225059\tbest: 0.2225059 (12)\ttotal: 31ms\tremaining: 208ms\n",
            "13:\tlearn: 0.1643732\ttest: 0.2216124\tbest: 0.2216124 (13)\ttotal: 32.8ms\tremaining: 201ms\n",
            "14:\tlearn: 0.1505507\ttest: 0.2129927\tbest: 0.2129927 (14)\ttotal: 34.2ms\tremaining: 194ms\n",
            "15:\tlearn: 0.1483391\ttest: 0.2124294\tbest: 0.2124294 (15)\ttotal: 35.8ms\tremaining: 188ms\n",
            "16:\tlearn: 0.1463872\ttest: 0.2113799\tbest: 0.2113799 (16)\ttotal: 37.5ms\tremaining: 183ms\n",
            "17:\tlearn: 0.1442123\ttest: 0.2118635\tbest: 0.2113799 (16)\ttotal: 39.1ms\tremaining: 178ms\n",
            "18:\tlearn: 0.1421739\ttest: 0.2120533\tbest: 0.2113799 (16)\ttotal: 41.1ms\tremaining: 175ms\n",
            "19:\tlearn: 0.1380256\ttest: 0.2119506\tbest: 0.2113799 (16)\ttotal: 42.6ms\tremaining: 170ms\n",
            "20:\tlearn: 0.1365476\ttest: 0.2098197\tbest: 0.2098197 (20)\ttotal: 43.9ms\tremaining: 165ms\n",
            "21:\tlearn: 0.1318830\ttest: 0.2072708\tbest: 0.2072708 (21)\ttotal: 45.2ms\tremaining: 160ms\n",
            "22:\tlearn: 0.1314183\ttest: 0.2072865\tbest: 0.2072708 (21)\ttotal: 46.4ms\tremaining: 155ms\n",
            "23:\tlearn: 0.1298300\ttest: 0.2079978\tbest: 0.2072708 (21)\ttotal: 47.7ms\tremaining: 151ms\n",
            "24:\tlearn: 0.1283244\ttest: 0.2067642\tbest: 0.2067642 (24)\ttotal: 49ms\tremaining: 147ms\n",
            "25:\tlearn: 0.1246617\ttest: 0.2074187\tbest: 0.2067642 (24)\ttotal: 50.3ms\tremaining: 143ms\n",
            "26:\tlearn: 0.1208404\ttest: 0.2061350\tbest: 0.2061350 (26)\ttotal: 51.7ms\tremaining: 140ms\n",
            "27:\tlearn: 0.1203888\ttest: 0.2061810\tbest: 0.2061350 (26)\ttotal: 53.1ms\tremaining: 136ms\n",
            "28:\tlearn: 0.1176739\ttest: 0.2054805\tbest: 0.2054805 (28)\ttotal: 54.7ms\tremaining: 134ms\n",
            "29:\tlearn: 0.1162178\ttest: 0.2058461\tbest: 0.2054805 (28)\ttotal: 56ms\tremaining: 131ms\n",
            "30:\tlearn: 0.1145304\ttest: 0.2068018\tbest: 0.2054805 (28)\ttotal: 57.3ms\tremaining: 128ms\n",
            "31:\tlearn: 0.1110839\ttest: 0.2060492\tbest: 0.2054805 (28)\ttotal: 58.7ms\tremaining: 125ms\n",
            "32:\tlearn: 0.1081517\ttest: 0.2049874\tbest: 0.2049874 (32)\ttotal: 60.1ms\tremaining: 122ms\n",
            "33:\tlearn: 0.1072746\ttest: 0.2041089\tbest: 0.2041089 (33)\ttotal: 61.3ms\tremaining: 119ms\n",
            "34:\tlearn: 0.1042451\ttest: 0.2054678\tbest: 0.2041089 (33)\ttotal: 62.6ms\tremaining: 116ms\n",
            "35:\tlearn: 0.1035142\ttest: 0.2052176\tbest: 0.2041089 (33)\ttotal: 63.9ms\tremaining: 114ms\n",
            "36:\tlearn: 0.0999809\ttest: 0.2047600\tbest: 0.2041089 (33)\ttotal: 65.2ms\tremaining: 111ms\n",
            "37:\tlearn: 0.0986073\ttest: 0.2049975\tbest: 0.2041089 (33)\ttotal: 66.7ms\tremaining: 109ms\n",
            "38:\tlearn: 0.0972883\ttest: 0.2048916\tbest: 0.2041089 (33)\ttotal: 68ms\tremaining: 106ms\n",
            "39:\tlearn: 0.0957783\ttest: 0.2026484\tbest: 0.2026484 (39)\ttotal: 69.3ms\tremaining: 104ms\n",
            "40:\tlearn: 0.0951465\ttest: 0.2028671\tbest: 0.2026484 (39)\ttotal: 70.6ms\tremaining: 102ms\n",
            "41:\tlearn: 0.0946396\ttest: 0.2027185\tbest: 0.2026484 (39)\ttotal: 71.9ms\tremaining: 99.4ms\n",
            "42:\tlearn: 0.0933647\ttest: 0.2036920\tbest: 0.2026484 (39)\ttotal: 73.3ms\tremaining: 97.1ms\n",
            "43:\tlearn: 0.0912984\ttest: 0.2029868\tbest: 0.2026484 (39)\ttotal: 74.5ms\tremaining: 94.8ms\n",
            "44:\tlearn: 0.0892129\ttest: 0.2026534\tbest: 0.2026484 (39)\ttotal: 75.8ms\tremaining: 92.6ms\n",
            "45:\tlearn: 0.0890171\ttest: 0.2027459\tbest: 0.2026484 (39)\ttotal: 77ms\tremaining: 90.4ms\n",
            "46:\tlearn: 0.0883848\ttest: 0.2023187\tbest: 0.2023187 (46)\ttotal: 78.3ms\tremaining: 88.3ms\n",
            "47:\tlearn: 0.0881272\ttest: 0.2023995\tbest: 0.2023187 (46)\ttotal: 79.6ms\tremaining: 86.2ms\n",
            "48:\tlearn: 0.0865421\ttest: 0.2040891\tbest: 0.2023187 (46)\ttotal: 80.9ms\tremaining: 84.2ms\n",
            "49:\tlearn: 0.0836068\ttest: 0.2061516\tbest: 0.2023187 (46)\ttotal: 82.2ms\tremaining: 82.2ms\n",
            "50:\tlearn: 0.0834288\ttest: 0.2062128\tbest: 0.2023187 (46)\ttotal: 83.5ms\tremaining: 80.2ms\n",
            "51:\tlearn: 0.0815570\ttest: 0.2074784\tbest: 0.2023187 (46)\ttotal: 84.8ms\tremaining: 78.3ms\n",
            "52:\tlearn: 0.0794653\ttest: 0.2098601\tbest: 0.2023187 (46)\ttotal: 86.1ms\tremaining: 76.4ms\n",
            "53:\tlearn: 0.0781475\ttest: 0.2104135\tbest: 0.2023187 (46)\ttotal: 87.5ms\tremaining: 74.5ms\n",
            "54:\tlearn: 0.0770968\ttest: 0.2110293\tbest: 0.2023187 (46)\ttotal: 88.9ms\tremaining: 72.8ms\n",
            "55:\tlearn: 0.0760652\ttest: 0.2112443\tbest: 0.2023187 (46)\ttotal: 90.2ms\tremaining: 70.9ms\n",
            "56:\tlearn: 0.0746163\ttest: 0.2130808\tbest: 0.2023187 (46)\ttotal: 91.7ms\tremaining: 69.2ms\n",
            "57:\tlearn: 0.0729827\ttest: 0.2108189\tbest: 0.2023187 (46)\ttotal: 93ms\tremaining: 67.3ms\n",
            "58:\tlearn: 0.0721851\ttest: 0.2106498\tbest: 0.2023187 (46)\ttotal: 94.2ms\tremaining: 65.5ms\n",
            "59:\tlearn: 0.0707040\ttest: 0.2110896\tbest: 0.2023187 (46)\ttotal: 95.5ms\tremaining: 63.7ms\n",
            "60:\tlearn: 0.0700404\ttest: 0.2108402\tbest: 0.2023187 (46)\ttotal: 96.8ms\tremaining: 61.9ms\n",
            "61:\tlearn: 0.0684081\ttest: 0.2112733\tbest: 0.2023187 (46)\ttotal: 98.2ms\tremaining: 60.2ms\n",
            "62:\tlearn: 0.0659915\ttest: 0.2092610\tbest: 0.2023187 (46)\ttotal: 99.5ms\tremaining: 58.5ms\n",
            "63:\tlearn: 0.0658353\ttest: 0.2093582\tbest: 0.2023187 (46)\ttotal: 101ms\tremaining: 56.7ms\n",
            "64:\tlearn: 0.0645020\ttest: 0.2093474\tbest: 0.2023187 (46)\ttotal: 102ms\tremaining: 55ms\n",
            "65:\tlearn: 0.0640767\ttest: 0.2096204\tbest: 0.2023187 (46)\ttotal: 103ms\tremaining: 53.3ms\n",
            "66:\tlearn: 0.0632948\ttest: 0.2097932\tbest: 0.2023187 (46)\ttotal: 105ms\tremaining: 51.6ms\n",
            "67:\tlearn: 0.0616737\ttest: 0.2104949\tbest: 0.2023187 (46)\ttotal: 106ms\tremaining: 49.9ms\n",
            "68:\tlearn: 0.0601474\ttest: 0.2124747\tbest: 0.2023187 (46)\ttotal: 107ms\tremaining: 48.2ms\n",
            "69:\tlearn: 0.0597984\ttest: 0.2125175\tbest: 0.2023187 (46)\ttotal: 109ms\tremaining: 46.6ms\n",
            "70:\tlearn: 0.0597668\ttest: 0.2124670\tbest: 0.2023187 (46)\ttotal: 110ms\tremaining: 44.9ms\n",
            "71:\tlearn: 0.0589984\ttest: 0.2132152\tbest: 0.2023187 (46)\ttotal: 111ms\tremaining: 43.3ms\n",
            "72:\tlearn: 0.0584411\ttest: 0.2129927\tbest: 0.2023187 (46)\ttotal: 113ms\tremaining: 41.7ms\n",
            "73:\tlearn: 0.0573935\ttest: 0.2143368\tbest: 0.2023187 (46)\ttotal: 114ms\tremaining: 40ms\n",
            "74:\tlearn: 0.0571082\ttest: 0.2144414\tbest: 0.2023187 (46)\ttotal: 116ms\tremaining: 38.6ms\n",
            "75:\tlearn: 0.0562033\ttest: 0.2161883\tbest: 0.2023187 (46)\ttotal: 117ms\tremaining: 37.1ms\n",
            "76:\tlearn: 0.0555412\ttest: 0.2171535\tbest: 0.2023187 (46)\ttotal: 119ms\tremaining: 35.4ms\n",
            "77:\tlearn: 0.0538789\ttest: 0.2181783\tbest: 0.2023187 (46)\ttotal: 120ms\tremaining: 33.8ms\n",
            "78:\tlearn: 0.0523635\ttest: 0.2202782\tbest: 0.2023187 (46)\ttotal: 121ms\tremaining: 32.2ms\n",
            "79:\tlearn: 0.0515590\ttest: 0.2190171\tbest: 0.2023187 (46)\ttotal: 122ms\tremaining: 30.6ms\n",
            "80:\tlearn: 0.0505325\ttest: 0.2194122\tbest: 0.2023187 (46)\ttotal: 124ms\tremaining: 29ms\n",
            "81:\tlearn: 0.0497542\ttest: 0.2194290\tbest: 0.2023187 (46)\ttotal: 125ms\tremaining: 27.5ms\n",
            "82:\tlearn: 0.0489519\ttest: 0.2206942\tbest: 0.2023187 (46)\ttotal: 126ms\tremaining: 25.9ms\n",
            "83:\tlearn: 0.0484106\ttest: 0.2213425\tbest: 0.2023187 (46)\ttotal: 128ms\tremaining: 24.3ms\n",
            "84:\tlearn: 0.0479234\ttest: 0.2216240\tbest: 0.2023187 (46)\ttotal: 129ms\tremaining: 22.8ms\n",
            "85:\tlearn: 0.0466322\ttest: 0.2214310\tbest: 0.2023187 (46)\ttotal: 130ms\tremaining: 21.2ms\n",
            "86:\tlearn: 0.0457532\ttest: 0.2224375\tbest: 0.2023187 (46)\ttotal: 132ms\tremaining: 19.7ms\n",
            "87:\tlearn: 0.0448020\ttest: 0.2223687\tbest: 0.2023187 (46)\ttotal: 133ms\tremaining: 18.2ms\n",
            "88:\tlearn: 0.0443147\ttest: 0.2228961\tbest: 0.2023187 (46)\ttotal: 135ms\tremaining: 16.7ms\n",
            "89:\tlearn: 0.0438053\ttest: 0.2236549\tbest: 0.2023187 (46)\ttotal: 136ms\tremaining: 15.1ms\n",
            "90:\tlearn: 0.0425878\ttest: 0.2240156\tbest: 0.2023187 (46)\ttotal: 137ms\tremaining: 13.6ms\n",
            "91:\tlearn: 0.0417157\ttest: 0.2255213\tbest: 0.2023187 (46)\ttotal: 139ms\tremaining: 12.1ms\n",
            "92:\tlearn: 0.0416606\ttest: 0.2255624\tbest: 0.2023187 (46)\ttotal: 140ms\tremaining: 10.6ms\n",
            "93:\tlearn: 0.0413931\ttest: 0.2253552\tbest: 0.2023187 (46)\ttotal: 142ms\tremaining: 9.04ms\n",
            "94:\tlearn: 0.0407601\ttest: 0.2270410\tbest: 0.2023187 (46)\ttotal: 143ms\tremaining: 7.52ms\n",
            "95:\tlearn: 0.0403133\ttest: 0.2277606\tbest: 0.2023187 (46)\ttotal: 144ms\tremaining: 6.01ms\n",
            "96:\tlearn: 0.0396765\ttest: 0.2258435\tbest: 0.2023187 (46)\ttotal: 146ms\tremaining: 4.5ms\n",
            "97:\tlearn: 0.0394616\ttest: 0.2258920\tbest: 0.2023187 (46)\ttotal: 147ms\tremaining: 3ms\n",
            "98:\tlearn: 0.0389990\ttest: 0.2274748\tbest: 0.2023187 (46)\ttotal: 148ms\tremaining: 1.5ms\n",
            "99:\tlearn: 0.0389040\ttest: 0.2274698\tbest: 0.2023187 (46)\ttotal: 150ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.2023187114\n",
            "bestIteration = 46\n",
            "\n",
            "Training on fold [2/4]\n",
            "0:\tlearn: 0.5145852\ttest: 0.5133551\tbest: 0.5133551 (0)\ttotal: 3.46ms\tremaining: 343ms\n",
            "1:\tlearn: 0.3927118\ttest: 0.4062883\tbest: 0.4062883 (1)\ttotal: 4.84ms\tremaining: 237ms\n",
            "2:\tlearn: 0.3178624\ttest: 0.3112318\tbest: 0.3112318 (2)\ttotal: 6.21ms\tremaining: 201ms\n",
            "3:\tlearn: 0.2802929\ttest: 0.2855842\tbest: 0.2855842 (3)\ttotal: 7.48ms\tremaining: 180ms\n",
            "4:\tlearn: 0.2599554\ttest: 0.2611876\tbest: 0.2611876 (4)\ttotal: 8.77ms\tremaining: 167ms\n",
            "5:\tlearn: 0.2343908\ttest: 0.2371996\tbest: 0.2371996 (5)\ttotal: 10ms\tremaining: 157ms\n",
            "6:\tlearn: 0.2244228\ttest: 0.2272309\tbest: 0.2272309 (6)\ttotal: 11.3ms\tremaining: 150ms\n",
            "7:\tlearn: 0.2157454\ttest: 0.2249943\tbest: 0.2249943 (7)\ttotal: 12.6ms\tremaining: 145ms\n",
            "8:\tlearn: 0.2049511\ttest: 0.2184360\tbest: 0.2184360 (8)\ttotal: 13.9ms\tremaining: 140ms\n",
            "9:\tlearn: 0.1907767\ttest: 0.1955564\tbest: 0.1955564 (9)\ttotal: 15.1ms\tremaining: 136ms\n",
            "10:\tlearn: 0.1826246\ttest: 0.1914204\tbest: 0.1914204 (10)\ttotal: 16.4ms\tremaining: 133ms\n",
            "11:\tlearn: 0.1803686\ttest: 0.1878317\tbest: 0.1878317 (11)\ttotal: 17.9ms\tremaining: 131ms\n",
            "12:\tlearn: 0.1776176\ttest: 0.1860412\tbest: 0.1860412 (12)\ttotal: 19.2ms\tremaining: 129ms\n",
            "13:\tlearn: 0.1738395\ttest: 0.1792058\tbest: 0.1792058 (13)\ttotal: 20.5ms\tremaining: 126ms\n",
            "14:\tlearn: 0.1659861\ttest: 0.1738610\tbest: 0.1738610 (14)\ttotal: 21.9ms\tremaining: 124ms\n",
            "15:\tlearn: 0.1624422\ttest: 0.1728453\tbest: 0.1728453 (15)\ttotal: 23.3ms\tremaining: 122ms\n",
            "16:\tlearn: 0.1598417\ttest: 0.1697564\tbest: 0.1697564 (16)\ttotal: 24.6ms\tremaining: 120ms\n",
            "17:\tlearn: 0.1571168\ttest: 0.1692597\tbest: 0.1692597 (17)\ttotal: 25.9ms\tremaining: 118ms\n",
            "18:\tlearn: 0.1534862\ttest: 0.1666970\tbest: 0.1666970 (18)\ttotal: 27.2ms\tremaining: 116ms\n",
            "19:\tlearn: 0.1501156\ttest: 0.1672109\tbest: 0.1666970 (18)\ttotal: 28.6ms\tremaining: 115ms\n",
            "20:\tlearn: 0.1477453\ttest: 0.1661171\tbest: 0.1661171 (20)\ttotal: 29.9ms\tremaining: 113ms\n",
            "21:\tlearn: 0.1462643\ttest: 0.1650556\tbest: 0.1650556 (21)\ttotal: 31.2ms\tremaining: 111ms\n",
            "22:\tlearn: 0.1435393\ttest: 0.1634184\tbest: 0.1634184 (22)\ttotal: 32.4ms\tremaining: 109ms\n",
            "23:\tlearn: 0.1406041\ttest: 0.1607612\tbest: 0.1607612 (23)\ttotal: 33.7ms\tremaining: 107ms\n",
            "24:\tlearn: 0.1378590\ttest: 0.1590722\tbest: 0.1590722 (24)\ttotal: 35.3ms\tremaining: 106ms\n",
            "25:\tlearn: 0.1350497\ttest: 0.1592570\tbest: 0.1590722 (24)\ttotal: 36.7ms\tremaining: 105ms\n",
            "26:\tlearn: 0.1315891\ttest: 0.1612563\tbest: 0.1590722 (24)\ttotal: 38.1ms\tremaining: 103ms\n",
            "27:\tlearn: 0.1285249\ttest: 0.1621802\tbest: 0.1590722 (24)\ttotal: 39.4ms\tremaining: 101ms\n",
            "28:\tlearn: 0.1257506\ttest: 0.1578691\tbest: 0.1578691 (28)\ttotal: 40.7ms\tremaining: 99.5ms\n",
            "29:\tlearn: 0.1235112\ttest: 0.1534881\tbest: 0.1534881 (29)\ttotal: 42.1ms\tremaining: 98.2ms\n",
            "30:\tlearn: 0.1218113\ttest: 0.1534852\tbest: 0.1534852 (30)\ttotal: 43.5ms\tremaining: 96.9ms\n",
            "31:\tlearn: 0.1193868\ttest: 0.1527587\tbest: 0.1527587 (31)\ttotal: 44.9ms\tremaining: 95.4ms\n",
            "32:\tlearn: 0.1191022\ttest: 0.1526629\tbest: 0.1526629 (32)\ttotal: 46.1ms\tremaining: 93.7ms\n",
            "33:\tlearn: 0.1172481\ttest: 0.1519605\tbest: 0.1519605 (33)\ttotal: 47.4ms\tremaining: 92ms\n",
            "34:\tlearn: 0.1146282\ttest: 0.1513669\tbest: 0.1513669 (34)\ttotal: 48.7ms\tremaining: 90.5ms\n",
            "35:\tlearn: 0.1119583\ttest: 0.1509557\tbest: 0.1509557 (35)\ttotal: 50.2ms\tremaining: 89.2ms\n",
            "36:\tlearn: 0.1102777\ttest: 0.1521294\tbest: 0.1509557 (35)\ttotal: 51.6ms\tremaining: 87.9ms\n",
            "37:\tlearn: 0.1089086\ttest: 0.1526514\tbest: 0.1509557 (35)\ttotal: 52.9ms\tremaining: 86.3ms\n",
            "38:\tlearn: 0.1065210\ttest: 0.1524026\tbest: 0.1509557 (35)\ttotal: 54.2ms\tremaining: 84.8ms\n",
            "39:\tlearn: 0.1038513\ttest: 0.1529199\tbest: 0.1509557 (35)\ttotal: 55.6ms\tremaining: 83.4ms\n",
            "40:\tlearn: 0.1017546\ttest: 0.1542880\tbest: 0.1509557 (35)\ttotal: 56.9ms\tremaining: 81.9ms\n",
            "41:\tlearn: 0.1011039\ttest: 0.1543776\tbest: 0.1509557 (35)\ttotal: 58.3ms\tremaining: 80.5ms\n",
            "42:\tlearn: 0.0981796\ttest: 0.1578209\tbest: 0.1509557 (35)\ttotal: 59.6ms\tremaining: 78.9ms\n",
            "43:\tlearn: 0.0961890\ttest: 0.1558194\tbest: 0.1509557 (35)\ttotal: 60.9ms\tremaining: 77.5ms\n",
            "44:\tlearn: 0.0947535\ttest: 0.1563983\tbest: 0.1509557 (35)\ttotal: 62.2ms\tremaining: 76ms\n",
            "45:\tlearn: 0.0946260\ttest: 0.1563763\tbest: 0.1509557 (35)\ttotal: 63.5ms\tremaining: 74.5ms\n",
            "46:\tlearn: 0.0918504\ttest: 0.1526104\tbest: 0.1509557 (35)\ttotal: 65.1ms\tremaining: 73.4ms\n",
            "47:\tlearn: 0.0917559\ttest: 0.1525888\tbest: 0.1509557 (35)\ttotal: 66.4ms\tremaining: 71.9ms\n",
            "48:\tlearn: 0.0905332\ttest: 0.1531042\tbest: 0.1509557 (35)\ttotal: 67.6ms\tremaining: 70.4ms\n",
            "49:\tlearn: 0.0885681\ttest: 0.1523724\tbest: 0.1509557 (35)\ttotal: 68.9ms\tremaining: 68.9ms\n",
            "50:\tlearn: 0.0877299\ttest: 0.1513225\tbest: 0.1509557 (35)\ttotal: 70.2ms\tremaining: 67.4ms\n",
            "51:\tlearn: 0.0865494\ttest: 0.1507893\tbest: 0.1507893 (51)\ttotal: 71.4ms\tremaining: 66ms\n",
            "52:\tlearn: 0.0848201\ttest: 0.1495647\tbest: 0.1495647 (52)\ttotal: 72.7ms\tremaining: 64.5ms\n",
            "53:\tlearn: 0.0839597\ttest: 0.1509945\tbest: 0.1495647 (52)\ttotal: 74ms\tremaining: 63ms\n",
            "54:\tlearn: 0.0820457\ttest: 0.1517000\tbest: 0.1495647 (52)\ttotal: 75.2ms\tremaining: 61.5ms\n",
            "55:\tlearn: 0.0810169\ttest: 0.1521612\tbest: 0.1495647 (52)\ttotal: 76.6ms\tremaining: 60.2ms\n",
            "56:\tlearn: 0.0804199\ttest: 0.1522941\tbest: 0.1495647 (52)\ttotal: 78ms\tremaining: 58.9ms\n",
            "57:\tlearn: 0.0802876\ttest: 0.1521768\tbest: 0.1495647 (52)\ttotal: 79.3ms\tremaining: 57.4ms\n",
            "58:\tlearn: 0.0801271\ttest: 0.1521484\tbest: 0.1495647 (52)\ttotal: 80.8ms\tremaining: 56.1ms\n",
            "59:\tlearn: 0.0784137\ttest: 0.1532081\tbest: 0.1495647 (52)\ttotal: 82.1ms\tremaining: 54.7ms\n",
            "60:\tlearn: 0.0774032\ttest: 0.1537081\tbest: 0.1495647 (52)\ttotal: 83.3ms\tremaining: 53.3ms\n",
            "61:\tlearn: 0.0744967\ttest: 0.1559987\tbest: 0.1495647 (52)\ttotal: 84.6ms\tremaining: 51.9ms\n",
            "62:\tlearn: 0.0727592\ttest: 0.1559979\tbest: 0.1495647 (52)\ttotal: 85.9ms\tremaining: 50.5ms\n",
            "63:\tlearn: 0.0726278\ttest: 0.1561103\tbest: 0.1495647 (52)\ttotal: 87.2ms\tremaining: 49ms\n",
            "64:\tlearn: 0.0720225\ttest: 0.1554621\tbest: 0.1495647 (52)\ttotal: 88.5ms\tremaining: 47.6ms\n",
            "65:\tlearn: 0.0704570\ttest: 0.1554327\tbest: 0.1495647 (52)\ttotal: 89.8ms\tremaining: 46.2ms\n",
            "66:\tlearn: 0.0691722\ttest: 0.1545431\tbest: 0.1495647 (52)\ttotal: 91ms\tremaining: 44.8ms\n",
            "67:\tlearn: 0.0688561\ttest: 0.1546765\tbest: 0.1495647 (52)\ttotal: 92.6ms\tremaining: 43.6ms\n",
            "68:\tlearn: 0.0677803\ttest: 0.1552047\tbest: 0.1495647 (52)\ttotal: 93.9ms\tremaining: 42.2ms\n",
            "69:\tlearn: 0.0669205\ttest: 0.1549289\tbest: 0.1495647 (52)\ttotal: 95.6ms\tremaining: 41ms\n",
            "70:\tlearn: 0.0655798\ttest: 0.1520043\tbest: 0.1495647 (52)\ttotal: 97ms\tremaining: 39.6ms\n",
            "71:\tlearn: 0.0646413\ttest: 0.1514305\tbest: 0.1495647 (52)\ttotal: 98.3ms\tremaining: 38.2ms\n",
            "72:\tlearn: 0.0636469\ttest: 0.1534420\tbest: 0.1495647 (52)\ttotal: 99.6ms\tremaining: 36.8ms\n",
            "73:\tlearn: 0.0631576\ttest: 0.1538596\tbest: 0.1495647 (52)\ttotal: 101ms\tremaining: 35.5ms\n",
            "74:\tlearn: 0.0625274\ttest: 0.1535381\tbest: 0.1495647 (52)\ttotal: 102ms\tremaining: 34.1ms\n",
            "75:\tlearn: 0.0614498\ttest: 0.1531464\tbest: 0.1495647 (52)\ttotal: 104ms\tremaining: 32.7ms\n",
            "76:\tlearn: 0.0609182\ttest: 0.1531206\tbest: 0.1495647 (52)\ttotal: 105ms\tremaining: 31.3ms\n",
            "77:\tlearn: 0.0606562\ttest: 0.1532750\tbest: 0.1495647 (52)\ttotal: 106ms\tremaining: 30ms\n",
            "78:\tlearn: 0.0603420\ttest: 0.1537475\tbest: 0.1495647 (52)\ttotal: 107ms\tremaining: 28.6ms\n",
            "79:\tlearn: 0.0596639\ttest: 0.1548253\tbest: 0.1495647 (52)\ttotal: 109ms\tremaining: 27.2ms\n",
            "80:\tlearn: 0.0587633\ttest: 0.1547948\tbest: 0.1495647 (52)\ttotal: 110ms\tremaining: 25.8ms\n",
            "81:\tlearn: 0.0567237\ttest: 0.1547388\tbest: 0.1495647 (52)\ttotal: 111ms\tremaining: 24.4ms\n",
            "82:\tlearn: 0.0564974\ttest: 0.1550684\tbest: 0.1495647 (52)\ttotal: 113ms\tremaining: 23.1ms\n",
            "83:\tlearn: 0.0562605\ttest: 0.1548458\tbest: 0.1495647 (52)\ttotal: 114ms\tremaining: 21.7ms\n",
            "84:\tlearn: 0.0558521\ttest: 0.1541539\tbest: 0.1495647 (52)\ttotal: 115ms\tremaining: 20.3ms\n",
            "85:\tlearn: 0.0552879\ttest: 0.1537711\tbest: 0.1495647 (52)\ttotal: 116ms\tremaining: 18.9ms\n",
            "86:\tlearn: 0.0546069\ttest: 0.1533173\tbest: 0.1495647 (52)\ttotal: 118ms\tremaining: 17.6ms\n",
            "87:\tlearn: 0.0541778\ttest: 0.1532674\tbest: 0.1495647 (52)\ttotal: 119ms\tremaining: 16.3ms\n",
            "88:\tlearn: 0.0532365\ttest: 0.1511687\tbest: 0.1495647 (52)\ttotal: 120ms\tremaining: 14.9ms\n",
            "89:\tlearn: 0.0523889\ttest: 0.1501898\tbest: 0.1495647 (52)\ttotal: 122ms\tremaining: 13.5ms\n",
            "90:\tlearn: 0.0515389\ttest: 0.1494533\tbest: 0.1494533 (90)\ttotal: 123ms\tremaining: 12.2ms\n",
            "91:\tlearn: 0.0508855\ttest: 0.1490322\tbest: 0.1490322 (91)\ttotal: 124ms\tremaining: 10.8ms\n",
            "92:\tlearn: 0.0508362\ttest: 0.1490070\tbest: 0.1490070 (92)\ttotal: 126ms\tremaining: 9.46ms\n",
            "93:\tlearn: 0.0502875\ttest: 0.1497026\tbest: 0.1490070 (92)\ttotal: 127ms\tremaining: 8.1ms\n",
            "94:\tlearn: 0.0500048\ttest: 0.1502529\tbest: 0.1490070 (92)\ttotal: 128ms\tremaining: 6.75ms\n",
            "95:\tlearn: 0.0489029\ttest: 0.1493539\tbest: 0.1490070 (92)\ttotal: 130ms\tremaining: 5.4ms\n",
            "96:\tlearn: 0.0476225\ttest: 0.1482475\tbest: 0.1482475 (96)\ttotal: 131ms\tremaining: 4.05ms\n",
            "97:\tlearn: 0.0470745\ttest: 0.1484859\tbest: 0.1482475 (96)\ttotal: 132ms\tremaining: 2.7ms\n",
            "98:\tlearn: 0.0463379\ttest: 0.1489145\tbest: 0.1482475 (96)\ttotal: 133ms\tremaining: 1.35ms\n",
            "99:\tlearn: 0.0456348\ttest: 0.1476437\tbest: 0.1476437 (99)\ttotal: 135ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1476437475\n",
            "bestIteration = 99\n",
            "\n",
            "Training on fold [3/4]\n",
            "0:\tlearn: 0.4855135\ttest: 0.4877946\tbest: 0.4877946 (0)\ttotal: 2.58ms\tremaining: 256ms\n",
            "1:\tlearn: 0.3831421\ttest: 0.3813864\tbest: 0.3813864 (1)\ttotal: 4.01ms\tremaining: 196ms\n",
            "2:\tlearn: 0.3297379\ttest: 0.3309462\tbest: 0.3309462 (2)\ttotal: 5.49ms\tremaining: 177ms\n",
            "3:\tlearn: 0.3033346\ttest: 0.2967306\tbest: 0.2967306 (3)\ttotal: 7.15ms\tremaining: 172ms\n",
            "4:\tlearn: 0.2772268\ttest: 0.2665820\tbest: 0.2665820 (4)\ttotal: 8.47ms\tremaining: 161ms\n",
            "5:\tlearn: 0.2585550\ttest: 0.2485231\tbest: 0.2485231 (5)\ttotal: 9.77ms\tremaining: 153ms\n",
            "6:\tlearn: 0.2409179\ttest: 0.2267964\tbest: 0.2267964 (6)\ttotal: 11ms\tremaining: 146ms\n",
            "7:\tlearn: 0.2316088\ttest: 0.2171726\tbest: 0.2171726 (7)\ttotal: 12.3ms\tremaining: 141ms\n",
            "8:\tlearn: 0.2238588\ttest: 0.2090644\tbest: 0.2090644 (8)\ttotal: 13.6ms\tremaining: 137ms\n",
            "9:\tlearn: 0.2068996\ttest: 0.1905999\tbest: 0.1905999 (9)\ttotal: 14.9ms\tremaining: 134ms\n",
            "10:\tlearn: 0.1996305\ttest: 0.1849534\tbest: 0.1849534 (10)\ttotal: 16.2ms\tremaining: 131ms\n",
            "11:\tlearn: 0.1966503\ttest: 0.1828394\tbest: 0.1828394 (11)\ttotal: 17.6ms\tremaining: 129ms\n",
            "12:\tlearn: 0.1951386\ttest: 0.1812776\tbest: 0.1812776 (12)\ttotal: 18.9ms\tremaining: 127ms\n",
            "13:\tlearn: 0.1907667\ttest: 0.1779577\tbest: 0.1779577 (13)\ttotal: 20.3ms\tremaining: 125ms\n",
            "14:\tlearn: 0.1902691\ttest: 0.1781822\tbest: 0.1779577 (13)\ttotal: 21.7ms\tremaining: 123ms\n",
            "15:\tlearn: 0.1878616\ttest: 0.1782985\tbest: 0.1779577 (13)\ttotal: 23.1ms\tremaining: 121ms\n",
            "16:\tlearn: 0.1856443\ttest: 0.1756702\tbest: 0.1756702 (16)\ttotal: 24.4ms\tremaining: 119ms\n",
            "17:\tlearn: 0.1838749\ttest: 0.1744742\tbest: 0.1744742 (17)\ttotal: 25.7ms\tremaining: 117ms\n",
            "18:\tlearn: 0.1765953\ttest: 0.1680896\tbest: 0.1680896 (18)\ttotal: 27ms\tremaining: 115ms\n",
            "19:\tlearn: 0.1726262\ttest: 0.1619757\tbest: 0.1619757 (19)\ttotal: 28.3ms\tremaining: 113ms\n",
            "20:\tlearn: 0.1653706\ttest: 0.1534759\tbest: 0.1534759 (20)\ttotal: 29.5ms\tremaining: 111ms\n",
            "21:\tlearn: 0.1639010\ttest: 0.1519674\tbest: 0.1519674 (21)\ttotal: 30.8ms\tremaining: 109ms\n",
            "22:\tlearn: 0.1610185\ttest: 0.1508683\tbest: 0.1508683 (22)\ttotal: 33.5ms\tremaining: 112ms\n",
            "23:\tlearn: 0.1592130\ttest: 0.1493784\tbest: 0.1493784 (23)\ttotal: 34.9ms\tremaining: 111ms\n",
            "24:\tlearn: 0.1541541\ttest: 0.1471048\tbest: 0.1471048 (24)\ttotal: 36.2ms\tremaining: 109ms\n",
            "25:\tlearn: 0.1535125\ttest: 0.1465600\tbest: 0.1465600 (25)\ttotal: 37.5ms\tremaining: 107ms\n",
            "26:\tlearn: 0.1515857\ttest: 0.1464478\tbest: 0.1464478 (26)\ttotal: 38.8ms\tremaining: 105ms\n",
            "27:\tlearn: 0.1484931\ttest: 0.1461816\tbest: 0.1461816 (27)\ttotal: 40.1ms\tremaining: 103ms\n",
            "28:\tlearn: 0.1478758\ttest: 0.1462778\tbest: 0.1461816 (27)\ttotal: 41.3ms\tremaining: 101ms\n",
            "29:\tlearn: 0.1447239\ttest: 0.1461470\tbest: 0.1461470 (29)\ttotal: 42.6ms\tremaining: 99.4ms\n",
            "30:\tlearn: 0.1419640\ttest: 0.1434317\tbest: 0.1434317 (30)\ttotal: 43.9ms\tremaining: 97.7ms\n",
            "31:\tlearn: 0.1390313\ttest: 0.1429757\tbest: 0.1429757 (31)\ttotal: 45.1ms\tremaining: 95.9ms\n",
            "32:\tlearn: 0.1377620\ttest: 0.1430112\tbest: 0.1429757 (31)\ttotal: 46.5ms\tremaining: 94.3ms\n",
            "33:\tlearn: 0.1348488\ttest: 0.1409536\tbest: 0.1409536 (33)\ttotal: 47.7ms\tremaining: 92.6ms\n",
            "34:\tlearn: 0.1326775\ttest: 0.1405747\tbest: 0.1405747 (34)\ttotal: 49.1ms\tremaining: 91.1ms\n",
            "35:\tlearn: 0.1310512\ttest: 0.1406559\tbest: 0.1405747 (34)\ttotal: 50.3ms\tremaining: 89.5ms\n",
            "36:\tlearn: 0.1304953\ttest: 0.1408542\tbest: 0.1405747 (34)\ttotal: 51.6ms\tremaining: 87.8ms\n",
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            "98:\tlearn: 0.0603575\ttest: 0.1359965\tbest: 0.1330140 (53)\ttotal: 142ms\tremaining: 1.43ms\n",
            "99:\tlearn: 0.0597274\ttest: 0.1363290\tbest: 0.1330140 (53)\ttotal: 143ms\tremaining: 0us\n",
            "\n",
            "bestTest = 0.1330139901\n",
            "bestIteration = 53\n",
            "\n",
            "{'depth': 4, 'l2_leaf_reg': 4, 'learning_rate': 0.30100000000000005}\n"
          ]
        }
      ],
      "source": [
        "cb_params = {\n",
        "    \"depth\": range(2,5),\n",
        "    'learning_rate': np.arange(0.001,0.5,0.1),\n",
        "    'l2_leaf_reg': range(1,5),\n",
        "}\n",
        "x_train_transform = preprocessor.fit_transform(X_train)\n",
        "train_dataset = Pool(data=x_train_transform, label=y_train)\n",
        "x_test_transform = Pool(data=preprocessor.transform(X_test))\n",
        "cb_grid = model.grid_search(cb_params, cv=4, X=x_train_transform,\n",
        "                y=y_train, verbose=True)\n",
        "print (cb_grid[\"params\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {},
      "outputs": [],
      "source": [
        "model = CatBoostClassifier(iterations=100,\n",
        "        learning_rate=cb_grid[\"params\"][\"learning_rate\"],\n",
        "        depth=cb_grid[\"params\"][\"depth\"],\n",
        "        l2_leaf_reg=cb_grid[\"params\"][\"l2_leaf_reg\"],\n",
        "        random_seed=17, loss_function=\"Logloss\",\n",
        "        bootstrap_type=\"MVS\", custom_metric=\"F1\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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          ]
        }
      ],
      "source": [
        "model.fit(train_dataset)\n",
        "y_pred = model.predict(x_test_transform)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7824933687002652"
            ]
          },
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fbeta_score(y_test, y_pred == 'True', beta = 2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Модель Catboost по точности предсказания сопоставима с XGBoost."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Модель RandomForestClassifier\n",
        "\n",
        "Попробуем улучшить предсказательную способность случайного леса путем подбора гиперпараметров по сетке. Возможно получить лучше результат, чем в предоставленном базавом варианте"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 4 folds for each of 840 candidates, totalling 3360 fits\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "GridSearchCV(cv=4,\n",
              "             estimator=RandomForestClassifier(n_estimators=1000,\n",
              "                                              random_state=42),\n",
              "             n_jobs=2,\n",
              "             param_grid={'max_depth': range(3, 10), 'max_features': range(2, 6),\n",
              "                         'min_samples_leaf': range(2, 5),\n",
              "                         'n_estimators': range(20, 30)},\n",
              "             scoring=make_scorer(fbeta_score, beta=2), verbose=True)"
            ]
          },
          "execution_count": 89,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Для начала обучим алгоритм на самом простом, но надежном классификаторе\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Инициализируем модель состоящую из 1000 решающих деревьев\n",
        "rf = RandomForestClassifier(n_estimators = 1000, random_state = 42)\n",
        "\n",
        "tree_params = {\n",
        "    'max_depth': range(3,10),\n",
        "    'max_features': range(2,6),\n",
        "    'n_estimators': range(20, 30),\n",
        "    'min_samples_leaf': range(2,5)\n",
        "}\n",
        "tree_grid = GridSearchCV(rf, tree_params, cv=4, n_jobs=2,\n",
        "            verbose=True, scoring=ftwo_scorer)\n",
        "tree_grid.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'max_depth': 9, 'max_features': 5, 'min_samples_leaf': 2, 'n_estimators': 27}\n"
          ]
        }
      ],
      "source": [
        "print (tree_grid.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {},
      "outputs": [],
      "source": [
        "rf = RandomForestClassifier(random_state=42,\n",
        "        min_samples_leaf=tree_grid.best_params_['min_samples_leaf'],\n",
        "        max_features=tree_grid.best_params_['max_features'],\n",
        "        max_depth=tree_grid.best_params_['max_depth'],\n",
        "        n_estimators=tree_grid.best_params_['n_estimators'])\n",
        "rf.fit(X_train, y_train)\n",
        "y_pred = rf.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7392473118279571"
            ]
          },
          "execution_count": 92,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fbeta_score(y_test, y_pred, beta=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Точность предсказания увеличилась относительно модели случайного леса без подбора гиперпараметров, однако данная модель все же более слабая, чем CatBoost и XGBoost."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Модель нейронной сети (Keras)\n",
        "\n",
        "Попробуем построить модель двухслойной нейронной сети."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {},
      "outputs": [],
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Подготовка данных"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 226,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0., 0., 1., ..., 0., 0., 0.], dtype=float32)"
            ]
          },
          "execution_count": 226,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np_x_train = preprocessor.fit_transform(X_train)\n",
        "np_y_train = y_train.to_numpy().astype(\"float32\")\n",
        "\n",
        "np_x_test = preprocessor.transform(X_test)\n",
        "np_y_test = y_train.to_numpy().astype(\"float32\")\n",
        "np_y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 242,
      "metadata": {},
      "outputs": [],
      "source": [
        "def build_model():\n",
        "    model = keras.Sequential([\n",
        "        layers.Dense(22, activation=\"relu\"),\n",
        "        layers.Dense(22, activation=\"relu\"),\n",
        "        layers.Dense(1, activation=\"sigmoid\")\n",
        "    ])\n",
        "    model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"binary_crossentropy\",\n",
        "              metrics=[\"accuracy\"])\n",
        "    return model\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 229,
      "metadata": {},
      "outputs": [],
      "source": [
        "x_val = np_x_train[:1600]\n",
        "partial_x_train = np_x_train[1600:]\n",
        "y_val = y_train[:1600]\n",
        "partial_y_train = np_y_train[1600:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 243,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "5/5 [==============================] - 1s 35ms/step - loss: 0.6184 - accuracy: 0.7218 - val_loss: 0.5725 - val_accuracy: 0.7962\n",
            "Epoch 2/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.5638 - accuracy: 0.8139 - val_loss: 0.5351 - val_accuracy: 0.8375\n",
            "Epoch 3/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.5303 - accuracy: 0.8402 - val_loss: 0.5087 - val_accuracy: 0.8500\n",
            "Epoch 4/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.5055 - accuracy: 0.8477 - val_loss: 0.4876 - val_accuracy: 0.8544\n",
            "Epoch 5/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.4849 - accuracy: 0.8477 - val_loss: 0.4717 - val_accuracy: 0.8556\n",
            "Epoch 6/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.4687 - accuracy: 0.8496 - val_loss: 0.4568 - val_accuracy: 0.8550\n",
            "Epoch 7/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.4541 - accuracy: 0.8515 - val_loss: 0.4433 - val_accuracy: 0.8556\n",
            "Epoch 8/100\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.4403 - accuracy: 0.8515 - val_loss: 0.4318 - val_accuracy: 0.8556\n",
            "Epoch 9/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.4288 - accuracy: 0.8534 - val_loss: 0.4218 - val_accuracy: 0.8556\n",
            "Epoch 10/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.4174 - accuracy: 0.8534 - val_loss: 0.4137 - val_accuracy: 0.8556\n",
            "Epoch 11/100\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.4088 - accuracy: 0.8534 - val_loss: 0.4077 - val_accuracy: 0.8556\n",
            "Epoch 12/100\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.4009 - accuracy: 0.8534 - val_loss: 0.4017 - val_accuracy: 0.8556\n",
            "Epoch 13/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.3936 - accuracy: 0.8534 - val_loss: 0.3963 - val_accuracy: 0.8556\n",
            "Epoch 14/100\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.3867 - accuracy: 0.8534 - val_loss: 0.3924 - val_accuracy: 0.8556\n",
            "Epoch 15/100\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.3805 - accuracy: 0.8534 - val_loss: 0.3884 - val_accuracy: 0.8556\n",
            "Epoch 16/100\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.3746 - accuracy: 0.8534 - val_loss: 0.3842 - val_accuracy: 0.8556\n",
            "Epoch 17/100\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.3692 - accuracy: 0.8534 - val_loss: 0.3804 - val_accuracy: 0.8556\n",
            "Epoch 18/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.3638 - accuracy: 0.8534 - val_loss: 0.3787 - val_accuracy: 0.8556\n",
            "Epoch 19/100\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.3615 - accuracy: 0.8534 - val_loss: 0.3756 - val_accuracy: 0.8556\n",
            "Epoch 20/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.3565 - accuracy: 0.8534 - val_loss: 0.3736 - val_accuracy: 0.8556\n",
            "Epoch 21/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.3532 - accuracy: 0.8534 - val_loss: 0.3705 - val_accuracy: 0.8562\n",
            "Epoch 22/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.3491 - accuracy: 0.8571 - val_loss: 0.3684 - val_accuracy: 0.8562\n",
            "Epoch 23/100\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.3460 - accuracy: 0.8571 - val_loss: 0.3670 - val_accuracy: 0.8562\n",
            "Epoch 24/100\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.3436 - accuracy: 0.8571 - val_loss: 0.3644 - val_accuracy: 0.8569\n",
            "Epoch 25/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.3399 - accuracy: 0.8571 - val_loss: 0.3630 - val_accuracy: 0.8569\n",
            "Epoch 26/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.3370 - accuracy: 0.8571 - val_loss: 0.3619 - val_accuracy: 0.8569\n",
            "Epoch 27/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.3343 - accuracy: 0.8571 - val_loss: 0.3601 - val_accuracy: 0.8569\n",
            "Epoch 28/100\n",
            "5/5 [==============================] - 0s 7ms/step - loss: 0.3310 - accuracy: 0.8590 - val_loss: 0.3591 - val_accuracy: 0.8569\n",
            "Epoch 29/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.3288 - accuracy: 0.8628 - val_loss: 0.3578 - val_accuracy: 0.8575\n",
            "Epoch 30/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.3261 - accuracy: 0.8590 - val_loss: 0.3559 - val_accuracy: 0.8569\n",
            "Epoch 31/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.3241 - accuracy: 0.8628 - val_loss: 0.3549 - val_accuracy: 0.8562\n",
            "Epoch 32/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.3222 - accuracy: 0.8609 - val_loss: 0.3549 - val_accuracy: 0.8581\n",
            "Epoch 33/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.3202 - accuracy: 0.8628 - val_loss: 0.3537 - val_accuracy: 0.8575\n",
            "Epoch 34/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.3176 - accuracy: 0.8628 - val_loss: 0.3519 - val_accuracy: 0.8606\n",
            "Epoch 35/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.3151 - accuracy: 0.8590 - val_loss: 0.3513 - val_accuracy: 0.8612\n",
            "Epoch 36/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.3134 - accuracy: 0.8609 - val_loss: 0.3497 - val_accuracy: 0.8606\n",
            "Epoch 37/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.3109 - accuracy: 0.8590 - val_loss: 0.3495 - val_accuracy: 0.8612\n",
            "Epoch 38/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.3089 - accuracy: 0.8628 - val_loss: 0.3484 - val_accuracy: 0.8619\n",
            "Epoch 39/100\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 0.3069 - accuracy: 0.8590 - val_loss: 0.3473 - val_accuracy: 0.8625\n",
            "Epoch 40/100\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 0.3051 - accuracy: 0.8590 - val_loss: 0.3458 - val_accuracy: 0.8619\n",
            "Epoch 41/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.3028 - accuracy: 0.8647 - val_loss: 0.3460 - val_accuracy: 0.8625\n",
            "Epoch 42/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.3013 - accuracy: 0.8665 - val_loss: 0.3454 - val_accuracy: 0.8619\n",
            "Epoch 43/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2986 - accuracy: 0.8684 - val_loss: 0.3449 - val_accuracy: 0.8619\n",
            "Epoch 44/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2970 - accuracy: 0.8703 - val_loss: 0.3443 - val_accuracy: 0.8631\n",
            "Epoch 45/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2947 - accuracy: 0.8759 - val_loss: 0.3417 - val_accuracy: 0.8625\n",
            "Epoch 46/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2928 - accuracy: 0.8759 - val_loss: 0.3425 - val_accuracy: 0.8625\n",
            "Epoch 47/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2913 - accuracy: 0.8759 - val_loss: 0.3422 - val_accuracy: 0.8637\n",
            "Epoch 48/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2891 - accuracy: 0.8778 - val_loss: 0.3409 - val_accuracy: 0.8644\n",
            "Epoch 49/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2880 - accuracy: 0.8816 - val_loss: 0.3405 - val_accuracy: 0.8637\n",
            "Epoch 50/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2857 - accuracy: 0.8778 - val_loss: 0.3406 - val_accuracy: 0.8644\n",
            "Epoch 51/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2837 - accuracy: 0.8778 - val_loss: 0.3390 - val_accuracy: 0.8675\n",
            "Epoch 52/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2824 - accuracy: 0.8853 - val_loss: 0.3392 - val_accuracy: 0.8656\n",
            "Epoch 53/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2802 - accuracy: 0.8853 - val_loss: 0.3389 - val_accuracy: 0.8662\n",
            "Epoch 54/100\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.2784 - accuracy: 0.8816 - val_loss: 0.3378 - val_accuracy: 0.8669\n",
            "Epoch 55/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2766 - accuracy: 0.8835 - val_loss: 0.3374 - val_accuracy: 0.8669\n",
            "Epoch 56/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2743 - accuracy: 0.8872 - val_loss: 0.3373 - val_accuracy: 0.8681\n",
            "Epoch 57/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2726 - accuracy: 0.8872 - val_loss: 0.3364 - val_accuracy: 0.8675\n",
            "Epoch 58/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2706 - accuracy: 0.8891 - val_loss: 0.3362 - val_accuracy: 0.8687\n",
            "Epoch 59/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2702 - accuracy: 0.8891 - val_loss: 0.3363 - val_accuracy: 0.8681\n",
            "Epoch 60/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2677 - accuracy: 0.8872 - val_loss: 0.3361 - val_accuracy: 0.8700\n",
            "Epoch 61/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2652 - accuracy: 0.8929 - val_loss: 0.3359 - val_accuracy: 0.8662\n",
            "Epoch 62/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2643 - accuracy: 0.8966 - val_loss: 0.3355 - val_accuracy: 0.8700\n",
            "Epoch 63/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2616 - accuracy: 0.8947 - val_loss: 0.3360 - val_accuracy: 0.8706\n",
            "Epoch 64/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2599 - accuracy: 0.8947 - val_loss: 0.3351 - val_accuracy: 0.8687\n",
            "Epoch 65/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2580 - accuracy: 0.8985 - val_loss: 0.3368 - val_accuracy: 0.8706\n",
            "Epoch 66/100\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.2574 - accuracy: 0.8966 - val_loss: 0.3354 - val_accuracy: 0.8712\n",
            "Epoch 67/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2546 - accuracy: 0.8985 - val_loss: 0.3360 - val_accuracy: 0.8712\n",
            "Epoch 68/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2543 - accuracy: 0.8985 - val_loss: 0.3342 - val_accuracy: 0.8719\n",
            "Epoch 69/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2506 - accuracy: 0.8985 - val_loss: 0.3317 - val_accuracy: 0.8719\n",
            "Epoch 70/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2488 - accuracy: 0.9060 - val_loss: 0.3305 - val_accuracy: 0.8744\n",
            "Epoch 71/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2469 - accuracy: 0.9023 - val_loss: 0.3309 - val_accuracy: 0.8725\n",
            "Epoch 72/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2454 - accuracy: 0.8966 - val_loss: 0.3312 - val_accuracy: 0.8744\n",
            "Epoch 73/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2431 - accuracy: 0.9004 - val_loss: 0.3304 - val_accuracy: 0.8725\n",
            "Epoch 74/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2414 - accuracy: 0.9060 - val_loss: 0.3305 - val_accuracy: 0.8744\n",
            "Epoch 75/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2393 - accuracy: 0.9004 - val_loss: 0.3310 - val_accuracy: 0.8700\n",
            "Epoch 76/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2382 - accuracy: 0.9117 - val_loss: 0.3304 - val_accuracy: 0.8712\n",
            "Epoch 77/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2361 - accuracy: 0.9098 - val_loss: 0.3296 - val_accuracy: 0.8725\n",
            "Epoch 78/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2344 - accuracy: 0.9135 - val_loss: 0.3289 - val_accuracy: 0.8769\n",
            "Epoch 79/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2324 - accuracy: 0.9079 - val_loss: 0.3294 - val_accuracy: 0.8725\n",
            "Epoch 80/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2301 - accuracy: 0.9135 - val_loss: 0.3296 - val_accuracy: 0.8750\n",
            "Epoch 81/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2280 - accuracy: 0.9098 - val_loss: 0.3296 - val_accuracy: 0.8731\n",
            "Epoch 82/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2264 - accuracy: 0.9098 - val_loss: 0.3299 - val_accuracy: 0.8750\n",
            "Epoch 83/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2243 - accuracy: 0.9079 - val_loss: 0.3305 - val_accuracy: 0.8731\n",
            "Epoch 84/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2220 - accuracy: 0.9098 - val_loss: 0.3293 - val_accuracy: 0.8750\n",
            "Epoch 85/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2205 - accuracy: 0.9135 - val_loss: 0.3303 - val_accuracy: 0.8712\n",
            "Epoch 86/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2179 - accuracy: 0.9192 - val_loss: 0.3296 - val_accuracy: 0.8744\n",
            "Epoch 87/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2154 - accuracy: 0.9173 - val_loss: 0.3269 - val_accuracy: 0.8750\n",
            "Epoch 88/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2146 - accuracy: 0.9229 - val_loss: 0.3273 - val_accuracy: 0.8769\n",
            "Epoch 89/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2115 - accuracy: 0.9229 - val_loss: 0.3297 - val_accuracy: 0.8756\n",
            "Epoch 90/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2101 - accuracy: 0.9211 - val_loss: 0.3286 - val_accuracy: 0.8763\n",
            "Epoch 91/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2073 - accuracy: 0.9229 - val_loss: 0.3273 - val_accuracy: 0.8750\n",
            "Epoch 92/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2050 - accuracy: 0.9267 - val_loss: 0.3288 - val_accuracy: 0.8775\n",
            "Epoch 93/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.2032 - accuracy: 0.9267 - val_loss: 0.3305 - val_accuracy: 0.8756\n",
            "Epoch 94/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2007 - accuracy: 0.9248 - val_loss: 0.3295 - val_accuracy: 0.8744\n",
            "Epoch 95/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.1984 - accuracy: 0.9305 - val_loss: 0.3275 - val_accuracy: 0.8794\n",
            "Epoch 96/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.1965 - accuracy: 0.9305 - val_loss: 0.3266 - val_accuracy: 0.8775\n",
            "Epoch 97/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.1934 - accuracy: 0.9323 - val_loss: 0.3230 - val_accuracy: 0.8806\n",
            "Epoch 98/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.1917 - accuracy: 0.9305 - val_loss: 0.3252 - val_accuracy: 0.8806\n",
            "Epoch 99/100\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.1900 - accuracy: 0.9361 - val_loss: 0.3235 - val_accuracy: 0.8825\n",
            "Epoch 100/100\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.1870 - accuracy: 0.9361 - val_loss: 0.3251 - val_accuracy: 0.8800\n"
          ]
        }
      ],
      "source": [
        "history = build_model().fit(partial_x_train,\n",
        "                        partial_y_train,\n",
        "                        epochs=100,\n",
        "                        batch_size=128,\n",
        "                        validation_data=(x_val, y_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 244,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "history_dict = history.history\n",
        "loss_values = history_dict[\"loss\"]\n",
        "val_loss_values = history_dict[\"val_loss\"]\n",
        "epochs = range(1, len(loss_values) + 1)\n",
        "plt.plot(epochs, loss_values, \"bo\", label=\"Training loss\")\n",
        "plt.plot(epochs, val_loss_values, \"b\", label=\"Validation loss\")\n",
        "plt.title(\"Training and validation loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 245,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.clf()\n",
        "acc = history_dict[\"accuracy\"]\n",
        "val_acc = history_dict[\"val_accuracy\"]\n",
        "plt.plot(epochs, acc, \"bo\", label=\"Training acc\")\n",
        "plt.plot(epochs, val_acc, \"b\", label=\"Validation acc\")\n",
        "plt.title(\"Training and validation accuracy\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 184,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "17/17 [==============================] - 0s 918us/step - loss: 0.2656 - accuracy: 0.9195\n"
          ]
        }
      ],
      "source": [
        "model = keras.Sequential([\n",
        "    layers.Dense(22, activation=\"relu\"),\n",
        "    layers.Dense(64, activation=\"tanh\"),\n",
        "    layers.Dense(1, activation=\"sigmoid\")\n",
        "])\n",
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"binary_crossentropy\",\n",
        "              metrics=[\"accuracy\"])\n",
        "model.fit(np_x_train, y_train, epochs=100, batch_size=128, verbose=0)\n",
        "results = model.evaluate(np_x_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 247,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.6578947368421053"
            ]
          },
          "execution_count": 247,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred = (model.predict(np_x_test) > 0.5).reshape(1, -1)[0]\n",
        "fbeta_score(y_test.to_numpy(), y_pred, beta = 2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Нейросеть оказалось хуже, чем случайный лес и модели бустинга."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Метод опорных векторов (SVM)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.svm import SVC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.46042003231017764"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pipe_svm = pipe_with_transform(SVC(kernel='linear', probability=True, max_iter=5))\n",
        "pipe_svm.fit(X_train, y_train)\n",
        "y_pred = pipe_svm.predict(X_test)\n",
        "fbeta_score(y_test, y_pred, beta = 2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Логистическая регрессия"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.25787965616045844"
            ]
          },
          "execution_count": 100,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pipe_logr = pipe_with_transform(LogisticRegression(max_iter=500))\n",
        "pipe_logr.fit(X_train, y_train)\n",
        "log_y_pred = pipe_logr.predict(X_test)\n",
        "fbeta_score(y_test, log_y_pred, beta = 2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# <text style=\"color:red; font-size:120%\">Ансамбль классификации</text>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Сформируем параллельный ансамбль из RandomForestClassifier, LightGBM, XGBoost, Catbost и нейросети.\n",
        "Для формирования ансамбля возьмем лучшие параметры, подобранные ранее. Итоговое предсказание рассчитаем на основании весов моделей и вероятности предсказания класса (т.е. \"мягким\" голосованием)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Сгруппируем код для удобства"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "train = pd.read_csv('train.csv')\n",
        "test = pd.read_csv('test.csv')\n",
        "\n",
        "columns_to_del = ['State', 'Total day minutes', 'Total eve minutes', 'Total night minutes', 'Total intl minutes']\n",
        "\n",
        "train = train.drop(columns_to_del, axis = 1)\n",
        "\n",
        "train['International plan'] = train['International plan'].replace({\"Yes\":1,\"No\":0}).astype('int64')\n",
        "train['Voice mail plan'] = train['Voice mail plan'].replace({\"Yes\":1,\"No\":0}).astype('int64')\n",
        "\n",
        "test = test.drop(columns_to_del, axis = 1)\n",
        "\n",
        "test['International plan'] = test['International plan'].replace({\"Yes\":1,\"No\":0}).astype('int64')\n",
        "test['Voice mail plan'] = test['Voice mail plan'].replace({\"Yes\":1,\"No\":0}).astype('int64')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Разделим train датасет на данные для обучения и валидации\n",
        "X_train, X_test, y_train, y_test = train_test_split(train.drop(['Churn'],axis=1),\n",
        "                                                    train['Churn'], test_size=0.2, \n",
        "                                                    random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import fbeta_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Предобратка данных (организация конвейров)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "categorical_features = ['Area code', 'International plan', 'Voice mail plan']\n",
        "numeric_features = X_train.columns.drop(categorical_features)\n",
        "\n",
        "numeric_transformer = StandardScaler()\n",
        "categorical_transformer = OneHotEncoder(handle_unknown=\"ignore\")\n",
        "\n",
        "def pipe_with_transform(clf):\n",
        "    preprocessor = ColumnTransformer(\n",
        "        transformers=[\n",
        "            (\"num\", numeric_transformer, numeric_features),\n",
        "            (\"cat\", categorical_transformer, categorical_features),\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    return Pipeline(\n",
        "        steps=[(\"scl\", preprocessor), (\"clf\", clf)]\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Построение базовых моделей"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Случайный лес (RandomForestClassifier)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7277628032345014"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_rf = RandomForestClassifier(\n",
        "        min_samples_leaf=3,\n",
        "        max_features=5,\n",
        "        max_depth=9,\n",
        "        n_estimators=27)\n",
        "model_rf.fit(X_train, y_train)\n",
        "rf_y_pred = model_rf.predict(X_test)\n",
        "fbeta_score(y_test, rf_y_pred, beta = 2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Модель градиентного бустинга XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [],
      "source": [
        "from xgboost import XGBClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7804232804232804"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pipe_xgb = pipe_with_transform(\n",
        "    XGBClassifier(\n",
        "        n_estimator=80,\n",
        "        verbosity=0))\n",
        "pipe_xgb.fit(X_train, y_train)\n",
        "\n",
        "xgb_y_pred = pipe_xgb.predict(X_test)\n",
        "fbeta_score(y_test, xgb_y_pred, beta=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Модель градиентного бустинга LightGBM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {},
      "outputs": [],
      "source": [
        "import lightgbm as lgb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7579787234042552"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pipe_lgbm = pipe_with_transform(lgb.LGBMRegressor())\n",
        "pipe_lgbm.fit(X_train, y_train)\n",
        "\n",
        "lgbm_y_pred = pipe_lgbm.predict(X_test) > 0.5\n",
        "fbeta_score(y_test, lgbm_y_pred, beta=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Модель градиентного бустинга Catboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {},
      "outputs": [],
      "source": [
        "from catboost import Pool, CatBoostClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7611548556430447"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "preprocessor_cb = ColumnTransformer(\n",
        "    transformers=[\n",
        "        (\"num\", numeric_transformer, numeric_features),\n",
        "        (\"cat\", categorical_transformer, categorical_features),\n",
        "    ]\n",
        ")\n",
        "x_train_transform = preprocessor_cb.fit_transform(X_train)\n",
        "train_dataset = Pool(data=x_train_transform, label=y_train)\n",
        "x_test_transform = Pool(data=preprocessor_cb.transform(X_test))\n",
        "\n",
        "model_catboost = CatBoostClassifier(\n",
        "        iterations=100,\n",
        "        learning_rate=1,\n",
        "        depth=4,\n",
        "        l2_leaf_reg=4,\n",
        "        loss_function=\"Logloss\",\n",
        "        bootstrap_type=\"MVS\",\n",
        "        custom_metric=\"F1\"\n",
        "        )\n",
        "\n",
        "model_catboost.fit(train_dataset, verbose=0)\n",
        "catboost_y_pred = model_catboost.predict(x_test_transform) == 'True'\n",
        "fbeta_score(y_test, catboost_y_pred, beta=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Модель двухслойной нейронной сети"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {},
      "outputs": [],
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {},
      "outputs": [],
      "source": [
        "preprocessor_nn = ColumnTransformer(\n",
        "    transformers=[\n",
        "        (\"num\", numeric_transformer, numeric_features),\n",
        "        (\"cat\", categorical_transformer, categorical_features),\n",
        "    ]\n",
        ")\n",
        "np_x_train = preprocessor_nn.fit_transform(X_train)\n",
        "np_y_train = y_train.to_numpy().astype(\"float32\")\n",
        "\n",
        "np_x_test = preprocessor_nn.transform(X_test)\n",
        "np_y_test = y_train.to_numpy().astype(\"float32\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "17/17 [==============================] - 0s 1ms/step - loss: 0.2522 - accuracy: 0.9213\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0.6692913385826772"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_nn = keras.Sequential([\n",
        "    layers.Dense(22, activation=\"relu\"),\n",
        "    layers.Dense(22, activation=\"tanh\"),\n",
        "    layers.Dense(1, activation=\"sigmoid\")\n",
        "])\n",
        "model_nn.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"binary_crossentropy\",\n",
        "              metrics=[\"accuracy\"])\n",
        "model_nn.fit(np_x_train, y_train, epochs=100, batch_size=128, verbose=0)\n",
        "results = model_nn.evaluate(np_x_test, y_test)\n",
        "nn_y_pred = (model_nn.predict(np_x_test) > 0.5).reshape(1, -1)[0]\n",
        "fbeta_score(y_test.to_numpy(), nn_y_pred, beta = 2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Расчет предсказаний"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Расчет итогово голосования будет происходить по \"мягкой\" схеме"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {},
      "outputs": [],
      "source": [
        "target_test_rf_proba = pd.DataFrame(model_rf.predict_proba(X_test))\n",
        "target_test_xgb_proba = pd.DataFrame(pipe_xgb.predict_proba(X_test))\n",
        "target_test_lgb_proba = 1 - pd.DataFrame(pipe_lgbm.predict(X_test))\n",
        "target_test_lgb_proba[1] = 1 - target_test_lgb_proba[0]\n",
        "target_test_cb_proba = pd.DataFrame(\n",
        "    model_catboost.predict_proba(\n",
        "        Pool(data=preprocessor_cb.transform(X_test))\n",
        "        ))\n",
        "target_test_nn_proba = 1 - pd.DataFrame(\n",
        "    model_nn.predict(\n",
        "        preprocessor_nn.transform(X_test)\n",
        "        ))\n",
        "target_test_nn_proba[1] = 1 - target_test_nn_proba[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Функция необходима для выбора класса при \"мягком\" голосования\n",
        "def vote_class (x):\n",
        "    a = np.argmax(x.values)\n",
        "    return a"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {},
      "outputs": [],
      "source": [
        "target_test_proba = target_test_rf_proba.copy()\n",
        "for i in range(0, 2):\n",
        "    target_test_proba[i] = target_test_rf_proba[i]\n",
        "    target_test_proba[i] += target_test_xgb_proba[i]\n",
        "    target_test_proba[i] += target_test_lgb_proba[i]\n",
        "    target_test_proba[i] += target_test_cb_proba[i]\n",
        "    target_test_proba[i] += target_test_nn_proba[i]\n",
        "target_test_proba[\"target\"] = target_test_proba.apply(vote_class, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {},
      "outputs": [
        {
          "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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.426870</td>\n",
              "      <td>4.573130</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.856603</td>\n",
              "      <td>0.143397</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.911709</td>\n",
              "      <td>0.088291</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.188786</td>\n",
              "      <td>4.811214</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.151902</td>\n",
              "      <td>4.848098</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          0         1  target\n",
              "0  0.426870  4.573130       1\n",
              "1  4.856603  0.143397       0\n",
              "2  4.911709  0.088291       0\n",
              "3  0.188786  4.811214       1\n",
              "4  0.151902  4.848098       1"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "target_test_proba.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7936507936507936"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fbeta_score(y_test, target_test_proba['target'], beta = 2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Поищим лучшие веса для вероятностей"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {},
      "outputs": [],
      "source": [
        "from itertools import product\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(7776, 5)"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "arr_search_weight = []\n",
        "for p in product(range(0, 105, 20), repeat=5):\n",
        "    arr_search_weight.append(p)\n",
        "arr_search_weight = np.array(arr_search_weight) / 10\n",
        "arr_search_weight.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 7776/7776 [01:16<00:00, 102.02it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.8072916666666665, array([ 0.,  0., 10., 10.,  2.])]\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "best_weight_comb = [0.5, arr_search_weight[1]]\n",
        "for comb in tqdm(arr_search_weight):\n",
        "    target_test_proba = target_test_rf_proba.copy()\n",
        "    for i in range(0, 2):\n",
        "        target_test_proba[i] = comb[0]*target_test_rf_proba[i]\n",
        "        target_test_proba[i] += comb[1]*target_test_xgb_proba[i]\n",
        "        target_test_proba[i] += comb[2]*target_test_lgb_proba[i]\n",
        "        target_test_proba[i] += comb[3]*target_test_cb_proba[i]\n",
        "        target_test_proba[i] += comb[4]*target_test_nn_proba[i]\n",
        "    target_test_proba[\"target\"] = target_test_proba.apply(vote_class, axis=1)\n",
        "    fbeta = fbeta_score(y_test, target_test_proba['target'], beta = 2)\n",
        "    if best_weight_comb[0] < fbeta:\n",
        "        best_weight_comb[0] = fbeta\n",
        "        best_weight_comb[1] = comb\n",
        "print(best_weight_comb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Лучший набор весов для моделей \"мягкого\" голосования."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_weight_comb = [0.8072916666666665, np.array([ 0.,  0., 10., 10.,  2.])]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Вывод:\n",
        "Случайный лес и XGBoost отбрасываются из ансамбля.\n",
        "Итоговый вариант ансамбля будет состоять из 3 моделей:\n",
        "1. LightGBM;\n",
        "2. Catboost;\n",
        "3. Нейросеть.\n",
        "\n",
        "Дообучим их на всем объеме данных и сделаем итоговое предсказание."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Подготовка предсказания"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_train = train.drop(columns='Churn')\n",
        "y_train = train['Churn']\n",
        "X_test = test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Pipeline(steps=[('scl',\n",
              "                 ColumnTransformer(transformers=[('num', StandardScaler(),\n",
              "                                                  Index(['Account length', 'Number vmail messages', 'Total day calls',\n",
              "       'Total day charge', 'Total eve calls', 'Total eve charge',\n",
              "       'Total night calls', 'Total night charge', 'Total intl calls',\n",
              "       'Total intl charge', 'Customer service calls'],\n",
              "      dtype='object')),\n",
              "                                                 ('cat',\n",
              "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
              "                                                  ['Area code',\n",
              "                                                   'International plan',\n",
              "                                                   'Voice mail plan'])])),\n",
              "                ('clf', LGBMRegressor())])"
            ]
          },
          "execution_count": 112,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pipe_lgbm = pipe_with_transform(lgb.LGBMRegressor())\n",
        "pipe_lgbm.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {},
      "outputs": [],
      "source": [
        "x_train_transform = preprocessor_cb.fit_transform(X_train)\n",
        "train_dataset = Pool(data=x_train_transform, label=y_train)\n",
        "\n",
        "model_catboost = CatBoostClassifier(\n",
        "        iterations=100,\n",
        "        learning_rate=1,\n",
        "        depth=4,\n",
        "        l2_leaf_reg=4,\n",
        "        loss_function=\"Logloss\",\n",
        "        bootstrap_type=\"MVS\",\n",
        "        custom_metric=\"F1\"\n",
        "        )\n",
        "\n",
        "model_catboost.fit(train_dataset, verbose=0)\n",
        "catboost_y_pred = model_catboost.predict(x_test_transform) == 'True'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x2b347cd1a90>"
            ]
          },
          "execution_count": 118,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "preprocessor_nn = ColumnTransformer(\n",
        "    transformers=[\n",
        "        (\"num\", numeric_transformer, numeric_features),\n",
        "        (\"cat\", categorical_transformer, categorical_features),\n",
        "    ]\n",
        ")\n",
        "np_x_train = preprocessor_nn.fit_transform(X_train)\n",
        "np_y_train = y_train.to_numpy().astype(\"float32\")\n",
        "\n",
        "model_nn = keras.Sequential([\n",
        "    layers.Dense(22, activation=\"relu\"),\n",
        "    layers.Dense(22, activation=\"tanh\"),\n",
        "    layers.Dense(1, activation=\"sigmoid\")\n",
        "])\n",
        "model_nn.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"binary_crossentropy\",\n",
        "              metrics=[\"accuracy\"])\n",
        "model_nn.fit(np_x_train, y_train, epochs=100, batch_size=128, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {},
      "outputs": [],
      "source": [
        "target_test_lgb_proba = 1 - pd.DataFrame(pipe_lgbm.predict(X_test))\n",
        "target_test_lgb_proba[1] = 1 - target_test_lgb_proba[0]\n",
        "target_test_cb_proba = pd.DataFrame(\n",
        "    model_catboost.predict_proba(\n",
        "        Pool(data=preprocessor_cb.transform(X_test))\n",
        "        ))\n",
        "target_test_nn_proba = 1 - pd.DataFrame(\n",
        "    model_nn.predict(\n",
        "        preprocessor_nn.transform(X_test)\n",
        "        ))\n",
        "target_test_nn_proba[1] = 1 - target_test_nn_proba[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {},
      "outputs": [],
      "source": [
        "target_test_proba = best_weight_comb[1][2]*target_test_lgb_proba\n",
        "for i in range(0, 2):\n",
        "    target_test_proba[i] += best_weight_comb[1][3]*target_test_cb_proba[i]\n",
        "    target_test_proba[i] += best_weight_comb[1][4]*target_test_nn_proba[i]\n",
        "target_test_proba[\"target\"] = target_test_proba.apply(vote_class, axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<text style=\"color:green;font-size: 250%\">Итоговое предсказание ID\t5759214</text>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "metadata": {},
      "outputs": [],
      "source": [
        "target_test_proba[\"target\"].astype('bool').to_csv('submit_ans.csv')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Baseline.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}